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Approaches for computational performance optimization ofinnovative adaptive façade conceptsCitation for published version (APA):Loonen, R. C. G. M. (2018). Approaches for computational performance optimization of innovative adaptivefaçade concepts. Technische Universiteit Eindhoven.
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/ Department of the Built Environment
bouwstenen 253Roel Loonen
Approaches for computational performance optimization of innovative adaptive façade concepts
APPROACHES FOR COMPUTATIONAL
PERFORMANCE OPTIMIZATION OF
INNOVATIVE ADAPTIVE FAÇADE
CONCEPTS
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de rector magnificus prof.dr.ir. F.P.T. Baaijens, voor een commissie aangewezen door het College voor Promoties, in het openbaar te verdedigen op
donderdag 6 september 2018 om 13.30 uur
door
Roel Cornelis Gerardus Maria Loonen
geboren te Grubbenvorst
Dit proefschrift is goedgekeurd door de promotoren en de samenstelling van de promotiecommissie is als volgt: voorzitter: prof.ir. E.S.M. Nelissen
1e promotor: prof.dr.ir. J.L.M. Hensen
co-promotor: dr.dipl.-ing. M. Trčka (United Technologies Research Center)
leden: prof.dr. J.A. Clarke (Strathclyde University)
prof.dr. M. Perino (Politecnico di Torino)
prof.dr.ing. U. Knaack (Technische Universiteit Delft)
prof.dr. A.P.H.J. Schenning
prof.dr.ing. A.L.P. Rosemann
Het onderzoek dat in dit proefschrift wordt beschreven is uitgevoerd in
overeenstemming met de TU/e Gedragscode Wetenschapsbeoefening.
APPROACHES FOR COMPUTATIONAL
PERFORMANCE OPTIMIZATION OF
INNOVATIVE ADAPTIVE FAÇADE CONCEPTS
The research reported in this dissertation was supported by RVO through the
Long-Term Energy Research Strategy (EOS-LT) project, FACET: Façade als
Adaptief Comfortverhogend Energiebesparend Toekomstconcept
© Roel Loonen, 2018
A catalogue record is available from the Eindhoven University of Technology
library
ISBN: 978-90-386-4554-4 NUR: 955
Published as issue 253 in the Bouwstenen series of the Department of the Built Environment, Eindhoven University of Technology
Cover design: GRFK grafisch ontwerp
Printed by: TU/e print service - Dereumaux Eindhoven, the Netherlands
v
Contents
Summary .........................................................................................................................vii
Acronyms ......................................................................................................................... xi
1 Introduction .................................................................................................................. 1
1.1 Trends in the built environment ................................................................... 1
1.2 The promise of adaptive façades .................................................................. 5
1.3 Adaptive façades – research and development needs ........................... 6
1.4 Adaptive façades – modeling, simulation and optimization .................. 8
1.5 Aims and objectives ........................................................................................ 10
1.6 Scope of the work ............................................................................................ 11
1.7 Research methods .......................................................................................... 12
1.8 Thesis outline .................................................................................................. 14
2 State-of-the-art: Adaptive facades ....................................................................... 17
2.1 Introduction ..................................................................................................... 17
2.2 Adaptive façades: background and characteristics ................................ 18
2.3 Decision support for product development of new adaptive façade concepts ............................................................................................................ 21
2.4 Façade adaptation at longer time-scales – an optimization example
............................................................................................................................ 30
3 Principles and requirements for simulation-based optimization of adaptive façades ............................................................................................................................. 41
3.1 Introduction ..................................................................................................... 41
3.2 Modelling and simulation techniques for performance prediction of adaptive façades ............................................................................................. 43
3.3 Control aspects – modeling and optimizing the operation of adaptive
façades ............................................................................................................... 57
3.4 Performance aspects and performance indicators ............................... 62
vi
4 Development of a toolchain for performance optimization of adaptive
façades ............................................................................................................................ 65
4.1 Introduction .................................................................................................... 65
4.2 Performance simulation of adaptive façades ......................................... 66
4.3 Co-simulation approach ............................................................................... 70
4.4 Coupling between ESP-r and BCVTB ......................................................... 71
4.5 MBC implementation using Matlab, BCVTB, ESP-r and Radiance ..... 74
4.6 Overview of the toolchain ............................................................................ 87
5 Quantifying the performance potential of adaptive façades – a case study
........................................................................................................................................... 91
5.1 Introduction ..................................................................................................... 91
5.2 Description of the case study ..................................................................... 92
5.3 Results – performance potential compared to reference case ......... 98
5.4 Discussion and conclusion ......................................................................... 106
6 Investigating trade-offs between performance and façade system
complexity .................................................................................................................... 107
6.1 Introduction ................................................................................................... 107
6.2 Description of the case study .................................................................... 109
6.3 Simulation strategy and settings ............................................................... 113
6.4 Results .............................................................................................................. 113
6.5 Discussion and conclusion .......................................................................... 118
7 Concluding remarks ................................................................................................. 121
References .................................................................................................................... 131
Curriculum Vitae ......................................................................................................... 149
List of Publications ..................................................................................................... 155
Acknowledgements ..................................................................................................... 151
vii
Summary
Adaptive façades give buildings the flexibility to act in response to varying weather conditions and occupant preferences. They are increasingly
recognized as a promising option for achieving high levels of indoor environmental quality (IEQ), while offering potential for low-energy building
operation. Despite the opportunities that arise from the fact that adaptive façades can consolidate the complementary benefits of passive and active
building design strategies, the number of adaptive façade applications in the current building stock is still limited, and the present focus is mostly on
individual projects rather than on solutions that promote widespread adoption. In research and product development contexts, however, a rapid
increase in activities that explore the potential of innovative adaptable building envelope components can be observed.
This doctoral dissertation sets out to investigate how computational
modeling, simulation and optimization techniques can be used to support, stimulate and accelerate the transition towards next-generation adaptive
building envelopes. More specifically, the objective of this work is to develop a computational methodology that can be used to facilitate design analysis
and performance-based design space exploration in the product
development process of innovative adaptable building envelope components and concepts. The computational tools are developed, tested and evaluated
with the aim of assessing the viability of adaptive façades as a design strategy in general, and to guide research and development (R&D) processes towards
the most promising solutions.
The development of the computational methodology begins by analyzing the characteristics of the problem at hand. A literature review that covers the
interface between adaptive façades and simulation support for R&D of innovative building envelope components identifies the needs and
possibilities for building performance simulation (BPS)-based support in this domain. Preliminary simulation studies and an analysis of software
capabilities furthermore identify that current simulation workflows and software lack the necessary capabilities to model the effects of time-varying
viii
building shell properties. Together, these findings serve as input for the
functional requirements of the newly-developed simulation-based optimization approach.
The focus in this thesis is on developing a toolchain that can help in gaining
a better understanding of the effect of adaptive façades on the dynamic interactions between energy saving potential and IEQ in terms of thermal and
visual performance. To achieve these goals, a co-simulation strategy using a middleware software was deployed to couple high-resolution tools for
building energy simulation and dynamic daylight simulation. The code of existing simulation programs was modified to enable performance prediction
of façades that change their properties during simulation run-time. In buildings with adaptive façades, there is a tight coupling between design and
operational aspects. To address this tight coupling in the performance prediction framework, a receding time horizon approach for optimization-
based supervisory control of adaptive façades was implemented in the simulation framework. This simulation-assisted control approach makes use
of genetic algorithms to efficiently search the large option space of possible façade adaptation scenarios.
The capabilities of the whole toolchain are illustrated in a typical commercial office zone in Dutch climate conditions. The results show that adaptive
façades are able to improve the performance of all relevant aspects compared to the best-performing design with non-adaptable façade. These gains are
achieved by influencing the varying trade-offs between multiple competing performance requirements over time. The toolchain is also able to identify
the most promising region in the large option space of possible façade adaptation sequences.
The usability of the newly developed toolchain is further demonstrated by
showing how it can help in addressing the trade-offs between façade complexity and performance. The focus in this second case study is on a
fenestration system that has the ability to selectively control the transmission/reflection of (i) visible light, (ii) near-infrared solar radiation,
(iii) and longwave infrared radiation. The results show that the method is able to identify high-potential directions for further R&D in building envelope
materials and components. Moreover, it enables a better comprehension of the causal relationships between adaptability of building shell parameters
and performance.
ix
By showing the development, testing and evaluation of a computational
approach for analysis and performance-based design space exploration of adaptable building envelopes, this thesis demonstrates how modeling and
simulation techniques can be a valuable tool for supporting both design and product development. Continued application of such virtual testbeds can
help to exploit the lingering potential of adaptive façades.
x
xi
Acronyms
ACH Air changes per hour
BCVTB Building controls virtual test bed
BES Building energy simulation
BPS Building performance simulation
CABS Climate adaptive building shell
CTF Conduction transfer function
DGP Daylight glare probability
EC European Commission
GA Genetic algorithm
HVAC Heating, ventilation and air conditioning
IAQ Indoor air quality
IEA International Energy Agency
IEQ Indoor environmental quality
IPC Inter-process communication
MOO Multi-objective optimization
MBC Model-based control
MPC Model predictive control
PCM Phase change material
PI Performance indicator
RBE Responsive building element
UDI Useful daylight illuminance
WWR Window-to-wall ratio
xii
1
1
Introduction
1.1 Trends in the built environment
The future of buildings and cities will be shaped by a variety of factors. Among the most influential drivers that will affect the way we design and operate
buildings are the need for decarbonization and supply of energy from clean and renewable sources. As the world’s population, average standard of living
(GDP) and urbanization rate continue to rise, the necessity to develop more environmentally-conscious buildings will only become more compelling.
Under a business-as-usual scenario, market forecasts project a twofold increase in global building-related energy consumption (heating and cooling)
and associated emissions by the year 2050 compared to 2010 (Figure 1.1) [IPCC 2014].
The built environment is partly responsible for the current situation1, but also
offers ample opportunities for new solutions to address the societal challenges of climate change and sustainable development.
1 In 2010, the energy consumption in residential and commercial buildings accounted for 35% of the total end-use
energy and 30% of energy-related CO2 emissions [IEA 2013b; Ürge-Vorsatz et al. 2015].
2
Figure 1.1. Trends and drivers for global heating and cooling thermal energy consumption in commercial buildings under a frozen efficiency scenario [IPCC 2014].
The European Commission (EC) has identified the building sector as a key
enabler in its long-term decarbonization strategy, by targeting a reduction in
CO2 emissions of at least 80% by the year 2050 [EC 2011]. This strategy has been translated into immediate action plans and policy instruments that
address various aspects of design, construction, operation and refurbishment of buildings and equipment with less use of fossil fuels.
Nowadays, the technical means are available to develop positive-energy
buildings, which, on an annual basis, consume less energy than they generate with on-site renewable energy systems [Kolokotsa et al. 2011; Cole and
Fedoruk 2015]. However, the deployment of such buildings is so far mostly limited to demonstration projects, while wider market penetration is
impeded by technological as well as non-technological barriers [Ryghaug and Sørensen 2009; Hakkinen and Belloni 2011; Prieto et al. 2017].
A recent analysis by the International Energy Agency (IEA) shows that the
buildings sector is off-track with long-term sustainability objectives, and that concerted efforts are needed to adjust this direction [IEA 2016].
Technological change, driven by research and development (R&D) of innovative building materials and components, with attention for high market
3
penetration potential, forms a key element throughout all phases of this
transition.
One of the core needs for technology development centers around building envelope systems [IEA 2014]. Building envelopes form the boundary between
the conditioned interior spaces of a building and the outdoor environment. As such, they play a dominant role in a building’s energy balance2, and due to
the large building envelope area in the total building stock, they represent a huge cumulative mitigation potential.
Over the past four decades, the main driver for progress in building envelope
technologies has been the need for heating and cooling energy demand reductions [Sadineni et al. 2011; Konstantinou and Knaack 2013]. Significant
results with profound impacts have been achieved, as is for example illustrated by the continuous reductions in window U-values3 (Figure 1.2).
Figure 1.2. Development of U-values and window technologies between 1910 and 2020. The overview is based on literature, interviews with window manufacturers and other
professionals related to the industry in Sweden (adapted from: Kiss and Neij [2011]).
2 Estimates of the total energy consumption in buildings that is directly affected by the design and construction of
building envelopes range from 20 to 60% [IEA 2013a]. 3 Other significant facade-related advances include better airtightness, solar control coatings, natural ventilation
systems and low-conductivity opaque insulation materials.
4
Two notable observations can be derived from the trend in Figure 1.2:
⎯ Progress in building energy efficiency has gone hand in hand with
technological innovations. Regular technology breakthroughs, supported by suitable policy interventions, have always been the
main driving force for moving to the next level of insulated windows [Kiss and Neij 2011; Jakob and Madlener 2004; Papadopoulos 2016].
⎯ Window improvement through reduced U-values, like most energy efficiency measures, is subject to the law of diminishing returns [IEA
2013a]. More resources (e.g. money, time, material) are needed for continued improvements, but the incremental gains are not
proportional to the investments. To break with this trend and attain the next generation of building envelopes, it is worthwhile to explore
alternative strategies and pursue the integration of multiple functions in the building envelope [Knaack et al. 2008].
The upgrade of building envelope components, with an emphasis on energy
conservation and cost saving arguments, undeniably forms an important step towards meeting the goals for a more sustainable built environment.
However, this strategy is also subject to debate [Ucci and Yu 2014; Perino and
Serra 2015]. The strong focus on reducing energy losses tends to approach sustainable building design from only a single direction. To create buildings
that deliver high performance in multiple dimensions, there are several other, occupant-related performance aspects that deserve at least an equal amount
of attention. It is contended that these opportunities are sometimes overlooked with energy efficiency in the spotlight [Bluyssen 2010; Altomonte
et al. 2015; Marszal et al. 2011]. This viewpoint is further supported by a number of recent studies demonstrating that design solutions that are
optimized for low building energy demand are not always conducive to creating a comfortable, healthy, productive and/or positive indoor
environment for occupants in a robust way [Deuble and de Dear 2012; Sundell et al. 2011; Roulet et al. 2006; Baird and Field 2013; McLeod et al. 2013].
Given this context, the premise is that a reconsideration of widely accepted
building design principles is needed to come up with new prospects and integrated solutions that can spearhead the development of future high-
performance, environmentally-conscious buildings. This thesis investigates adaptive façades as a possible design strategy for reconciling the seemingly
contradictory requirements of low-energy building operation with high indoor environmental quality (IEQ).
5
1.2 The promise of adaptive façades
Adaptive façades, sometimes referred to as climate adaptive building shells (CABS), can adjust their properties or configuration over time, in response to
variable weather conditions and comfort preferences [Loonen et al. 2013]. This gives buildings with adaptive façades the unique ability to manage
energy flows for the benefit of occupants, by taking advantage of the dynamic conditions it is exposed to [Heiselberg 2009] (Figure 1.3).
Figure 1.3. Illustration of the dynamic energy flows and interactions in buildings with adaptive façades (from: IEA EBC Annex 44, adapted by Fernández Solla [2014]).
In this way, adaptive façades represent a distinct alternative to the passive design approach, which achieves energy savings, primarily through air-tight, highly-insulated building envelopes, thereby reinforcing a disconnection
between a building and its environment. In contrast, adaptive façades have
potential to influence the multivariate building envelope trade-offs in a dynamic way, and can actively take advantage of the natural energy sinks and
sources in its environment [Davies 1981]. For this reason, many technology roadmaps identify adaptive façades as a promising direction for achieving
forthcoming targets for design and operation of nearly zero-energy buildings [IEA 2013a; EC 2013; US DOE 2014].
Well-known examples of adaptive façades include switchable glazing
[Baetens et al. 2010], dynamic insulation [Kimber et al. 2014; Claridge 1977] or façade systems with phase-change materials [Favoino et al. 2014].
Nevertheless, the number of adaptive façade applications in the current
6
building stock is still limited, with a focus on individual projects rather than
larger-scale solutions [Loonen et al. 2013]. Increased deployment and market uptake of advanced innovative building envelope technologies is needed to
move from niche projects to mainstream construction and to play a role in meeting the 21st century sustainability targets [IEA 2013a; US DOE 2014].
1.3 Adaptive façades – research and
development needs
In recent years, the number of proposals for new adaptive façade systems is increasing at an exponential rate [Loonen 2018; Loonen 2015]. Two main
development directions can be discerned: (i) kinetic components with actuation of movable parts via mechanical systems; and (ii) responsive
materials, with the ability to change the physical behavior of building elements in response to dynamic conditions. Especially the developments in
material science laboratories have been identified as promising for the construction sector, because of the possibilities for scalable solutions with
potential for wide applicability [Bastiaansen et al. 2013; Loonen, Singaravel, et al. 2014]. After discovery of a new principle or material, academic research
groups (e.g. in physics and chemistry labs) typically have the expertise, interest and resources to develop such concepts up to a relatively low
technology readiness level (TRL4) (Figure 1.4).
4 Technology readiness levels were defined by NASA in the 1980s as a system for measuring and comparing the
maturity of innovative technologies. This rating system has been adopted by many organizations and governmental
agencies, and is nowadays frequently used as a policy-making instrument for research and innovation [Mankins
2009].
7
Figure 1.4. Overview of activities at different technology readiness levels (TRL). A detailed description of different R&D challenges at TRL 4-7 is given.
The subsequent phases of testing, refining, technology transfer and
commercialization into marketable products tend not to be straightforward
[Shove 1999; Arora et al. 2014]. Several reasons can be identified for this challenging situation:
⎯ Basic research at low TRLs is mainly done with public funding,
whereas private investors are mostly interested in working towards commercial viability. This leaves a void in the middle.
⎯ Many resources are generally needed throughout the whole R&D cycle, but the certainty of success is relatively low. Only few
technology concepts will eventually develop into successful commercial façade products.
⎯ There is a lack of tools that can provide insights into building-
integration issues at an early R&D phase (TRL 1-5). This results in a
mismatch between information need and availability and complicates decision-making to move future iterations of the product towards the
direction of high-potential solutions.
⎯ The process requires an interdisciplinary approach. The right combination of skills and expertise (materials development and
building application) may not always be available.
8
Figure 1.5. Availability of resources for new product development at various TRLs. The gap in the middle is sometimes referred to as “The Valley of Death” (adapted from: Coyle
[2011]).
The “Valley of Death” is sometimes used as an analogy to describe the aforementioned discontinuity in innovation processes (Figure 1.5) [Markham
et al. 2010; Hensen et al. 2015]. Developing methods and tools that can bridge this valley is identified as an essential stepping stone for practical deployment
of innovative sustainable technologies, and is therefore high on the agenda of policy programmes, such as Horizon 2020, the EU Framework Programme for
Research and Innovation [EC, 2013].
The work presented in this thesis is part of this ongoing development in
relation to emerging building envelope components. More specifically, computational building performance simulation and optimization is put
forward as a tool for supporting informed decision-making in research and development processes of innovative, next-generation adaptive façade
concepts.
1.4 Adaptive façades – modeling, simulation and
optimization
Over the last decades, building performance simulation (BPS) has evolved into
a well-established design support tool in the construction industry [Clarke and Hensen 2015]. BPS takes into account the dynamic interactions between
9
building shape and structure, systems, user behavior and climatic conditions,
and is therefore regarded as a valuable tool in many building design processes [Hand 1998; Bleil de Souza and Tucker 2014]. Because of these attributes, BPS
can also be used as a virtual test-bed for supporting informed decision-making in the R&D phase of emerging adaptive façade concepts. However,
due to (i) the limited flexibility for modeling adaptable building envelope components in state-of-the-art simulation software, and (ii) difficulties with
implementing advanced façade control strategies during the simulation, such possibilities have thus far only been explored to a limited extent [De Klijn-
Chevalerias et al. 2017].
Another opportunity for advanced innovation support with the use of BPS, is to formulate the requirements for building envelope design as an
optimization problem. This leads to an inverse approach in which intelligent algorithms are used to drive a parameter search, until a design solution is found that meets the specified objectives in the best way possible. Through
such structured design space explorations, the trade-offs between various performance aspects can be analyzed in a systematic way [Deb and Srinivasan
2006; Radford and Gero 1987; Turrin et al. 2011]. With application to conventional, static façades, numerous studies have successfully
demonstrated the value of combining BPS models with optimization techniques, such as genetic algorithms [Attia et al. 2013; Evins 2013].
Applications of optimization for adaptive building envelopes are not yet extensively studied in scientific literature [Loonen et al. 2017; Favoino,
Overend, et al. 2015].
Figure 1.6. Building performance simulation has been linked to adaptive façades, optimization and product development in an isolated way. The aim of this research is to
combine these concepts in one framework.
10
It is hypothesized that the combination of computational modeling,
simulation and optimization can form an essential resource in supporting, stimulating and accelerating the development process of high-performance
adaptive façade concepts. However, this is a complex task, and currently available simulation tools and workflows lack capabilities to support this
process (Figure 1.6).
1.5 Aims and objectives
The main aim of this research is to develop, test and evaluate a computational approach that can be used to support design analysis and performance-based
design space exploration in the development process of innovative adaptable building envelope components and concepts.
Four objectives are directly related to this research aim:
⎯ To develop an effective modeling and simulation strategy for
integrated performance prediction of buildings with time-varying
building shell properties.
⎯ To develop and test a computational approach, based on simulation and optimization techniques, that is capable of exploring the
performance potential/limits of adaptive façades.
⎯ To illustrate, on a case study basis, how this approach can be used to
identify high-potential, i.e. high-performance, low-complexity, directions for future adaptive façade concepts (Figure 1.7).
⎯ To better understand the causal relationships between adaptability of building shell parameters and performance (energy and comfort)
in a number of demonstration examples.
11
Figure 1.7. High-potential adaptive façades are identified as having relatively low complexity, but high performance.
1.6 Scope of the work
The field of adaptive façades is a multi-disciplinary design and research area
that covers a relatively broad scope and receives attention from a diverse set of stakeholders. The work presented in this thesis targets a specific subarea
within the larger field of adaptive façades. Before describing the methods and research approach in more detail, a further clarification of the specific scope
of this work is given first.
Performance aspects: Building performance can be analyzed from many
different perspectives, including e.g. structural, aesthetic, air quality, lighting,
financial, durability, energy (operational and embodied) and acoustic aspects. In this thesis, the assessment of building performance concentrates on
energy saving potential for space conditioning (HVAC and lighting) and indoor environmental quality in terms of thermal and visual comfort.
Type of adaptability: The adaptation frequency in adaptive façades can range
from the time-scale of seconds (e.g. responding to variations in wind speed) to decades (e.g. responding to global warming or changes in building use as a
result of functional changes) [Fernández Solla 2014]. In this thesis, the main focus is on façade adaptation in the middle of this range (minutes-hours-
days).
Envelope construction types: Adaptability of building shell features focuses
on thermophysical and optical properties of the exterior façade. For example,
Com
plex
ity
high
low
12
airflow in multiple skin façades, adaptable roof systems and adaptability in
natural ventilation apertures are not within the scope of this study.
Level of abstraction and role in the building design process: The modeling
and simulation work described in this thesis targets to support the product development phase of innovative building envelope components. The
computational tools are developed with the aim of assessing the viability of adaptive façades as a design strategy in general, and to guide future research
and development processes towards the most promising solutions. It is developed for scoping or pre-feasibility studies that take a higher-level
perspective, i.e., the aim is to cover various different adaptive façade concepts, without focusing on the practical implementation aspects of
specific commercial building envelope components or prototypes. In
addition, the work is not necessarily limited to currently existing/available materials, but intends to facilitate the exploration of next-generation
adaptive façade concepts. The goal of the tools is to offer guidance for several stakeholders, including façade manufacturers, consulting engineers and
material scientists, to focus their attention on the most promising development directions with the highest impact.
Software development: The computational tools are developed in the form
of a “software prototype”, i.e. they are developed in a rapid way for research
purposes [Trčka 2008]. In this phase, little attention is being paid to facilitate
robust use for practitioners and other third-party users. Consequently, the software prototype does not yet include extensive documentation, tutorials,
interface development, etc.
1.7 Research methods
The work described in this thesis focuses on the research activities regarding development, testing and deployment of a toolchain5 for computational
performance simulation and optimization of buildings with adaptive façades. The research methodology follows the steps that are taken in the workflow
of software prototyping [Boehm 1988]. In this thesis, this is approached by iteratively addressing the various phases of the modeling and simulation life
5 A suite of software tools, scripts and algorithms, linked together in such a way to achieve capabilities that cannot
be achieved by the individual software tools.
13
cycle, as described by Balci [1994]. The four steps in this process are
described below.
1 Analysis of requirements
Development of the computational approach for performance-based analysis of innovative adaptive façade concepts begins by decomposing the
characteristics of the problem at hand. A literature review that covers the interface between adaptive façades and simulation support for R&D of
innovative building envelope components is conducted first. Together with
preliminary simulation studies and an analysis of capabilities of state-of-the-art BPS programs, including an inter-model comparison, this leads to the
specification of functional requirements for the toolchain.
2 Prototype development
The second step consists of model development and implementation and subsequent initial testing of the toolchain, leading to the development of a
working software prototype. After an initial phase of developing conceptual
models [Robinson 2007], three methods are used to accomplish this step:
⎯ Code modifications in existing BPS software to allow performance prediction with controllable time-varying building envelope
properties;
⎯ Integration of simulation programs in a receding time horizon control
framework with optimization algorithms to address the tight coupling between design and control aspects in dynamic systems such as adaptive façades;
⎯ Development of a co-simulation strategy to enable run-time communication between simulation solvers in the thermal and visual
domains.
3 Review of functionality
After a first version of the toolchain is developed, its performance is evaluated
with respect to the formulated requirements, to validate the functional performance of the toolchain and to examine if further refinements are
necessary. Iterative loops in combination with step 2 are carried out. This research step includes:
⎯ Illustration of the main features and capabilities of the whole
toolchain in a typical application example.
14
⎯ Critical examination of the quality (i.e. sensibility) of optimization
outcomes by comparing the outcomes with principles from building physics and results published in literature.
4 Demonstrate usability
Finally, the usability of the computational approach is further demonstrated in an application-specific case study. This demonstration aims to show the
potential for giving more advanced decision-support in response to requirements posed by real-world R&D processes at different modeling
resolutions and levels-of-detail. Moreover, it helps in building a better understanding of the causal relationships between adaptability of building
shell parameters and performance.
1.8 Thesis outline
Chapter 2 presents an analysis of the unique characteristics of adaptive façades and introduces the opportunities for computational simulation to
support research and development of innovative building envelope components. Together with the lessons learned from preliminary simulation
studies, this information is used to derive the functional needs for effective performance prediction and optimization of adaptive façades.
Chapter 3 forms the link between the research context (Chapter 2) and the
development of the new computational approach (Chapter 4). It presents higher-level requirements for performance prediction of façades with time-
varying properties, and discusses the limitations and opportunities for accomplishing this in existing BPS software. This chapter further introduces
the specific challenges that arise from optimization of dynamic properties in
adaptive façades, and discusses the performance aspects and corresponding performance indicators that are used throughout this thesis.
Chapter 4 provides considerations and detailed information about the
toolchain implementation. It describes the software programs that are used, how they were modified, and in which way they are connected in one co-
simulation framework, that couples thermal and daylighting aspects, and ensures high-performance supervisory control strategies for time-varying
façade properties.
Chapter 5 illustrates various capabilities of the toolchain in a typical application example. The focus in this chapter is on exploring the
15
performance potential of future-oriented adaptive façade concepts that can
simultaneously adjust optical properties and thermal resistance. This study intends to help identifying high-potential R&D directions for emerging
adaptive façade materials.
Chapter 6 demonstrates the usability of the newly developed toolchain, by showing how it can help in addressing the trade-offs between façade
complexity and performance. The subject in this case study is a fenestration system that has the ability to selectively control the transmission/reflection
of (i) visible light, (ii) near-infrared sunlight, (iii) and longwave infrared radiation.
Chapter 7 summarizes the main outcomes, discusses limitations of this work
and provides directions for future research.
16
17
2
State-of-the-art: Adaptive facades
2.1 Introduction
There are numerous research activities all over the world that seek to advance the design and development of buildings with adaptive façades.
These developments cover many different aspects that have conventionally been scattered across various engineering disciplines. In recent years, a
number of dedicated scientific conferences on adaptive building envelopes have been held, and efforts are coordinated via international collaborations
such as IEA ECB Annex 44: Responsive Building Elements [Heiselberg 2009],
and EU COST Action TU1403: Adaptive Façades Network [Luible 2015]. The work presented in this thesis connects to the search for new solutions in
these ongoing developments. A broad overview of challenges and achievements in the larger field of adaptive façades’ research can be found in
Loonen et al. [2013]. The purpose of this chapter is to provide an abridged review of background concepts and new research findings, with relevance for
narrowing down the scope of this thesis. While doing so, the details of two
18
preliminary simulation studies are briefly introduced (Sections 2.2 and 2.3).
These case studies show the type of performance information that can currently be generated in terms of simulation support for adaptive façades,
but also serve to illustrate a number of shortcomings of these existing approaches. Together, this then serves as the basis for functional
requirements of the new computational simulation approach in Chapter 3.
2.2 Adaptive façades: background and
characteristics
The envelope of a building performs many different functions [Hutcheon
1963]. On the one hand, it offers safety, security, privacy, and protection against fire, wind and rain. On the other hand, it functions as the connecting
element between building occupants and the outside world, by regulating the exchange of energy and admitting access to e.g. views, daylight and fresh air.
The design of a building envelope is of crucial importance for the quality of
the indoor environment it encloses. A growing awareness of the positive influences of good indoor conditions on health, well-being and occupant
productivity [Bluyssen 2010; Jin and Overend 2014] prompts increasing interest for finding new ways of achieving this with less energy consumption
[Brager et al. 2015]. Building envelopes are exposed to an environment that is highly dynamic – weather conditions change continuously with seasonal and
daily patterns, and also the influence of occupants (presence, activity, comfort needs) varies with time. Regular building envelopes are designed as
static elements and have limited ability to act in response to these changes. Adaptive façades, on the other hand, try to exploit these dynamics.
Many different adjectives are in use to refer to the virtues of buildings with time-varying façade characteristics. The most common variations include:
adaptable, adaptive, dynamic, flexible, kinetic, intelligent, interactive,
responsive, smart and switchable. Throughout this thesis, we use adaptive façades, and define it as:
An adaptive façade has the ability to repeatedly and reversibly change some of its functions, features or behavior over time in response to changing
performance requirements and variable boundary conditions, and does this
with the aim of improving overall building performance in terms of energy use and IEQ.
19
More specifically, this research draws upon three concepts from systems
engineering literature to describe the positive features of adaptive façades: adaptability, multi-ability and evolvability. The next subsections describe
these concepts in more detail.
2.2.1 Adaptability
The definition of adaptability that is used in this thesis is the one proposed by Ferguson et al. [2007]: the ability of a system to deliver intended functionality during operation, considering multiple criteria under variable
conditions through design variables that change their physical values over
time.
Building shells with this attribute can seize the opportunity to deliberately act in response to changes in IEQ requirements and ambient conditions, such
as solar radiation, wind speed and direction, temperature, rainfall, etc. Doing this offers a potential for energy savings compared to conventional, static
buildings because the valuable energy resources in the local environment can be actively exploited, but only at times when these effects are deemed
favorable. adaptive façades can thus act as weather mediator, negotiating between IEQ needs and what is available in the ambient environment
[Wigginton and Harris 2002]. With embedded adaptability, façades no longer have to be a compromise solution for the whole year, performing acceptable
under a wide range of conditions, but never optimal regarding a specific situation [Ochoa et al. 2012; Goia 2016; C. S. Lee 2017]. Moreover, it gives
opportunities to adjust to the individual user [Bakker et al. 2014; Sadeghi et al. 2016], rather than a best average for all, as usually prescribed in comfort
standards.
In addition to the immediate effect, adaptability of building envelopes also
helps in achieving gains through smart utilization of the thermal storage capacity of building constructions. By controlling the energy flux to/from
structural thermal mass, adaptive façades can help in mitigating overheating risk, and also limit redundancy in installed heating and cooling capacity [Hoes
and Hensen 2016; De Witte et al. 2017].
20
2.2.2 Multi-ability
The concept of multi-ability originates from the existence of non-simultaneous performance requirements, or the need to fulfil new roles over
time. The ‘balcony that can be folded’ [Hofman and Dujardin 2008] and the Velux Cabrio system [Petersen et al. 1990] are illustrative examples of a
responsive building envelope that features multi-ability: depending on ambient conditions and users’ preference, it changes function from window
to a balcony-on-demand [Weaver et al. 2008].
Multi-ability differs from adaptability in the sense that multiple requirements
are fulfilled successively, not at the same time [Ferguson et al. 2007]. Unlike conventional systems, designed to satisfy a single set of conditions, it allows
for addressing change via a plurality of individually-optimized states. In this way, multi-ability promotes more efficient use of resources, which adds to
the list of benefits already mentioned in the previous paragraph. A second application of multi-ability is the potential for spatial versatility. At the same
moment in time, the properties of the building envelope can be different for various positions of the building shell. In this way, different orientations of
the building can independently react to the ambient conditions or to distinct comfort preferences requested by individual users in separate zones.
2.2.3 Evolvability
Whereas adaptability and multi-ability mostly handle short-term changes, evolvability is a property of flexible systems that deals with variations over a
longer time-horizon [Silver and de Weck 2007]. Perhaps even more than uncertainty in day-to-day operation, future building requirements and
boundary conditions are highly unpredictable, or cannot even be known in the design stage [Holmes and Hacker 2007; De Wilde et al. 2011; Grant and
Ries 2013]. Building shells that can evolve over time are a means of extracting value from the uncertainty of these unforeseen events [Struck et al. 2014].
Evolvability is nevertheless considered more a positive side-effect, rather than a main design objective; the ability to keep options open preserves
opportunities to react to changes as they unfold in the future or perhaps might not occur. Concerning the building and construction industry,
evolvability, or sometimes called survivability or resilience [Stevenson et al. 2016], can be used to deal with changing conditions coming from the outside
(e.g. climate change, changing urban environment, deterioration of building
21
components) or from the inside (e.g. organizational function changes of the
building, new space layout). In all these cases, having a façade that can react to changes increases the chances that the building can continue operation as
intended, without getting impaired by potential negative impacts of unforeseen future conditions [Fawcett et al. 2012].
2.3 Decision support for product development
of new adaptive façade concepts
2.3.1 Introduction
Transitioning adaptive façades from niche application to mainstream design
option will require continued development of innovative adaptive façade materials, components and systems. However, it turns out that just having a
good idea is not always enough; the success or failure of innovations in the construction industry is influenced by several other factors. The ability of
innovation teams to show that their new product will reduce cost and
enhance quality or performance, has been recognized as one of the key enablers for success in this context [Toole 2001; Loosemore and Richard 2015;
Klein 2014]. A lack of effective communication about performance aspects, by contrast, was identified as one of the significant barriers hindering technical
innovation in construction [Gambatese and Hallowell 2011]. In Loonen et al. [2014], fifteen recently published projects describing R&D steps of various
innovative adaptable building envelope components were reviewed and classified according to four phases of development: laboratory scale,
reduced-scale experiment, full-scale mock-up, and pilot study. Figure 2.1 presents a summary of these projects.
[Xu et al. 2007; Xu and Van Dessel 2008; Chun et al. 2008; Imbabi 2006; Elsarrag et al. 2012; Granqvist et al. 2010;
Piccolo and Simone 2009; Assimakopoulos et al. 2007; E. S. Lee et al. 2012; Lienhard et al. 2011; Viereck et al. 2010;
He and Hoyano 2011; Ismail 2001; Grynning et al. 2013; Goia et al. 2013; Goia et al. 2014; Davidsson et al. 2010; Runsheng et al. 2003; Givoni 2011; E. S. Lee et al. 2006; Erell 2004; Leal and Maldonado 2008; Carbonari et al. 2012;
Debije 2010; Nitz and Hartwig 2005; Seeboth et al. 2010; E. S. Lee et al. 2013]
22
Figure 2.1. Classification of adaptive façades product development publications in relation to the characteristic R&D phases.
23
A number of inefficient elements in the R&D process were identified from
these fifteen studies:
⎯ Mismatch between information need and availability: Product
development of building envelope components often takes a linear solution approach (e.g. the stage-gate approach), even though the
design problems tend to be ill-defined [Eppinger et al. 1997; Kiss and Neij 2011; De Klijn-Chevalerias et al. 2017]. Decisions in the early
stages have the highest impact on the end result, but in the absence of detailed whole-building performance information, tend to be
based on intuition rather than analysis. Because the available performance information tends to be limited in scope and level of
detail, it is difficult to set goals, and evaluate whether they are
achieved. Moreover, early-stage identification of most promising directions for further development is a challenging task.
⎯ Disconnect between material science and building scale: Multiple
stakeholders with diverse interests are involved in the product
development process. Each stakeholder has a different perception of the value of the future façade product [Den Heijer 2013]. As a result,
there is a need for objective performance information to assist in decision-making trade-offs. The innovation process moreover
typically spans across multiple engineering disciplines. The expertise required for contributing to progress in technology development
tends to be in a different domain than the expertise required to assess what impacts this has on the built environment. In the existing
workflow, material scientists have limited guidance as to which properties are optimal [E. S. Lee et al. 2013], and it may not be
straightforward to appraise how modifications on the material scale affect performance aspects on the building level (e.g. indoor
environmental quality). There is a demand for integrated, multi-scale, multi-disciplinary tools to provide such complex insights.
⎯ Lack of information on building integration issues: The longitudinal
performance of building envelope components is strongly coupled to building-specific design attributes (e.g. glazing percentage,
orientation) and dynamic disturbances (e.g. climatic conditions, occupants’ behavior). Component-level performance metrics like U-
value and solar heat gain coefficient, which are determined under a single set of standard test conditions, can capture this type of
complexity only partially [Dave and Andersen 2011; De Wilde et al.
24
2002]. For example, Saelens [2002] has shown that active façades
feature a significant dependence between environmental conditions and U-value or g-value. Pushing component-level properties of
adaptive façades towards either high or low extremes might not always be the best solution considering the multitude of competing
performance criteria over the whole building life cycle [Ochoa et al. 2012]. Moreover, component-level performance metrics have very
limited use in assessing the dynamic performance of buildings with adaptive façades.
⎯ Limited scope of experiments: The task of obtaining reliable
performance information on the basis of experiments is not always
straightforward. This is the case for measuring the different building
energy flow paths [Judkoff et al. 2008], and also applies to objective quantification of thermal and visual comfort perception. Moreover,
conducting series of experiments with different product variants is a time-consuming and labor-intensive activity. Because of planning
and budget constraints, the number of product iterations in experiments often needs to be kept as low as possible [Strachan
2008].
⎯ No what-if analysis: In the conventional product development
process, only a limited number of scenarios, related to such factors
as orientation, building typology and climate can be examined. It is difficult to make projections of performance outside the range of
tested conditions on the basis of this bounded and incomplete knowledge. Technological product development can also be
hampered by the state of innovation itself. The envisioned directions for further development may be clear, but the technology is still
immature, or evaluated on the basis of semi-functional prototypes [E. S. Lee et al. 2013]. Test output from experiments may consequently
give a distorted view of reality, and thereby introduce the risk that the actual performance of the end product is misinterpreted
[Thomke 1998]. Physical tests thus provide only limited insights into possibilities of products with specifications that push the edge of
what is possible. To explore future directions, it can sometimes be worthwhile to assess the performance of visionary, hypothetical
product variants with properties and/or dimensions that cannot yet be manufactured [O’Connor and Veryzer 2001; C. S. Lee 2017]. The
25
virtual world of computer simulations is well-suited for supporting
this type of analyses.
Considering these limitations in existing product design and development processes, it is likely that some emerging adaptive façade technologies do
currently not reach their full potential. Innovation processes in the
construction industry tend to be based on technology-push (what is
possible?), instead of market-pull (what is needed?) [Nam and Tatum 1992]. This situation potentially leads to sub-optimal solutions that have negative
impact on the competitive position of product developers, but is also a missed opportunity for the innovations to contribute to sustainability goals.
This thesis explores the use of BPS to assist decision-making in product development processes of adaptive façades. The application example
presented in Section 2.3.2 will illustrate the added value of the current use of BPS in a typical R&D project. The opportunities can be contrasted with the
inefficiencies presented in this section. At the same time, the case study also highlights some of the existing limitations in the use of simulations with
respect to the specific characteristics of adaptive façades.
2.3.2 Case study and simulation method
Windows with switchable reflection/transmission properties in the near-
infrared (non-visible) part of the solar spectrum are considered as a viable future option for significant reductions in the energy use for heating, cooling
and lighting in buildings [DeForest et al. 2013; Ye et al. 2013; Khandelwal et al. 2017]. Recently, such a switchable IR reflector with high transparency in the
visible wavelength range has been fabricated with the use of cholesteric liquid crystals [Khandelwal et al. 2015]. Throughout this development process,
simulations acted as a virtual test-bed, providing feedback to the developers by evaluating the whole-building performance impacts of switchable IR
reflectors with different properties.
A medium-size office building (Table 2.1), with properties as defined by the U.S. Department of Energy reference buildings, was simulated using TRNSYS
simulation software [TRNSYS 2017]. In these dynamic whole-building performance predictions, the effect of the switchable IR reflector on
potential primary energy savings for heating, cooling and artificial lighting was analyzed. The window was switched to the reflecting state when the
indoor operative temperature was above 22 °C during daytime. In this way,
26
solar heat gains are allowed to the building when it is cold, but reflected when
there is a risk of indoor overheating.
Table 2.1. Details and assumptions of the case study building. See [Deru et al. 2011] for a full description.
Building description
Window-to-wall ratio 48%
U-value window 1.3 W/m2K
Opaque façade elements Heavyweight, thermal insulation
according to ASHRAE 90.1 [2013]
Room depth 6.4 m
Conversion to primary energy
Heating system efficiency 0.9
COP heat pump 3
Primary energy conversion factor for
electricity
2.5
Building usage scenario
Climate data TMY2 weather files
Occupied hours 09:00 – 17:00 on weekdays
Occupant heat loads 6 W/m2
Equipment loads 13 W/m2
Lighting power density 9 W/m2
Heating and cooling setpoint (Top) 20°C and 26°C
The window properties that were used in the simulations were obtained from spectral measurements on small-scale samples, and converted to the right format, using the calculation methods in the software Optics [LBNL 2017a] (Table 2.2).
27
Table 2.2. Window properties of the double glazing units in the simulations
Type of Window Tsol Tvis g-value
Reference 0.60 0.76 0.70
Switchable IR reflector – transparent state 0.59 0.72 0.68
Switchable IR reflector – reflecting state 0.48 0.69 0.56
Three different climates were selected to understand the relation between environmental conditions and energy savings in buildings with switchable IR
reflectors: (1) Abu Dhabi, United Arab Emirates (2) Amsterdam, the Netherlands and (3) Madrid, Spain. The results are compared to a reference
configuration employing normal double glazing (Ref, Figure 2.2) for a south facing office. We have also compared the results of the switchable IR reflector
(R-IR) with a static permanent IR reflector (StIR) in the ‘off’ state [Khandelwal et al. 2014]. This evaluation shows the impact that a switchable system may
have in comparison to static IR reflectors that are already available in the market.
2.3.3 Results
The simulations show that the energy-saving potential of switchable infrared reflectors in office buildings strongly depends on the local environmental
conditions (Figure 2.2). In Abu Dhabi, which has a warm and sunny climate throughout the year, the application of a switchable IR reflector (R-IR, 178.1
kWh/m2.yr) leads to cooling energy savings of > 15% compared to a normal double glazing window (Ref, 211.2 kWh/m2.yr). However, in such locations,
the demand for cooling is high throughout the year, which makes non-visible solar gains unwanted at all times. Given the current window switching
control strategy, the window would be in the transparent state for only 23 hours of the year. Hence, similar energy savings are achieved with static IR
reflection (Figure 2.2, StIR versus R-IR). In the Amsterdam environment, the window would be switched from the reflective state to the transmission state
for a considerable amount of time (1684 hours). The simulation results indicate that in heating dominated climates such as Amsterdam, there is no
need for either switchable or static IR reflectors. The decrease in utilization of passive solar heat gains would lead to an increase in demand of energy
required for heating.
28
Figure 2.2. Comparison of energy use intensity for a normal double glazed window (Ref), static IR reflector (stIR) and the switchable (responsive) infrared reflector (R-IR) for
three different climates.
In climates such as Madrid which have more seasonal influences than
Amsterdam or Abu Dhabi, for example, trade-offs between heating and cooling are better balanced. The ability of the windows to switch between a
transparent and reflecting state thus becomes more interesting, as well-controlled solar gains have benefits during both heating and cooling periods.
The total energy that can be saved by replacing the normal double glazing window (Ref, 126.1 kWh/m2.yr) with the fabricated switchable IR reflector (R-
IR, 110.6 kWh/m2.yr) is 12.3 %. For the same situation, a static IR reflector (121.9 kWh/m2.yr) would only result in 3% energy savings. The climate in
Madrid is predominantly sunny, yet has a relatively large number of cold days, so continuous reflection of NIR sunlight is not always a good strategy. The
window would need to be switched to the transparent state for an estimated 870 hours per year. It is advised that switchable IR reflectors are used in this
case to utilize passive solar gains in a dynamic way.
2.3.4 Discussion and lessons learned
Although relatively simple in scope and application, the example of windows
with controllable IR reflection properties clearly illustrates the value of using BPS to support the innovation process of an innovative adaptive façade
concept in an early phase of the R&D process. With respect to the inefficiencies introduced in Section 2.3.1, it shows that simulations can be
used to:
29
⎯ Benchmark the performance of new components, relative to current-
practice design solutions and other competing innovations.
⎯ Quantify the performance of building envelope products on the
whole-building level considering the influence of dynamic boundary conditions and indoor comfort criteria.
⎯ Provide insight into the performance under different climatic
conditions. The parametric analyses can easily be extended to include variations in e.g. façades orientations, window-to-wall ratios,
building usage scenarios, etc.
Despite all the opportunities, the simulation process also faced a number of
challenges, most of which were caused by software limitations. The following limitations and needs for further development are identified:
⎯ The computational algorithms in most building performance
simulation programs focus on a single physical aspect (e.g. thermal, ventilation, daylight) [Crawley et al. 2008], with no possibility to
explicitly model the interactions between domains. Proper analysis of advanced building envelope systems requires an integrated
performance assessment method that covers all relevant physical domains and interactions at an appropriate level of detail [Citherlet
et al. 2001]. In this present case, it was not possible to account for all interactions between daylight utilization, visual comfort, electrical
light usage and passive solar energy gains. It is therefore difficult to
investigate the trade-offs between these complementary domains.
⎯ The possibilities to model time-varying façade properties in available simulation tools are very limited. This limits the scope and level-of-
detail at which different adaptive façade solutions can be assessed. The implications of such simulation-specific issues will be further
explored in Chapter 3.
⎯ Only very simplified control strategies for façade adaption could be
modeled. The control logic had to be defined before the start of a simulation. Alternative modeling and simulation approaches are
needed to support performance prediction with more advanced façade control strategies.
⎯ The current simulation study is mostly a post-hoc analysis that
rationalizes the performance of windows with properties that were
30
already fabricated in the lab. To use the power of simulation to its full
extent, the process could also be inversed, by examining the potential of materials with properties that are not (yet) fabricated in the lab. In
that case, the innovation process would shift from technology-push
toward market-pull. However, simulation-based testing of the performance of many window variants with different properties is a labor-intensive task. There is a need for a structured approach to
streamline this design space exploration process.
2.4 Façade adaptation at longer time-scales –
an optimization example
2.4.1 Introduction
The bulk of research on adaptive façades focuses on components that can
change their properties on a high-frequency basis, i.e., in the order of seconds, minutes or hours [Wigginton and Harris 2002]. Examples include
the deployment of switchable glazing technology [Baetens et al. 2010], dynamic thermal insulation [Kimber et al. 2014], advanced solar shading
systems [Schumacher et al. 2010; Fiorito et al. 2016], and materials with variable solar absorptance/emittance properties [Park and Krarti 2016].
These short-term adaptation mechanisms enable the façade dynamics to synchronize with changing boundary conditions, and as a result, they are
expected to represent the highest potential in terms of performance improvements [Selkowitz and Bazjanac 1981; Davies 1981; Moloney 2011]. It is
also the reason why the new computational approach in this thesis focuses on short-term adaptive façades.
An alternative to short-term adaptive façades are façades that can adapt their
properties in response to changing conditions over the seasons [Loonen et al. 2011]. Previous studies have identified that building designs with seasonal
adaptation strategies can enhance energy and comfort performance compared to the best static situation (e.g. [Claridge 1977; Lorenz 1998; Dubois
1999]). In this context, improvements in building performance have already been demonstrated in cases with seasonal variation in: solar shading design,
window properties, thermal insulation, thermal mass, natural ventilation
strategies and temperature setpoints [Kasinalis et al. 2014]. It is noted that adaptive façade technologies with short response time can also be operated
31
in response to long-term variations. However, compared to short-term
adaptability, seasonal façade adaptation has a number of possible advantages:
⎯ The technical feasibility, robustness and durability are expected to be higher, since long-term adaptive façades are more likely to be built as
low-cost add-on solutions, with less challenging technologies.
⎯ There is no need for complex control strategies and user interaction
mechanisms as the façade needs to change only few times during the year.
⎯ The risk for disturbance or reduced occupant satisfaction due to
moving façade components is very low.
Additionally, it turns out that the analysis of the performance potential of
seasonal façade adaptation is not affected by most of the simulation process-related limitations for short-term adaptive façades (as presented in Section
2.3.4), and can be done using already existing simulation approaches. Long-term adaptive façades furthermore allow for easy integration with existing
building envelope optimization workflows. The next example illustrates the benefits of such an approach.
2.4.2 Case study and simulation method
The purpose of this case study is to analyze the possible improvement in energy performance and indoor environmental quality due to monthly
adaptation of six building envelope parameters. The simulations are based on coupled building energy and daylight simulations, conducted under multi-
objective optimization (MOO) scenarios with genetic algorithms, following the method that is introduced in Kasinalis et al. [2014].
The study considers a single-person south-facing perimeter office zone (3.6
m × 5.4 m × 2.7 m), situated at an intermediate floor, surrounded by identical office zones and a corridor at the back. The building, which is evaluated under
Dutch climate conditions, is occupied on weekdays from 8 to 17 h. Heating is assumed to be supplied with an ideal system with unlimited capacity, but no
active cooling system is employed. Ventilation with outside air is provided at a rate of 2 ACH during occupied hours. The opaque part of the external façade
is modeled as a single layer with a thickness of 0.35 m. Window-to-wall ratio (WWR) as well as thermophysical and optical material properties are
32
determined by optimization. Table 2.3 gives the dynamic range of the design
parameters that are considered in this study.
Table 2.3. Overview and range of design parameters.
Parameter Range Unit
Density (ρ) 50 - 3000 [kg/m3]
Specific heat (cp) 0.8 – 2.0 [kJ/kgK]
Thermal conductivity (λ) 0.1 – 2.5 [W/mK]
External surface absorptance (α) 0.1 – 0.9 [-]
Window to wall ratio (WWR) 0.1 – 0.8 [-]
Glazing ID (see Table 2.4) 1-7 [-]
Allowing the different optical and thermal glazing properties to vary independently from each other can easily lead to fenestration typologies that
are unrealistic from a physical point of view. To avoid this, we used existing window systems with meaningful properties. Table 2.4 shows the properties
of the glazing types that correspond to the glazing IDs from Table 2.3.
Table 2.4. Overview of glazing properties.
Glazing ID U-value [W/mK] g-value [-]
1 5.7 0.86
2 2.8 0.76
3 1.4 0.59
4 1.4 0.62
5 1.3 0.59
6 1.3 0.62
7 0.9 0.59
An exterior shading system (venetian blinds) is applied to control the
admittance of solar gains. These blinds are “ideally” controlled on the basis of
33
an active users profile [Reinhart 2004]. This stochastic algorithm assumes
that the blinds are rearranged on a regular basis with the aim of maximizing daylight availability while preventing glare and direct sunlight on the work
plane. Artificial lighting with a lighting power density of 10 W/m2 is switched on, only when daylight availability is not sufficient to meet the illuminance
target of 500 lx on the work plane.
Dynamic simulations were conducted using the multi-zone building model in TRNSYS [TRNSYS 2017]. These energy simulations were coupled with the
outcomes of daylight simulations in DAYSIM [DAYSIM 2015]. The long-term adaptation was investigated using twelve separate analyses, in which each
month of the year was addressed by a different set of simulations and optimization. The annual performance was then obtained by the combination
of monthly results as described in detail in Kasinalis et al. [2014]. By combining these simulations in a multi-objective optimization framework,
the following two optimization objectives were minimized:
⎯ Primary energy consumption [kWh/m2] (for heating and artificial
lighting).
⎯ Number of hours per year when |Tdiff| > 3 K, where Tdiff is the
temperature difference between the indoor operative temperature and the comfort/neutral temperature (corresponding to Category II
in EN 15251 [2007]).
2.4.3 Results
Optimal performance of seasonal adaptation in adaptive façades
Figure 2.3 shows the annual results for different façade configurations in two dimensions: energy consumption and thermal comfort. Each dot in this
scatter plot corresponds to a possible combination of façade properties, i.e. a façade design. The black dots represent the Pareto front with annual
optimization results for the façade with non-adaptive properties (note that
the cloud with dominated non-adaptive design solutions is omitted in this figure). Optimization results for the adaptable façade are obtained by making
all possible combinations from Pareto solutions of the individually optimized monthly simulations. For the case study, this cloud of results is shown with
pink dots in Figure 2.3, with its corresponding Pareto set in red.
34
Figure 2.3. Comparison of the annual Pareto fronts for the static (black) and monthly adaptive façades (red).The pink dots represent the cloud of available solutions for the
case of the adaptive façade. See text for descriptions of points A, B, B’ and C.
It is worth noting that all solutions that were aggregated from the sets of monthly optimal results are non-dominated with respect to the Pareto-
optimal design solutions with a static façade. Due to the large number of possible combinations originating from the points in the twelve Pareto fronts,
the density of the created cloud is significant. Therefore, it is more difficult to distinguish individual solutions (dots) in comparison with the cloud
representing the performance of the same zone with a non-adaptive façade. This finding indicates that many adaptive façades solutions lead to
approximately similar performance, and that additional preferences could be introduced to select the best alternative. When evaluated under only the
scope of energy consumption reduction, the application of a monthly adaptable façade shows a moderate improvement. For example, if the design
team accepts discomfort in the range of 240 hours per year (with respect to EN 15251 standard, category II), then the reduction in energy consumption
amounts to 18% compared to the best performing static building shell design (compare points B and B’ in Figure 2.3). This saving potential is not remarkably
high, but it should be considered that these savings are compared to an optimized static façade, and most buildings do not have optimized static
façades. Moreover, savings around 18% would provide, for example, four
credits in the LEED green building certification system, which is a good result
35
considering that the intervention is focused only on seasonal adaptability of
the building envelope.
The potential of seasonal adaptive façades stands out better when not only the effects on energy conservation, but simultaneously also the impact on
indoor environmental quality is considered. The office zone with adaptive features achieves a high-quality indoor environment (up to zero discomfort
hours, point C) while achieving energy savings around 15% in comparison with the best performing static design, point B’. This performance gain points
out that further developments of innovative adaptable building shell concepts seem worthwhile, particularly considering the positive correlation between
indoor environmental quality and increased employee productivity in office spaces.
Optimum properties of seasonal adaptive façades
In addition to the assessment of the performance potential of a building shell with monthly adaptable properties, it is also useful to gain better
understanding of the optimum design characteristics of such an innovative façade.
Figure 2.4. Optimum window-to-wall ratio of monthly adaptive façades for the case study office building in the Netherlands. Points A, B and C correspond to the three
designs in Figure 2.3. The dotted line represents B’, an optimal non-adaptive design solution.
Figure 2.4 shows the time evolution of optimal WWR values for the three
representative points of Figure 2.3. The three lines represent point B (with 240 discomfort hours), and the two Pareto extremes with maximum (point A)
36
and minimum discomfort level (point C). The fixed value of the best
performing static façade design, point B’, is indicated with the dashed line.
The results in Figure 2.4 indicate a clear preference for low WWR values during winter months. This result implies, in accordance with previous
findings [Peippo et al. 1999; Ochoa et al. 2012], that under winter conditions in temperate climates, passive solar gains and daylight utilization are
insufficient to compensate for conduction losses through windows. Due to the lower thermal resistance of fenestration compared to opaque wall parts,
the given optimization formulation consistently favors low window to wall ratios in winter. In mid-season months, however, the reduced heating
demand in combination with the low risk for overheating promotes higher levels of daylight utilization. Therefore, relatively high values of WWR are the
optimum during these months. In the summer period, the substantially higher risk of thermal discomfort leads to smaller window areas than in the
mid-season, but larger in comparison to winter months. The differences in performance between points A, B and C in Figure 2.3 are clearly reflected in
the optimum WWR for each case. During the winter months, all three points have the same values due to the absence of overheating risk, and hence the
mild conflict between performance objectives. During the mid-season and summer, two groups can be identified. On one hand, the designs with priority
in energy consumption (points A and B) have larger windows, reducing the use of artificial lighting but increasing the number of overheating hours. On
the other hand, the design with priority for comfort (point C) has reduced windows sizes, spending more energy for artificial lighting but avoiding
comfort problems caused by excessive solar heat gains.
A comparison between the properties of point B and the static optimum shell
(B’ represented by the dashed line) in Figure 2.4 shows that the improvements in performance are based on relatively large adaptations in WWR. It is
remarkable that, for most months in the year, the adaptable WWR values are larger than the static value. Larger WWR values indicate that in this case
study, adaptive façades also increase the view to the outside, which is an important performance aspect in buildings [Aries et al. 2010]. As such, this
finding shows a clear example of the promising role of adaptive façades, together with the type of concessions [Radford and Gero 1980] that is needed
to design static building envelopes which are supposed to perform well under the wide range of different boundary conditions.
37
Figure 2.5. Optimum properties of monthly adaptive façades for the case study office building in the Netherlands.
Figure 2.5 presents the optimum monthly values of the other building shell
properties in the case study. The overall trend for most properties is in
accordance with the expected behavior. External surface absorptance has higher values in winter (improving passive solar gains) and has lower values
in summer (reflecting solar radiation to reduce overheating). Thermal conductivity has lower values in the winter and mid-season (reducing
conduction losses) and has higher values in summer to improve heat losses and avoid overheating. The combined effects of density and specific heat
suggest that optimal building shells are lightweight in winter, and heavyweight in summer. This finding is consistent with the results in Hoes et
al. [2011]. Finally, the evolution of glazing ID over time points out that it is important to have high-performance fenestration properties, i.e. low U-value
(0.9 W/m2K) and relatively low g-value (0.59), throughout the entire year.
Monthly optimized adaptive façade properties in Figure 2.5 also show some erratic behavior that cannot be directly explained by physical principles. Part
of this behavior may be caused by limitations of the genetic algorithm, which cannot guarantee that the outcome of the optimization process is the global
optimum. Although measures have been taken to ensure convergence of the optimization, an in-depth analysis of the impact of different settings of the
algorithm was not the focus of this research. These results, however, do
38
demonstrate the need for further research on robustness of optimization
outcomes with respect to adaptive façades. Another reason for the erratic behavior of some properties pertains to sensitivities in the input-output
relationship between design parameters and objectives. Two different effects play a role here:
⎯ There is an inherent difference in sensitivity of the performance
indicators to each adaptive façade property in the design option space [Struck et al. 2009; Tian 2013].
⎯ The effect of certain design variables (e.g. solar absorptance) can become negligible when another variable (e.g. thermal insulation) has
a first-order effect in driving the optimization search [Brownlee and Wright 2012].
The effect of different input-output relationships on the performance of
adaptive façades is further exemplified in Figure 2.6 which presents the cloud of design options investigated during the optimization run for the month
April.
Figure 2.6. Bubble plots of all the design solutions evaluated in the optimization process for the month of April, with colors based on the values for (A) thermal conductivity and
(B) specific heat capacity.
In Figure 2.6, each design alternative is colored according to the value of the conductivity (Figure 2.6A) and specific heat capacity (Figure 2.6B). For
thermal conductivity, it can be noticed that design solutions converge to a specific tint of the color map as the design solutions approach the Pareto
front. Such kind of ordered patterns in the optimization cloud and along the
front contain useful cues about the significance of design variables to the optimal trade-off in multi-objective problems [Chichakly and Eppstein 2013].
39
Based on the results in Figure 2.6A, conductivity seems to have a dominant
influence on performance as all Pareto-optimal solutions converge to approximately the same value. This dominant effect reduces the erratic
behavior of this property in the optimization results, as seen in the winter months of Figure 2.5B. This is in contrast with the seemingly random spread
of values in the Pareto front for specific heat capacity, and in this case the optimum solution in Figure 2.5D should be adopted with caution.
2.4.4 Discussion and lessons learned
The previous paragraphs presented an example of the analysis of adaptive
façades with coupled daylighting and thermal simulations. Although the focus was on thermal comfort and energy consumption, it did also take daylighting
and electricity use for lighting into account. Moreover, the study highlighted the merits of automated design space explorations through the coupling
between BPS tools, optimization algorithms and post-optimization analysis of the results. The study shows a significant performance potential for
seasonal adaptability of building façades because they take care of energy
savings while upgrading the indoor environment. Whereas the optimization of the static façade has to make compromises to achieve satisfactory
performance over the whole year (e.g. resulting in small window areas), long-term adaptive façades can strategically take advantage of the variable
conditions by adapting over time (e.g. large window areas during most part of the year).
It is hypothesized that adaptive façades with shorter-term (e.g. hourly)
adaptation options can achieve even better performance, because the adaptive façade actions/responses can be synchronized with changes in
internal and external boundary conditions. The here presented assessment method is, however, not well-suited to explore this potential. In these
simulations, the changing of façade configurations did not take place during simulation run-time, but was approximated by coupling the results from a
series of disconnected, monthly simulations. With a higher façade adaptation frequency, the simulation periods would get shorter, meaning that
shortcomings in the initialization process (i.e. the end state of one simulation (surface and construction node temperatures) are different from the starting
conditions of the subsequent simulation) effects start playing a more prominent role. Using a series of decoupled simulations, the correctness of
thermal history effects at the transition between two successive simulations
40
cannot be guaranteed [Loonen, Hoes, et al. 2014]. With short-term adaptation
cycles (e.g. hours), in particular, this can lead to significant prediction errors, as the repeated start-ups would almost defeat the purpose of dynamic
simulations.
41
3
Principles and requirements for
simulation-based optimization of
adaptive façades
3.1 Introduction
The relationship between façades and building performance is multi-faceted.
In comparison with conventional buildings, the various IEQ elements (e.g. thermal, visual, air quality, etc.) tend to be more interconnected in buildings
with adaptive façades. Moreover, their interactions and priorities are changing dynamically, and can be influenced over time. Modelling and
simulation studies that consider adaptive building envelopes have to accurately represent a sequence of time-varying building envelope system
states (or properties), instead of a static representation of the building enclosure. Moreover, for effective performance prediction of adaptive
building envelope systems, it is essential to simultaneously consider multiple
levels, in terms of (i) spatial scales, (ii) time resolutions, and (iii) physical domains.
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Compared to simulation-based design optimization of conventional, static
façades, two major additional requirements for the computational performance prediction framework are identified:
⎯ Modeling time-varying façade properties: Façade specifications (i.e.
material properties or position of components) need to be
changeable during simulation run-time to properly account for transient heat transfer and energy storage effects in building
constructions [Loonen, Hoes, et al. 2014]. The motivation for doing this, as well as the challenges and opportunities for accomplishing
this in existing state-of-the-art BPS software, are discussed in Section 3.2.
⎯ Modeling the dynamic operation of façade adaptation: The dynamic
interactions in adaptive façades give rise to a strong mutual dependence between design and control aspects. The performance
of adaptive systems fully depends on the scheduling strategy (i.e. control logic) for façade adaptation during operation. Moloney [2011]
describes it as: “The design outcome in a project with kinetic façades
is a process, rather than a static object or artifact”. Thus, to identify
the characteristics of optimal adaptive façade systems, it requires not only design considerations (i.e. façade system design parameters), but
also insights into high-performance automated operation strategies of the dynamic façade to be considered, already during the product
development or design phase [Hoes et al. 2012]. Moreover, effective design and operation of dynamic façade systems depends also on the
integration with operations of the other building services. For example, limited lighting energy savings could be achieved if the
operation of dynamic solar shading is not integrated with a lighting dimming system. Similarly, the integration with HVAC, and renewable
energy systems needs to be carefully considered. This coupling between engineering domains leads to a bi-level optimization
formulation [Fathy et al. 2001; Evins and Orehounig 2014], which is further discussed in Section 3.3.
Details from the translation of the above two requirements into a software
implementation are described in Chapter 4. An important characteristic in the development of the simulation-based optimization framework is that the
optimization of building performance primarily takes place via changes in building shell configurations. This is a relatively unique situation, which
requires that the performance aspects and corresponding performance
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indicators that are used in the software implementation need to match this
situation. The way in which the framework addresses these issues is therefore also described first, at the end of the current Chapter, in Section
3.4.
3.2 Modelling and simulation techniques for
performance prediction of adaptive façades
3.2.1 Modelling challenges
A large number of software tools is available for predicting the energy and comfort performance of buildings6. Each program has unique features in
terms of modelling resolution, solution algorithms, intended target audience, modeling options, ease of use vs. flexibility, etc. Generally, these tools are
used to support informed decision-making in the later phases of building and HVAC system design/sizing [Hensen and Lamberts 2011]. The simulation
tools with most powerful modeling capabilities, and which have undergone
most rigorous validation studies (e.g. ESP-r, EnergyPlus, TRNSYS), are legacy software programs [Crawley et al. 2008]. Although these tools have active
development communities, and receive regular updates and extension of modeling capabilities, their underlying concepts and basic software
architecture do not change. Most tools stem from a time when adaptability of building components was not a primary consideration [Ayres and Stamper
1995; Oh and Haberl 2015]. Consequently, the building shape and material properties are usually not changeable during simulation run-time in these
tools, which restricts the options for modelling adaptive façades. There are three main reasons for the present difficulties:
1. User interface: Input for constructions and material properties to
BPS programs is normally given in the form of scalar values. These parameters are either directly entered by the user, or imported from
pre-configured databases. The same static representation is implemented for the size, geometry and orientation of the various
surfaces that together form the building envelope. In the most common approach, this information is then processed once, prior to
6 The database of building energy analysis software maintained by the International Building Performance
Simulation Association USA currently consists of 174 different tools [IBPSA-USA 2018]
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the actual simulation run, and is not updated further during the
simulation. In many simulation programs, users have very limited flexibility to extend the functionality for modelling adaptive façades
through the non-modifiable user interface and the restricted access to the source code of (proprietary) simulation tools. Typical
exceptions of non-constant elements in most simulation programs are the deployment of solar shading systems and operable windows
for natural ventilation, both of which can be functions or time series.
2. Solution routines for transient heat conduction through building elements. Many of the widely used BPS tools such (e.g. Trnsys and
EnergyPlus) adopt response factor techniques (e.g. Thermal Response Factors (TRF) or Conduction Transfer Functions (CTF)) to
solve the differential equations governing the heat transfer phenomena through opaque building elements [Spitler 2011]. These
methods are optimized for computational efficiency, but by virtue of their design, they can only work with time-invariant thermophysical
properties (i.e. density, specific heat capacity, and thermal conductivity) [Clarke 2001]. This is because the coefficients that are
used in the equations are constant and determined only once for each building envelope element at the beginning of the simulation. As such,
response factor methods do not permit variations in thermophysical material properties during simulation run-time [Delcroix et al. 2012;
Pedersen 2007]. The default solution routine in EnergyPlus uses the CTF methods, but the software was recently extended with a new
finite difference scheme for conduction, to allow for modelling temperature- or time-dependent material properties [Pedersen
2007; Tabares-Velasco and Griffith 2012]. Practical use of these new algorithms is still limited [Roberz et al. 2017], and its potential largely
unexploited. The code in the simulation program ESP-r, on the other hand, is based on the control volume method. This numerical
methods adopt an iterative procedure, thereby allowing for updates
of the matrix coefficients that describe heat transfer, as time steps of the simulation proceed. This makes the simulation of variable
thermo-physical properties possible [Clarke 2001].
3. Control strategies. Control strategies in BPS models provide the link
between sensed variables and actuator actions by means of a certain control logic. This feature is mostly used for the control of HVAC
systems, but other opportunities also exist. The (non-)availability of
45
actuator options is what in the end determines the types of adaptive
façade technologies that can be modelled in a simulation tool. The general architecture for the control of building systems (including
adaptive building envelope systems) in BPS tools can be divided into 3 parts: (i) sensors level (climatic boundary conditions, building
internal boundary conditions and occupant preferences); (ii) control logic level and (iii) actuators level, that is, any building component
that can be controlled (including HVAC, artificial lighting and adaptive building envelope systems).
Most BES tools use simplified expressions such as time-based
schedules or hardcoded if-then-else statements as strategies for building systems control. Moreover, they allow a limited range of
sensor and actuator options [Hoes et al. 2012]. Advanced control of dynamic properties is, however, identified as one of the major
elements needed for performance assessment of adaptive façades. The lack of options is currently a significant barrier for performance
prediction of advanced operation strategies with adaptive façade components as time-varying actuators.
3.2.2 Opportunities using existing simulation software
A short review of the possibilities for modeling adaptive façades in state-of-the-art BPS tools was conducted to identify the current possibilities and
existing development needs. This analysis is based on the information in user manuals, software tutorials, release notes and contextual help facilities of the
BPS tools, as well as communication with their development teams. Furthermore, scientific articles, dissertations and the information exchange
in mailings lists online forums were used to gather input. Two types of modeling features are distinguished: (i) application-specific and (ii) general-
purpose. Here, application-specific indicates that the simulation model was implemented in the software with a specific adaptive façade concept in mind.
The adaptive mechanism and how it is triggered are, therefore, already embedded in the specific model, and users can activate it easily by means of
the GUI, but are limited to the presets available. The general-purpose features, on the other hand, are not restricted to a specific technology, but
offer flexibility for user-defined combinations of adaptive thermo-physical property variations and/or triggering mechanisms. This higher abstraction
level affords more freedom for exploring innovative adaptive building
46
envelope systems, although it requires the BPS user to define and code the
control mechanism that triggers adaptation in the building element.
1 - Application-specific capabilities
An analysis of the capabilities in six commonly-used software tools resulted in six different application-specific adaptive features (Table 3.1); each of them
is discussed below.
Table 3.1. Overview of application-specific features for modeling adaptive façades in six commonly used BPS tools.
EnergyPlus7 ESP-r eQUEST IDA ICE IES VE TRNSYS
Electrochromic glazing x x x o x o
Thermochromic glazing x x o o o
Insulating solar shading x o x x o
Phase change materials x x o x
Green walls and roofs x o x
Movable insulation x o o o
x - The modeling capability is readily available from the graphical user interface and can
directly be used by advanced users.
o - The modeling capability is present in the software, but can only be activated by expert
users/developers via source-code modifications or custom-made scripts.
Different types of switchable windows, including electrochromic glazing, are commercially available, and many research papers have been written about their application in buildings and architecture [Baetens et al. 2010]. As a result
of their presence in the market, options for modelling switchable glazing technologies are embedded in several simulation tools. All the software tools
analyzed offer the possibility to control the properties of the fenestration system during simulation run-time. The differences between the various
implementations are the number of possible window states (e.g. on/off versus gradual transitions) and the simulation state variables that can be used
for the control of adaptation (e.g. room temperature, ambient temperature and incident radiation).
Thermochromic windows are slightly more complicated to simulate than other switchable window types because of their ‘intrinsic’ control character; adaptation of the fenestration properties is directly triggered by window
surface temperature instead of a control signal that is based on more general simulation variables [Favoino, Cascone, et al. 2015]. A thermochromic window
7 Some of the features in native EnergyPlus are also available in GUIs that use EnergyPlus as simulation engine e.g.
DesignBuilder, OpenStudio, Simergy, Sefaira.
47
capability was implemented in EnergyPlus (since v3.1, 2009) and ESP-r [Evans
and Kelly 1996]. The input of these models consists of sets of window properties at various temperatures. During the simulation, the
thermochromic layer temperature of the previous time step is automatically fed into a window control algorithm, which then selects the window
properties that best match with the given temperature. In IDA ICE, IES VE and Trnsys, it is also possible to model thermochromic windows, but a
significantly higher level of work and expertise is required from the user side.
The GUIs of EnergyPlus, IDA ICE and EnergyPlus offer the possibility to give dynamic shading devices additional thermal resistance properties. This
makes it possible to simulate the performance of insulating solar shading
systems [Hashemi and Gage 2012]. In such an implementation, dynamic thermal insulation and solar shading are coupled; their separate effects cannot be analyzed.
Prediction models for wall-integrated phase change materials (PCM) are present in EnergyPlus [Tabares-Velasco et al. 2012], ESP-r [Heim and Clarke 2004], IDA ICE [Plüss et al. 2014] and TRNSYS [Kuznik et al. 2010]. These
models influence heat transfer in constructions via either the ‘effective heat capacity’ or the ’additional heat source’/‘enthalpy’ method. The need to
implement PCM features led the developers of EnergyPlus to abandon the Conduction Transfer Functions approach and introduce a numerical finite
difference conduction algorithm [Pedersen 2007]. This new algorithm also has a provision for including a temperature coefficient that makes thermal
conductivity variable during the simulation [Tabares-Velasco and Griffith 2012]. No applications of this latter model were found in literature.
EnergyPlus, ESP-r, and TRNSYS support the simulation of green walls and
roofs. The models account for: (i) long-wave and short-wave radiative exchange within the plant canopy, (ii) plant canopy effects on convective heat transfer, (ii) evapotranspiration from the soil and plants, and (iv) heat
conduction and storage in the soil layer [Sailor 2008; Djedjig et al. 2015]. In the EnergyPlus model, it is possible to include material properties that
change over time with fluctuations in plant growth and moisture content [Sailor and Bass 2014].
Finally, EnergyPlus and IDA ICE [Bionda et al. 2014] have the option to predict
the performance of building envelopes with moveable insulation. A controllable layer can be applied to the interior or exterior side of a construction (not windows) to temporarily increase its thermal resistance.
48
These materials are massless, which means that no thermal energy can be
stored in a moveable insulation layer.
2 - General-purpose modeling options
The application-specific adaptive features presented in the previous paragraph only allow for a restricted level of flexibility. The tendency of BPS
tools to lag behind the market availability of adaptive technologies limits the number of application-specific modelling capabilities available in a specific
BPS tool, compared to what is technologically available. As such, there are many adaptive building envelope systems (either at prototype or at product
stage), whose performance cannot be evaluated yet with the existing application-specific simulation models. Examples include tunable switchable
windows with selective light transmission in different parts of the solar spectrum [Llordés et al. 2013], water-carrying transparent façades with solar
control [Ritter 2014], solar-tracking photovoltaic façades [Nagy et al. 2016] and luminescent solar concentrating façade elements with variable
transmission and scattering properties [Sol et al. 2018]. Therefore, from a product development and innovation point-of-view, it is more desirable to
allow for bottom-up or general-purpose approaches to be able to simulate emerging or not-yet-existing adaptive building envelope materials and
technologies.
Because of their particular background and open make-up, the BPS tools
TRNSYS and ESP-r both offer a number of attractive capabilities for modeling and simulation of adaptive building shell behavior in a generic way. A brief
overview of adaptive features in both tools is included here to provide further background for the decisions that are made in the newly-developed
simulation approach.
TRNSYS
The approach that TRNSYS takes towards managing complexity in the built environment is characterized by breaking down the problems into a series of
smaller components. One of these components is a multi-zone building model — in TRNSYS called TYPE 56 — that can be connected to a large number
of other components, including weather data, HVAC systems, occupancy schedules, controllers, output functions, thermal energy storage, renewable
(solar) energy systems, etc. This particular configuration allows the user to set up and manipulate the connections between the building and various
other subsystems/components in the simulation environment.
49
TRNSYS TYPE 56 offers the possibility to change the thermal and optical
window properties during run-time with a function called variable window ID. Additionally, it is also possible to control the ratio of window/frame area,
which influences the degree of transparent façade elements. Recently, TRNSYS was extended with a bidirectional scattering distribution function
(BSDF) model that can be changed at every time step of the simulation [Hiller and Schöttl 2014]. All the other adaptive mechanisms in TRNSYS are not found
in the (non-modifiable) building model itself, but in the connections with
other components. Using equations in TRNSYS enables the application of Boolean logic and algebraic manipulations to almost all state variables in the
simulation. This flow of information can then be used to drive a control algorithm that is able to dynamically ‘switch on’, ‘switch off’ or modulate, for
example, overhangs and wingwalls (TYPE 34), shading masks (TYPE 64), attached sunspaces (with or without movable thermal insulation) (TYPE 37),
windows with variable insulation properties (TYPE 35) and photovoltaic modules (TYPES 94, 180 and 194). In addition, it is also possible to adjust the
connections with weather files and radiation processors. In this way, the effects of changing orientations (e.g. rotating buildings) can be mimicked.
Even more control flexibility can be achieved by connecting TRNSYS models to the W-editor (Type 79) [Keilholz et al. 2009]. Type 79 makes use of W, a
simple programming language that can influence the connection between the inputs and outputs of TRNSYS components at every iteration of the
simulation.
The standard TRNSYS distribution already comes with an extensive library of components. Yet, one of the distinct benefits of TRNSYS’ modular structure
is the fact that it allows users to add content by introducing new components
[McDowell et al. 2004]. With some coding efforts, it is possible to encapsulate the desired adaptive façade behavior in a new TRNSYS TYPE which can then
be linked to the building model. Due to constraints in TRNSYS’ CTF method, coupling of these new TYPES with the building envelope model works in a
rather indirect way via the so-called slab-on-grade approach. In TRNSYS it is not possible to substitute building shell constructions/properties during
simulation run-time. Instead, developers can impose the desired behavior by overwriting the inside surface layer temperatures of adjacent zones and the
respective heat transfer coefficients. With respect to adaptive façades, Kuznik et al. [2010] and Claros-Marfil et al. [2014] recently demonstrated this
approach for a new PCM wallboard TYPE, and Djedjig et al. [2015] developed a model for green walls.
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ESP-r
ESP-r is a multi-domain research-oriented BPS tool with an active development community and a source code that is accessible and modifiable.
The tool is used for prediction of thermal, visual, indoor air, electrical and acoustic performance of buildings. Users can extend or modify existing
features in the source code, according to their needs. Over the course of the years, several functionalities that can be used to model adaptive behavior in
the building shell have already been implemented. Nevertheless, the use of these capabilities has remained limited, possibly because the features lack
extensive documentation or are concealed somewhere in the distributed menu-structure of ESP-r. This section summarizes five of such features:
⎯ One of the control laws in ESP-r is called thermophysical property
substitution mode. It is the only strategy that is not used for controlling the operation of HVAC systems. Instead of this, this
control strategy can replace the thermophysical properties (λ, cp, ρ) of
a construction during the course of the simulation. In essence, this control works like any other control algorithm in ESP-r, in the way
that actions are triggered based on ‘tests’ applied to sensed variables during run-time [MacQueen 1997]. Unfortunately, this feature does
not allow for full flexibility since it only affects opaque wall elements and the only ‘sensor variable’ is indoor air temperature.
⎯ The previous feature dealt with opaque construction elements only,
however, ESP-r also has a similar functionality available for modeling the dynamic behavior of windows; transparent multi-layer
construction control. This functionality can for example be used for performance prediction of switchable glazing technologies.
Currently, it is possible to replace window properties (.tmc-files) based on time, temperature, solar radiation level or illuminance level.
Restrictions are that no more than two window states are supported without the possibility for gradual transitions. Recently, the
capabilities of ESP-r have been further extended with the
implementation of two new facilities for modeling transparent façade systems. Both the complex fenestration constructions (CFC)
[Lomanowski and Wright 2012] and the advanced optics [Kuhn et al. 2011] module have powerful options for façade systems with dynamic
fenestration properties.
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⎯ In ESP-r, the special materials facility was introduced to model ‘active
building elements’ [Evans and Kelly 1996]. This universal functionality may be applied to any node within a multi-layer construction. The
special material subroutines can actively modify the matrix coefficients of these specific nodes at every time-step. By doing this,
it directly changes basic thermophysical or optical properties and/or the associated energy flows at the equation-level, based on the
respective physical relationships. Currently, the following special materials are implemented: building-integrated photovoltaics,
ducted wind turbines, solar thermal collectors, thermochromic glazing, evaporating surfaces and phase change materials. It is
possible to add new user-defined special materials; however this may require time-intensive programming work.
⎯ ESP-r offers the unique possibility to use roaming files. This facility is used to change the location of a building as a function of time, and
was originally intended to be used for cruise ships. Because this roaming file not only includes coordinates but also orientation of the
zone, it is very well suited for simulation of rotating buildings.
⎯ Nakhi [1995] introduced variable thermophysical properties in ESP-r
with the aim to model heat transfer in building slabs in a more accurate way. The model takes into account that the properties of
most construction materials are not constant, but change as a function of temperature and/or moisture content. This dependency
is implemented via transient thermophysical material properties (λ, cp, ρ) that are linear or polynomial functions of layer temperature or
moisture content. The same functionality can be used to model certain types of adaptive façades.
3.2.3 Simulation strategies and workarounds
Aside from the above-mentioned possibilities, researchers and engineers have developed numerous customized simulation strategies for predicting
the performance of RBEs in their preferred whole-building performance simulation program [Loonen, Hoes, et al. 2014]. Such approaches often call
upon the use of workaround simulation strategies [Brahme et al. 2009], that are devised in view of various legitimate reasons such as the complete
absence of existing models for certain adaptive façade technologies, a lack of user expertise/experience or limited project resources (time and money) to
52
move towards more complex models and the absence of advanced control
options for actuating the adaptive façade system. In many of these cases, the reuse of validated, high-resolution models is an important argument in favor
of using existing software instead of the development of custom-made simulation code from scratch [Wetter 2011a], such as the approach taken by
Liu et al. [2014]. A main drawback of using workarounds is that they tend to rely on approximations or simplifications that might infringe the physics of
model representations and, consequently, also put the credibility of simulation outcomes at risk.
Arguably, the simplest approach for representing adaptive façades is by
subdividing the simulation period into several simulation runs with shorter periods (e.g. seasons), each with distinct building properties [Joe et al. 2013;
Hoes et al. 2011; Loonen et al. 2011] (Figure 3.1A). The downside of this approach is that it cannot accurately model short-term adaptive façade
dynamics, as already described in Section 2.4.4., and thus results in ‘continuity’ errors.
Figure 3.1. Schematic representation of workaround strategies for modeling the performance of adaptive façades. Case A represents the discrete approach that combines a number of short-term simulations. Case B represents the approach that assembles the
results of simulations with static façades during post-processing.
An alternative approach uses separate models for the whole simulation period, each with static properties that represent different states of the
adaptive building envelope system. At a post-processing stage, the results of these independent simulation models are combined in a single
representation of the performance of the building, according to a certain control strategy for the adaptive façade (Figure 3.1B). Examples of this
approach are presented by DeForest et al. [2013], who used simulations in the EnergyPlus-based program COMFEN, to predict the performance of smart
windows that switch optical properties in the infrared wavelength range, and
53
by Du Montier et al. [2013], who used IES VE to predict the performance of
movable insulation panels. This modelling approach can have the advantage of (i) mimicking more advanced building operation controls than what is
offered by the simulation software and/or (ii) simulating adaptive building envelope technologies and materials for which a model does not exist yet.
Even though such a modelling method is well able to capture switching of instantaneous solar gains, for example, due to changing window-to-wall ratio
[Goia and Cascone 2014] or glazing properties [DeForest et al. 2013; C. S. Lee 2017], it fails to account for the effect of delayed thermal response due to the
capacitance of building components (i.e. slabs, walls and internal partitions). Therefore, in cases where thermal mass is involved in adaptive building
envelope operations, the use of these approximate models would probably lead to significant errors in the results, because they do not correctly handle
transient thermal energy storage effects [Erickson 2013]. These inaccuracies may eventually compromise decision-making based on simulation outcomes,
but little information about this issue is reported in the literature.
3.2.4 Comparison between modeling approaches
This section presents the results of an inter-model comparison that was set
up to assess the impact of different adaptive façade modeling approaches on the accuracy of simulation results. The simple, discontinuous approach,
which combines the results from separate simulation runs with fixed properties (Figure 3.1B) is compared with the “exact” solution, obtained with
run-time adaptation using ESP-r. The simulation results that are analyzed concern the opaque exterior construction (Rc value: 5 m2K/W) of a single-
floor detached residential building in the Netherlands. Thermotropic coatings, which can change the surface properties of a material depending
on temperature [Karlessi et al. 2009; Park and Krarti 2016], are investigated as the adaptive façade concept. At low temperatures, these thermotropic
layers absorb most of the incoming solar radiation, whereas at high temperatures, the coatings help reduce cooling load via increased reflection
and/or enhanced longwave radiation exchange. Different thermotropic technologies are currently under development, but most of them are still in
the earlier phases of the innovation process [Agrawal and Loverme 2011; Bergeron et al. 2008; Zheng et al. 2015].
54
Two types of thermotropic coatings were investigated in this study. This was
done by changing the properties of all opaque interior or exterior surfaces according to the values in Table 3.2.
Table 3.2. Material properties of the thermotropic coatings.
Low High Default Absorptivity (α) (λ: 0.28 – 2.8 μm) 0.3 0.7 0.65 Emissivity (ε) (λ > 3 μm) 0.3 0.9 0.84
The coatings are modelled to switch instantaneously, but only one of the properties at a time. This means that when the case with variable absorptivity
is investigated, the value for emissivity is left in the default state.
Thermotropic coatings are an example of self-adjusting, intrinsic adaptive
façades [Loonen et al. 2013]. In this study, the threshold surface temperature for state switching was assumed to be 20°C. Two situations are investigated.
The first one considers application of the thermotropic coating on the exterior surface. The second situation investigates application on the
innermost surface.
Results – outdoor application
Figure 3.2A shows the surface temperature of the exterior roof layer for three days in summer (4 - 6 July). In the situation with fixed high absorptance
(dashed line), higher temperatures are reached than is the case for fixed low absorptance (solid black line). Surface temperature of the thermotropic
coating closely follows one of the two states with static properties around the switching point of 20°C.
(A) (B)Figure 3.2. Exterior surface temperature. (A) Thermotropic α coating and fixed low and high absorptivity (4 – 6 July); (B) Thermotropic ε coating and fixed low and high emissivity, (30
August – 1 September).
0
10
20
30
40
50
Tem
pera
ture
[oC]
High abs. Low abs. Thermotropic α
day 1 day 2 day 30
10
20
30
40
50
Tem
pera
ture
[oC]
Low ems. High ems. Thermotropic ε
day 1 day 2 day 3
55
Similar behavior is observed in the results with variable emissivity (Figure
3.2B, period: 30 August – 1 September). In this situation, a temperature difference between the high and low case is not only present during the day,
but also at night when the radiant heat transfer coefficient from the roof to the sky and surroundings changes as a result of the difference in emissivity
value.
To evaluate the effect of using the two different modelling strategies, a comparison of heating energy consumption and thermal comfort, predicted
by the two methods is presented in Table 3.3. The differences in heating energy consumption are very small (less than three percent). The difference
in discomfort hours is also negligible. Use of the simplified, discontinuous, modelling approach in this case could therefore be justified, because the
predicted difference will likely not lead to a different recommendation or design decision.
Table 3.3. Comparison of results for the two modelling approaches (discontinuous and run-time).
Discontinuous “Exact”
Error
Abs. Rel. Heating Energy (kWh)Thermotropic α 2492 2525 33 1.3%
Thermotropic ε 2321 2393 72 2.9%
Thermal Comfort (wPPDh)Thermotropic α 65 67 2 3.0%
Thermotropic ε 74 76 2 2.6%
This result is not unexpected because the coating is applied outside of the thermal insulation layer. Therefore, temperature changes immediately follow
switching actions, because almost no thermal energy is stored in the construction.
Results – indoor application
Thermotropic coatings are not only applied to the exterior surfaces of
buildings, but can also be useful for indoor applications, especially to control the release of energy to/from constructions with thermal mass. Figure3.3A
shows the indoor surface temperature for the case with an internal thermotropic emissivity coating for the period 7-10 March. In contrast to the
exterior application, the thermotropic coating is in direct contact with
56
materials that have high thermal storage capacity. Because the switching of
surface properties in this case significantly influences the thermal history of the construction, there is hardly any overlap between the exact solution and
any of the two static surface temperature curves. In terms of surface temperature, the thermotropic coating is therefore not well-represented by
either one of the static cases. A discontinuous modelling approach would therefore not lead to reliable results, especially when high-resolution thermal
comfort evaluations are desired.
(A) (B)Figure 3.3. Interior surface temperature. Thermotropic ε coating and fixed low and high
emissivity during two periods: (A) 7-10 March, (B) 27-29 April.
Figure 3.3B shows that the thermal mass phenomenon can even lead to more unexpected effects (27-29 April). In several periods in this interval, the
temperature of the thermotropic layer rises higher than what would have happened in the static case. The exact reason for such effects is hard to
identify, but comes from multimode heat transfer effects, including non-linearities of longwave heat transfer and convection regimes. The occurring
heat transfer phenomena also depends on whether, during state transitions, the construction is already charged with energy, relative to its surroundings
and heating system operation.
The effect in Figure 3.3B is not reproducible with discontinuous simulations. Only when adaptation of construction properties takes place during
simulation run-time, it is possible to analyze and quantify the impacts of this effect.
18
20
22
24
Tem
pera
ture
[oC]
Low ems. High ems. Thermotropic ε
day 1 day 2 day 3 day 418
20
22
24
Tem
pera
ture
[oC]
Low ems. High ems. Thermotropic ε
day 1 day 2 day 3
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Summary
The above presented simulation study has demonstrated that:
⎯ Thermal mass has a big influence on the proper selection of
performance prediction strategies for adaptive façades.
⎯ In cases where the operation of adaptive façades is decoupled from
thermal storage (e.g. exterior coatings with varying surface properties), a decoupled simulation approach is adequate.
⎯ When the operation of adaptive façades does affect the amount of
energy stored in the thermal mass, these dynamic effects have to be
taken into account during simulation run-time.
⎯ The simplified approach is not always able to capture all heat transfer phenomena during RBE state transitions.
⎯ Choosing a non-appropriate simulation strategy can lead to significant prediction errors that, in turn, can result in sub-optimal
design decisions.
3.3 Control aspects – modeling and optimizing
the operation of adaptive façades
3.3.1 Control considerations
In buildings with adaptive façades, there is a strong coupling between design
and (façade) control aspects. It is not difficult to understand that a good control strategy is of principal importance for materializing the performance
potential of adaptive façades during operation. To identify directions for high-potential adaptive façade design concepts, these control considerations
therefore deserve significant attention.
Two types of supervisory strategies for automation of dynamic systems in buildings are commonly distinguished: rule-based control (RBC) and model-
based control (MBC) [Oldewurtel et al. 2012; Henze and Neumann 2011]. RBC
is powerful for simple control problems. It connects measurements (sensors) to actions (actuators) via if-then-else control statements. These heuristic
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control strategies, often represented in the form of decision trees [CIBSE
2009], can, however, get excessively complex in situations with multiple interacting sub-systems [Spindler and Norford 2009; Liu et al. 2015]. As a
consequence, RBC lacks the flexibility that is necessary to ensure high building performance in complex multi-input, multi-output problems.
One of the difficulties of RBC comes from the fact that sensed environmental
variables, setpoints, thresholds, and the order of control actions have to be predefined. MBC approaches, on the other hand, are less rigid, because the
best control strategy can be found after comparing different control
scenarios on the basis of model predictions. MBC approaches describe what the controller should achieve (e.g. minimizing energy use while maintaining
comfort) without prescribing how the controller should do this. This flexibility increases the option space of the control system to reconcile varying performance trade-offs over time in a way that best responds to the
dynamically changing conditions as they occur during building operation. Whereas RBC is a rigid approach that decouples optimal design and
operational aspects of a system, MBC integrates the two and allows for concurrent optimization of both features [Evins and Orehounig 2014]. This is
the main reason why the use of MBC is explored in this research.
3.3.2 Model-based control of adaptive façades
Figure 3.4 introduces the principles of the optimization-based approach for
offline control of dynamic building shell properties that is developed in this
thesis. In this simulation-based, in silico study, the building in the upper part of Figure 3.4 is represented by a whole-building performance simulation
model with actuators in the form of time-varying building envelope properties. This model can be considered as a detailed virtual representation
of the real-world building with adaptive façades.
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Figure 3.4. Relationships between different components in the model-based adaptive façade control strategy
The task of the building shell controller, as represented by the bottom part in
Figure 3.4, is to drive the search for control sequences (i.e. time varying façade system states) that would optimize building performance. Each of
these control sequences represents a possible scenario of dynamic façade behavior, which is evaluated in the second building model that is integrated
in the building shell controller. This so-called embedded building model is repeatedly called to evaluate the performance of various sequences of time-varying façade properties over a predicted optimization horizon. It takes into
account scenarios for future ambient conditions and occupant behavior. The search for the best control sequence is driven by an optimization algorithm
that ranks the performance of each solution (i.e. sequence). Façade objectives/goals are transformed in an objective/penalty function, based on
the difference between desired (i.e. ideal) and actual responses. The best control sequence is found by minimizing this penalty function within the
optimization window. When an optimization loop is completed, the control sequence with the best performance is selected, and the corresponding set
of dynamic façade parameter values for the current control horizon is transferred from the controller to the building.
MBC is based on iterative, finite-horizon optimization runs with function
evaluations in the embedded building model. Three different time horizons/periods are considered in this control approach, as Figure 3.5
illustrates.
Sensors Actuators
Input OutputBuilding model
Objective function
Future disturbances
Best control sequence
Optimization algorithm
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Figure 3.5. Illustration of different time horizons in the building and controller models. In this situation with three optimization horizons, each control horizon consists of six adaptation periods. Note that only the shaded façade states are actually applied to the
building.
⎯ Optimization horizon: Using a sequence of multiple, relatively short
optimization horizons (e.g. hours or days) in the building shell controller, the simulation process successively steps forward in time.
This approach is used to evaluate the longer-term (e.g. annual) performance of adaptive façades. Selecting an adequate length of the
optimization horizon should find a balance between conflicting requirements. The look-ahead period needs to be sufficiently long to
exploit dynamic energy storage effects and include future occupancy changes, but short enough to keep prediction uncertainties and the
size of the search space manageable [Zavala et al. 2009; Gunay et al. 2014].
⎯ Control horizon: In MBC approaches, feedback is introduced by only
implementing the first step(s) of the optimized control strategy in the building. Then, calculations are repeated, starting from the new
current state, and shifting the optimization horizon forward in time.
It is for this reason that MBC is sometimes also referred to as receding
time horizon control. The term control horizon is used to describe how many façade states from one optimization horizon (open-loop) are
applied to the building. The length of a control horizon can be as short as one adaptation period until the length of the whole optimization
horizon.
Optimization horizon
Control horizon
Adaptationperiod
Time
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⎯ Adaptation period: The adaptation period represents the shortest
period of time during which façade properties are constant. Each
adaptation period is therefore characterized by one façade state. One adaptation period can consist of multiple simulation time steps. It
generally makes sense to synchronize the adaptation period with the rate-of-change in exterior boundary conditions and occupancy
conditions inside the building. In most practical building performance simulation studies that use typical meteorological years
(TMY) or other standard weather files, this means that the adaptation period is at least one hour.
3.3.3 Model resolution
To ensure good performance in MBC approaches, it is important that there is a high level of agreement between controller model predictions and the
situation as it would actually take place in the building [May-Ostendorp and Henze 2013]. Achieving this good fit in real-time building-integrated model
predictive control (MPC) tends to be a challenging task, because constraints on computation time prompt developers to use models that either have
relatively low modeling resolution (e.g. reduced-order models) or are not based on first principles (e.g. grey box models) [Prívara et al. 2012]. In
addition, difficulties with parameter estimation and setting of initial conditions may compromise the performance of full-scale MPC [Chen and
Athienitis 2003]. In this research, which is situated in the pre-design and product development phase, the optimization framework is used to calculate
adaptive façade control strategies “offline”, i.e. not coupled to the operation of a real-world system [Coffey 2013]. Computation-time reduction measures
are therefore not a primary concern, and as such, it appears adequate to make use of controller models at a high modeling resolution [Clarke et al. 2002;
Coffey et al. 2010; Hoes et al. 2012]. The integration of high-resolution models in the MBC formulation offers a number of advantages, because the method:
⎯ preserves the interrelated physical effects of all energy flow paths in the simulation process. As a result, energy and comfort performance
can be analyzed at a high level of detail (see Section 3.4 for more details).
⎯ does not rely on availability of model identification data, which is
favorable considering the exploratory nature of the optimization
problem.
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⎯ is scalable, and can be applied to different climates and building types.
⎯ offers flexibility to deal with loosely specified and innovative building
envelope components (as long as they can be modeled in the selected
simulation program).
⎯ uses dynamic models that take transient effects into account in an explicit way.
⎯ allows for defining a performance bound8 based on “ideal” control actions. The predicted performance of adaptive façades is not
affected by interference from model-plant mismatch.
3.4 Performance aspects and performance
indicators
3.4.1 Thermal comfort and energy performance
Energy performance of buildings is closely linked to the levels of indoor
environmental quality they maintain. Better indoor comfort can generally be obtained by increasing energy use intensity, and vice versa. In this
dissertation, the focus is on analyzing the impact of adaptable façade design on the trade-offs between indoor environmental quality and the energy
consumption for heating, cooling and artificial lighting. In terms of space conditioning for thermal comfort, the goal is to keep the building in free-
running mode (i.e. no energy consumption for active heating or cooling) as much as possible. This goal is achieved by adjusting façade properties in such
a way that operative temperature is steered toward the deadband zone for HVAC control.
The HVAC system in this study is assumed to be an ideal system. Therefore,
temperature limits can always be satisfied in situations when façade
adaptation alone is not sufficient. As a consequence, thermal comfort is not considered as a primary performance indicator, but its importance is
8 The performance bound is a virtual construct that describes the performance of an ideal controller with an infinite
prediction horizon and perfect knowledge about the system dynamics and all future disturbances. It is often used
as a benchmark in control systems design, because no other controller will perform better [Oldewurtel et al. 2012;
Hoes 2014].
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incorporated by imposing it as a constraint in the optimization process. The
performance of various façade alternatives is directly reflected in the energy consumption that is necessary to achieve satisfactory thermal comfort
conditions.
3.4.2 Visual comfort
Façade-related visual performance is influenced by a complex interplay among numerous different factors [Reinhart and Wienold 2011]. In terms of
daylight, an ideal façade would continuously provide, at least, (i) sufficient levels of well-distributed daylight illuminance, (ii) the absence of discomfort
glare for all occupants, and (iii) full view to the outside. In this research, three performance indicators are considered for evaluating to what extent a
certain adaptable façade configuration is in agreement with this ultimate solution:
⎯ Useful daylight illuminance9 (UDI). Daylight utilization is assessed by
computing the percentage of occupied hours that daylight
illuminance on the work plane falls within certain bounds. Four different categories according to the classification of [Mardaljevic et
al. 2012] are considered: fell short (<100 lux), supplementary (100-300 lux), autonomous (300-3000 lux) and exceeded (>3000 lux).
⎯ Daylight glare probability (DGP). Discomfort glare is assessed using
DGP, as it was shown to be the most robust glare metric in a comparative study under a wide range of ambient conditions
[Jakubiec and Reinhart 2011]. Research that explores the impact of view direction and user occupant influence on glare discomfort is still
in the early phases [Sarey Khanie et al. 2017; Bakker et al. 2014]. Throughout this thesis, a conservative glare scenario is considered in
which the occupant’s view direction is always facing the façade, to investigate the potential of a dynamic façade to mitigate glare risk
under all conditions. Three different categories for discomfort glare are distinguished, according to the classification in [Reinhart and
9 In the original definition, UDI is calculated for whole-year periods. Because this research also uses shorter
simulation periods, the results might not always be directly comparable with results from other studies.
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Wienold 2011], where the risk is (i) intolerable (DGP > 0.45), (ii)
disturbing (0.35 < DGP < 0.45), or (iii) not disturbing (DGP < 0.35).
⎯ View to outside. Although the effects on visual performance and user
satisfaction are difficult to quantify, it is widely believed that improved view type and quality can affect these aspects in a positive
way [Aries et al. 2010; Hellinga and Hordijk 2014]. In this research, view performance is assessed by computing the potential for visual
interaction with the outside environment. This is done by taking the sum of averaged façade element transparencies over a period of time,
and quantifying this relative to the fully-glazed configuration (i.e. 100% view). This PI is therefore not based on simulation output, but
directly calculated for each façade configuration sequence on the
basis of visible transmittance.
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4
Development of a toolchain for
performance optimization of
adaptive façades
4.1 Introduction
Taking the functional requirements and performance indicators that were introduced in the previous Chapters as a starting point, this Chapter
describes the details of how these specifications were interpreted in the form of a software implementation. This Chapter is split into five parts that each
cover a different aspect. Section 4.2 introduces characteristics of the simulation strategy for daylight and energy performance prediction of
façades with time-varying properties. Section 4.3 gives a higher-level description of the co-simulation approach that facilitates (i) the mutual
coupling between these energy and daylight simulation models, and (ii) the coupling between models representing the adaptive façades’ ‘real’ building
model, and those embedded in the building shell controller. The code modifications necessary for the coupling between the dynamic thermal
simulation program (ESP-r) and the middleware software that facilitates co-
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simulation (BCVTB) are described in Section 4.4. Section 4.5 gives an overview
of the integration of four different software tools in the MBC approach. Finally, Section 4.6 presents how to various parts come together, by giving a
graphical overview of the interaction among all elements in the toolchain.
4.2 Performance simulation of adaptive façades
4.2.1 Dynamic daylight simulations
Daylight simulations are carried out using the Radiance three-phase method.
This method is a validated, new addition to the Radiance suite of daylight simulation tools [Ward and Shakespeare 1998], which was specifically
developed to enable high-resolution annual daylight performance prediction at reduced computational cost [Saxena et al. 2010; McNeil and Lee 2013]. It is
called the three-phase method because the calculation of light transport between sky patches and illuminance sensor points is separated into three
parts: exterior transport, fenestration transmission and interior transport.
Each phase of light transport is simulated independently and stored in a matrix form. The resulting climate-based indoor illumination (sensor points
or rendered images) is obtained using matrix multiplication [McNeil and Lee 2013]. The main reason for using the three-phase method in this research is
the possibility for analyzing the dynamic performance of multiple façade configurations in a quick, yet accurate way. By splitting up the daylight
prediction in three parts, different façade/fenestration options can be tested, without recomputation of the other two parts (i.e. light transport
inside and outside the building). The reuse of the already calculated ray-tracing results is responsible for a significant increase in computational
efficiency compared to traditional daylight simulation (e.g. Daysim or
rvu/rpict modules in Radiance), and therefore especially useful for modeling scenes with variable fenestration optics [Saxena et al. 2010].
To fit the specific purpose of this work, i.e. optimization of adaptability in
adaptive façades, a slightly modified workflow for the “standard” three-phase
method was developed, which puts emphasis on the changeability of individual elements in the façade (Figure 4.1).
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Figure 4.1. Schematic overview that shows the interactions between the various components of the daylight performance prediction strategy.
The simulation process in this workflow is subdivided into a pre-processing
phase and a run-time phase as follows:
Pre-processing
i Sky vectors are generated in gendaylit for each daylight hour in the
simulation period. The Radiance function gendaylit works with the
Perez all weather model [Perez et al. 1993] to create exterior sky scenes using a standard weather file with hourly values as input for
direct and diffuse solar irradiance.
ii Daylight matrices (DMX) represent the luminous flux from sky
divisions to façade elements. They are calculated using rtcontrib to
establish the relationship between each façade surfcace and the sky patches from step 1, using a daylight coefficient method coupled to a
Reinhart sky division of 2305 patches plus ground surface.
iii Optical façade properties are characterized by bidirectional scattering distribution functions (BSDF). These transmission matrices
are calculated in WINDOW7 software [LBNL 2017b]. One BSDF is created for each of the possible fenestration properties, which are
then assigned to the transparent parts of the façade. Using this
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approach, both specular windows and optically complex light-
redirecting glazing systems can be evaluated.
iv For each façade element, rtcontrib is used to create indoor view matrices (VMX), which model interior light transport by relating outgoing façade directions to observation points and planes in the
zone. To be able to calculate both UDI and DGP, (i) a grid of sensor points for daylight illuminance evaluation, and (ii) a fish-eye
projection image for assessment of glare discomfort are defined.
Run-time
i The contribution that each individual façade surface/element makes
to overall daylighting performance is calculated on a time step basis, by combining sky vectors, DMXs, BSDFs and VMXs in the
dctimestep module. These calculations are based on fast matrix multiplications; no ray-tracing is performed during this run-time simulation phase.
ii The total daylight illuminance coming from all different transparent
façade surfaces/elements is reconstructed using rlam and rcalc for a grid of sensor points. The values for daylight illuminance obtained
in this step are later also used to calculate the required energy demand and internal gains for artificial lighting.
iii A superposition approach using pcomb is employed to generate a single high dynamic range (HDR) image for the entire scene. This
image is supplied to the evalglare program [Wienold 2015] , which is used to calculate glare discomfort performance.
In the daylight performance prediction approach described above, the majority of computational load for the simulations is shifted to the pre-
processing phase, which takes place before the actual optimization process. Within the run-time optimization loop, only fast matrix multiplications are
needed; no ray-tracing calculations. A validation study by McNeil and Lee [2013] shows that the increased simulation speed does not come at the
expense of inaccuracy of predicted results. Currently, the bottleneck in computation time comes from the relatively resource-intensive pixel analysis
of DGP calculations in evalglare. Future work should investigate the potential of using other glare indices (e.g. simplified DGP) [Wienold 2009] or calculation methods (e.g. radiosity-based) [Kleindienst and Andersen 2012]
for this part of the analysis.
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4.2.2 Building energy simulation with adaptable façade properties
Building energy simulations (BES) are carried out using ESP-r [Clarke 2001]. ESP-r is an integrated, research-oriented BPS tool with a long validation
history and an active development community. The tool can be used for prediction of thermal, visual, indoor air, electrical and acoustic performance
of buildings and systems. Unlike most other high-resolution BES tools, ESP-r does already offer some possibilities to model building components with
time-varying thermophysical properties (Section 3.2.2). In this research, these capabilities were further extended. The limited flexibility in the default
ESP-r “BCL99 control” facility for thermophysical property substitution, as implemented by MacQueen [1997], was avoided by re-programming parts of
the source code to replace these properties during simulation run-time by reading from an external signal. Section 4.3 will describe where this external
signal comes from.
Performance prediction with variable thermophysical properties requires
that the storage and transfer properties of energy conservation equations are estimated at each time step [Nakhi 1995]. The main difference with normal
ESP-r calculations for non-adaptive façades is that not just in the beginning of a simulation run, but during every time step, the building-side matrix
equation is re-established after reading new construction properties for the future time row [MacQueen 1997]. ESP-r has two main subroutines for setting
coefficients of the implicit difference equations. Normally, MZCOE1 sets up those coefficients (or partial coefficients) of the equations that are constant. For regular building envelopes, the thermophysical properties (thermal
conductivity (W/m.K), density (kg/m3) and specific heat capacity (J/kg.K)) are independent of time and need therefore only be computed once per
simulation run. Subroutine MZCOE3, on the other hand, initiates the computation of the energy transport that varies with time (e.g. solar gains
impinging on internal and external surfaces, external longwave radiation exchanges, internal casual gains, and ventilation exchanges), and its
coefficients are therefore computed during every time step. In the present implementation, the thermophysical building envelope properties are made
variable by making extra calls to MZCOE1 on a time-step basis, after reading updated construction properties for recomputing the matrix coefficients.
This step happens right before the call to MZCOE3.
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4.3 Co-simulation approach
To accomplish implementation of the performance prediction framework as proposed in the previous Chapters, a UNIX-based toolchain was developed
that integrates the solvers for daylight (Radiance) and energy performance (ESP-r) prediction together with optimization routines and scripts for
receding time horizon control. This so-called co-simulation approach is a valuable method to deal with this type of coupled simulation studies, and has the advantage that it can reuse existing code, models and toolboxes in
distributed software programs [Trcka et al. 2009; Wetter 2011b].
In the present toolchain implementation, the co-simulation process is supervised by master algorithms implemented in Matlab [Matlab 2015]. The
master algorithms are responsible for starting and finishing the simulation, preparing simulation input files, extracting outputs, and a number of other
tasks as will be described in the next Sections. Matlab was chosen for these tasks because it offers a high-level programming language in a flexible
environment.
The actual run-time communication (i.e. data exchange) between the various
programs in the toolchain takes place through the Building Controls Virtual Test Bed (BCVTB) as a middleware tool [Wetter 2011b]. BCVTB uses a quasi-
dynamic coupling scheme (Figure 4.2). The inter-process communication (IPC) between software programs happens at fixed time steps, without
iteration. Many simulation programs, including Matlab and Radiance, are already linked to the BCVTB. ESP-r has been coupled to the BCVTB as part of
this research10. This software development was needed to enable controlled adaptation of building shell properties during simulation run-time (Figure
4.3). More details about the changes that were made to ESP-r are presented next.
10 The connection with ESP-r is now part of the standard BCVTB distribution. It can be downloaded from the BCVTB
website and includes example models and user instructions.
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Figure 4.2. Quasi-dynamic coupling scheme used in BCVTB. The dashed lines indicate data exchange between the tools per time
step; t = time step, yt =data of tool 1 at time step t, ut = data of tool 2 at time step t.
Figure 4.3. Coupling of tools in the co-simulation framework. Master algorithms are implemented in Matlab. The communication through BCVTB takes place between ESP-r (‘real’ building) and Matlab
(building shell controller).
4.4 Coupling between ESP-r and BCVTB
The coupling between ESP-r and BCVTB is based on the FORTRAN90 libraries
that are included in the standard BCVTB distribution. The functions in these libraries make use of Berkeley software distribution (BSD) sockets; BCVTB’s
mechanism of connecting different software programs. They fulfill three main functions:
i establish a connection with BCVTB (establishclientsocket);
ii exchange data (exchangedoubleswithsocket);
iii close the connection (ipcclose).
To couple these functions with ESP-r, new subroutines were added, and other subroutines were adapted to integrate the additional functionality in
the source code of ESP-r11. In addition, a new header file (BCVTB.h) was added to all relevant subroutines to declare the parameters and variables that are
used for communication with BCVTB. The most important variables are those that are used during the data exchange with BCVTB:
11 A version of ESP-r with basic BCVTB functionality is available for download from the ESP-r source code repository
(branch ESP-r_BCVTB). It also includes example models and user instructions.
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⎯ bcvtb_y: an array that contains all sensor variables sent to BCVTB;
⎯ bcvtb_u: an array that contains all actuator variables (control signals)
received from BCVTB.
Figure 4.4. Overview of ESP-r code modifications to enable coupling with BCVTB, showing modified subroutines (left) and new subroutines (right). ESP-r subroutine
names are underlined.
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Figure 4.4 presents an overview of all the additions and changes that were
made to the ESP-r code. The calls to the new subroutines are made from
MZNUMA, the backbone subroutine of ESP-r that includes its simulation clock. Before the start of the first simulation time step, the connection with BCVTB
is established by calling bcvtbestablish. The second step in the preparation phase is to exchange initial values of sensor and actuator values
by calling bcvtbexchange. During the actual simulation loop, every time
increment in MZNUMA is interrupted with a data communication call via
bcvtbexchange. Finally, after the last time step in the simulation, the
connection with BCVTB is terminated, by calling bcvtbclose.
The coupling between ESP-r and BCVTB is designed to be as generic as possible. It can therefore be used for various purposes such as solar shading
operation, window opening strategies, or for controlling the temperature and/or flow rate in thermally activated building systems [Hoes et al. 2012;
Hoes 2014]. The specific application is determined by the user-defined ESP-r simulation variable(s) that gets overwritten with actuator signal(s) received
through IPC with BCVTB (i.e. bcvtb_u).
With respect to performance prediction of adaptive façades, the coupling between ESP-r and BCVTB in this research takes place at two different levels,
(i) between Radiance and ESP-r, and (ii) between Matlab (building shell control) and ESP-r. More details are described in the next sub-sections.
4.4.1 Coupling of visual and thermal domains to obtain integrated
results.
The basics of the Radiance-to-ESP-r integration between the thermal and
daylighting domains (Figure 4.1) are very similar to the principles for simulation of daylight-responsive building systems as described in e.g. Janak
[1997]. However, the implementation in the present work is different. Instead of using a direct link to Radiance functions in the ESP-r code, the coupling is
established through BCVTB (as indicated by the blue and red arrows in Figure
4.5). At a time-step basis, daylight illuminance values are computed, and fed into the lighting controller of the thermal simulation model, which then
assigns/updates the right level of internal gains corresponding to the current façade configuration and climatic conditions.
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4.4.2 Data exchange and synchronized communication between
building model and building shell control.
The time-varying building shell properties are assigned to ESP-r through an external signal (Section 4.2.2). This external signal is based on the outcome of
the model-based control strategy, which is implemented in Matlab. In this process, BCVTB is responsible for synchronizing the data exchange between
both programs. Details of the coupling between building model and building shell control, and the role of BCVTB in this process, are described in the next
Section.
Figure 4.5. Schematic overview of coupling between the various software tools. The two large boxes indicate the master algorithms in Matlab. Black arrows indicate data
exchange through BCVTB.
4.5 MBC implementation using Matlab, BCVTB,
ESP-r and Radiance
4.5.1 Implementation of the receding time horizon control
approach
To achieve the MBC performance evaluation as described in Section 3.3, a procedure is proposed in which an instance of the same high-resolution BPS
model that is used to predict adaptive façade performance, is also embedded in the controller that determines its operation. With respect to their
Build
ing
Build
ing
shel
l con
trol
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connection to BCVTB, there are two main differences between these models,
as is also indicated with the differences between left and right in Figure 4.3:
⎯ Contrary to the ‘real’ building model where communication takes place as simulation time proceeds, these external simulation calls
from Matlab to the embedded building model are run from start to finish, for the length of one optimization horizon (Figure 3.5). The
calls are implemented via Matlab system commands to (i) Unix shell
scripts, for ESP-r simulation (bps) and results extraction (res), and (ii) the run-time part of Radiance functions from Section 4.2.1.
⎯ For the model that represents the ‘real’ building, the coupling is
directly integrated in ESP-r’s algorithms (Section 4.4). In the building shell control, BCVTB does not directly communicate with the
simulation tools, but with a master algorithm, implemented in Matlab (Figure 4.5). Apart from this communication, the master algorithm
takes care of time step bookkeeping, preparing simulation input files with correct start/stop time, making calls to Radiance and ESP-r (i.e.
new instantiations of the embedded building model), handling post-processing, and managing the optimization procedure
The optimization procedure in the receding time horizon formulation
consists of seven steps. A graphical representation is given in Figure 4.6.
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Figure 4.6. Schematic overview of the optimization procedure. Each cell represents one façade configuration, while different colors (greyscale) show how it changes over time. The numbers correspond to the different steps in the optimization procedure described
in Section 4.5.1. Only three iterations of the optimization algorithm are shown. The arrow (step 5) indicates the iterative loop that ends when a stopping condition is
satisfied.
i At the beginning of time step tn, simulation of the ‘real’ building is
temporarily paused, to start a new search for optimized control
sequences in the next control horizon (from tn to tn+m). The task of the controller is to decide if and how the façade configuration should be
adjusted in the upcoming period.
ii All state variables representing the thermal state of the building (i.e.
node and surface temperatures) after time step tn-1 are stored in a file.
iii New versions of the BPS model are instantiated at time tn. In these thermal simulations, the initial states of the models are overwritten
with stored state variable values inherited from the previous time step of the ‘real’ building simulation model. This step is needed to
ensure consistency of transient thermal effects, because the default initialization would not always lead to accurate results, as is
described in Section 4.5.2.
iv Multiple scenarios with different façade adaptation strategies are evaluated in the MBC embedded BPS models (both Radiance and ESP-
r). These simulations are executed over a future optimization horizon
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of m time steps, assuming perfect knowledge about future boundary conditions and building use. An overview of source code
modifications to accomplish this is presented in Section 4.5.3.
v The search for optimal control sequences is driven by a selected optimization algorithm which iteratively suggests new sequences of
façade parameters as it attempts to minimize an objective function (Section 4.5.4). The optimization loop is repeated until the given
stopping criterion (e.g. based on time or relative performance improvement) for the current optimization horizon is satisfied. In this
study, genetic algorithms are used. Additional considerations about the search algorithm and its modifications are discussed in Section
4.5.5.
vi The values of time-varying façade properties that together constitute
the optimal control sequence for the current control horizon (open-loop) are sent as actuator values from Matlab through BCVTB to the ‘real’ building simulation model. In addition, the artificial lighting
needs that are required to complement daylighting in the selected scenario are sent to the thermal model.
vii Performance simulation of the ‘real’ building simulator is resumed
with optimized dynamic façade states from step 6 and continues until an updated adaptive façade sequence is requested at the end of the
control horizon (Figure 3.5). This cycle repeats until the total simulation period is expires.
4.5.2 Explicit state initialization
Despite multiple favorable aspects of the use of high-resolution BPS models
as function evaluators in simulation-based or experimental MBC studies [Clarke et al. 2002], the number of applications in literature is very limited
[Prívara et al. 2012]. One of the main drawbacks of using detailed BPS tools for MBC are the time-consuming pre-conditioning periods. In reduced-order
models or data-driven models, one can explicitly impose the correct initial conditions of embedded building models to avoid continuity erros. With
detailed BPS models, warm-up periods, which lead to considerable overhead in computation time, are usually needed to guarantee correct starting
conditions [Wetter and Haugstetter 2006; Coffey et al. 2010]. Depending on operational schedules and thermal time constants of constructions, this
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initialization period may consume up to 80% or more of the actual simulation
time (e.g. four days of pre-conditioning for an optimization horizon of one day) [Nghiem and Pappas 2011; Corbin et al. 2013; Favoino et al. 2017].
In earlier MBC studies with BPS as embedded building model, it was not, or only partly [Coffey 2013] possible to remove/reduce this initialization period,
because the programs that were used (e.g. TRNSYS and EnergyPlus) did not allow access to the state variables (the underlying reason for this can be
found in the solution routines for energy balance equations, as described in Section 3.2.1). An advantage of ESP-r is that it does give the user access to the
building and system’s state variables. This flexibility was used to circumvent the need for repeatedly running initialization periods.
Clarke [2001] uses the following matrix notation to describe the set of conservation equations that are solved simultaneously during each time step
of the simulation: 𝑨𝜽𝒏 𝟏 = 𝑩𝜽𝒏 + 𝑪
where 𝑨 and 𝑩 are sparse matrices containing the nodal equation coefficients
for energy conservation that describe the coupling between nodal regions of
various construction elements in the model. The column matrix 𝑪 contains
the influence of ambient boundary conditions, and the column matrices 𝜽𝒏 𝟏
and 𝜽𝒏 contain the nodal temperature terms and heat injection/extraction at the future and present time-rows, respectively. To enable explicit state
initialization of the embedded building model, state variables in the matrix 𝜽𝒏 of the last time step of the ‘real’ building model, before the start of a new MBC loop, are stored in a file. These values are then used to overwrite the default
initial values of the embedded building model. To completely avoid the computational overhead caused by repeated initializations, a simulation start-up period of zero days was used in the embedded building model.
4.5.3 Code modifications in ESP-r
Previous Sections already introduced the ESP-r code modifications that were needed to include time-varying façade properties (4.2.2), and to connect ESP-
r with BCVTB (Section 4.4). This Section discusses the modifications that were additionally needed to implement the receding time horizon control
formulation, in both the ‘real’ building model and the embedded building model.
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‘Real’ building model
State variables of the thermal model are written to an external file at the right
time step during the simulation, i.e. at the end of the control horizon, just before a new call to the embedded building model is made. A new variable,
MBCupdate with an incremental counter is added to determine the right time step.
Embedded building model
Because the ESP-r algorithm of the embedded building model is not
connected to BCVTB, another way of introducing the dynamic façade properties and lighting gains to ESP-r was implemented here. Matlab writes
each façade sequence as suggested by the optimization algorithm to an external file. This file is opened by ESP-r, and in every time-step of the
simulation, the corresponding values are read and updated by the program.
The ESP-r code is designed to simulate full days, always starting at 00:00 h
and running for x times 24 hours. This can be inconvenient in a receding time horizon control formulation, because it restricts the flexibility of horizon
sizes. However, to be able to start a simulation in the middle of the day would require a major overhaul of ESP-r, with impacts in many parts of the code.
The workaround solution that was implemented to simulate incomplete days (either starting before 00:00h, ending before 24:00h, or both) is presented in
Figure 4.7.
Figure 4.7. Optimization horizon starting after 00:00h. t1 = start of the optimization horizon; t2 = end of the optimization horizon. Explicit state initialization happens at t1.
Simulation results before t1 and after t2 are discarded.
An extra variable MBCtsread was added as a counter for the number of simulation time steps until t1; the time at which the state variable are
overwritten. ESP-r receives control signals with “dummy” variables for the
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periods before t1 and after t2, and all results in these periods are not included
in the performance evaluation.
4.5.4 Objective function
Function evaluations in the embedded building model are ranked and compared with respect to an objective function that takes the multiple
performance criteria of interest (Section 3.4) into account. More specifically,
performance is expressed using penalty functions (P) and weighting factors
(w) for both energy and daylighting aspects.
As described in Section 3.4.2, visual comfort evaluation is further subdivided
into three components: UDI, DGP and view to outside. These factors are also embedded in the objective function so that the desired goals can be achieved
by optimization. The approach adopted in this research is similar to the concept of “daylight credits” [Gagne and Andersen 2012; Kleindienst and
Andersen 2012] and “preference functions” [Mahdavi 2008]. However, to allow for minimization of both performance criteria (energy and visual
comfort) in the mathematical sense, the credits are transformed into penalty functions. Figure 4.8 introduces the penalty functions for daylight
illuminance, glare and view.
Figure 4.8. Penalty functions for daylight performance aspects.
For each daylight hour in the optimization horizon, penalty values are
computed for the various façade configurations, and then aggregated into one value using weighting factors that are determined by the decision-maker:
𝑃 = 𝑤 ∙ 𝑃 + 𝑤 ∙ 𝑃 + 𝑤 ∙ 𝑃 4.1
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By combining daylight performance with primary energy consumption for
heating, cooling and lighting, the total objective function can be described as:
𝑃 = 𝑓 𝑤 ∙ 𝑃 + 𝑤 ∙ 𝑃 4.2
Further enhancements to this objective function formulation, and ways of assessing tradeoffs between energy and daylight performance are included
in the next Section.
4.5.5 Genetic algorithm + modifications
A genetic algorithm (GA) is used to drive the parameter search in the
optimization of façade properties. GAs are metaheuristic, population-based optimization algorithms. Numerous studies have shown that these algorithms
are effective and robust for reconciling the competitive requirements in optimization of building envelope design based on “black-box” simulation
models, such as ESP-r, EnergyPlus and Trnsys [Evins 2013; Attia et al. 2013]. In this study, the task of the algorithm is to propose new façade adaptation
sequences, to be evaluated in the controller model. This approach is markedly different from conventional design optimization studies where the algorithm
acts on time-invariant parameters of a simulation model.
Because the properties of each adaptable façade element can be described as
a discrete set of predefined options, the problem is encoded in an integer optimization formulation, with one identifier per façade element. GA
functions in Matlab were used in this implementation. To reduce optimization time, the default GA algorithm was extended with code that
avoids already simulated solutions from being evaluated again.
The dynamic behavior in adaptive façades raises a few specific challenges for the optimization procedure. A number of additional measures were taken in
response to these challenges, to ensure a search process that is both efficient and effective. These are discussed in this Section (Figure 4.9).
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Figure 4.9. Overview of process steps in the genetic algorithm. Implications of adaptive façade dynamics on four aspects (indicated with numbers) are further explored in this
Section.
1. Seeding of initial population
In addition to the default random way, a number of members in the initial
population are seeded with manually generated solutions. Seeding with high-potential solutions based on problem-specific knowledge is an established
technique for improving optimization efficiency with evolutionary algorithms [Hamdy et al. 2011], and has been mentioned as an important supporting
mechanism for reaching convergence in design optimization of cellular façades [Wright et al. 2014]. In the present approach, all uniform façade
configurations with non-adaptive properties are added to the initial population. In addition, the best solution from the same period in the
previous day (if available), and the tail part of the optimal solution in the previous optimization horizon (indicated in white in Figure 3.5), are added to
the initial population. Initial trials revealed that, without seeding, it is very difficult to obtain good optimization results in the large search space of
possible solutions. In the context of this project, seeding hopes to enhance optimization efficiency in the following ways:
⎯ Ensuring good spread and coverage of the entire design option space.
⎯ Making sure that end points of the design option space belong to the searched solutions. Previous research shows that lower and upper
bounds of the search space are the places where good solutions tend to be [Radford 1981]. This also minimizes the risk that “conventional”
solutions are missed by the optimization algorithm.
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⎯ Speeding up convergence because genes of good candidate solutions
are likely introduced early in the search process.
2. Objective function and normalization
The dynamic optimization process seeks to improve two different performance aspects at the same time; energy consumption and visual
comfort. This translates into a two-dimensional performance space in which Pareto optimal solutions can be identified [Radford and Gero 1980]. The
optimization of adaptive façades is based on successive decision moments in the MBC control formulation. Because the dynamic optimization procedure
needs to move forward automatically (i.e. no human intervention), manual inspection of Pareto fronts in each control horizon is not possible. As an
alternative, performance preferences that rank the two non-commensurable
objectives have to be articulated a priori.
Which of the façade adaptation sequences gets identified as most preferred solution by the genetic algorithm depends on two elements:
i the relative magnitude of each performance indicator compared to
the other indicators (multi-objective optimization);
ii the range of potential gains that can be achieved within each criterion
(i.e. the swing from worst to best) [Choo et al. 1999].
In conventional façade design optimization, the weights in Equation 4.2 can be tuned in such a way that a balanced trade-off point is found that is in line
with the goals of the design team [Hopfe 2009; Mela et al. 2012]. In adaptive façade optimization, this tuning of weights is less trivial because the
importance of trade-offs is intrinsically subject to change. Due to dynamic
effects, the values for 𝑷𝒆𝒏𝒆𝒓𝒈𝒚 and 𝑷𝒗𝒊𝒔𝒖𝒂𝒍 can differ several orders of
magnitude from horizon to horizon. This can for example happen when the building is in free-running mode (i.e. no energy consumption) for an entire
optimization horizon; in this horizon, the energy penalty (Penergy) would be zero, whereas in the next horizon, a minimum energy consumption of several
kWh could be found. This large variability in the results makes it difficult to find balanced trade-off solutions with respect to the other performance
criterion, in this case visual comfort. As a consequence, it is not straightforward to determine fixed criteria weights that will lead to
satisfactory performance over the whole simulation period.
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A solution to this difficulty is found by selection of optimal solutions with
reference to an anchor point. The position of this anchor point is recalculated in every optimization horizon by determining the performance of all possible
non-adaptive façade options for the current optimization horizon. Note that these non-adaptive solutions are part of the initial population as an outcome
of the seeding procedure described in step 1 above. Based on predefined weights that represent the relative importance of energy and visual
performance, these values are scaled in an iterative process (i.e. vector normalization) until the preferred non-dominated solution is located at the
position [1,1] in an xy-coordinate system. A graphical summary of this procedure is presented in Figure 4.10.
Figure 4.10. Overview of the multi-criteria decision-making approach. The left figure shows the performance for different adaptive façade system sequences (dots) as well as
reference solutions with fixed façade configuration (diamonds). The axis values are imaginary, but are chosen to indicate that the values for different indicators can be
widely different. The right figure shows the normalized performance indicators with an anchor solution at [1,1]. The circle arcs indicate the shortest Euclidean distance to the ideal solution [0,0], in this case indicating that many adaptive façade solutions can
improve performance on both objectives, compared to the best performing static façade solution.
The normalization weights are re-calculated for every optimization horizon and applied to the fitness function values of all adaptive façade solutions by
dividing the raw penalty value with the values of the solution at the anchor point.
Finally, the best performing sequence is selected using a TOPSIS decision-
making approach. This method finds the compromise solution as the point with the shortest Euclidean distance to the ideal solution, and the farthest
distance from the negative-ideal solution [Opricovic and Tzeng 2004; Triantaphyllou 2000], with:
Objective 1
Objective 2
Objective 1, normalized
Objective 2, no
rmalized
1.0
1.0
0.10.2
0.3
100 200 300Anchor point, best non-adaptive solutionNon-adaptive design solutionCandidate solution, adaptive facadeOptimum solution
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⎯ Ideal solution: the Utopia solution (point [0,0]) [Zavala 2012] having
zero energy consumption and zero comfort penalty.
⎯ Negative-ideal solution: the anchor point [1,1] representing the best
possible non-adaptable solution. In this way, the distance from worst to best becomes an explicit part of the decision-making process in
the MBC formulation.
The mathematical representation for the TOPSIS case where normalized
penalty is calculated as the shortest Euclidean distance to the ideal solution is presented in Equation 4.3.
P = w ∙ PP | + w ∙ PP | [-] 4.3
3. Stopping criterion
Stopping criteria are an important consideration in population-based
optimization studies, as they determine the interaction between optimality of the results and computational efficiency. In most building-related
optimization studies, the optimization search is stopped after a maximum number of generations in the GA is exceeded [Attia et al. 2013]. In is not
uncommon, however, that the optimum solution lies at the edge of the search space, and is evaluated already as part of the seeded initial population. In such
cases, it is unnecessary to run the GA for all generations. A check was implemented to track the relative improvement in normalized objective
function values. If the relative improvement in equation 4.2 is less than 5% in five consecutive generations, we assume that convergence has been reached,
and terminate the optimization.
4. Recombination
Recombination, or crossover, forms an essential part of the working principle
of GAs. In its default implementation with uniform crossover, however, there is a risk that unfavorable crossover points hinder the convergence because
they can break the gene sequence of good candidate solutions. In order to prevent such effects, in the current approach, crossover points are only
allowed per entire façade sequence, i.e. between adaptation periods (Figure 3.5), not within. Mutation, on the other hand, can take place on any gene to
support sufficient diversity throughout the whole sequence.
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5. Soft constraints
The optimization formulation as presented in this Chapter, gives rise to an
enormous number of possible façade adaptation strategies; the size of the search space grows exponentially as the optimization horizon gets longer.
However, as it turns out, many of these options lead to equivalent performance. For example, at times when the temperature difference
between inside and outside is small, the thermal resistance of the façade plays only a minor role. Similarly, at night, the influence of façade transparency can
be ignored. In terms of performance as it is defined in the objective function (Equation 4.2), it is often impossible for the optimization algorithm to
differentiate between all the different alternatives. The consequence is that there is a risk that random components or confounding variables enter the
decision making process in lieu of meaningful variations. When one is only interested in the performance potential of adaptive façades, these effects
have no direct consequences. However, when the aim is to also derive the properties of high-performance adaptive façade concepts, this can become
problematic, because it is not directly possible to tell which suggested adaptation actions actually drive the parameter search, and which come from
meaningless variations that go without physical explanation. To avoid such issues as much as possible, the objective function is extended with two
additional hierarchic preference terms in the form of a soft constraint [Kerrigan et al. 2000]. These preference terms are based on the following
three considerations:
⎯ Undue complexity of building envelope components should be
avoided as much as possible. As long as the performance differences are negligibly small, the façade sequence with lowest complexity (i.e.
most limited in frequency and extent of adaptation) is preferred.
⎯ Excessive changes in the façade zone may lead to increased chances
for disturbance and discomfort [Stevens 2001; Bakker et al. 2014]. From the perspective of user acceptance, the façade sequence that is
least obtrusive (i.e. most limited in frequency and extent of adaptation) is preferred, in cases with otherwise equal performance.
⎯ When the optimization algorithm cannot discriminate between
multiple options, it keeps unintentional diversity in the gene pool, rather than narrowing down on a specific subset of high-
performance solutions. Due to a lack of consistency, such erratic changes complicate the way to convergence. Additional preference
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terms are useful for facilitating effective ranking of all alternative
solutions. There is a risk however, that the optimization gets stuck in a local minimum. Crossover and mutation settings need extra
attention to avoid premature convergence.
Taking Equation 4.3 as a starting point for calculating 𝑃 , these considerations translate into the following hierarchic preference terms: 𝑃 = 𝑃 + 0.01 ∗ 𝑃 , + 0.01 ∗ 𝑃 , [-] 4.4
Where: 𝑃 , limits the variation within an adaptation period: if
performance of more than one design alternative is equivalent on all other
performance aspects, the one with the most uniform appearance is selected;
and: 𝑃 , limits the variation from adaptation period to adaptation
period: if the performance of more than one design alternative is equivalent
on all other performance aspects, the one that requires the lowest number of changing elements is selected.
4.6 Overview of the toolchain
This Chapter is concluded by giving an overall view of the various elements
in the toolchain, and their interactions (Figure 4.11). Different combinations of control horizons and optimization horizons are possible in the current
implementation. This overview shows data exchange for the simplified example of an optimization horizon of 3 time steps with a control horizon of
2 time step (see also Figure 3.5).
The “sensor” variables y are sent to BCVTB by the ‘real’ building model, the
“actuator” values u are sent by the controller. State variables are only transferred at the start of a new optimization horizon (t=1 and t=3; indicated
with dark grey dots). Depending on whether it is in the middle of a control horizon (e.g. at t=2) or at the start of a new one, Matlab sends either data
stored from a previous optimization horizon (e.g. at t’=2) or invokes a new call to the optimization algorithm to receive the data after the optimization (ESP-
r and Radiance) run is finished. In the first situation, the data exchange happens very fast, whereas in the second case, possibly thousands of
scenarios are evaluated in the embedded building model, which may take minutes to hours.
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Figure 4.11. Example of the data exchange per time step. t= time step, yt = sensor value at time step t, ut = control signal at time step t. Optimization horizon length is 3 time steps
and control horizon length is 2 time steps.
Figure 4.12 demonstrates how the toolchain steps forward in time. Three consecutive adaptation periods are shown, indicating the optimization
process finds different optimal values when more information about the future becomes available.
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Figure 4.12. Example of façade optimization sequences over time. Three consecutive adaptation periods are shown. The length of the adaptation period in this example is
46h, the control horizon is 12h and the adaptation period is 6h.
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5
Quantifying the performance
potential of adaptive façades –
a case study
5.1 Introduction
So far, this dissertation has mostly focused on the development of a methodology for computational performance optimization of adaptive
façades. This has resulted in a toolchain that couples different simulation tools for building energy and daylight performance prediction with a control
approach that can ensure high-performance adaptive façade operation. In the present and the following Chapter, two selected case studies are
presented to illustrate potential applications of the toolchain, and to show how it can be used to analyze the performance of innovative adaptive façade
concepts.
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5.1.1 Background and objectives of the case study
The case study that is presented in this chapter is framed within the context of EOS-lt project FACET [Loonen et al. 2015]. The goal of the FACET project
was to identify the ideal adaptable building envelope properties for different building types in the Netherlands. To achieve this objective, the project
proposed an inverse approach. Instead of starting from a certain adaptive façade material or technology and analyzing its performance, the project
began by postulating the desired end result (i.e. optimal IEQ conditions with minimal energy expenditure) and a subsequent search for adaptable façade
system states that best approach the ideal end result. The hypothesis is that, based on this theoretically-derived optimum which can also include building
materials with hypothetical properties, promising suggestions for R&D paths can be outlined.
5.2 Description of the case study
5.2.1 Building model and simulation scenario
This study considers a typical single-zone perimeter office space with
dimensions (LxWxH) of 3.6 m x 5.4 m x 3 m. The office faces south, is located on an intermediate floor, and is surrounded by similar spaces and a corridor
at the back. Construction and material properties of the external façade are determined via optimization as described in Section 5.2.2; the properties of
the other construction elements are given in Table 5.1.
Table 5.1. Constructions and material properties of the case study.
Thickness(mm)
Conductivity(W/mK)
Density (kg/m3)
Specific heat (J/kgK)
Floor Floor tiles 6 0.60 500 750
Concrete blocks 140 1.06 1950 1000
Ceiling Suspended ceiling (gypsum) 13 0.42 1200 837
Internal walls Lightweight concrete blocks 150 0.44 1500 650
Plasterboard 12 0.18 800 837
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Occupancy and usage
On working days, the zone is occupied by two persons. The office workers
are present from 8:00 to 17:00 and account for internal heat gains of 65 W per person (metabolism) and 120 W per person for appliances. For glare
evaluation, we assume that the persons are looking in the direction of the window (worst-case scenario).
Heating, cooling, lighting and ventilation
Operative temperature inside the room is controlled on the basis of adaptive
temperature setpoints according to EN 15251, class B. The setpoints vary over time according to the running mean outdoor temperature in the previous five
days [Nicol and Humphreys 2010]. Outside occupied hours, temperature setpoints for heating and cooling are adjusted to 17°C and 30°C respectively.
Dimmable artificial lighting with a maximum lighting power density of 10 W/m2 is used. Daylight is controlled automatically to maintain a minimum
work plane illuminance of 500 lux. Illuminance sensors for controlling artificial lights are placed in the center of the room at work plane level (0.9
m).
During occupied hours, outdoor air with a flow rate of 2 ACH is used for ventilation, with a heat recovery efficiency of 90%. The infiltration rate is
assumed to be constant, and set at 0.24 ACH.
Weather conditions
Simulations are carried out with the weather data reference year for energy
simulations for the Netherlands [NEN5060 2008]. The analysis focuses on two typical weeks, one in winter, and one in spring. Figure 5.1A shows incident
solar radiation on the south façade and Figure 5.1B shows ambient temperature for these weeks.
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A
B Figure 5.1. Weather conditions (the Netherlands) during selected weeks in winter, spring
and summer.
Primary energy conversion
To make fair comparisons with respect to the various energy flows, overall energy performance is assessed on the basis of primary energy consumption.
In the case study, we assume that the seasonal heating efficiency = 0.9, cooling COP = 3, and the primary energy conversion factor for electricity =
2.56 [NEN7120 2011].
5.2.2 Adaptive façades: adaptable properties and adaptation
ranges
To analyze the effect of adaptability of adaptive façades, the external façade is subdivided into a number of smaller elements, or cells. The physical
properties of each of these elements can be controlled individually. Instead of having fixed walls and fenestration areas, such a decomposition enables
flexible optimization of shape, number, position, transparency and thermal properties of “window” elements. Previous research by Wright et al. [2014]
shows that a subdivision of the façade into smaller elements can be a valuable approach, because it allows for façade optimization in response to the
temporal and spatial characteristics of daylight. In contrast to previous work, however, this research not only deals with design optimization of ‘cellular’
façades, we also account for the fact that in adaptive façades, these properties can change over time. Discretization of the façade is done to gain
insight into performance trends and dynamic spatial effects of adaptive façades in an abstract but generic way. Actual adaptive façade concepts
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deriving from this approach may or may not have this type of grid-like
appearance.
In this demonstration example, the façade is subdivided into a grid of 4*4 elements. Material properties in terms of solar energy transmittance (Tsol)
and U-value of each of these elements (Table 5.2) are adaptable per hour. The option space for thermal transmittance consists of six steps, ranging from
thermal resistances equivalent of a conventional glazing system up to the passive house insulation standard. The option space for solar transmittance
consists of four steps, ranging from values equivalent of a clear glazing to a dark tinted glazing or roller shade system. Overall, this leads to a design
option space of 6*4=24 possible combinations per element, and 24^(4*4)=1.2e22 possible façade configurations per adaptation period.
Table 5.2. Option space of material properties for the adaptive façade.
Design option 1 2 3 4 5 6
U-value [W/m2K] 0.2 0.5 1 2 3 4
Tsol [-] 0.05 0.25 0.50 0.75
5.2.3 Objective function
Multiple performance criteria are transformed into in a single objective optimization function, following the formulation in Section 4.5.4 (Equation
4.4) to compute the dimensionless total penalty function 𝑃 .
𝑃 = 𝑤 ∙ | + 𝑤 ∙ | + 0.01 ∗ 𝑃 , + 0.01 ∗ 𝑃 , [-] 5.1
In this case study, the weighting factors for energy (w ) and visual (w ) performance are chosen to have an equal value of 0.5. These equal weights
ensure that the optimization formulation can find a balanced trade-off point between these competing objectives.
As described in Section 3.4.2, visual comfort is further subdivided into three
components: UDI, DGP and view to outside. These factors are also embedded in the objective function so that the desired goals can be achieved by
optimization. The threshold values for daylight illuminance, glare and view
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are indicated in Figure 5.2. For each daylight hour in the optimization horizon,
penalty values are computed, and then aggregated into one value using weighting factors. The penalty functions consist of ramps instead of discrete
step functions, as this aids in detecting slight differences between the façade configurations, and thus expedites the design space exploration.
Figure 5.2. Penalty function for daylight performance with threshold values
To achieve the desired performance, it is important to choose appropriate weighting factors for the combined visual comfort performance function
(Equation 4.1). Several factors need to be taken into account. For example, glare risk occurs during only a relatively limited period of the year. There are
many instances when glare considerations are not relevant for determining façade operation, but when the risk occurs, it is important that the
consequences are effectively blocked. On the other hand, view to outside is only of interest after the basic performance criteria have been met. By giving
view to outside a relatively low weight, the parameter search will be nudged towards solutions that maximize openness of the façade while not
compromising on daylight illuminance and glare discomfort. After performing several test runs, it was found that the weights in Equation 5.2
lead to the desired results for calculating 𝑃 as in input for Equation 5.1. Note that only the relative difference between the weights is important,
because the aggregated result will always be normalized.
𝑃 = 1 ∙ 𝑃 + 10 ∙ 𝑃 + 0.2 ∙ 𝑃 [-] 5.2
It should also be noted that the weightings and setting of threshold values involve subjective and contextual considerations. The present case study
aims to demonstrate capabilities of the performance simulation framework. Future work should focus on identifying the impacts of different weights on
performance. As indicated before by Mahdavi [2008] performance
preferences do not have to be constant over time.
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5.2.4 Optimization horizon and control update
In the present case study, an optimization horizon of 24 hours was chosen. Ideally, we would want to work with a quasi-infinite horizon, because it
theoretically leads to the best results [Zavala et al. 2009]. A disadvantage of increasing the length of this forward-looking horizon is that it leads to an
exponential increase of the search space per control sequence, and as such, the increased risk of finding only a local optimum with non-exhaustive
optimization algorithms. On the other hand, a too short horizon also has drawbacks. When the horizon is too short, e.g. 1 hour, there are potentially
many cases with for example no energy consumption and no discomfort, in which the objective function is not able to rank performance of the different
alternatives. In such cases, however, this does not mean that each façade adaptation option is equally good. Because of transient energy storage
effects, the current control decision affects the system states that can be reached in the future, possibly leading to performance degradation (e.g.
higher energy demands or comfort violations) that could not be foreseen in
the short horizon. For buildings with very low thermal mass, such as highly-glazed spaces, the consequences might be insignificant [C. S. Lee 2017], but
for typical office buildings, these temporal effects are worth considering. In addition, a longer forward-looking horizon is needed to plan façade
sequences with smooth transitions (Section 4.5.5), because some fluctuating façade schedules could only be prevented with consideration of further
points in the future [Coffey et al. 2010].
The case study uses an adaptation horizon, and also update frequency of 12 hours. In this way, the positive effects of receding, partly overlapping, time
horizons are accomplished. Without receding time horizon, thermal energy storage potential of construction elements would be just exhausted/emptied
at the end of each horizon, and indoor temperature could be at the edge of becoming uncomfortable, leaving unfavorable starting conditions for the
next horizon. The selected adaptation period is one hour and the simulation time step for thermal simulations is 10 minutes.
5.2.5 Reference façade
To establish a baseline for further comparison, an office zone with typical façade properties is defined. The building envelope of this reference situation
has a window-to-wall ratio of 40%. The opaque parts have an Rc-value of 5
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m2K/W, while for the windows, low-emissivity double glazing (g-value: 0.50,
U-value: 1.78 W/m2K), with external solar shading screens is applied. The shading system is activated when incident solar radiation on the façade is
higher than a fixed threshold value of 300 W/m2.
5.3 Results – performance potential compared
to reference case
5.3.1 Spring week
Performance
Figure 5.3 shows a comparison of the energy performance for different design
options in a week in spring (18 – 22 May). Case 1 to 4 represent the energy performance for a façade with non-adaptive properties at the endpoints of
the values given in Table 5.2. The performance of these design options was selected as a baseline, because these points can give an indication of the
potential in best/worst case scenarios for single objectives, but often fail to perform well on the other aspects. Column 5 represents the office with
reference building envelope as presented in Section 5.2.5, and column 6 shows the optimized result for the adaptive façade.
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Figure 5.3. Optimization results for a spring week. A: Energy performance, B: glare discomfort, C: daylight illuminance.
The results indicate that thermal insulation has a dominant effect on the
energy consumption in spring. With a highly insulated envelope (e.g. case 2 or case 3), the building needs a considerable amount of mechanical cooling
to maintain comfortable conditions because the heat that is trapped inside cannot be easily released to the environment. In addition, the results show
that a reduction in the contribution for lighting, by making a transparent façade, can have a significant impact on the building’s energy balance. With
adaptive façades, the overall energy consumption can be reduced by 60% compared to the reference situation (Figure 5.3A). This is, however, not the
lowest possible energy demand, which would have been achieved with a fully glazed, low-insulation design variant (Case 1). Looking at the visual
performance indicators DGP (Figure 5.3B) and UDI (Figure 5.3C), it is clear why the adaptive façade solution is chosen as optimal scenario. Even though
UDI levels are favorable, a fully glazed façade leads, as expected, to a high risk of intolerable glare discomfort. In the reference solution, the solar shading
system is closed throughout most of the day, resulting in restricted view to outside and very low levels of UDI in the autonomous category. An optimized
adaptive façade, on the other hand, is able to find a good trade-off point considering all performance aspects.
0
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AdaptiveRefU=4T=0.05
U=0.2T=0.05
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U=4T=0.75
AdaptiveRefU=4T=0.05
U=0.2T=0.05
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Figure 5.4. Scatter plot showing progression of the design space exploration. Green dots indicate static façade configurations. Grey dots indicate adaptive façade configurations; the darker the color, the higher the iteration number in the GA. The red dot represents
the optimized solution that was selected by the GA.
Figure 5.4 shows a scatter plot with performance considering the two optimization objectives: visual performance (horizontal) and energy
performance (vertical). Each dot represents the performance of a certain façade adaptation strategy for the optimization horizon of 18 May from 0h00
to 24h00. In green, the non-adaptive façade solutions are shown as reference
points. Points closer to the origin (Utopia point) have better performance because they have the lowest penalty values. The greyscale colors give an
indication of the progression towards better performing solutions over time (the darker the color, the higher the generation of the genetic algorithm), and
the red point is the façade solution that is finally chosen by the optimization objective.
Design considerations
To better understand the performance of the optimized adaptive façade, it is important to analyze how the façade properties vary over time. Figure 5.5
shows the optimized façade configurations for the 55 occupied hours during the week.
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Figure 5.5. Optimized façade configurations for the spring week. Each block represents a façade configuration as seen from the inside. Black corresponds to Tsol = 0.05, dark grey
to Tsol = 0.25, light grey to Tsol = 0.50 and white to Tsol = 0.75
The following principles can be observed:
⎯ In the mornings, the façade is highly transparent to enable maximum
daylight utilization and a view to outside.
⎯ During periods of high incident radiation (midday), the optimization
algorithm suggests a façade configuration with low solar transmittance. On the overcast day (Thursday) the façade remains
mostly transparent.
⎯ Later in the afternoon, passive solar energy gains are unwanted
because the building is already warmed up. Configurations with intermediate transparency are found as a balanced trade-off solution
considering all performance aspects involved. The non-uniform façade patterns are the result of (unwanted) randomness that is
sometimes present in the optimization outcomes. In these cases, the soft constraints were not effective in ensuring uniform façade
appearance.
⎯ Sun-tracking behavior of opaque façade elements can be observed to some degree. In the mornings, a higher density of dark cells is
situated on the left (East) side, whereas in the afternoon, the majority of dark cells shifts to the right (West) side. This effects is most
noticeable on the Wednesday, and indicates how the optimization framework is able to identify façade regions with high luminance
contrast to promote the effect of glare-free daylight utilization. The
fact that this effect is not evident on all sunny days highlights that the
16 17 189 10 11 12 13 14 15
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existence of multiple local optima and the existence of the large
design option space in combination the flatness of the fitness landscape (i.e. the fact that there are many solutions with nearly
identical performance) make it difficult to reach clear patterns in the final solution.
⎯ On days with similar weather conditions (e.g. Monday and Tuesday)
and IEQ requirements, the algorithm finds a different optimal trade-off solution during many hours of the day. This observation provides
another illustration of the complexity of the optimization problem.
The façade-averaged solar transmittance and U-value for each hour of the
simulation period are represented in Figure 5.6. By analyzing this data, information can be extracted to develop a better understanding of the
adaptive façade properties that will lead to high performance. Three notable findings can be observed:
⎯ The ability to chance thermal insulation properties is effectively
utilized. On evenings and nights with relatively cool outside temperatures, this feature is used for passive cooling of the interior
space.
⎯ Whereas solar transmittance values frequently use all values of the
design option space, U-value relies almost exclusively on the end points. The use of developing façade materials or technologies with
many intermediate thermal insulation states seems limited.
⎯ There are many instances where the optimization algorithm suggests
the combination of high thermal resistance with high solar transmittance. It seems worthwhile to focus R&D efforts on
developing transparent insulation systems that also have the ability to change visible light transmittance.
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Figure 5.6. Optimization results: surface-averaged dynamic façade properties for the spring week.
5.3.2 Winter week
Performance
Figure 5.7 shows a comparison of the energy performance over the whole winter week (12 – 16 Jan). Compared to spring, the contribution of useful solar
gains is small. More artificial light is needed, and the differences between extremes in transparency are relatively small. Depending on the insulation
level, either heating or cooling energy is needed. In the case with adaptive façade, heating energy can completely be eliminated, while only a small
fraction of the cooling energy is still needed. Compared to the reference case, an overall energy reduction of 43% can be achieved, which can mostly be
attributed to smart use of passive solar heating. Due to the low angle of the sun, and the possibility for low visible light transmittance with the adaptive
façade, there is no risk for glare discomfort during this week, provided that façade transparency is properly controlled on sunny days.
00.511.522.533.544.5
T sol[-] and U-v
alue [W/m2 K]
Mon Tue Wed Thu FriTsol U-value
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Figure 5.7. Optimization results for a winter week.
Design considerations
Compared to the week in spring, the average transparency of the façade is
much higher in winter (Figure 5.8). There is a high correlation between façade transparency and incident solar radiation during this period. At times when
irradiance levels are low, and there is little or no risk for glare discomfort, the design exploration algorithm suggests highly transparent façade states that
maximize the view to outside. Sun tracking behavior is less visible during this winter week. This is likely due to the position of the sun, which is lower in the
sky during this period. As a consequence, the façade configurations that are found by the genetic algorithm resemble the characteristics of a conventional
solar shading system to a large extent.
Figure 5.8. Optimized façade configurations for the winter week. Each block represents a façade configurations as seen from the inside. Black corresponds to Tsol = 0.05, dark grey
to Tsol = 0.25, light grey to Tsol = 0.05 and white to Tsol = 0.75
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The combination of insulation and transmission properties of the optimized
façade states is presented in Figure 5.9. During most of the time, a transparent insulation state is selected as the optimal solution, but during
sunny moments, the solar transmittance goes down to reduce glare risk. A low thermal resistance value is identified as beneficial on two of the
afternoons. In this way, heat trapping can be prevented – and cooling load avoided – by releasing heat to the cooler ambient environment.
Figure 5.9. Optimization results: surface-averaged dynamic façade properties for the winter week.
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T sol[-] and U-
value [W/m2 K]
Mon Tue Wed Thu FriTsol U-value
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5.4 Discussion and conclusion
This Chapter has demonstrated the capabilities of the toolchain, by presenting a case study that explores the performance potential of adaptive
façades for an office building in the Netherlands. Results that have been analyzed in detail for one week in spring and one week in winter indicate that
the simulation-based optimization methodology is able to highlight the capabilities of adaptive façades in terms of finding balanced trade-off
solutions considering multiple competing objectives. In this way, significant performance benefits compared to state-of-the-art façade alternatives can
be obtained.
A rather large option space of dynamic façade properties was used to be able to provide recommendation for further R&D and product development of
adaptive façade technologies and materials. It was found that dynamic thermal insulation and sun-tracking solar shading solutions are mostly
responsible for the improved building performance with adaptive façades. More R&D to develop new design solutions in these directions is therefore
recommended.
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6
Investigating trade-offs between
performance and façade system
complexity
6.1 Introduction
The goal of this chapter is to demonstrate application-specific aspects of the toolchain on the basis of a case study. The intention is to extend the usability
of the simulation methods, and to illustrate how it can help in developing a better understanding of the tradeoffs between façade performance and
complexity.
6.1.1 Background and objectives of the case study
Careful selection of the properties and dimensions of windows is essential for
optimizing the indoor environmental quality and energy performance of
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buildings. Nowadays, special layers, such as low-emissivity coatings and solar
control films, are routinely applied for enhanced control of solar heat gains and higher thermal resistance. Although these technologies have
substantially improved the performance of fenestration systems, they also have some drawbacks. One of the limitations of such glazing systems is that
the properties are fixed over time, and can therefore not respond to, or take advantage of, the variability in climatic conditions. A solar control film, for
example, will typically help reduce cooling energy in summer, but also impairs view to outside and blocks useful solar gains in periods when no
cooling is needed. As a result, compromises in the design phase or often
needed to achieve satisfactory performance under all conditions.
Breakthroughs in materials science now open up a range of new possibilities that will allow building designers to influence window-related trade-offs in a
more dynamic way. Recently, materials with switchable reflection/transmission properties in three parts of the electromagnetic
spectrum have been reported in literature. The ability to selectively control the transmission of (i) visible light, (ii) near-infrared sunlight, (iii) and
longwave infrared radiation (emissivity) generates many promising opportunities because through this adaptability, multiple optimized states
can be reached. There is, however, still a lack of guidance on how such innovative technologies should be integrated in buildings.
The objective of this study is to evaluate and compare the performance of such windows on a whole-building level under a range of different conditions.
This scoping study is performed by means of a co-simulation approach, as described in Chapter 4 that uses Radiance, ESP-r and BCVTB to
simultaneously predict energy performance, dynamic daylight illuminance, and glare discomfort. In this study, independent switching of glazing
properties is addressed in three parts of the spectrum, as well as the various interactions, and it is investigated which strategy works best depending on
certain circumstances. The parameters that are taken into account include different façade orientations, climatic conditions and window control
strategies. Finally, the findings of this study are synthesized in the form of general recommendations that can be used by both building designers and
the research community as an outline for future materials science development directions.
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6.2 Description of the case study
The methodology for the demonstration study that is presented in this chapter is described in five steps: (i) baseline model, (ii) window properties,
(iii) performance indicators, (iv) simulation scenarios, and (v) window control strategies.
6.2.1 Baseline model
The baseline simulation model is a single-zone office space (3.6*5.4*2.7 m)
with a window-to-wall ratio of 60%. The perimeter façade is modelled as an external wall while all other surfaces face similar office spaces. All opaque
construction elements in this reference model have medium thermal mass. The external wall has a thermal resistance (R-value) equal to 3.5 m2K/W
The perimeter office zone is occupied by two people who are present on weekdays from 8-18 h. Each occupant represents an internal heat gain of 120
W. Internal gains from electronic equipment (laptop, printer, etc.) corresponds to 50 W/person, operating only during occupied hours.
Artificial lights are controlled by a sensor inside the zone, 1.5 m from the façade at work plane level. The lights with a nominal lighting power density
of 12 W/m2 are continuously dimmed to meet the setpoint of 500 lux.
The heating, ventilation and air conditioning (HVAC) system is modeled as an ideal convective system, because the main interest of this work is on daylight
control and management of solar gains. The HVAC system has a heating setpoint 21ºC starting one hour before the beginning of the occupied hours
until the zone is empty again (7-18 h.) and 14ºC during the rest of unoccupied hours. The cooling setpoint is set to 25ºC for occupied and 30ºC for
unoccupied hours. Ventilation with heat recovery (90% efficiency) is set to 3 air changes per hour (ACH) during occupied hours and 0.2 ACH during
unoccupied hours, while infiltration was assumed to have a constant value of 0.3 ACH throughout the year.
6.2.2 Window properties
Windows with three different types of switchable glazing properties are analyzed in this study. All switchable windows share one common reference
110
state (sref), having the properties of a regular low-emissivity double glazed
window.
Figure 6.1. Relationship between visible transmittance and solar transmittance. Note that window properties below the maximum light-to-solar heat gain (LSG) line and
above the minimum LSG line are physically impossible.
Figure 6.1 shows how the properties of the windows with low visible
transmittance (s1) and high NIR reflectance (s2) relate to this reference situation (sref, low-e). The switchable window state with high NIR reflection
(s2) has a very high light-to-solar heat gain (LSG) ratio. In this case, it means
that all solar energy in the wavelength range λ > 700 nm is reflected, without
affecting the transmittance of visible light. For s1 the case with low visible transmittance, it was assumed that the absorption of sunlight is equally
spread over the entire solar spectrum. This case therefore results in the situation that switching of window properties happens between two points
with equal LSG value. The end point of VLT = 0.05 is selected because it is generally considered that values as low as this lead to a moderate risk for
glare discomfort [E. S. Lee et al. 2013; Piccolo and Simone 2009].
The third type of adaptability (s3), the changing of properties in the longwave radiation range is achieved by varying the emissivity of the inside surface of
the outside glazing pane from 0.05 to 0.84. The algorithms in LBNL Window 7 [LBNL 2017b] indicate that doing this would lead to an increase in window
U-value from 1.6 W/m2K to 2.7 W/m2K.
A summary of all relevant properties of the different switchable glazing
systems is presented in Table 6.1.
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
Tso
l
Tvis
NIR
sref
Max. LSG
Min. LSG
s2s1
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Table 6.1. Overview of the switchable glazing properties in the [i] visible (VLT), [ii] near-infrared (NIR) and [iii] longwave part of the solar spectrum.
Tvis[-]
Tsol[-]
𝜺[-]
U-value[W/m2K]
VLT s1 0.05 0.22 0.05 1.6
sref 0.74 0.54 0.05 1.6
NIR s2 0.74 0.35 0.05 1.6sref 0.74 0.54 0.05 1.6
Longwave s3 0.74 0.54 0.84 2.7
sref 0.74 0.54 0.05 1.6
6.2.3 Performance indicators
Two aspects are considered in the switchable window performance assessment: energy and comfort.
To evaluate energy performance, the total primary energy use intensity (EUI) in kWh/m2 for heating, cooling and lighting is computed. Throughout this
Chapter, we assume that the seasonal heating efficiency = 0.9, cooling COP = 3, and the primary energy conversion factor for electricity = 2.56, according
to (NEN7120 2011).
Façade-related visual comfort is influenced by a number of factors with complex interactions [Reinhart and Wienold 2011]. The target is to use the
switchable windows to contribute to well-distributed daylight illuminance in the absence of discomfort glare. This is evaluated with two complementary
indicators:
⎯ Useful daylight illuminance (UDI). Daylight utilization is assessed by
computing the percentage of occupied hours that daylight
illuminance on the work plane falls within certain bounds. Four different categories according to the classification of [Mardaljevic et
al. 2012] are considered: fell short (<100 lux), supplementary (100-300 lux), autonomous (300-3000 lux) and exceeded (>3000 lux).
⎯ Daylight glare probability (DGP). Discomfort glare is assessed using
DGP, as it was shown to be the most robust glare metric in a comparative study under a wide range of ambient conditions
[Jakubiec and Reinhart 2011]. In this study, a conservative glare scenario is considered in which the occupants’ view direction is
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always facing the façade. Three different categories for discomfort
glare are distinguished, according to the classification in [Reinhart and Wienold 2011], where the risk is (i) intolerable (DGP > 0.45), (ii)
disturbing (0.35 < DGP < 0.45), or (iii) not disturbing (DGP < 0.35).
6.2.4 Simulation scenarios
Various integrated daylight and thermal simulations are conducted to assess the performance of the switchable window types. Here, one baseline
simulation scenario is defined that considers variations in terms of climate, façade orientation and window-to-wall ratio. Table 6.2 gives an overview of
the different scenarios that are investigated in this study; items in bold represent the baseline scenario.
Table 6.2. Overview of variants in the parametric study (bold represents baseline scenario).
Climate Stockholm, Amsterdam, Berlin, Madrid
Orientation East, South, West
WWR 30%, 60%, 90%
The four locations were selected because together they cover a wide range
of climate types, representative for the European continent. Table 6.3 presents an overview of heating and cooling degree days for each of the four
cities as an indication of the range of climate conditions that is covered in this study.
Table 6.3. Overview of heating degree days (HDD) and cooling degree days (CDD) for the four climates.
HDD CDD
Stockholm 4286 49Amsterdam 3038 65
Berlin 3317 147Madrid 2023 612
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6.2.5 Window control strategies
The definition of an appropriate window control strategy is of essential importance for analyzing the performance of switchable glazing technologies
[Jonsson and Roos 2010]. This demonstration example uses rule-based control strategies, to be able to study the performance of the different
window systems in a realistic application environment. Table 6.4 summarizes the control strategies that are considered in this study.
Table 6.4. Window control strategies
Switch from sref other state, if
VLT Incident solar radiation > 250 W/m2
NIR Incident solar radiation > 250 W/m2
Longwave Ambient temperature < indoor temperature
AND building is not in heating mode
6.3 Simulation strategy and settings
This study uses a coupled simulation approach to analyze the interactions
between daylight performance and energy efficiency. Annual dynamic
daylight simulations for the different window states are carried out using the Radiance three-phase method, while Building energy simulations (BES) are
performed using ESP-r. The control strategies are directly implemented in the BCVTB using actors from its Ptolemy II library. This coupling strategy also
takes care of assigning the correct internal heat gains for lighting, depending on daylight illuminance corresponding to the values that were computed in
the pre-processing phase.
6.4 Results
6.4.1 Daylight performance
Figure 6.2 compares the simulation results in terms of UDI for the reference
window (sref) and the case with switchable VLT (s1). The difference is most
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notable for the number of occupied hours that the illuminance threshold of
3000 lux gets exceeded.
Figure 6.2. Useful daylight illuminance results for the reference window (top, sref) and switchable VLT window (bottom, s1). Results are shown for two façade orientations and
three window-to-wall ratios.
The extent of possible discomfort due to the too high daylight illuminance
levels gets significantly reduced in the case of controlled light transmittance,
most notably for the South façade.
During the hours with relatively low daylight illuminance, the difference between the two cases is relatively low. This effect can be explained by the
fact that during most of these hours, the switchable glazing system is switched in the transparent mode, and hence leads to similar lighting
conditions as the regular façade.
The results for glare discomfort are presented in Table 6.5, showing again the comparison between switchable and non-switchable visible light
transmittance. The difference in results for DGP is even more pronounced than the results for UDI. The amount of discomfort hours shows a substantial
reduction when the window transparency is reduced to 5% during times with high incident solar radiation.
0
20
40
60
80
100
South West South West South West
0
20
40
60
80
100
South West South West South West
% o
f occ
up
ied
tim
e%
of o
ccu
pie
d ti
me
WWR = 30 WWR = 60 WWR = 90
WWR = 30 WWR = 60 WWR = 90
Fell short Supplementary Autonomous Exceeded
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Table 6.5. Daylight glare probability classification for the regular window (top) and switchable VLT window (bottom). Results are shown for tow façade orientations and
three window-to wall ratios.
Orientation WWR DGP Disturbing [h] Intolerable [h]Reference case (sref)
South 30 98 11260 114 198
90 182 325
West 30 179 25660 172 378
90 212 421
Switchable VLT (s1)
South
30 31 4
60 52 990 56 23
West
30 22 12
60 44 2190 51 56
6.4.2 Energy performance
Figure 6.3 shows a comparison of the primary energy demand for different
WWR values (South façade, Amsterdam). The results show that for windows with switchable non-visible properties (NIR and longwave), the results are
highly sensitive to the window area. Especially larger windows lead to a large increase in energy consumption. On the other hand, switching in the visible
part of the spectrum leads to much less difference in the results. This finding indicates the possibility of designing buildings with large glazed façades,
without having a significant penalty in terms of increased energy consumption.
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Figure 6.3. Energy performance of the three switchable window types for a façade with South orientation in Amsterdam. Results are shown for window-to-wall ratios of 30, 60
and 90%.
In Figure 6.4, the results for the four different climates are presented. These results show that lighting energy consumption has a quite large influence on
the overall energy consumption. The windows that switch in the non-visible part of the spectrum need considerably less energy for lighting. However,
these cases also show a higher energy demand for cooling. The lowest cooling energy demand is observed in the situation with visible switching. Compared
to longwave switching, the dynamic NIR reflectivity also reduces cooling energy demand, but not as effective as the case with VLT control. These
results correspond with the values for solar transmittance, as presented in Table 6.1. However, these static material properties are not sufficient to
characterize the performance of switchable glazing systems, because (i) they do not account for the dynamic effects that occur at different control
strategies, (ii) do not take the total building energy balance in consideration, and (iii) do not distinguish whether most of the incident radiation is absorbed
(VLT case) or whether it is reflected (NIR case). The complex balance between heating, cooling and lighting energy use in Figure 6.4 clearly indicates the
need to develop and select switchable glazing technologies in response to the needs of local climatic conditions. For example, a switchable VLT window
offers the lowest total energy demand for Madrid, but such a window would
not be recommended for energy-saving purposes in Stockholm.
0
50
100
150
200
250
30 60 90 30 60 90 30 60 90Pri
mar
y en
ergy
con
sum
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on [k
Wh
/m2]
VLT NIR Longwave
Heating Cooling Lighting
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Figure 6.4. Energy performance of the three switchable window types for a façade with South orientation and 60% window-to-wall ratio. Results are shown for the climates of
Stockholm, Amsterdam, Berlin and Madrid.
Finally, Figure 6.5 shows the energy performance results as a function of façade orientation. Although these results are useful to obtain a better global
understanding of the performance of switchable glazing types, they do not lead to remarkable new insights. For all three window types, the lowest
energy consumption is achieved for the South façade. In all three cases, the East façade has the highest heating energy consumption. In the situation
where the admittance of solar gains is reduced (VLT and NIR), the heating load on the East façade is extra high because the contribution of passive solar
gains to room heating gets reduced when the room is heating up in the morning hours. The consequences of this orientation effect are also visible
for the West façade. By the time that high intensity solar radiation reaches the West façade, the office room is already warmed up. Because the longwave
case has no ability to control the amount of solar gains, we can see there a much larger increase in cooling energy demand, compared to the other two
cases (VLT and NIR).
0
50
100
150
200
250
300
STO AMS BER MAD STO AMS BER MAD STO AMS BER MAD
Pri
mar
y en
ergy
con
sum
pti
on [k
Wh
/m2]
VLT NIR Longwave
Heating Cooling Lighting
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Figure 6.5. Energy performance of the three switchable window types for a façade with 60% window-to-wall ratio in Amsterdam. Results are shown for three orientations,
East, South and West
6.5 Discussion and conclusion
This chapter has demonstrated the use of a high-resolution modeling approach to evaluate how three advanced switchable window types, active in
three different parts of the electromagnetic spectrum, are able to meet the goal of well-controlled daylight and solar gains in buildings.
The results are in line with the outcomes of various simulation studies that
present an in-depth analysis of the individual switchable window types, and with a strong focus on either the thermal or daylight aspect [DeForest et al.
2015; Fernandes et al. 2013; Loonen, Singaravel, et al. 2014; Tavares et al. 2014]. This is an important finding, because it gives additional information to
confirm that the co-simulation implementation that is presented in this thesis is able to produce meaningful results. Another unique characteristic of
the results presented here, is that it allows for a rapid side-by-side comparison of the relative performance of several technologies, because
their evaluation was subject to the same simulation scenarios and boundary conditions.
Pri
mar
y en
ergy
con
sum
pti
on [k
Wh
/m2]
VLT NIR Longwave
Heating Cooling Lighting
0
50
100
150
200
250
East South West East South West East South West
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This application study has led to the following main conclusions:
⎯ The concept of one-size-fits-all does not apply to switchable window
technology. To achieve good performance, product developers and building designers should adapt the properties of switchable glazing
technologies to the needs of individual situations.
⎯ Dynamic modulation of sunlight transmittance in the visible
wavelength range not only influences solar heat gains, but also has a positive effect on visual comfort conditions and lighting energy use.
Among the three tested glazing types, glazing systems that are active in this part of the spectrum have the highest energy-saving potential.
It is essential to use coupled daylighting and thermal simulations to gain insights into such effects.
⎯ Solar shading systems, such as blinds and screens or window coatings
with controllable VLT transmission are needed to ensure acceptable
visual comfort conditions, especially in cases with large window areas. It is not always fair to compare the energy performance of
windows with and without shading system, because they do not lead to equivalent indoor conditions [Ochoa et al. 2012; Goia 2016].
⎯ The performance of switchable windows is very climate-dependent.
Windows with switchable NIR reflection are most effective in climates which have both a considerable heating and cooling energy
demand, whereas the ability to switch VLT is most
effective/promising in sunny climates.
⎯ Compared to the other two technologies, the performance potential of adaptive low-emissivity coatings is relatively low. Its effect appears
to be almost insensitive to the façade orientation.
This study also led to a couple of limitations that are worth to be mentioned.
For example, the potential co-benefits of having switchable properties in multiple parts of the solar spectrum was not investigated. It is expected,
however, that independent control of transmitted visible and non-visible solar energy can lead to extra advantages for low-energy building operation
with high indoor environmental quality. This study only assessed two states per switchable window type, at the end points of the dynamic range. It would
be interesting to analyze if adding the option for switching to intermediate states would lead to different conclusions. Although it is known that the
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chosen window control strategy can have a significant influence on the
performance of switchable glazing, only a limited number of options were examined here.
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7
Concluding remarks
7.1 Conclusions
Driven by the need to reduce the negative environmental impact of building energy use, and motivated by the business opportunities that arise from
providing spaces with superior indoor environmental quality, there is currently a rapid transition towards more sustainability in the built
environment. In light of these developments, adaptive façades have been identified as a promising design solution, because their application can
enable high energy performance without compromising indoor comfort conditions. To further support the development and adoption of high-
performance adaptive façade systems, there is a need for tools and methodologies that can be used to facilitate informed decision-making in the
R&D process of innovative adaptive façade materials and technologies. Through literature review and initial simulation studies, it was identified in
Chapter 2 and Chapter 3 that currently available building performance
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simulation software and workflows have a number of important
shortcomings that limit their usability in the context of buildings with adaptable façade properties. The main aim of the research presented in this
thesis was therefore to develop, test and evaluate computational approaches that can be used to support design analysis and performance-based design
space exploration in the development process of innovative adaptable building envelope components and concepts.
This dissertation has addressed four objectives that are directly related to
this research aim:
⎯ To develop an effective modeling and simulation strategy for
integrated performance prediction of buildings with time-varying building shell properties.
⎯ To develop and test a computational approach, based on simulation
and optimization techniques, that is capable of exploring the performance potential/limits of adaptive façades.
⎯ To illustrate, on a case study basis, how this approach can be used to identify high-potential, i.e. high-performance, low-complexity,
directions for future adaptive façade concepts.
⎯ To better understand the causal relationships between adaptability
of building shell parameters and performance (energy and comfort) in a number of demonstration examples.
As a first step in this process, the requirements that are characteristic for
performance prediction and optimization of buildings with adaptive façades were identified. It was found that special attention needs to be paid to
adequately incorporating time-varying façade properties and dynamic thermal energy storage effects into the simulation models. In addition, when
predicting the performance of high-performance façades, there is a need for computational approaches that can simultaneously take into account the
interactions and trade-offs between thermal and daylight domains. A third important requirement in the present context is the necessity to consider
advanced control strategies because of the tight coupling between design and operational aspects, and the fact that different levels of control
complexity would lead to different optimal solutions.
In response to these requirements, a simulation toolchain was developed
using the building energy simulation program ESP-r and the three-phase
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simulation approach in Radiance for dynamic daylight predictions. A co-
simulation implementation using the building controls virtual test bed (BCVTB) was set up to enable on-the-fly coupling between the models in
these two software environments. The possibility to incorporate time-varying façade construction properties was achieved by extending the
thermophysical property substitution feature that was implemented by MacQueen [1997]. In this way, a flexible yet accurate method of simulating
dynamic thermophysical façade properties was established.
An important element of this study is the idea to formulate the design exploration process of adaptable building envelopes as an optimization
problem. The fact that adaptive façades change over time makes this study quite different from conventional design optimization studies, because it is
not just one façade configuration, but a whole sequence of time varying system states (i.e. façade properties) that needs to be considered in the
optimization. In this thesis, this challenging task was addressed through the development of an offline receding-horizon model-based control strategy.
This approach contains two instances of the BPS models; one model is a
virtual representation of the real building, and the other model is used inside the simulated control system to evaluate the performance of various façade
adaptation strategies. This whole process was coordinated using Matlab. A genetic algorithm was used to efficiently navigate through the large search
space of façade adaptation strategies, while an approach for explicit state initialization was developed to avoid the significant computational overhead
that would occur in the repeated warm-up phases of the simulations.
Two case studies were presented to illustrate the usability of the tool chain
in different application areas.
The first case study was set up to quantify the performance potential of future-oriented adaptive façade systems that can simultaneously change
both thermophysical and optical material properties. The façade was subdivided into a grid of elements with individually controllable façade
properties to provide insights into performance trends and dynamic spatial effects of adaptive façades in an abstract but generic way. Through an
analysis of optimized solutions, it was found that the toolchain leads to meaningful results that match well with principles known from building
physics and other results published in literature. Based on this positive confirmation, the results were then further analyzed to suggest R&D
pathways in materials science that could be pursued in the coming years to
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bring the identified high-performance adaptive façade systems within reach
of façade designers.
The second case study focused on spectrally selective switchable glazing systems with adaptive properties in different parts of the electromagnetic
spectrum. Multiple window-to-wall ratios, façade orientations and climatic conditions were investigated to evaluate the performance of the fenestration
system in different scenarios. The results from this demonstration example showed how different glazing types can be developed to meet the specific
requirements of different building types and boundary conditions. As such, it could be illustrated that it is not always necessary to have the widest adaption
range possible, but that tailored solutions can lead to similar performance at much lower complexity. It was found that the toolchain that is developed in
this thesis is a useful instrument for providing decision support in settings where such complex problems are being addressed.
7.2 Limitations
During the development of the toolchain, many decisions about the structure
of the models and inter-process communication between the models had to be made. Although these decisions were always based on careful analysis of
the advantages and drawbacks of state-of-the-art options, it was not always possible to verify that they would lead to the most desirable result. In other
words, even though the case studies demonstrated several advantageous characteristics of the simulation approach, it is not possible to claim with
confidence that the approach to simulation and optimization taken here is the most effective or efficient.
For example, the toolchain contains a considerable number of simulation parameters (e.g. simulation time step) and optimization settings (e.g. length
of optimization horizon) that can be tuned by the user. It has been observed that setting these user-defined variables appropriately is very important,
because implementing incorrect settings can lead to sub-optimal results, thereby jeopardizing the effectiveness of the toolchain. Despite this
importance, due to limited resources, it was not possible to carry out a systematic study that would allow advice to be provided to the end user on
how to select robust simulation settings. It is also worth noting that many of these settings are very case-specific (e.g. the length of the optimization
horizon should be chosen in accordance with the thermal time constant of
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the building), which makes it difficult to arrive at generally applicable
guidelines.
The optimization method that is currently implemented in the toolchain is based on genetic algorithms (GA). GAs are the preferred method of choice for
providing optimal solutions in many multi-criteria building performance problems, and, also in this study, it was found that they can work well. This
kind of meta-heuristic algorithm, however, also introduces an important limitation in the context of the present work. When using GAs, it is not
possible to guarantee that the best design solutions, as identified by the algorithm, are actually global optimum solutions. This is not a serious issue
when one is interested in quantifying the performance potential (e.g. energy-saving or comfort improvement) of a certain adaptive façade technology.
However, in many cases, it is also interesting to gain an in-depth understanding of which design parameters and façade adaptation strategies
are responsible for the prospective high performance. In such circumstances, it is important that a certain level of consistency can be identified in the
optimized results. When this consistency is missing, it is very difficult to distinguish random artefacts of the optimization algorithm (e.g. due to
finding local instead of global optima) from meaningful differences that are actually driving the parameter search. Because of this difficulty, it can
become highly problematic to discern patterns, and to identify optimal designs for adaptive façades. Several cases with inconsistencies in optimal
façade properties were found in this study. These inconsistencies can be partly attributed to the intrinsic complexity of the optimization problem and
to the fact that the fitness landscape is rather flat, but they are also partly due to the local optima that are found by the GA. Various measures such as soft
constraints and careful selection of the bounds of the design option space were implemented to eliminate the negative consequences of this effect as
much as possible. Nevertheless, it was not possible within the limitations of this thesis to test whether different optimization algorithms of the same type
(e.g. particle swarm optimization), or algorithms of a different category (e.g. deterministic algorithms or gradient-based algorithms) could possibly lead to
more consistency in the results. Consequently, there is a need for comparison
studies that test the relative effectiveness of optimization algorithms in the context of adaptive façades.
It has been demonstrated in this thesis that formulating the design space
exploration of adaptive façade systems in the form of an optimization problem can be a very powerful approach. Nevertheless, there are also points
of attention, arising from the fact that solving the optimization problem at
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every n time steps of the simulation drastically increases the computational time that is needed to arrive at optimal or near-optimal solutions. Possible
ways to reduce computation time are through parallel computing, or by improving the search efficiency of optimization algorithms. Here it is argued
that there are also application-specific elements in which knowledge of the optimization problem at hand can be exploited to make the search process
more efficient. For instance, adaptation periods could have a variable length, to allow for frequent transitions of façade properties at times when there are
more dynamics in the environment (e.g. when direct sunlight is incident on the façade), but with a lower degree of adaptability under stable boundary
conditions (e.g. during nighttime). In addition, the idea of using pattern recognition could be explored. Using this approach, the façade control
intelligence could detect certain trends or characteristic periods in IEQ requirements and environmental boundary conditions. Based on this
information, valuable computation time can be saved by using an archive or look-up table function to retrieve high-performance façade sequences that
have proven to work well for similar conditions in the past. Comparable approaches have already been demonstrated for control of HVAC systems
[Coffey 2013], and it is expected that this knowledge can also be transferred to the domain of adaptive façades. In the current implementation, a high-
resolution ESP-r model is used to assess the performance of different proposed façade sequences inside the virtual controller. It would be
worthwhile to investigate if simplified or data-driven models (e.g. grey-box models) could be used instead of the high-resolution first-principles based
model to reduce computation time without significant loss of prediction quality.
In Chapter 1, it was identified that high-potential adaptive façade systems are the ones that combine high performance with relatively low complexity in
terms of e.g. the number of adaptive vs. fixed design variables, their modulation range (i.e. high and low value of adaptive design variables) and
corresponding adaptation frequency. Using the case of switchable glazing as an example, Chapter 6 subsequently demonstrated how the developed
toolchain can help designers and product developers to quantify the prevailing trade-offs between performance and complexity of façade
systems, thereby enabling them to make informed decisions that can lead to desired outcomes. By means of this demonstration, only a fraction of the
possibilities of how the toolchain could be used for taming complexity were addressed. Many other possibilities were left unexplored, but the toolchain
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has been developed in such a way that it can be used to help answering the
following questions:
⎯ What is the relationship between spatial resolution (e.g. the number of adaptable façade elements) and façade performance?
⎯ Do different façade orientations (e.g. north, no direct sun vs. sun-exposed façades) and preferences for performance criteria (e.g.
different scenarios for glare perception, multiple occupants) lead to different optimal façade solutions?
⎯ Is there a critical minimum adaptation frequency to achieve certain
performance targets with different design variables? Does this
frequency differ for buildings with low and high thermal mass?
⎯ What is the loss in performance when the façade is changing its configuration on a less frequent basis?
⎯ Should this frequency be constant throughout the year, or can we identify periods where less frequent transitions would lead to the
same performance?
7.3 Future research
While developing, implementing and testing the functionality of the toolchain for performance optimization of adaptive façades, this study has opened up
various promising directions for future research. On one hand, these suggestions for further work focus on broadening the scope and application
domain of the toolchain. On the other hand, they refer to the integration of knowledge or methodologies from adjacent research disciplines to further
enhance the potential added value of the approach.
The performance of adaptive façades has so far been analyzed for a relatively
small number of performance indicators, mostly related to energy efficiency and thermal and visual comfort. It is expected that the application of adaptive
façades in buildings can lead to a number of other benefits, such as cost-reductions due to smaller installed capacity of HVAC systems, increased
robustness/resilience with respect to unexpected events (e.g. heatwaves or persistent changes in building usage), or synergistic interactions with other
building services. The toolchain that has been developed in this thesis has all
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the capabilities to become a useful asset in future studies that quantify such
effects for various stakeholders. In doing so, it would be worthwhile to make the toolchain part of a larger virtual test environment that also contains the
necessary elements for extensive uncertainty propagation (e.g. weather variability and occupant behavior) and sensitivity analysis studies. It might be
wise to further develop dedicated methodologies such as dynamic sensitivity analysis [Loonen and Hensen 2013], or to adjust existing approaches, to take
full advantage of the intrinsic value of these advanced computational support tools in the context of adaptive façades.
The application potential of the work presented in this thesis could be
broadened by adapting it in such a way that additional façade technologies can also be included. It would, for example, be interesting to extend its use
towards onsite harvesting of renewable energy (e.g. façade-integrated PV or solar thermal), or to include controlled use of façade openings to optimize
the design of natural ventilation and night-time ventilative cooling. There is also potential for incorporating interior slabs with adaptable thermal storage
[Hoes and Hensen 2016].
By embedding the simulation programs in a receding time horizon control
framework, this research has established a thorough coupling between the design and control aspects that determine the performance of adaptive
façades. Advanced, optimization-based control sequences were considered to evaluate how the operation of the façade plays a defining role in identifying
optimal adaptive façade properties. All the analyses have targeted the product development and/or façade design phase, prior to the building’s
operation. The actual control of adaptive façade systems when the building is in use was beyond the scope of this study. Nevertheless, it seems possible
to use the tools and methods of this study to support the operational phase of buildings with adaptive façades. For example, the receding horizon
approach could be integrated into a real-time simulation-assisted control framework, similar to the work described by e.g. Clarke et al. [2002] and
Xiong and Tzempelikos [2016]. As indicated by e.g. Favoino et al. [2017], the ability to use a digital twin [Boschert and Rosen 2016] of an adaptive façade
system can be very beneficial for supporting complex decision-making during operation. Another option would be to use the simulation framework
to extract control rules that can be implemented in a conventional building management system [May-Ostendorp et al. 2013].
In a wider sense, it would be profitable to apply the toolchain developed in
this work to further investigate and improve the mutual interaction between
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adaptive façades and building occupants. The developed toolchain can be
used to investigate the impact of user influence (e.g. control override actions) on adaptive façades and in turn support the development of interfaces that
enable a better human-façade interaction experience. Moreover, instead of using the toolchain to quantify how adaptive façades can minimize the
occurrence of discomfort compared to other façade solutions, there is also scope for increased use of simulation-based research to further explore how
adaptive façades can lead to increased occupant satisfaction, wellbeing and productivity [Loonen et al. 2015].
Researchers in the field of flexible systems engineering have developed a
range of design support tools that can be used to appraise the sustained value of reconfigurable or adaptive systems over their entire lifecycle [Cardin 2013].
Furthermore, these tools can be used to systematically evaluate the relative influence of adaptive vs. fixed design variables, and to prioritize which
variables should be changeable in operation. Notable examples include epoch-era analysis with Pareto trace [Fitzgerald and Ross 2012], selection-
integrated optimization [Khire and Messac 2008] and time-expanded decision networks [Silver and de Weck 2007]. Thus far, these methodologies
have mostly been applied for e.g. automotive and aerospace applications, and to improve the design of manufacturing systems. Because of the many
similarities with adaptive façades, it would be interesting to transfer such methodologies to the domain of building engineering. The neighboring field
of multi-criteria decision-making could also provide interesting connection points to further enrich design support for adaptive façades. It would, for
example, be interesting to process the outcomes of the toolchain using post-optimization analysis [Brownlee and Wright 2012], visualization [Chichakly
and Eppstein 2013], and data-mining approaches [Bandaru et al. 2017] as a step forward in reaching a better understanding of the causal relationships
between adaptive façade design and performance.
The toolchain that is presented in this thesis is developed using the building
energy simulation tool ESP-r. Several decisions about the design of the toolchain were made in response to specific characteristics in terms of
structure and algorithms of the program. ESP-r is a legacy simulation program. It is a robust tool that is evolving on top of many thousands of man-
hours of software development and testing. The core of ESP-r was developed in the 1970s and has remained largely unchanged since then. As a result, there
are some archaic features in the program (e.g. need for recompilation, and the relatively monolithic code structure) that limit the flexibility and options
for versatile use of the toolchain. Modern scripting and modeling languages
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such as Python and Modelica offer a number of advantages that would enable
scalability, modular extension of capabilities, options for interfacing with external software (e.g. through functional mock-up units) and user-friendly
access to advanced simulation settings [Nouidui et al. 2012; Wetter 2009]. It is expected that the lessons that have been learned during the development
of the present toolchain can provide a valuable starting point for the implementation of a similar workflow in another software environment, or in
a hybrid fashion, combining the strengths of both approaches.
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Curriculum Vitae
Roel Loonen (1986) holds a BSc and MSc (cum laude) in Building Services from Eindhoven University of Technology (TU/e). His PhD research – carried out at the Unit Building Physics and Services (TU/e) – developed approaches for computational performance optimization of innovative adaptive façade concepts. Roel spent the summer of 2012 in Philadelphia, US, for a research stay with Pennsylvania State University at DOE’s Energy Efficient Buildings Hub. Roel’s research and teaching focus on the development and application of modeling and simulation strategies to provide decision support for designing buildings that combine high indoor quality with low or no impact on the environment. Roel collaborates with SMEs in various R&D projects, aspiring to accelerate the process of bringing innovative building envelope technologies and renewable energy systems to the market. In addition, he is an active participant in EU COST Action 'Adaptive Facades Network' and IEA SHC Task 56 'Solar Building Envelopes'. Since 2012, he is board member of IBPSA—NVL and has acted as a reviewer for 21 international scientific journals. Roel has received prizes for his Master thesis (REHVA International Student Competition 2011), innovation competitions (Team Energy Challenge 2015) and the Best PhD Supervisor Award of the Department of the Built Environment (2017). From the summer of 2018, Roel continues as Assistant Professor in the Building Performance group at TU/e.
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Acknowledgements
If we want to think like a scientist more often in life, then there are three key
objectives to keep in mind — be humbler about what we know, more confident
about what’s possible, and less afraid of things that don’t matter.” ~ Tim Urban
The list of metaphors that is being used to give outsiders an idea of what it is like to do a PhD is nearly endless, and includes words such as: a symphony, a
knot, a pilgrim's journey, a tree, a school of fish, a battlefield, learning how to drive, building a ship, etc.
The one that resonates most with me lies in the distinction between puzzles and mysteries, as it was put forward by Gregory F. Treverton12:
"There’s a reason millions of people try to solve crossword puzzles each day. Amid the well-ordered combat between a puzzler’s mind and the
blank boxes waiting to be filled, there is satisfaction along with frustration. Even when you can’t find the right answer, you know it exists. Puzzles can
be solved; they have answers. But a mystery offers no such comfort. It poses a question that has no definitive answer because the answer is
contingent; it depends on a future interaction of many factors, known and unknown. A mystery cannot be answered; it can only be framed, by
identifying the critical factors and applying some sense of how they have interacted in the past and might interact in the future. A mystery is an
attempt to define ambiguities."
Looking at it from this perspective, the PhD process can undeniably be
categorized as mystery solving. As we all know, mysteries can sometimes be wicked, while it also ought to be realized that the methods that are used for
puzzles generally don’t work for solving mysteries, or worse, could even
impair the situation.
As such, contingency becomes the main ingredient for the successful completion of a PhD thesis. Contingency can happen in a variety of different
ways, at work or elsewhere, by reading the work of others, and through a range of possible interactions, both planned or in serendipitous situations. In
any case, contingency is not to be confused with randomness. I have
12 https://www.smithsonianmag.com/history/risks-and-riddles-154744750/
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experienced that the interaction with people is of particular importance
during a PhD, especially when these interactions go beyond the level of what can reasonably be expected. Numerous of such wonderful interactions have
helped me to accomplish the end result that is reported in this thesis. I am very grateful for this.
First and foremost, I would like to express my gratitude to prof.dr.ir. Jan Hensen. Thank you Jan for your constant support, guidance and trust. Your
enthusiasm about how the proper application of building performance simulation can help to make the built environment a better place to live in is
contagious. The way you manage to combine sincere ambition with congeniality, and the way you give others the chance to develop while always
noticing the important things in every project, is appreciated by many. On top of that, I think that the way you always care for the human behind the
researcher is more than commendable.
I am also grateful to dr.dipl.-ing Marija Trčka. Marija, you have been a great
source of inspiration throughout the first phases of my doctorate research. Under your tutelage, the main ideas for the project were developed, many of
which have found their way into this thesis. I also want to thank you for helping to arrange my research stay in Philadelphia.
I would like to thank the other members of the committee for their comments and valuable suggestions to improve this dissertation: prof.dr. Joe Clarke,
prof.dr. Marco Perino, prof.dr.ing. Ulrich Knaack, prof.dr. Albert Schenning, prof.dr.ing. Alex Rosemann and prof.ir. Elphi Nelissen.
The development phase of this project has greatly benefited from close collaboration with dr.ir. Pieter-Jan Hoes. Pieter-Jan, thank you very much for
the shared developments and all other collegial and scientific advice throughout the years.
During my Master studies, and in the early phases of my PhD, I have learned a lot from dr. Daniel Cóstola. Daniel, thanks for sharing your knowledge and
experiences, related to research as well as many other things.
dr. Bruno Lee and dr. Chul-sung Lee also deserve a special acknowledgement
in this section. Bruno and Chul-sung, thanks a lot for helping to create the friendly environment during the years that we have shared the same office
space.
Further, I would like to thank Ignacio for sharing your knowledge, wittiness
and your couch; Isabella for challenging me with a continuous stream of creative ideas; Rebeca and Petr for laughs, reflection and always making me
look forward to the next rendezvous; Rajesh and Vojta for the many merry
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moments while you were competently overtaking me; and Hamid for
generous advice, support and simply for being a very good friend.
Having had the fortunate possibility to be co-supervisor of various PhD
candidates and PDEng trainees might not have expedited the completion of this thesis, but has undoubtedly enriched my research and personal
development in countless different ways. I would like to thank Finn, Mohammad, Marie, Hemshikha, Adam, Samuel, Antía and Dmitry for your
enthusiasm and the pleasant collaboration.
Apart from providing an excellent research environment, the Building Physics
and Services Unit at TU/e stimulates an atmosphere that blurs – if not vanishes – the line between colleagues and friends. I consider it as a great
bonus to have the privilege to be part of this cohesive group. In addition to those mentioned above, I would like to thank for all the support, useful
distractions and cultural learning: Adam, Adelya, Alessia, Ana Paula, Asit, Benedetto, Carlos, Christina, Christof, Christos, Erkki, Evangelos, Fotis,
Gerardo, Giovanni, Johann, John, Katarina, Luyi, Marcel, Maryam, Massimo, Mike, Mohamed, Mohammad, Olga, Parisa, Qimiao, Raúl, Rizki, Rongling,
Rubina, Sanja, Sanket, Tomaž, Wiebe, Yasin, and Zahra.
The work described in this thesis has, directly and indirectly, benefitted from
many inspiring discussions with the master thesis students I have worked with. Sundar, Alexander, Babis, Michalis, Charlotte, Daan, Nikos, Bas,
Franziska, João, Riccardo, Wouter, Daniela, Prahaladh, Stefan, Remco, Teun, Adheesh, Canan, David, Helena, Niels, Puji, Suryadi and Vignesh, thank you
all.
Without the cooperation of many people outside the BPS Unit at TU/e, who
have supported me in this research and thesis, it would have never been possible to achieve this end result.
This research was financially supported by RVO through the EOS-lt project: FACET. I would like to thank Bart de Boer from TNO for the skillful project
management, and all project members for all the valuable input and insightful suggestions for my research.
I am very fortunate to have met dr. Fabio Favoino, as an aspiring researcher who is on a similar mission to materialize the benefits of adaptive facades
through increased use of building performance simulation. Fabio, it was a great pleasure to work together and to be able to discuss research ideas and
everyday challenges, without the hassle of needing to explain all the context. I am really happy to see you back in academia, and look forward to continuing
the collaboration. I am also grateful to dr. Mauro Overend for the support and constructive feedback.
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I am thankful to dr. Hitesh Khandelwal, dr. Michael Debije and prof.dr. Albert
Schenning for introducing me to the wonders of stimuli-responsive functional materials and devices. You have provided me with a great number
of ideas and I appreciate that you were always there to answer my questions.
I would further like to thank the project members of COST Action TU1403
‘Adaptive Facades Network’, IEA SHC Task 56 ‘Solar Building Envelopes’ and the 4TU.Lighthouse initiatives for feedback, support and providing an
international perspective.
Duncan Harkness, thanks a lot for the much-needed support during the last
phases of the project.
Apart from the contacts in academia, this work has also significantly
benefited from information exchange with industrial partners. I would like to thank Teun Wagenaar, Jasper van den Muijsenberg and dr. Casper van Oosten
from Merck Window Technologies and Sam Kin, Stan de Ridder and Matthijs Damen from Kindow / Wellsun for giving me the much-appreciated reality-
checks and for letting me have a first-hand insight into what it takes to really bring an innovative façade concept from initial idea to the market.
My dear friends from Lottum, thanks for being there during the day of the defense, for being curious about my project, at times, but most notably for
always reminding me that the most important things in life happen outside the university.
Finally, a special word of thanks to my parents and to Ingrid and Remon. I don’t express it often enough, but I really appreciate the outstanding home
base and your unquestioned support.
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List of Publications
Journal articles
1. Capperucci, R., Loonen, R.C.G.M., Hensen, J.L.M. & Rosemann, A.L.P. (2018). Angle-dependent optical properties of advanced fenestration systems - finding a right balance between model complexity and prediction error. Building Simulation. Article in Press.
2. Koenders, S.J.M., Loonen, R.C.G.M. & Hensen, J.L.M. (2018). Investigating the potential of a closed-loop dynamic insulation system for opaque building elements. Energy and Buildings, 173, 409-427.
3. Sarakinioti, M.V., Konstantinou, T., Turrin, M., Tenpierik, M., Loonen, R.C.G.M., de Klijn - Chevalerias, M.L. & Knaack. U. (2018). Developing an integrated 3D-printed façade for thermal regulation in complex geometries. Journal of Façade Design and Engineering, 6(2): 29-40.
4. Roberz, F., Loonen, R.C.G.M., Hoes P. & Hensen, J.L.M. (2017). Ultra-lightweight concrete: energy and comfort performance evaluation in relation to buildings with low and high thermal mass. Energy and Buildings, 138, 432-442.
5. De Witte, D., de Klijn – Chevalerias, M.L., Loonen, R.C.G.M., Hensen, J.L.M., Knaack, U. & Zimmermann, G. (2017). Convective Concrete: Additive Manufacturing to facilitate activation of thermal mass. Journal of Façade Design and Engineering 5(1): 107-117.
6. Loonen, R.C.G.M., Favoino, F., Hensen, J.L.M. & Overend, M. (2017). Review of current status, requirements and opportunities for building performance simulation of adaptive facades. Journal of Building Performance Simulation, 2017(2): 205-223. (most read article in Journal of Building Performance Simulation since 2011)
7. Rezaeian, M., Montazeri, H. & Loonen, R.C.G.M. (2017). Science foresight using life-cycle analysis, text mining and clustering: a case study on natural ventilation. Technological Forecasting and Social Change, 118, 270-280.
8. De Witte, D., Knaack, U., de Klijn - Chevalerias, M.L., Loonen, R.C.G.M., Hensen, J.L.M. & Zimmermann, G. (2017). Convective Concrete: additive manufacturing to facilitate activation of thermal mass. Spool 4(2): 13-16.
9. Sarakinioti, V., Turrin, M., Teeling, M., De Ruiter, P., Van Erk, M., Tenpierik, M., Konstantinou, T., Knaack, U., Pronk, A.D.C., Teuffel, P.M., van Lier, A., Vorstermans, R., Dolkemade, E., de Klijn - Chevalerias, M.L., Loonen, R.C.G.M., Hensen, J.L.M. & Vlasblom, D. (2017). Spong3d: 3D printed facade system enabling movable fluid heat storage. Spool. 4(2):57-60.
10. Khandelwal, H., Loonen, R.C.G.M., Hensen, J.L.M., Debije, M.G., & Schenning, A.P.H.J. (2015). Electrically switchable polymer stabilized broadband infrared reflectors and their potential as smart windows for energy saving in buildings. Nature Scientific Reports 5, 11773.
11. Bakker, L.G., Hoes - Van Oeffelen, E.C.M., Loonen, R.C.G.M. & Hensen, J.L.M. (2014). User satisfaction and interaction with automated dynamic facades: a pilot study. Building and Environment, 78, 44-52.
12. Kasinalis, C., Loonen, R.C.G.M., Cóstola, D. & Hensen, J.L.M. (2014). Framework for assessing the performance potential of seasonally adaptable facades using multi-objective optimization. Energy and Buildings, 79, 106-113.
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13. Khandelwal, H., Loonen, R.C.G.M., Hensen, J.L.M., Schenning, A.P.H.J. & Debije, M.G. (2014). Application of broadband infrared reflector based on cholesteric liquid crystal polymer network to windows and its impact on reducing the energy consumption in buildings. Journal of Materials Chemistry A, 2, 14622-14627.
14. Loonen, R.C.G.M., Singaravel, S., Trčka, M., Cóstola, D. & Hensen, J.L.M. (2014). Simulation-based support for product development of innovative building envelope components. Automation in Construction, 45, 86-95.
15. Loonen, R.C.G.M., Trčka, M., Cóstola, D. & Hensen, J.L.M. (2013). Climate adaptive building shells: state-of-the-art and future challenges. Renewable & Sustainable Energy Reviews, 25, 483-493.
16. Franchimon, F., Loonen, R.C.G.M. & Bronswijk, J.E.M.H. van (2008). Towards an economically acceptable prevention of Legionnaire's disease. Gerontechnology, 7(2): 107-107.
Book chapters
1. Aelenei, D., Aelenei, L., Loonen, R.C.G.M., Perino, M. & Serra, V. (2018). Adaptive Facades. In Desideri, U. & Asdrubali, F. (Eds.), Handbook of Energy Efficiency in Buildings. Elsevier.
2. Loonen, R.C.G.M. (2015). Bio-inspired adaptive building skins. In F. Pacheco Torgal, J.A. Labrincha, M.V. Diamanti, C.P. Yu & H.K. Lee (Eds.), Biotechnologies and Biomimetics for Civil Engineering (pp. 115-134). Springer International Publishing.
Conference Contributions
1. Bognar, A., Loonen, R.C.G.M., Valckenborg, R. & Hensen, J.L.M. (2018). Modeling Reflected Irradiance in Urban Environments – A Case Study for Simulation-Based Measurement Quality Control for an Outdoor PV Test Site. Conference Paper: Proceedings of EUPVSEC 2018 – European PV Solar Energy Conference and Exhibition. Brussels, Belgium.
2. Tzikas, C., Valckenborg, R., van den Donker, M., Bognar, A., Duque Lozano, D., Loonen, R.C.G.M., Hensen, J.L.M. & Folkerts, W. (2018). Outdoor characterization of colored and textured prototype PV façade elements. Conference Paper: Proceedings of EUPVSEC 2018 – European PV Solar Energy Conference and Exhibition. Brussels, Belgium.
3. Bianco, L., Loonen, R.C.G.M., Koenders, S.J.M., Avesani, S., Vullo, P., Bejat, T., Goia, F., Cascone, Y., Serra, V. & Favoino, F. (2018). Towards new metrics for the characterization of the dynamic performance of adaptive façade systems. Conference Paper: Proceedings of FACADE2018 – final conference of COST TU1403 “Adaptive Facades Network”. Lucerne. Switzerland.
4. De Michele, G., Loonen, R.C.G.M., Saini, H., Favoino, F., Avesani, S., Papaiz, L. & Gasparella, A. (2018). Opportunities and challenges for performance prediction of dynamic Complex Fenestration Systems (CFS). Conference Paper: Proceedings of FACADE2018 – final conference of COST TU1403 “Adaptive Facades Network”. Lucerne. Switzerland.
5. Juaristi, M., Loonen, R.C.G.M., Monge-Barrio, A. & Gómez-Acebo, T. (2018). Dynamic Analysis of Climatic Conditions for deriving suitable Adaptive Façade responses. Conference Paper: Proceedings of FACADE2018 – final conference of COST TU1403 “Adaptive Facades Network”. Lucerne. Switzerland.
157
6. Luna-Navarro, A., Loonen, R.C.G.M., Attia, S., Juaristi, M., Monge-Barrio, A., Rabensheifer, R., Donato, M., & Overend, M. (2018). Occupant-Adaptive Façade Interaction: Relationships and Conflicts. Conference Paper: Proceedings of FACADE2018 – final conference of COST TU1403 “Adaptive Facades Network”. Lucerne. Switzerland.
7. Meskinkiya, M., Loonen, R.C.G.M., Paolini, R. & Hensen, J.L.M. (2018). Calibrating Perez Model Coefficients Using Subset Simulation. Conference Paper: Proceedings of the 9th international SOLARIS Conference. Chengdu, China.
8. Saini, H., Loonen, R.C.G.M. & Hensen, J.L.M. (2018). Simulation-based performance prediction of an energy-harvesting façade system with selective daylight transmission. Conference Paper: ICAE2018 – International Congress on Architectural Envelopes, San Sebastian, Spain.
9. de Klijn, M.L., Loonen, R.C.G.M., Zarzycka, A., de Witte, D., Sarakinioti, M.V. & Hensen, J.L.M. (2017). Assisting the development of innovative responsive façade elements using building performance simulation. Conference Paper: Proceedings of SimAUD 2017, Toronto, Canada. pp:243-250.
10. Favoino, F., Giovannini, L. & Loonen, R.C.G.M. (2017). Smart Glazing in Intelligent Buildings: What Can We Simulate? Conference Paper: Proceedings of Glass Performance Days 2017, Tampere Finland, pp:212-219
11. de Witte, D., de Klijn, M.L., Loonen, R.C.G.M., Hensen, J.L.M., Knaack, U. & Zimmermann, G. (2017). Convective concrete: additive manufacturing to facilitate activation of thermal mass. Conference Paper: Proceedings of POWERSKIN, Munich, Germany.
12. Luna-Navarro, A., Loonen, R.C.G.M., Attia, S., Juaristi, M., Bilir S., Favoino, F., Monteiro Silva, S., Mateus, R., Almeida, M., Petrovski, A. & Overend, M. (2017). A roadmap for capturing user-adaptive façade interaction. Conference Paper: Proceedings of NEXT-Facades 2017: mid-term conference of COST Action TU 1403 Adaptive Facades Network, Munich, Germany. pp: 84-85
13. Bognár, Á., Loonen, R.C.G.M., Valckenborg, R.M.E. & Hensen, J.L.M. (2017). Identifying local PV shading in urban areas based on AC power and regional irradiance measurement. Conference Poster: Sunday conference.
14. Surugin, D., Loonen, R.C.G.M., Valckenborg, R.M.E. & Hensen, J.L.M. (2017). Performance prediction of BIPV: approach for development of a model selection guideline. Conference Poster: Sunday conference.
15. Ghasempourabadi, M., Sinapis. K., Loonen, R.C.G.M., Valckenborg, R.M.E., Hensen, J.L.M. & Folkerts, W. (2016). Towards simulation-assisted performance monitoring of BIPV systems considering shading effects. Conference Paper: Proceedings of IEEE PVSC 43, 5-10 June 2016, Portland, Oregon, US, (pp. 6). IEEE.
16. Loonen, R.C.G.M. & Hensen, J.L.M. (2015). Smart windows with dynamic spectral selectivity : a scoping study. Conference Paper: Proceedings Building Simulation '15, 7-9 December 2015, Hyderabad, India, (pp. 8). IBPSA.
17. Loonen, R.C.G.M., Loomans, M.G.L.C. & Hensen, J.L.M. (2015). Towards predicting the satisfaction with indoor environmental quality in building performance simulation. Conference Paper: Proceedings of Healthy Buildings Europe 2015: 18-20 May 2015, Eindhoven, The Netherlands, (pp. 1-8). ISIAQ International.
18. Loonen, R.C.G.M., Rico-Martinez, J.M., Favoino, F., Brzezicki, M., Menezo, C., La Ferla, G., & Aelenei, L. (2015). Design for facade adaptability - Towards a unified and systematic characterization. In Proceedings of the 10th Energy Forum - Advanced Building Skins. Bern, Switzerland. pp: 1274-1284
19. Attia, S., Favoino, F., Loonen, R.C.G.M., Petrovski, A. & Monge-Barrio, A. (2015). Adaptive façades system assessment: An initial review. In Proceedings of the 10th Energy Forum - Advanced Building Skins. Bern, Switzerland. pp: 1265-1273
158
20. Loonen, R.C.G.M. (2014). Shaping the next generation of adaptive facade concepts with the use of simulations. In A. Luible & U Zihlmann (Eds.), Conference Paper: Facade2014 - Conference on Building Envelopes, (pp. 84-95). Lucerne, Switzerland. Invited paper.
21. Hensen, J.L.M., Archontiki, M., Cóstola, D. & Loonen, R.C.G.M. (2014). Simulation support for research and development of advanced building skin concepts. Conference Paper: Proceedings of the 9th Energy Forum on Advanced Building Skins, 28-29 October 2014, Bressanone, Italy, (pp. 1091-1107).
22. Khandelwal, H., Roberz, F., Loonen, R.C.G.M., Hensen, J.L.M., Bastiaansen, C.W.M., Broer, D.J., Debije, M.G. & Schenning, A.P.H.J. (2014). Infrared reflector based on liquid crystal polymers and its impact on thermal comfort conditions in buildings. In I. C. Khoo (Ed.), Conference Paper: Liquid Crystals XVIII, August 17, 2014, San Diego, USA, (Proceedings of SPIE, 9182, pp. 91820S-1-91820S-8). Bellingham: SPIE.
23. Loonen, R.C.G.M., Hoes, P. & Hensen, J.L.M. (2014). Performance prediction of buildings with responsive building elements. Proceeding of IBPSA-England conference Building Simulation and Optimization (BSO14) 23-34 June 2014, London, UK.
24. Loonen, R.C.G.M. & Hensen, J.L.M. (2014). Considerations regarding optimization of dynamic facades for improved energy performance and visual comfort. Proceedings of COLEB2014, Computational optimization of low-energy buildings, 6-7 March, Zurich, Switzerland.
25. Lee, C., Cóstola, D., Loonen, R.C.G.M. & Hensen, J.L.M. (2013). Energy saving potential of long-term climate adaptive greenhouse shells. Conference Paper: Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association, 26-28 August 2013, Chambery, France, (pp. 954-961).
26. Loonen, R.C.G.M. & Hensen, J.L.M. (2013). Dynamic sensitivity analysis for performance-based building design and operation. Conference Paper: Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association, 26-28 August 2013, Chambery, France, (pp. 299-305).
27. Boer, B.J. de, Bakker, L., Oeffelen, E.C.M. van, Loonen, R.C.G.M., Cóstola, D. & Hensen, J.L.M. (2012). Future climate adaptive building shells - Optimizing energy and comfort by inverse modeling. Oral : Proceedings of the 8th Energy Forum on Solar Building Skins, 6-7 December 2012, Bressanone, Italy, (pp. 15-19). Bressanone.
28. Hoes, P., Loonen, R.C.G.M., Trčka, M. & Hensen, J.L.M. (2012). Performance prediction of advanced building controls in the design phase using ESP-r, BCVTB and Matlab. Oral : Paper presented at the First IBPSA-England conference Building Simulation and Optimization(BSO12) 10-11 September 2012, Loughborough, UK, (pp. 229-236). Loughborough, England.
29. Boer, B.J. de, Ruijg, G.J., Bakker, L.G., Kornaat, W., Zonneveldt, L., Kurvers, S.R., Alders, E.E., Raue, A., Hensen, J.L.M., Loonen, R.C.G.M. & Trčka, M. (2011). Energy saving potential of climate adaptive building shells - Inverse modelling of optimal thermal and visual behaviour. Adaptive Architecture, an international conference, London.
30. Boer, B.J. de, Ruijg, G.J., Loonen, R.C.G.M., Trčka, M., Hensen, J.L.M. & Kornaat, W. (2011). Climate adaptive building shells for the future – optimization with an inverse modelling approach. Proceedings ECEEE Summer Study 2011, Belambra Presqu'île de Giens, France, June 2011, (pp. 1413-1422). Presqu'île de Giens.
31. Loonen, R.C.G.M., Trčka, M. & Hensen, J.L.M. (2011). Exploring the potential of climate adaptive building shells. Conference Paper : Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney, Australia, November 2011, (pp. 2148-2155). Sydney, Australia.
159
32. Trčka, M., Loonen, R.C.G.M., Hensen, J.L.M. & Houben, J.V.F. (2011). Computational building performance simulation for integrated design and product optimization. Conference Paper : Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, Sydney, Australia, November 2011, (pp. 302-309). Sydney, Australia.
33. Loonen, R.C.G.M., Cóstola, D., Trčka, M. & Hensen, J.L.M. (2010). Prestatiesimulatie van adaptieve gevels - Smart Energy Glass als case-study. Proceedings IBPSA-NVL 2010 Event, Eindhoven, oktober 2010, (pp. 1-8 [Paper ID: 15]). Eindhoven: IBPSA-NVL.
34. Loonen, R.C.G.M., Trčka, M., Cóstola, D. & Hensen, J.L.M. (2010). Performance simulation of climate adaptive building shells - Smart Energy Glass as a case study. In V. Lemort, P. André & S. Bertagnolio (Eds.), Proceedings 8th International conference on System Simulation in Buildings, (pp. 1-19). Liège.
Professional journals and magazines
1. Roberz, F., Loonen, R.C.G.M., Hoes P. & Hensen, J.L.M. (2016). Ultra-lichtgewicht beton: bouwfysische alleskunner of simpelweg een ander type beton?. Bouwfysica, 27(3), 10-14. Best paper award.
2. Hensen, J.L.M., Loonen, R.C.G.M., Archontiki, M. & Kanellis, M. (2015). Using building simulation for moving innovations across the "Valley of Death". REHVA Journal, 52(3), 58-62.
3. Loonen, R.C.G.M., de Boer, B.J., Kornaat, W., Bakker, L.G. & Hensen, J.L.M. (2015). Zoektocht naar de ideale adaptieve gevel. TVVL Magazine, 44(7-8), 36-38.
4. Hensen, J.L.M., Loonen, R.C.G.M., Archontiki, M. & Kanellis, M. (2014). Modelirovaniye zdaniy dlya prodvizheniya innovatsiy cherez « dolinu smerti » [Modeling of buildings to promote innovation through the "valley of death"]. Zdaniâ vysokih tehnologij, 2014 (Autumn), 72-79.
5. Loonen, R.C.G.M. & Hensen, J.L.M. (2014). Bioadaptivnaya obolochka zdaniya [Bio-inspired adaptive facades]. Zdaniâ vysokih tehnologij, 2014 (Summer), 50-57.
6. Berk, A.B.M., Rutten, P.G.S., Loomans, M.G.L.C., Aarts, M.P.J. & Loonen, R.C.G.M. (2013). Gelijktijdig berekenen en beoordelen van gevelprestaties. TVVL Magazine, 42(1), 16-20.
7. Loonen, R.C.G.M., Trčka, M. & Hensen, J.L.M. (2012). Adaptieve gevels: een verkenning van het potentieel. TVVL Magazine, 41(5), 34-36.
Bouwstenen is een publicatiereeksvan de Faculteit Bouwkunde,Technische Universiteit Eindhoven.Zij presenteert resultaten vanonderzoek en andere activiteiten ophet vakgebied der Bouwkunde,uitgevoerd in het kader van dezeFaculteit.
Bouwstenen en andere proefschriften van de TU/e zijn online beschikbaar via: https://research.tue.nl/
KernredactieMTOZ
Reeds verschenen in de serieBouwstenen
nr 1Elan: A Computer Model for Building Energy Design: Theory and ValidationMartin H. de WitH.H. DriessenR.M.M. van der Velden
nr 2Kwaliteit, Keuzevrijheid en Kosten: Evaluatie van Experiment Klarendal, ArnhemJ. SmeetsC. le NobelM. Broos J. FrenkenA. v.d. Sanden
nr 3Crooswijk: Van ‘Bijzonder’ naar ‘Gewoon’Vincent SmitKees Noort
nr 4Staal in de WoningbouwEdwin J.F. Delsing
nr 5Mathematical Theory of Stressed Skin Action in Profiled Sheeting with Various Edge ConditionsAndre W.A.M.J. van den Bogaard
nr 6Hoe Berekenbaar en Betrouwbaar is de Coëfficiënt k in x-ksigma en x-ks? K.B. LubA.J. Bosch
nr 7Het Typologisch Gereedschap: Een Verkennende Studie Omtrent Typologie en Omtrent de Aanpak van Typologisch OnderzoekJ.H. Luiten nr 8Informatievoorziening en BeheerprocessenA. NautaJos Smeets (red.)Helga Fassbinder (projectleider)Adrie ProveniersJ. v.d. Moosdijk
nr 9Strukturering en Verwerking van Tijdgegevens voor de Uitvoering van Bouwwerkenir. W.F. SchaeferP.A. Erkelens
nr 10Stedebouw en de Vorming van een Speciale WetenschapK. Doevendans
nr 11Informatica en Ondersteuning van Ruimtelijke BesluitvormingG.G. van der Meulen
nr 12Staal in de Woningbouw, Korrosie-Bescherming van de Begane GrondvloerEdwin J.F. Delsing
nr 13Een Thermisch Model voor de Berekening van Staalplaatbetonvloeren onder BrandomstandighedenA.F. Hamerlinck
nr 14De Wijkgedachte in Nederland: Gemeenschapsstreven in een Stedebouwkundige ContextK. DoevendansR. Stolzenburg
nr 15Diaphragm Effect of Trapezoidally Profiled Steel Sheets: Experimental Research into the Influence of Force ApplicationAndre W.A.M.J. van den Bogaard
nr 16Versterken met Spuit-Ferrocement: Het Mechanische Gedrag van met Spuit-Ferrocement Versterkte Gewapend BetonbalkenK.B. LubirM.C.G. van Wanroy
nr 17De Tractaten van Jean Nicolas Louis DurandG. van Zeyl
nr 18Wonen onder een Plat Dak: Drie Opstellen over Enkele Vooronderstellingen van de StedebouwK. Doevendans
nr 19Supporting Decision Making Processes: A Graphical and Interactive Analysis of Multivariate DataW. Adams
nr 20Self-Help Building Productivity: A Method for Improving House Building by Low-Income Groups Applied to Kenya 1990-2000P. A. Erkelens
nr 21De Verdeling van Woningen: Een Kwestie van OnderhandelenVincent Smit
nr 22Flexibiliteit en Kosten in het Ontwerpproces: Een Besluitvormingondersteunend ModelM. Prins
nr 23Spontane Nederzettingen Begeleid: Voorwaarden en Criteria in Sri LankaPo Hin Thung
nr 24Fundamentals of the Design of Bamboo StructuresOscar Arce-Villalobos
nr 25Concepten van de BouwkundeM.F.Th. Bax (red.)H.M.G.J. Trum (red.)
nr 26Meaning of the SiteXiaodong Li
nr 27Het Woonmilieu op Begrip Gebracht: Een Speurtocht naar de Betekenis van het Begrip 'Woonmilieu'Jaap Ketelaar
nr 28Urban Environment in Developing Countrieseditors: Peter A. Erkelens George G. van der Meulen (red.)
nr 29Stategische Plannen voor de Stad: Onderzoek en Planning in Drie Stedenprof.dr. H. Fassbinder (red.)H. Rikhof (red.)
nr 30Stedebouwkunde en StadsbestuurPiet Beekman
nr 31De Architectuur van Djenné: Een Onderzoek naar de Historische StadP.C.M. Maas
nr 32Conjoint Experiments and Retail PlanningHarmen Oppewal
nr 33Strukturformen Indonesischer Bautechnik: Entwicklung Methodischer Grundlagen für eine ‘Konstruktive Pattern Language’ in IndonesienHeinz Frick arch. SIA
nr 34Styles of Architectural Designing: Empirical Research on Working Styles and Personality DispositionsAnton P.M. van Bakel
nr 35Conjoint Choice Models for Urban Tourism Planning and MarketingBenedict Dellaert
nr 36Stedelijke Planvorming als Co-ProduktieHelga Fassbinder (red.)
nr 37 Design Research in the Netherlandseditors: R.M. Oxman M.F.Th. Bax H.H. Achten
nr 38 Communication in the Building IndustryBauke de Vries
nr 39 Optimaal Dimensioneren van Gelaste PlaatliggersJ.B.W. StarkF. van PeltL.F.M. van GorpB.W.E.M. van Hove
nr 40 Huisvesting en Overwinning van ArmoedeP.H. Thung P. Beekman (red.)
nr 41 Urban Habitat: The Environment of TomorrowGeorge G. van der Meulen Peter A. Erkelens
nr 42A Typology of JointsJohn C.M. Olie
nr 43Modeling Constraints-Based Choices for Leisure Mobility PlanningMarcus P. Stemerding
nr 44Activity-Based Travel Demand ModelingDick Ettema
nr 45Wind-Induced Pressure Fluctuations on Building FacadesChris Geurts
nr 46Generic RepresentationsHenri Achten
nr 47Johann Santini Aichel: Architectuur en AmbiguiteitDirk De Meyer
nr 48Concrete Behaviour in Multiaxial CompressionErik van Geel
nr 49Modelling Site SelectionFrank Witlox
nr 50Ecolemma ModelFerdinand Beetstra
nr 51Conjoint Approaches to Developing Activity-Based ModelsDonggen Wang
nr 52On the Effectiveness of VentilationAd Roos
nr 53Conjoint Modeling Approaches for Residential Group preferencesEric Molin
nr 54Modelling Architectural Design Information by FeaturesJos van Leeuwen
nr 55A Spatial Decision Support System for the Planning of Retail and Service FacilitiesTheo Arentze
nr 56Integrated Lighting System AssistantEllie de Groot
nr 57Ontwerpend Leren, Leren OntwerpenJ.T. Boekholt
nr 58Temporal Aspects of Theme Park Choice BehaviorAstrid Kemperman
nr 59Ontwerp van een Geïndustrialiseerde FunderingswijzeFaas Moonen
nr 60Merlin: A Decision Support System for Outdoor Leisure PlanningManon van Middelkoop
nr 61The Aura of ModernityJos Bosman
nr 62Urban Form and Activity-Travel PatternsDaniëlle Snellen
nr 63Design Research in the Netherlands 2000Henri Achten
nr 64Computer Aided Dimensional Control in Building ConstructionRui Wu
nr 65Beyond Sustainable Buildingeditors: Peter A. Erkelens Sander de Jonge August A.M. van Vlietco-editor: Ruth J.G. Verhagen
nr 66Das Globalrecyclingfähige HausHans Löfflad
nr 67Cool Schools for Hot SuburbsRené J. Dierkx
nr 68A Bamboo Building Design Decision Support ToolFitri Mardjono
nr 69Driving Rain on Building EnvelopesFabien van Mook
nr 70Heating Monumental ChurchesHenk Schellen
nr 71Van Woningverhuurder naar Aanbieder van WoongenotPatrick Dogge
nr 72Moisture Transfer Properties of Coated GypsumEmile Goossens
nr 73Plybamboo Wall-Panels for HousingGuillermo E. González-Beltrán
nr 74The Future Site-ProceedingsGer MaasFrans van Gassel
nr 75Radon transport in Autoclaved Aerated ConcreteMichel van der Pal
nr 76The Reliability and Validity of Interactive Virtual Reality Computer ExperimentsAmy Tan
nr 77Measuring Housing Preferences Using Virtual Reality and Belief NetworksMaciej A. Orzechowski
nr 78Computational Representations of Words and Associations in Architectural DesignNicole Segers
nr 79Measuring and Predicting Adaptation in Multidimensional Activity-Travel PatternsChang-Hyeon Joh
nr 80Strategic BriefingFayez Al Hassan
nr 81Well Being in HospitalsSimona Di Cicco
nr 82Solares Bauen:Implementierungs- und Umsetzungs-Aspekte in der Hochschulausbildung in ÖsterreichGerhard Schuster
nr 83Supporting Strategic Design of Workplace Environments with Case-Based ReasoningShauna Mallory-Hill
nr 84ACCEL: A Tool for Supporting Concept Generation in the Early Design Phase Maxim Ivashkov
nr 85Brick-Mortar Interaction in Masonry under CompressionAd Vermeltfoort
nr 86 Zelfredzaam WonenGuus van Vliet
nr 87Een Ensemble met Grootstedelijke AllureJos BosmanHans Schippers
nr 88On the Computation of Well-Structured Graphic Representations in Architectural Design Henri Achten
nr 89De Evolutie van een West-Afrikaanse Vernaculaire ArchitectuurWolf Schijns
nr 90ROMBO TactiekChristoph Maria Ravesloot
nr 91External Coupling between Building Energy Simulation and Computational Fluid DynamicsEry Djunaedy
nr 92Design Research in the Netherlands 2005editors: Henri Achten Kees Dorst Pieter Jan Stappers Bauke de Vries
nr 93Ein Modell zur Baulichen TransformationJalil H. Saber Zaimian
nr 94Human Lighting Demands: Healthy Lighting in an Office EnvironmentMyriam Aries
nr 95A Spatial Decision Support System for the Provision and Monitoring of Urban GreenspaceClaudia Pelizaro
nr 96Leren CreërenAdri Proveniers
nr 97SimlandscapeRob de Waard
nr 98Design Team CommunicationAd den Otter
nr 99Humaan-Ecologisch Georiënteerde WoningbouwJuri Czabanowski
nr 100HambaseMartin de Wit
nr 101Sound Transmission through Pipe Systems and into Building StructuresSusanne Bron-van der Jagt
nr 102Het Bouwkundig ContrapuntJan Francis Boelen
nr 103A Framework for a Multi-Agent Planning Support SystemDick Saarloos
nr 104Bracing Steel Frames with Calcium Silicate Element WallsBright Mweene Ng’andu
nr 105Naar een Nieuwe HoutskeletbouwF.N.G. De Medts
nr 106 and 107Niet gepubliceerd
nr 108GeborgenheidT.E.L. van Pinxteren
nr 109Modelling Strategic Behaviour in Anticipation of CongestionQi Han
nr 110Reflecties op het WoondomeinFred Sanders
nr 111On Assessment of Wind Comfort by Sand ErosionGábor Dezsö
nr 112Bench Heating in Monumental Churches Dionne Limpens-Neilen
nr 113RE. ArchitectureAna Pereira Roders
nr 114Toward Applicable Green ArchitectureUsama El Fiky
nr 115Knowledge Representation under Inherent Uncertainty in a Multi-Agent System for Land Use PlanningLiying Ma
nr 116Integrated Heat Air and Moisture Modeling and SimulationJos van Schijndel
nr 117Concrete Behaviour in Multiaxial CompressionJ.P.W. Bongers
nr 118The Image of the Urban LandscapeAna Moya Pellitero
nr 119The Self-Organizing City in VietnamStephanie Geertman
nr 120A Multi-Agent Planning Support System for Assessing Externalities of Urban Form ScenariosRachel Katoshevski-Cavari
nr 121Den Schulbau Neu Denken, Fühlen und WollenUrs Christian Maurer-Dietrich
nr 122Peter Eisenman Theories and PracticesBernhard Kormoss
nr 123User Simulation of Space UtilisationVincent Tabak
nr 125In Search of a Complex System ModelOswald Devisch
nr 126Lighting at Work:Environmental Study of Direct Effects of Lighting Level and Spectrum onPsycho-Physiological VariablesGrazyna Górnicka
nr 127Flanking Sound Transmission through Lightweight Framed Double Leaf WallsStefan Schoenwald
nr 128Bounded Rationality and Spatio-Temporal Pedestrian Shopping BehaviorWei Zhu
nr 129Travel Information:Impact on Activity Travel PatternZhongwei Sun
nr 130Co-Simulation for Performance Prediction of Innovative Integrated Mechanical Energy Systems in BuildingsMarija Trcka
nr 131Niet gepubliceerd
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nr 132Architectural Cue Model in Evacuation Simulation for Underground Space DesignChengyu Sun
nr 133Uncertainty and Sensitivity Analysis in Building Performance Simulation for Decision Support and Design OptimizationChristina Hopfe
nr 134Facilitating Distributed Collaboration in the AEC/FM Sector Using Semantic Web TechnologiesJacob Beetz
nr 135Circumferentially Adhesive Bonded Glass Panes for Bracing Steel Frame in FaçadesEdwin Huveners
nr 136Influence of Temperature on Concrete Beams Strengthened in Flexure with CFRPErnst-Lucas Klamer
nr 137Sturen op KlantwaardeJos Smeets
nr 139Lateral Behavior of Steel Frames with Discretely Connected Precast Concrete Infill PanelsPaul Teewen
nr 140Integral Design Method in the Context of Sustainable Building DesignPerica Savanovic
nr 141Household Activity-Travel Behavior: Implementation of Within-Household InteractionsRenni Anggraini
nr 142Design Research in the Netherlands 2010Henri Achten
nr 143Modelling Life Trajectories and Transport Mode Choice Using Bayesian Belief NetworksMarloes Verhoeven
nr 144Assessing Construction Project Performance in GhanaWilliam Gyadu-Asiedu
nr 145Empowering Seniors through Domotic HomesMasi Mohammadi
nr 146An Integral Design Concept forEcological Self-Compacting ConcreteMartin Hunger
nr 147Governing Multi-Actor Decision Processes in Dutch Industrial Area RedevelopmentErik Blokhuis
nr 148A Multifunctional Design Approach for Sustainable ConcreteGötz Hüsken
nr 149Quality Monitoring in Infrastructural Design-Build ProjectsRuben Favié
nr 150Assessment Matrix for Conservation of Valuable Timber StructuresMichael Abels
nr 151Co-simulation of Building Energy Simulation and Computational Fluid Dynamics for Whole-Building Heat, Air and Moisture EngineeringMohammad Mirsadeghi
nr 152External Coupling of Building Energy Simulation and Building Element Heat, Air and Moisture SimulationDaniel Cóstola
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nr 153Adaptive Decision Making In Multi-Stakeholder Retail Planning Ingrid Janssen
nr 154Landscape GeneratorKymo Slager
nr 155Constraint Specification in ArchitectureRemco Niemeijer
nr 156A Need-Based Approach to Dynamic Activity GenerationLinda Nijland
nr 157Modeling Office Firm Dynamics in an Agent-Based Micro Simulation FrameworkGustavo Garcia Manzato
nr 158Lightweight Floor System for Vibration ComfortSander Zegers
nr 159Aanpasbaarheid van de DraagstructuurRoel Gijsbers
nr 160'Village in the City' in Guangzhou, ChinaYanliu Lin
nr 161Climate Risk Assessment in MuseumsMarco Martens
nr 162Social Activity-Travel PatternsPauline van den Berg
nr 163Sound Concentration Caused by Curved SurfacesMartijn Vercammen
nr 164Design of Environmentally Friendly Calcium Sulfate-Based Building Materials: Towards an Improved Indoor Air QualityQingliang Yu
nr 165Beyond Uniform Thermal Comfort on the Effects of Non-Uniformity and Individual PhysiologyLisje Schellen
nr 166Sustainable Residential DistrictsGaby Abdalla
nr 167Towards a Performance Assessment Methodology using Computational Simulation for Air Distribution System Designs in Operating RoomsMônica do Amaral Melhado
nr 168Strategic Decision Modeling in Brownfield RedevelopmentBrano Glumac
nr 169Pamela: A Parking Analysis Model for Predicting Effects in Local AreasPeter van der Waerden
nr 170A Vision Driven Wayfinding Simulation-System Based on the Architectural Features Perceived in the Office EnvironmentQunli Chen
nr 171Measuring Mental Representations Underlying Activity-Travel ChoicesOliver Horeni
nr 172Modelling the Effects of Social Networks on Activity and Travel BehaviourNicole Ronald
nr 173Uncertainty Propagation and Sensitivity Analysis Techniques in Building Performance Simulation to Support Conceptual Building and System DesignChristian Struck
nr 174Numerical Modeling of Micro-Scale Wind-Induced Pollutant Dispersion in the Built EnvironmentPierre Gousseau
nr 175Modeling Recreation Choices over the Family LifecycleAnna Beatriz Grigolon
nr 176Experimental and Numerical Analysis of Mixing Ventilation at Laminar, Transitional and Turbulent Slot Reynolds NumbersTwan van Hooff
nr 177Collaborative Design Support:Workshops to Stimulate Interaction and Knowledge Exchange Between PractitionersEmile M.C.J. Quanjel
nr 178Future-Proof Platforms for Aging-in-PlaceMichiel Brink
nr 179Motivate: A Context-Aware Mobile Application forPhysical Activity PromotionYuzhong Lin
nr 180Experience the City:Analysis of Space-Time Behaviour and Spatial Learning Anastasia Moiseeva
nr 181Unbonded Post-Tensioned Shear Walls of Calcium Silicate Element MasonryLex van der Meer
nr 182Construction and Demolition Waste Recycling into Innovative Building Materials for Sustainable Construction in TanzaniaMwita M. Sabai
nr 183Durability of Concretewith Emphasis on Chloride MigrationPrzemys�aw Spiesz
nr 184Computational Modeling of Urban Wind Flow and Natural Ventilation Potential of Buildings Rubina Ramponi
nr 185A Distributed Dynamic Simulation Mechanism for Buildings Automation and Control SystemsAzzedine Yahiaoui
nr 186Modeling Cognitive Learning of UrbanNetworks in Daily Activity-Travel BehaviorSehnaz Cenani Durmazoglu
nr 187Functionality and Adaptability of Design Solutions for Public Apartment Buildingsin GhanaStephen Agyefi-Mensah
nr 188A Construction Waste Generation Model for Developing CountriesLilliana Abarca-Guerrero
nr 189Synchronizing Networks:The Modeling of Supernetworks for Activity-Travel BehaviorFeixiong Liao
nr 190Time and Money Allocation Decisions in Out-of-Home Leisure Activity Choices Gamze Zeynep Dane
nr 191How to Measure Added Value of CRE and Building Design Rianne Appel-Meulenbroek
nr 192Secondary Materials in Cement-Based Products:Treatment, Modeling and Environmental InteractionMiruna Florea
nr 193Concepts for the Robustness Improvement of Self-Compacting Concrete: Effects of Admixtures and Mixture Components on the Rheology and Early Hydration at Varying TemperaturesWolfram Schmidt
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nr 194Modelling and Simulation of Virtual Natural Lighting Solutions in BuildingsRizki A. Mangkuto
nr 195Nano-Silica Production at Low Temperatures from the Dissolution of Olivine - Synthesis, Tailoring and ModellingAlberto Lazaro Garcia
nr 196Building Energy Simulation Based Assessment of Industrial Halls for Design SupportBruno Lee
nr 197Computational Performance Prediction of the Potential of Hybrid Adaptable Thermal Storage Concepts for Lightweight Low-Energy Houses Pieter-Jan Hoes
nr 198Application of Nano-Silica in Concrete George Quercia Bianchi
nr 199Dynamics of Social Networks and Activity Travel BehaviourFariya Sharmeen
nr 200Building Structural Design Generation and Optimisation including Spatial ModificationJuan Manuel Davila Delgado
nr 201Hydration and Thermal Decomposition of Cement/Calcium-Sulphate Based MaterialsAriën de Korte
nr 202Republiek van Beelden:De Politieke Werkingen van het Ontwerp in Regionale PlanvormingBart de Zwart
nr 203Effects of Energy Price Increases on Individual Activity-Travel Repertoires and Energy ConsumptionDujuan Yang
nr 204Geometry and Ventilation:Evaluation of the Leeward Sawtooth Roof Potential in the Natural Ventilation of BuildingsJorge Isaac Perén Montero
nr 205Computational Modelling of Evaporative Cooling as a Climate Change Adaptation Measure at the Spatial Scale of Buildings and StreetsHamid Montazeri
nr 206Local Buckling of Aluminium Beams in Fire ConditionsRonald van der Meulen
nr 207Historic Urban Landscapes:Framing the Integration of Urban and Heritage Planning in Multilevel GovernanceLoes Veldpaus
nr 208Sustainable Transformation of the Cities:Urban Design Pragmatics to Achieve a Sustainable CityErnesto Antonio Zumelzu Scheel
nr 209Development of Sustainable Protective Ultra-High Performance Fibre Reinforced Concrete (UHPFRC):Design, Assessment and ModelingRui Yu
nr 210Uncertainty in Modeling Activity-Travel Demand in Complex Uban SystemsSoora Rasouli
nr 211Simulation-based Performance Assessment of Climate Adaptive Greenhouse ShellsChul-sung Lee
nr 212Green Cities:Modelling the Spatial Transformation of the Urban Environment using Renewable Energy TechnologiesSaleh Mohammadi
nr 213A Bounded Rationality Model of Short and Long-Term Dynamics of Activity-Travel BehaviorIfigeneia Psarra
nr 214Effects of Pricing Strategies on Dynamic Repertoires of Activity-Travel BehaviourElaheh Khademi
nr 215Handstorm Principles for Creative and Collaborative WorkingFrans van Gassel
nr 216Light Conditions in Nursing Homes:Visual Comfort and Visual Functioning of Residents Marianne M. Sinoo
nr 217Woonsporen:De Sociale en Ruimtelijke Biografie van een Stedelijk Bouwblok in de Amsterdamse Transvaalbuurt Hüseyin Hüsnü Yegenoglu
nr 218Studies on User Control in Ambient Intelligent SystemsBerent Willem Meerbeek
nr 219Daily Livings in a Smart Home:Users’ Living Preference Modeling of Smart HomesErfaneh Allameh
nr 220Smart Home Design:Spatial Preference Modeling of Smart HomesMohammadali Heidari Jozam
nr 221Wonen:Discoursen, Praktijken, PerspectievenJos Smeets
nr 222Personal Control over Indoor Climate in Offices:Impact on Comfort, Health and ProductivityAtze Christiaan Boerstra
nr 223Personalized Route Finding in Multimodal Transportation NetworksJianwe Zhang
nr 224The Design of an Adaptive Healing Room for Stroke PatientsElke Daemen
nr 225Experimental and Numerical Analysis of Climate Change Induced Risks to Historic Buildings and CollectionsZara Huijbregts
nr 226Wind Flow Modeling in Urban Areas Through Experimental and Numerical TechniquesAlessio Ricci
nr 227Clever Climate Control for Culture:Energy Efficient Indoor Climate Control Strategies for Museums Respecting Collection Preservation and Thermal Comfort of VisitorsRick Kramer
nr 228Fatigue Life Estimation of Metal Structures Based on Damage ModelingSarmediran Silitonga
nr 229A multi-agents and occupancy based strategy for energy management and process control on the room-levelTimilehin Moses Labeodan
nr 230Environmental assessment of Building Integrated Photovoltaics:Numerical and Experimental Carrying Capacity Based ApproachMichiel Ritzen
nr 231Performance of Admixture and Secondary Minerals in Alkali Activated Concrete:Sustaining a Concrete Future Arno Keulen
nr 232World Heritage Cities and Sustainable Urban Development: Bridging Global and Local Levels in Monitor-ing the Sustainable Urban Development of World Heritage Cities Paloma C. Guzman Molina
nr 233 Stage Acoustics and Sound Exposure in Performance and Rehearsal Spaces for Orchestras: Methods for Physical MeasurementsRemy Wenmaekers
nr 234Municipal Solid Waste Incineration (MSWI) Bottom Ash:From Waste to Value Characterization, Treatments and ApplicationPei Tang
nr 235Large Eddy Simulations Applied to Wind Loading and Pollutant DispersionMattia Ricci
nr 236Alkali Activated Slag-Fly Ash Binders: Design, Modeling and ApplicationXu Gao
nr 237Sodium Carbonate Activated Slag: Reaction Analysis, Microstructural Modification & Engineering ApplicationBo Yuan
nr 238Shopping Behavior in MallsWidiyani
nr 239Smart Grid-Building Energy Interactions:Demand Side Power Flexibility in Office BuildingsKennedy Otieno Aduda
nr 240Modeling Taxis Dynamic Behavior in Uncertain Urban EnvironmentsZheng Zhong
nr 241Gap-Theoretical Analyses of Residential Satisfaction and Intention to Move Wen Jiang
nr 242Travel Satisfaction and Subjective Well-Being: A Behavioral Modeling Perspective Yanan Gao
nr 243Building Energy Modelling to Support the Commissioning of Holistic Data Centre OperationVojtech Zavrel
nr 244Regret-Based Travel Behavior Modeling:An Extended FrameworkSunghoon Jang
nr 245Towards Robust Low-Energy Houses: A Computational Approach for Performance Robustness Assessment using Scenario AnalysisRajesh Reddy Kotireddy
nr 246Development of sustainable and functionalized inorganic binder-biofiber compositesGuillaume Doudart de la Grée
nr 247A Multiscale Analysis of the Urban Heat Island Effect: From City Averaged Temperatures to the Energy Demand of Individual BuildingsYasin Toparlar
nr 248Design Method for Adaptive Daylight Systems for buildings covered by large (span) roofsFlorian Heinzelmann
nr 249Hardening, high-temperature resistance and acid resistance of one-part geopolymersPatrick Sturm
nr 250Effects of the built environment on dynamic repertoires of activity-travel behaviourAida Pontes de Aquino
nr 251Modeling for auralization of urban environments: Incorporation of directivity in sound propagation and analysis of a frame-work for auralizing a car pass-byFotis Georgiou
nr 252Wind Loads on Heliostats and Photovoltaic TrackersAndreas Pfahl
Propositions
associated with the dissertation
“Approaches for computational performance optimization of innovative adaptive façade concepts”
R.C.G.M. Loonen, September 2018, Eindhoven University of Technology
1. Neither truly passive designs nor building strategies that rely only on active systems to provide occupant comfort can solve all of the challenges necessary to achieve a sustainable built environment. Building elements that can respond to their environment by controlled adaptation to interior and exterior conditions unite the benefits of both approaches and are therefore a more promising way forward. <This dissertation>
2. Innovative adaptive façade systems can push the envelope of building performance by outperforming the best possible building envelope with fixed properties. These adaptive façade systems provide new windows of opportunity for high-performance building design and operation. <This dissertation>
3. Fifteen years ago, Selkowitz et al. remarked that “Delivering dynamic, responsive control of solar gain and glare, but permitting daylight use, is still the holy grail of façade technology”; this is still the case. <Selkowitz et al., 2003* and this dissertation (2018)>
4. ‘In silico’ research can provide insights that are impossible to obtain with physical experiments and is therefore a valuable method in product development of innovative building envelope components. <This dissertation>
5. Researchers who use and develop legacy simulation software are in the fortunate position to be standing on the shoulders of giants. This offers protection, while making it easy to rise fast and reach great heights. These heirs, however, should be aware that giants have not been storied for their accomplishments in software documentation and that some of the methods they are using were state-of-the-art in a different era.
6. The building industry is regularly denounced as being one of the most conservative sectors in science and technology. This tendency to preserve the status quo is a mixed blessing. It is indeed true that society is sometimes deprived of valuable innovations due to process inefficiencies and other seemingly irrational barriers. On the other hand, few aspects in life are regarded as being as vital to our well-being, protection and self-identity as the buildings we live in. The fact that buildings do not become a playground for passing fads and speculative technologies is therefore worth cherishing.
7. When the intricate knot between research and academic education gets disentangled, one ends up with two dysfunctional pieces of yarn. <Inspired by Rik Torfs, former Rector Magnificus KU Leuven>
8. Academic research requires both inspiration and transpiration. Those who plan to cherry-pick the low-hanging fruit will soon discover that it has already been harvested.
9. Stimulated by the virtues of the adage ‘if you can’t measure it, you can’t improve it’, metric-driven decisions increasingly permeate our everyday life. This can backfire when measurement of performance turns into metric fixation. We should be very careful that what is actually measured is a reasonable proxy of what is intended to be measured. Measurement is therefore not a substitute for judgement. <Inspired by Muller, 2018†>
10. When Hofstadter's law ("It always takes longer than you expect, even when you take into account Hofstadter's Law") coincides with Parkinson's law ("Work expands so as to fill the time available for its completion"), the likely result is a positive feedback loop with potentially negative consequences.
* Selkowitz, S., Aschehoug, Ø. and Lee, E.S. 2003. Advanced Interactive Facades – Critical Elements for Future Green Buildings?, in: Proceedings of GreenBuild, the Annual USGBC International Conference and Expo.
† Muller, J.Z. 2018. The Tyranny of the Metrics. Princeton University Press
/ Department of the Built Environment