politecnico di milano · rosalia sciortino matr. 740769 anno accademico 2010 - 2011. 2 all’amico...
TRANSCRIPT
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POLITECNICO DI MILANO
Facoltà di Ingegneria Industriale
Corso di Laurea in
Ingegneria Energetica
Dipartimento di Elettronica e Informazione
Object-Oriented scalable-detail with building simulation: a model
library and some comparisons with state-of-the-art tools.
Relatore: Prof. Francesco CASELLA
Co-relatore: Ing. Alberto LEVA
Ing. Marco BONVINI
Tesi di Laurea di:
Rosalia SCIORTINO
Matr. 740769
Anno Accademico 2010 - 2011
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All’amico
don Fabio Coppini
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Contents
1. INTRODUCTION ......................................................................................... 13
1.1 The energy problem ...................................................................................... 13
1.2 European legislation ...................................................................................... 15
1.3 The Italian scenario ....................................................................................... 16
1.4 Limits of the Italian legislation ..................................................................... 18
1.5 Advantages and disadvantages of “simplified” procedures .......................... 20
1.6 Usefulness of dynamic simulation ................................................................ 22
2. BUILDING SIMULATION TOOLS........................................................... 25
2.1 Classification of calculation codes ................................................................ 25
2.1.1 Clarke and Maver’s classification .................................................... 25
2.1.2 Sahlin’s classification ....................................................................... 26
2.1.3 CIRIAF’s classification .................................................................... 27
2.2 Thermal simulation methods for buildings ................................................... 29
2.2.1 The finite – difference method ......................................................... 30
2.2.2 The finite element method ................................................................ 32
2.3 A literature review ......................................................................................... 34
2.4 Peculiar difficulties of dynamic modeling .................................................... 37
2.4.1 The complexity of energy exchanges ............................................... 37
2.4.2 Difficulty in writing the equation for building simulation ............... 40
2.4.3 Different physical phenomena .......................................................... 42
2.4.4 Different disciplines.......................................................................... 43
2.4.5 Different time scales and spatial sizes .............................................. 43
2.5 Current dynamic tools ................................................................................... 44
3. AN OBJECT-ORIENTED SOLUTION BASED ON MODELICA ........ 45
3.1 Model library structuring .............................................................................. 45
3.1.1 Structuring step 1 .............................................................................. 45
3.1.2 Structuring step 2 .............................................................................. 47
3.2 Object Oriented Modeling (and Simulation) ................................................ 49
3.2.1 Physically meaningful connections .................................................. 49
3.2.2 Interface abstraction and a-causal approach ..................................... 50
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3.3 Modelica ........................................................................................................ 51
3.4 A Modelica library ........................................................................................ 56
3.4.1 Overview........................................................................................... 56
3.4.2 An example model ............................................................................ 59
4. VALIDATION AND CASE STUDIES ....................................................... 65
4.1 Test 1: dynamic simulation and (static) certification .................................... 66
4.2 Test 2: comparison with other simulation programs ..................................... 72
4.2.1 Simulation in winter conditions ........................................................ 74
4.2.2 Simulation in summer conditions ..................................................... 87
4.3 Test 3: a complete simulation model at various detail level ......................... 95
4.4 Test 4: including sub-zonal models1 ........................................................... 114
5. CONCLUSIONS AND FUTURE WORK ................................................ 121
6. ACRONYMS ............................................................................................... 122
7. BIBLIOGRAPHY ....................................................................................... 123
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List of figures
1.1 Distribution of European consumption for end-use................................. 14
1.2 Distribution of consumption in the European Community for final
uses .......................................................................................................... 14
1.3 Distribution of European consumption for end-use................................. 15
1.4 Effects of inertia on the wave thermal ..................................................... 19
1.5 A good way to design using dynamic simulation.................................... 23
2.1 Heat transfer simulation methods ............................................................ 30
2.2 Discretization of a multi-component ....................................................... 33
2.3 Relative distance between nodes ............................................................. 33
2.4 Energy flows in a building ...................................................................... 39
2.5 Factors that influence the air flow distribution in buildings ................... 40
3.1 Connection of three fluid subsystems ...................................................... 50
3.2 Simulation of model Example1 in listing 1 ............................................. 55
3.3 Organization of the BUILD Modelica library ......................................... 58
3.4 An example of room model ..................................................................... 59
4.1 A room model for test 1 ........................................................................... 66
4.2 Test 1: temperature trade of the North wall ............................................ 71
4.3 Test 1: temperature trade of the air-conditioned environment ................ 71
4.4 Test 1: plant and section of the building ................................................. 73
4.5 Test 2: the building model implemented in Modelica ............................. 75
4.6 Test 2: comparison of the primary specific energy demands
(kWh/m2y) for heating of the containment ............................................. 78
4.7 The Sun variable position through zenith and azimuth angles ................ 79
4.8 Example of the Sun path in January ........................................................ 79
4.9 Example of the Sun path in June ............................................................. 80
4.10 Changing of external temperature during the coldest month in 2010 ..... 81
4.11 Azimuth and zenith trades ....................................................................... 82
4.12 Sunrise and sunset hour trades in 31 days ............................................... 83
4.13 External temperature trade during the coldest month in 2010 ................ 83
4.14 Test 2: winter settings for Milan (second method) .................................. 84
4.15 Mean external temperature trade (°C) during the entire heating
season ...................................................................................................... 85
4.16 Sunrise and sunset hour trades in 183 days ............................................. 86
4.17 External temperature trade during the winter season .............................. 87
4.18 Test 2: comparison of the primary specific energy demands
(kWh/m2y) for cooling of the containment ............................................. 89
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4.19 Changing of external temperature during the hottest month in 2010 ...... 91
4.20 External temperature trade during the hottest month in 2010 ................. 91
4.21 Test 2: summer settings for Milan (third method) .................................. 92
4.22 Mean external temperature trade (°C) during the entire summer
season ...................................................................................................... 93
4.23 External temperature trade during the summer season ........................... 94
4.24 Test 2: total specific energy need (kWh/m²y) of the containment on
annual basis ............................................................................................. 95
4.25 Test 3: the building model ....................................................................... 96
4.26 Test 3: lost energy of walls by transmission at different exposures ....... 97
4.27 Test 3: dynamic study - Level 0 ............................................................ 100
4.28 AWPI analogue control ......................................................................... 102
4.29 Test 3: dynamic analysis of the room only and control system
for each local - Level 1a ....................................................................... 102
4.30 Test 3: temperature trades in adjacent rooms - Level 2 ........................ 103
4.31 Test 3: power supplied by the control system - Level 1a ..................... 103
4.32 Test 3: studies with decoupling control - Level 1b ............................... 104
4.33 Block system of a general decoupling ................................................... 105
4.34 Representation of a generic compensator .............................................. 105
4.35 “Backwards" decoupling for a 2×2 system ........................................... 108
4.36 Test 3: action and correction of the multivariable control system -
Level 1b ................................................................................................. 108
4.37 Test 3: building model in open loop ...................................................... 109
4.38 Test 3: temperature trends in open loop ................................................ 109
4.39 Test 3: simplified analysis of the heater only - Level 2 ........................ 110
4.40 Test 3: temperature trades in the rooms - Level 2 ................................. 111
4.41 Test 3: analysis of the heater only with control system - Level 3 ......... 112
4.42 Test 3: temperature trades in adjacent rooms - Level 3 ........................ 112
4.43 Test 3: power supplied by the control system - Level 3 ........................ 113
4.44 Test 3: comparison between level 1(a) and level 3 ............................... 113
4.45 Test 4: Modelica elements for the application example models ........... 114
4.46 Possible configurations of all the model elements ................................ 115
4.47 Test 4: control of the mean room temperature (K), levels L1- 4 ........... 116
4.48 Test 4: control of the power (W), levels L1 - 4 ..................................... 116
4.49 Test 4: the total computed consumption (J), levels L1 - 4 .................... 117
4.50 Test 4: temperature at different heights, levels L2 and L4 .................... 118
4.51 Different positions for a temperature sensor ......................................... 118
4.52 Test 4: temperature and power control, and the total consumption
with different sensor positions at L2 ..................................................... 119
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List of tables
1.1 Using the methodology of calculation of energy building
performance ............................................................................................. 17
2.1 Calculation codes of the first level for evaluating building energy ........ 28
2.2 Calculation codes of the second level for evaluating building energy .... 28
2.3 Comparison of E/E with ESP-r/DOE-2/BLAST Weather Data
Formats .................................................................................................... 36
4.1 Test 1: settings ......................................................................................... 67
4.2 Test 1: physical characteristics of walls .................................................. 68
4.3 Test 1: physical characteristics of the roof .............................................. 68
4.4 Test 1: physical characteristics of the floor ............................................. 69
4.5 The exposure factors ............................................................................... 69
4.6 Test 1: comparison in terms of energy lost by transmission ................... 70
4.7 Test 1: thermal transmittance .................................................................. 70
4.8 Test 1: energy performance index ........................................................... 70
4.9 Physical characteristics of the building ................................................... 74
4.10 Heating and cooling seasons ................................................................... 74
4.11 Test 2: transmission lost power (W) of the building located
in Milan ................................................................................................... 76
4.12 Test 2: energy performance index in winter conditions for
the building located in Milan.................................................................. 76
4.13 Test 2: transmission lost power (W) of the building located
in Rome .................................................................................................. 76
4.14 Test 2: energy performance index in winter conditions for
the building located in Rome................................................................... 76
4.15 Test 2: software results ............................................................................ 77
4.16 Test 2: physical proprieties in stationary conditions (first method) ........ 80
4.17 Test 2: physical proprieties for monthly calculation
(second method) ...................................................................................... 81
4.18 Mean monthly external temperatures (°C) during
the winter season ..................................................................................... 85
4.19 Test 2: physical proprieties for annual calculation (third method) ......... 86
4.20 Test 2: different energy performance index for the building
containment situated in the same location (Milan) ................................. 87
4.21 Test 2: energy performance index in summer conditions for
the building located in Milan.................................................................. 88
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4.22 Test 2: energy performance index in summer conditions for
the building located in Rome................................................................... 88
4.23 Software results in the summer season .................................................... 89
4.24 Test 2: stationary conditions in the summer season ................................ 90
4.25 Test 2: mean monthly external temperatures during the summer
season (°C) .............................................................................................. 93
4.26 Test 2: different energy performance index of the building situated
in the same location (Milan) .................................................................... 94
4.27 Test 3: settings ......................................................................................... 97
4.28 Test 3: physical characteristics of walls .................................................. 97
4.29 Test 3: physical characteristics of the roof .............................................. 97
4.30 Test 3: physical characteristics of internal walls ..................................... 97
4.31 Test 3: physical characteristics of the floor ............................................. 98
4.32 Test 3: physical characteristics of building components ......................... 98
4.33 Test 3: energy lost by transmission and ventilation for
each exposure ......................................................................................... 99
4.34 Test 3: comparison between static and dynamic calculation ................ 101
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Ringraziamenti
Vorrei ringraziare il Professore Francesco Casella, per la grande possibilità di
lavorare a questa Tesi. E ancor di più desidero ringraziare il Professore Alberto
Leva e Marco Bonvini per il loro sostegno ed aiuto durante l’intero periodo di
lavoro. La loro grande esperienza nel campo è stata di grande valore per i
risultati presentati in questa Tesi, ma è stata soprattutto la passione, attenzione,
dedizione e cura nel lavoro svolto insieme a stupirmi e ad incoraggiarmi.
E così ringrazio tutti quei professori a cui ho guardato con grande ammirazione
per l’amore all’insegnamento e l’interesse sincero mostratomi durante questi
anni, durante i quali la strada che portava all’università e poi al lavoro si faceva
sempre più chiara. Ricordo in particolare l’insegnante di italiano del liceo, che,
terminata la maturità, mi ragalò una tartarughina fatta da lei all’uncinetto.
Questo piccolo peluche porta al collo un foglio arrotolato con la seguente
citazione: «Il professore è uno che parla nel sonno altrui» (W. H. Auden).
E ora i ringraziamenti più difficili, non perché non riesca a trovare le parole
adatte, ma per l’impossibilità di poter arrivare - come io vorrei - a tutte le
persone incontrate lungo il cammino. Prima di tutto ringrazio i miei genitori,
Salvo e Laura, per il sostegno e l’amore ricevuto, ma soprattutto la pazienza.
E i miei più cari amici senza i quali non avrei potuto fare un passo. Il primo sei
tu, don Fabio, che mi hai insegnato ad amare, a saper guardare con il cuore e a
giudicare tutte le circostanze che vivevo affinchè potessi diventare una donna.
E il mio pensiero va ai più piccoli amici dell’oratorio, mi avete regalato la
possibilià di vedervi crescere nella vostra semplicità d’animo, e diventare grandi
e curiosi della vita. E a voi, amici incontrati in università o meglio compagni di
viaggio, a voi rivolgo tutto il mio affetto. Vivere l’università in vostra
compagnia ha dato un significato alla scelta dei miei studi. Così lo studio non
era più una fatica ma un modo attraverso il quale poter scoprire e conoscere di
più me stessa. E così tutto il tempo passato insieme portava a un “di più”.
Ho davvero davanti a me molti volti di cui vorrei scrivere per ciascuno il nome
in modo tale che rimanga inciso in questo lavoro che per me rappresenta il
completamento di un incredibile percorso umano. Decido di lasciarvi un breve
passo di Oscar Milosz tratto dal libro “Miguel Manara” che mi permette di poter
abbracciare e voler bene fino in fondo ciascuno di voi.
"Verrà forse un giorno in cui Dio ti permetterà di entrare brutalmente, come
una scure, nella carne dell'albero, di cadere pazzamente, come una pietra, nella
notte dell'acqua e di scivolare cantando, come il fuoco, nel cuore del metallo.
E quel giorno saprai di quale carne è fatto il mondo, e parlerai liberamente
all'anima del mondo dell'Albero, dell'Acqua e del Metallo, e gli parlerai con la
voce del vento e della pioggia e della donna innamorata"
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Abstract
The main idea behind this thesis is to show that dynamic simulation can help
achieve better energy efficiency in buildings. To this end, moreover, it is very
important to be able of conducting system-level studies with a scalable detail
level. This work presents the motivations for the statements above, and explains
how simulation can be a decision aid tool along the entire life of a project, be it
a new design or a refurbishing. Finally, OOMS (Object-Oriented Modeling and
Simulation) is proposed as the formalism for the required modeling and
simulation tasks, and the reasons for adopting that formalism in system-level
building simulation are illustrated by means of convenient examples.
The work also acknowledges the relevant problems involved in such a matter,
especially the necessity for dynamic simulation models to maintain steady-state
consistence with design-oriented ones (in the classical sense). In so doing, we
define the role and expected outcome of the major types of tools, for example
showing what one can (and cannot) expect from certification systems, sizing-
oriented models, and so forth.
The goal of the long-term research to which this thesis belongs is constructing a
set of dynamic simulation models and procedures capable of tackling the whole-
building problem in a unified framework, and with fully scalable complexity. In
order to do that in practice, a model library is thus introduced, and some
examples demonstrate the improvements yielded by OOMS to both the ease and
the usefulness of simulation in building design. By means of some case studies,
show the usefulness on two different fronts:
The same model can be used to conduct studies at different levels of detail, allowing the designer to base his/her decisions on simulation
outputs right from the beginning of a design, and maintaining coherence
along that design process;
Models conceived in this way allow to synthesize and compare different control systems, both at a high level (correctness of a strategy and
suitability for the specifications at hand) and with arbitrarily fine detail
(capability of the installed devices to actually realize that strategy).
Finally, this work preliminarily shows how OOMS allows to model relevant
facts such as the behavior of air movers, and sub-zonal airflow descriptions.
In doing so, OOMS allows to capture energy-relevant phenomena at a level that
is surely coarse with respect to fine-scale 3D CFD codes. However, this in
OOMS is done together with all other modeling task, in a single framework and
tool.
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Specifically, the contribution of this thesis consists in implementing some
library models, and especially in defining and conducting comparative tests to
relate the proposed approach to well assessed engineering techniques, thereby
evidencing the proposal’s advantages.
Keywords: Building simulation, object-oriented modelling, and scalable detail.
Sommario
L’idea principale che sta alla base di questa tesi è mostrare come la simulazione
dinamica possa aiutare a raggiungere una migliore efficienza energetica negli
edifici. A tal proposito, è molto importante che essa, inoltre, sia in grado di
condurre le analisi a livello di sistema di controllo con un livello di dettaglio
scalabile. Questo lavoro presenterà le motivazioni di quanto appena dichiarato, e
spiegherà come la simulazione possa essere uno strumento di aiuto decisionale
lungo l’intera vita di un progetto, sia che si tratti di nuova costruzione o di
rimessa a nuovo. Infine, il linguaggio orientato agli oggetti definito OOMS
(Object-Oriented Modeling and Simulation) viene proposto come formalismo
per la modellazione e simulazione, e alcuni esempi saranno presentati per
illustrare le ragioni per le quali si è scelto tale formalismo per la simulazione
degli edifici a livello di sistema di regolazione. Il lavoro riconosce anche i
problemi rilevanti coinvolti in tale materia, in particolare la necessità dei
modelli di simulazione dinamica di mantenere la consistenza dello stato
stazionario con quello orientato (nel senso classico). Così facendo, si definisce il
ruolo e i risultati attesi dei principali tipi di strumenti, mostrando per esempio
ciò che uno può (e non può) aspettarsi dai programmi di certificazione,
dimensionamento degli modelli orientati, e così via.
L’obiettivo della ricerca a lungo termine a cui appartiene questa tesi è di
formulare una serie di modelli di simulazione dinamica e procedure in grado di
affrontare la progettazione dell’intera costruzione attraverso un quadro unitario e
una complessità completamente scalabile. A tale scopo, nella pratica saranno
presentati un modello di libreria e alcuni esempi che dimostreranno i
miglioramenti apportati dal linguaggio OOMS sia per la facilità sia per l’utilità
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della simulazione nella progettazione edilizia. Per mezzo di alcuni casi di studio,
si dimostra tale utilità su due fronti differenti:
Lo stesso modello può essere utilizzato per condurre studi a diversi livelli di dettaglio, consentendo in tal modo al progettista di basare le
proprie decisioni progettuali sulle uscite di simulazione fin dall’inizio
della costruzione dell’edificio, e di mantenere la coerenza lungo quel
processo di progettazione;
Modelli concepiti in questo modo permettono di sintetizzare e confrontare i diversi sistemi di controllo, sia ad alto livello (correttezza
di una strategia e idoneità per le specifiche a portata di mano) sia con
dettagli arbitrariamente sottili (capacità dei dispositivi installati per
realizzare effettivamente la strategia).
Infine, questo lavoro mostra preliminarmente come il linguaggio OOMS
permetta di modellare alcune situazioni rilevanti come il comportamento dei
moti dell’aria (compreso il caso che si tratti di una descrizione sub-zonale). In
tal modo, la tecnica agli oggetti orientati permette di catturare fenomeni
energetici rilevanti ad un livello che è sicuramente grossolano rispetto alla scala 3D dei codici eleborati da CFD. Tuttavia, nel linguaggio di programmazione
scelto questo viene fatto insieme a tutti gli altri compiti di modellazione
attraverso un unico strumento. In particolare, il contributo di questa tesi consiste
nell’implementare alcuni modelli della libreria, e soprattutto nel definire e
condurre alcune prove comparative per mettere in relazione l’approccio
proposto con le tecniche di ingegneria ben consolidate, evidenziando così i
vantaggi della proposta.
Parole chiave: simulazione di edificio, modellazione object-oriented e studio di
dettaglio scalabile
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Chapter 1
Introduction
1.1 The energy problem Since 1997 the global climate change debate has focused largely around the
Kyoto Protocol that requires industrialized countries to reduce their emissions of
greenhouse gases. The identification of effective strategies to control climate
change is therefore a key challenge for the research undertaken on sustainability
issues.
Nowadays, energy savings and improved efficiency in end uses seem to be the
way forward to be able to resolve, at least partially, the serious energy problems
that are affecting all countries.
Since 1973, when the first oil crisis happened, the state of energy began to be
analyzed and assessed. Especially in the Anglo-Saxon world professions such as
Energy Managers were created with the task of analyzing and solving all the
problems related to improper use of energy resources. In support of this, a series
of regulations was subsequently launched that took into account energy saving
and provided guidance to improve end-use efficiency.
The work of the World Climate Conference (particularly the COP 3 in 1997 that
defined the Kyoto Protocol) has gradually promoted programs, strategies and
actions aimed at reducing air pollution and consumption of non-renewable
energy sources, the promotion of renewable energy and energy-saving incentive
[1]. The need to rethink the way we produce and use energy is inescapable: first,
the inadequacy of the current energy production in meeting the demands of
consumption growth and, secondly, the impact on the environment and quality
of life that an increase in production and consumption of fossil fuels would
bring.
The energy consumption of cities is particularly significant: according to recent
estimates, in fact, half the world's population lives in urban settlements. For
example, in Italy, one third of the population and most activities are
concentrated in a seventeenth of the national territory.
In terms of energy, in 2003 the Europe Union has spent a total of 1,505 million
tons of oil equivalent: a breakdown in end uses can see a large part of
consumption attributable to the residential and tertiary sector (see fig. 1.1).
Energy efficiency is the most important, fast and effective tool identified by the
European Union to ensure global competitiveness, security and quality of our
environment, reducing dependence on foreign supply of raw materials and
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energy. It has been estimated that the EU would be able to save, with
appropriate interventions, at least 20% of the current consumption of around 60
billion euro per year: on a smaller scale, an average family could save from 200
to 1,000 euro per year.
Figure 1.1. Distribution of European consumption for end-use (The Green Paper, 2000)
In the energy balance of EU countries an important role is played by the civil
sector, which includes energy consumption for the use and management of
residential and tertiary buildings. According to data presented by the EU itself,
this sector uses more than 40% of the total EU final energy demand (see fig.
1.2).
Figure 1.2. Distribution of consumption in the European Community for final uses (The
Green Paper, 2005)
The end-uses in a building are numerous and include the air-conditioned
(heating and cooling), the production of hot water, ventilation and air handling,
lighting, use of appliances and electronic equipment [2].
42%
28%
30%
Residential and tertiary Transport Industry
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Figure 1.3 - Final consumption of energy in residential use category (ENEA data
processing MAP – 2004)
The air conditioning in buildings in winter and summer season is the most
energy-consuming component, even considering the differences between
buildings due to different types of construction and intended use (see fig. 1.3).
In addition, the increased use of cooling systems in residential construction has
contributed in recent years, the significant increase in energy consumption for
air conditioning in summer.
Based on these evaluations and considering that existing building stocks are
many cases ancient and inefficient in terms of energy1, the EU and member
countries have identified a strategic sector for achieving the overall energy
efficiency.
Thus, in recent years, both at European and national level, a new legislative
framework has established, able to develop the necessary regulatory
requirements, financial, technological and cultural challenge for adequate
response to the energy efficiency of buildings.
1.2 European legislation In 2002 the European Parliament adopted Directive 2002/91/EC (Energy
Performance of Buildings Directive) on energy efficiency in buildings with the
aim of improving the energy performance of buildings within the Community.
This Directive [3] dictates, in fact, that each State shall provide an energy
performance certificate at the time of construction, sale and leasing of new or
existing building. This certificate shall be obtained based on a methodology for
1 in Italy there are 13 million of existing buildings, of which about 11 million prior to the Act 373/73 - Source: White Paper on Energy, Building, Environment
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calculating the energy performance of buildings. Standards are therefore needed
to define, quantify, and assess energy performance.
The objective of Energy Performance of Building Directive is to “promote the
improvement of the energy performance of buildings within the European
Community, taking into account outdoor climatic and local conditions, as well
as indoor climate requirements and cost-effectiveness” (Art.1).
This is to be achieved through five main actions:
The creation of a single methodology that can be used to calculate the energy performance of buildings.
The application of minimum requirements, to all new residential and tertiary (generally public and commercial) buildings and to the major
refurbishment of existing buildings with floor areas greater than 1,000
square meters.
The introduction of an energy performance certificate to be produced whenever a building is constructed, rented or sold.
Regular inspection of boilers with outputs of more than 20 kW and inspection every two years for boilers of more than 100 kW.
Regular inspection of air conditioning systems with outputs of more than 12 kW.
Confirming the increased focus on more effective integration of building into
the environment surrounding, Art.8 of the initial considerations provides that
“Member States shall set minimum energy performance requirements for
technical building systems that are installed in buildings”.
1.3 The Italian scenario
In Italy, Law 373 of 30 April 1976 concerns, in particular, limitations in energy
consumption for heating. It requires that the building envelope ensure the least
possible loss of heat to the outside.
By Act 10 of 9 January 1991 the approach to the problem of energy saving was
of a different nature, although in the analogous purpose of inducing the user to
reduce energy consumption for its own needs for environmental heating. In
practice, the observations should be concentrated on the energy needs of the user
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over an entire year in question. It is necessary to consider not only the effects of
isolation, but also the free contributions (people, lights, solar radiation, etc.) that
contribute to environmental heating.
Following the European Directive, we have seen a renewal of legislation [4],
that has led to the promulgation of national Legislative Decree 192 of 19 August
2005, which was, later, supplemented and corrected by legislative decree of 29
December 2006, n. 311 and more recently by the implementing decrees, D.P.R.
59/09 and D.M 29/06/2009 containing the "National guidelines for energy
certification of buildings".
These certifications are based on precise calculation methods of national
reference, divided by new and existing buildings, by type of building, size and
complexity of the same (see tab. 1.1).
In particular, new buildings are referred to the “method of calculation of
project” (paragraph 5.1 of Annex A of 06/26/2009 Decree). It refers to UNI/TS
11300-1 and UNI/TS 11300–2 [5] for index calculation of energy performance
for heating (EPi and EPi invol) and cooling (EPe,invol). This procedure is applicable
to all types of buildings, whatever their size.
Table 1.1. Using the methodology of calculation of energy building performance (Annex 3
of Annex A of 26/06/2009 Decree)
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For existing buildings, however, one has to refer to the "method of calculation to
relief on the building" (paragraph 5.2 of Annex A of 06/26/2009 Decree). It
refers to UNI/TS 11300-1 and UNI/TS 11300-2 for index calculation of energy
performance for heating (EPi and EPi,invol) and cooling (EPe,invol) and it provides
the following three levels of detail, depending on the type and size of the
building:
1. For all building types, regardless of their size, the technical standards UNI/TS 11300-1 and UNI/TS 11300-2 (and their simplifications provided for
existing buildings) are the national reference.
2. Only for residential buildings with floor area up to 3000 m2 it refers to the method of DOCET calculation, prepared by CNR and ENEA on the basis of
the technical standards UNI/TS 11300-1 and UNI/TS 11300-2. This method
meets the requirements of simplification, aimed at minimizing the burden on
applicants.
3. Only for residential buildings with floor area up to 1000 m2, the simplified method set out in Annex 2 of Annex A of 06/26/2009 Decree is used as a
reference for the calculation of building energy performance indices for
winter heating (EPi and EPiinvol).
1.4 Limits of the Italian legislation Designing second a proper approach due to the principles of sustainable
architecture, the building envelope will need to: release little heat and capture
solar energy from sunlight in the winter season, and reject the solar radiation
and release heat when necessary, if summer season.
In this regard, countries with a temperate climate (Southern Europe) will solve a
more difficult task: to design solutions that can deal with the cooling as well as
with the heating.
Much research [6] has been conducted to evaluate the performance of buildings
and their materials with the change in thermal inertia. The thermal inertia is
simply the ability of materials to store heat and release it gradually over time:
the energy received during the hottest hours is stored in the mass of the building
and then gradually released. Materials have the task to mitigate (damping) and
delay (time lag) entry in the environment of a heat wave (see fig 1.4) due to
incident solar radiation on the building envelope. This ability depends on the
thickness of the material, the thermal capacity and conductivity. This will
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19
determine, within the building, a lag and a reduction of fluctuations and peaks
that characterize the outside temperature.
Figure 1.4. Effects of inertia on the wave thermal (TERMOBUILD)
The conscious use of the mass has a significant positive effect on comfort
conditions, energy consumption and cooling loads, particularly those peak,
which is one of the reasons for the summer blackout.
In assessing the energy costs, one must therefore consider both the total
consumption and the maximum loads, which determine the sizing of air
conditioning. The mass is not in itself a solution applied indiscriminately to
automatically improve the energy performance. The use of a heavy containment
implies a deep understanding of the dynamic properties of the closures. It is a
solution that fits nicely with the passive cooling strategies also mentioned in the
initial note (n.18) by EPBD. The European Union, in this note, remember that in
recent years in countries of southern Europe, there has been an increased use of
facilities for air conditioning thus posing serious problems at peak load2.
Therefore, it states that priority with respect to energy consumption for cooling
rather than heating should be given in some European countries.
With reference to air conditioning in summer, the Legislative Decree 192/05 and
311/06 require (for some climate zones and destinations of use) the adoption of
certain solutions of containment without also requiring any calculations [7].
In particular, establishing a single limit, equal for all locations to the surface
mass of opaque component, is a simplistic prescription. It does not properly
consider some effects of various parameters (thermal, solar, user and
environmental) on summer loads and energy needs. Finally, the only indicator of
energy performance introduced by these regulations refers to the winter heating
(EPi). This seems at odds with the Directive 2002/91/EC which comprises the
2 it was summer-time record of power equal to 56,589 MW (source TERNA - July 20, 2007)
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20
total energy consumption of the building: winter heating, summer air
conditioning, domestic hot water, lighting, ventilation.
1.5 Advantages and disadvantages of “simplified” procedures When one plans to proceed with the simulation for the design of buildings, it is
convenient to wonder if it is advantageous to opt for a stationary rather than a
dynamic evaluation. To overcome the complexity of data collection and a real
dynamic simulation of energy behavior of the building, the standardization
organizations have called for simplified procedures.
Some software tools (CENED, DOCET, BESTClass, MC Impianto and so forth)
quantify the energy performance of a building [8]. They have a broad consensus
among experts thanks to several advantages:
- their immediacy of use,
- simplicity,
- repeatability,
- understandability for user,
- transparency to all actors involved (designer, project manager, tester),
- reduced expensiveness.
The implemented computational procedures (called “quasi-stationary”) were
translated into rules that require methodological simplifications, such as those
derived from CEN (Comitato Europeo di Normazione). The decrees transposing
EU Directive on energy efficiency in buildings are based on these procedures.
Currently, commercial software for the energy certification, as well as those
provided by ITC-CNR or by individual regions, returns a set of data and
indicators on energy performance on a monthly basis.
However, steady-state simulations can only partially investigate the actual
performance of a building, because they start from the assumption that the
periodic changes in temperature and the contribution of solar radiation can be
neglected or zeroed out by averaging. It is therefore possible to use highly
aggregated climate data. As such, these tools are not able to properly appreciate
the effects of climate change detectable within 24 hours. Therefore, static
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21
procedures are not sufficient to calculate the real thermal-dynamic behavior of
the constructive system.
Simulations carried out in dynamic conditions, instead, allow a much more
realistic and comprehensive analysis. They assess in detail the response of the
building affected by various external factors such as the outdoor temperature,
solar radiation, natural ventilation, the behavior of the occupants, the air
conditioning system.
In professional terms, it is therefore important to deepen as much as possible the
energy analysis, establishing means and skills to use the tools that operate in
dynamic regime, which can give particularly efficient and tangible support to
the design.
To understand how the energy behavior of the building changes with the change
in the methods of calculation, we advise the reader to analyze the search result
[9] of Simone Ferrari - Environmental Technical Assistant Professor of Physics
at the Department of the Polytechnic BEST Milan. The study reveals that the
simplified procedures are insensitive to appreciate the effects of heat capacity
building to respond to the variability of weather conditions. The change in
climate, especially on the contribution of solar radiation, measured with
sophisticated instruments, not only plays a key role in determining the energy
requirements for buildings, but allows a designer to appreciate the advantages
given by the heat capacity of the building.
The study [10] made in the University of Technology in Finland could be also
interesting. Researchers have analyzed the effects of thermal mass on heating
and cooling energy in Nordic climate and for modern, well-insulated Nordic
buildings. The effect of thermal mass was analyzed by calculations made by
seven different calculation programs. Six of these programs are simulation
programs (Consolis Energy, IDA-ICE, SciaQPro, TASE, VIP, VTT House
model) and one monthly energy balance method based on the standard EN 832,
which is the predecessor of ISO DIS 13790. The study purpose was to evaluate
the reliability of the monthly energy calculation method and especially its gain
utilization factor compared with the simulation programs. The results showed
that the simplified standard methods of EN 832 and of ISO DIS 13790 generally
give accurate results in calculating the annual heating energy, e.g., in the context
of energy design and energy certification. However, the gain utilization factor of
these standards was too low for very light buildings having no massive surfaces
resulting in too high energy consumption. The study showed that the differences
in input data cause often greater differences in calculation results than the
differences between various calculation and simulation methods.
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22
1.6 Usefulness of dynamic simulation The need to improve energy efficiency has also influenced the construction
sector. Improving the energy performance of buildings is, in fact, the basis of
new legislation (2010/31/EC) which aims to contribute to the efficiency-related
energy use. Specifically, in the context of production processes and buildings,
whether for residential, commercial or industrial, we can identify four main
types of intervention: energy savings associated with devices and/or good
design/restructuring, rationalization in use of energy, cogeneration, and
integration of different sources.
The fundamental problems of classical design/modeling are related to:
Ineffectiveness of the stationary approaches when the designed systems assume a substantial degree of complexity;
A lack of technology that allows simultaneous analysis of the interaction between buildings, variable weather conditions, presence of renewable
resources, issues of performance limits.
A significant advantage of a good simulation method is the ability to investigate
the sensitivity of individual parameters, which allows designers to efficiently
compare different designs. In fact, during the design or refurbishing of a
building, a designer has to take some complex decisions and dynamic simulation
is an optimal tool to do that as all the problems can be identified and solve in
advance. It can give a full and tangible support to the design (fig. 1.5),
simulating the project at any time, irrespective of what was already fully
designed. It makes also possible to move back and forth among the complexity
levels implicitly defined above, in the case some past decision needs re-
discussing.
Using a dynamic model of the system, one can evaluate the behavior of the
generating section while the heat and electrical load are not constant. This will
give a designer the possibility to assess the integration of more energy-efficient
technologies (renewables, CHP, solar-cooling, etc.) according to the weather
characteristics of the site and the demands of the territory.
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23
Figure 1.5. A good way to design using dynamic simulation
In some sense, the availability of a dynamic simulator “joins” the existing
instruments, and can provide additional information with respect to that
available in the planning, design or (semi) static evaluation. In fact, dynamic
simulation is a necessary tool in the design phase of thermal plants, especially
when it comes to test the responses of innovative systems. It plays a crucial role
in the early stages of a design, since the control strategies and the necessary
equipment are evaluated, to the certification of the validity of the control
system.
Dynamic modeling and transient analysis are however frequently thought to
require considerable effort and investment. Such investments are most often
paid off by savings to be gained by optimizing the configuration of the system
or by the discovery of potential vulnerabilities, instead of having to perform
timely and more costly in the future. Nonetheless, simulation models must be
efficient (in terms of machine time), modular (the model is made by assembling
appropriate models of individual components, each relating to a portion of
physical plant), transparent (the code must be legible and reflect the original
equations); said models also need to enforce integrity (the model is to grasp the
essential dynamics and be able to match the model of the control system,
demonstrating the ability of the system to work properly).
A complete building simulation tool has three main classes of potential users
with different requirements:
- building designers,
- government policy makers, and
- research scientists.
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24
These users can apply a simulation tool to pre-construction testing, indoor air
quality prediction, energy efficient heating and ventilation design, and design
validation (Kendrick 1993).
In so complex a panorama, this thesis belongs to a long-term research proposing
OOMS (Object-Oriented Modeling and Simulation) as a possible solution in
terms of a unified framework where to cast the overall problem.
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25
Chapter 2
Building simulation tools
After a brief introduction on the current legislation and the usefulness of
dynamic simulation, this chapter presents different ways to classify simulation
tools and shows some relevant problems in dynamic modeling.
2.1 Classification of calculation codes In building simulation the first software tools arose from the implementation of
“handbook” procedures, characterized by a simplified outline, and operating in
steady state. Therefore, such procedures provide only first-cut results.
Later on models appeared that took into account part of the energy dynamics in
buildings. These applications were however difficult to use, in particular
because of the lack of a graphical interface, and of limited usefulness because
they were aimed at resolving specific problems such as the sizing of air ducts, or
the determination of thermal loads.
In the current generation of software tools, the behavior of the entire building-
plant complex can in principle be simulated, matching both analytical and
numerical procedures. In particular the modeling of heat flows, electrical,
lighting, sound and behavior of the occupants can be solved simultaneously.
Although they present easier and more intuitive graphical interface and various
functions have been introduced to help the process of data entry, these software
however involve non trivial mechanism (up to co-simulation) and as a
consequence may often require considerable experience on the part of the user.
2.1.1 Clarke and Maver’s classification
A way to classify building simulation tools was proposed by Clarke and Maver
(1991), who suggest the following classification [11]:
1st generation: such tools are handbook oriented computer implementations, analytical in formulation, and biased towards
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26
simplicity. They often lack rigorous approach, and thus provide only
indicative results within constrained solution domains.
2nd generation: such tools are characterized by the introduction of the dynamics coming from the containment, but are decoupled in relation to
the treatment of air movement, systems and control. Early tools were
decoupled from the design process by limited interfaces and
computational requirements which were demanding for their time. Later
implementations are often still marketed owing to their ease of use and
speed of solution.
3rd generation: such tools are characterized by treating the entire building as a coupled field problem and employing a mix of numerical
and analytical techniques. These tools require considerable experience
and resources to go beyond simple problems. Interfaces are able to
reduce some barriers to their use. Modeling integrity is enhanced but the
tools are often used to derive information to be incorporated in
simplified techniques.
4th generation: such tools are characterized by full computer-aided building design integration and advanced numerical methods which
allow integrated performance assessments across analysis domains.
Interfaces and underlying data models are evolved to present and operate
on simulation entities as objects in the user’s domain. One common
evolution is the incorporation of knowledge bases within the tool
infrastructure.
The first and second generations refer to as simplified methods because of their
constrained treatment of the underlying physics, and the third and fourth
generations refer to as simulation or dynamic methods (Hand 1998).
It is worth noticing that nowadays, commercial tools have more or less reached
the performance of generation 3; the rest is still mostly research.
2.1.2 Sahlin’s classification
Another classification criterion was given by Sahlin (1996), who suggest
classifying simulation program by “modular” vs. “traditional” tools [11]. In
order to clarify the concept of modular software, two conditions are reported
that must be observed to fall into this category:
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27
Models are treated as data. The key characteristic of an MSE is that the mathematical models are exchangeable. The environment allows
radically different models to be used for the same physical device.
Software models for modeling and solution are separated. The software architecture allows exchange of solvers. Although only a few MSEs
really offer a selection of different solvers, they are flexible in this
respect.
2.1.3 CIRIAF’s classification
According to [12] the institution CIRIAF (Centro Interuniversitario di Ricerca
sull’Inquinamento da Agenti Fisici), classified computer codes in two levels,
based on the temporal scheme of calculation:
1st level: codes that work in steady regime.
2nd level: codes that work in dynamic regime.
Within each level, the computer codes can then be further subdivided in other
macro-categories based on:
The interface, dividing the codes in instruments backed by a computer graphics interface (input graph) and tools with no graphical interface;
The evaluated building systems, basically codes that allow the evaluation of energy performance in winter conditions and those that analyze the
summer conditions.
Table 2.1 and 2.2 show, for the first and second level respectively, the
classifications of tools to evaluate building energy more available and widely
used in the construction sector, accompanied by information concerning the
origin, the implementation team and possible Internet sites.
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28
Table 2.1. Calculation codes of the first level for evaluating building energy (CIRIAF)
Table 2.2. Calculation codes of the second level for evaluating building energy (CIRIAF)
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29
2.2 Thermal simulation methods for buildings Models can be either identified from data, or derived from first principles. To
describe something that does not exist yet, such as a building that is being
designed, the second way is the only possible, and thus this work focuses on it.
Dynamic first principle models are based on the (dynamic) balances of mass,
energy and momentum:
0)(
w
t
(Mass)
)()()( TTcwet
p
(Energy)
fpwwwt
)()( (Momentum)
The scalars p, T, cp and are respectively the fluid pressure, temperature, specific energy and density; the vectors w and f are the fluid velocity and the
possible motion driving forces, and the scalar parameters and cp are the fluid thermal conductivity and constant-pressure specific heat capacity. In this case
we consider air as a mixture of ideal gases, so it allows expressing the specific
energy e as cv*T, where cv is the constant - volume specific heat capacity.
Solving partial differential equations that are obtained from said balances means
finding the motion of the state variables that characterize it. The entire
resolution is reported in [13].
The solutions can be obtained by analytical or numerical way. In the first case
one can find the exact solution, limited to problems where the geometry and
boundary conditions are very simple; in the latter case, appropriate
approximations are used to help a simulator resolve any problems on computers
through the calculation programs and to identify the solution with reasonable
accuracy. Analytical solutions allow for the calculation of variables at any point
in the model, but with very long calculation time. Therefore, they are suitable
only to solve simple problems. With numerical methods, conversely, one can
easily solve problems of any degree of complexity: they have general
applicability, but refer only to points (segments, areas or volumes) by default.
The selected (or discrete) points will be characterized by the properties of a
small region which they belong. Such a discrete point is frequently termed a
nodal point (or simply a node), and the aggregate of points is termed a nodal
network, grid or mesh.
According to [11], Kallblad (1983) grouped building heat transfer simulation methods in time-dependent methods and simplified methods. The simplified
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30
methods can further be categorized ad steady-state heat balance, degree-day, and
other methods (see fig. 2.1).
Figure 2.1. Heat transfer simulation methods (Kallblad 1983)
Dynamic thermal simulation methods can be classified as heat balance and
weighting factor methods, with the heat balance method giving more detail
output data than the weighting factor method.
When the heat balance method is used, the solution of the time-dependent
temperature distribution within a solid during transient process is often difficult
to obtain. Therefore, where possible, a simple approach is preferred. One such
approach is termed the lumped capacitance method, where the temperature of
the solid is assumed spatially uniform at any instant during the transient process.
This assumption implies that the temperature gradient within the solid is
negligible (Incropera and De Witt 1990).
The numerical methods used are various: we report below those that are more
suited to solving the problem of heat transfer, in particular the technique of
finite differences and finite volume. Notice, with a view to evidencing the
peculiarity of this work, which OOMS tools allow to use either of them freely.
2.2.1 The finite – difference method
An approximate solution to solve a problem of heat conduction through a wall in
the direction of one-dimensional is given by the finite difference method
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31
(FDM). In the field of iterative methods it is the easiest to treat in terms of
writing equations.
The equations (2.1) and (2.2) that characterize the finite difference method
derived from the discretization of the derivative of a generic function in a
predefined point, made by Taylor series.
)(!3
)(2
)()()(32
xfh
xfh
xfhxfhxf (2.1)
)(!3
)(2
)()()(32
xfh
xfh
xfhxfhxf (2.2)
By the relations written above, it is possible to approximate the first derivative.
This can be done in two different ways (2.3) (2.4), “forward” and “backward”:
)(!3
)(2
)()()('
2
xfh
xfh
h
xfhxfxf
(2.3)
)(!3
)(2
)()()(
2
xfh
xfh
h
hxfxfxf
(2.4)
Then subtracting the former from the latter we get “central” discretization (2.5):
)(!32
)()()(
2
xfh
h
hxfhxfxf
(2.5)
Similarly, for the second derivative,
)(2 2
2
21 hoh
fffif iii
(forward)
)(2 2
2
12 hoh
fffif
iii
(backward)
)(2 2
2
11 hoh
fffif iii
(central)
Dividing the spatial and temporal domains with an arbitrary number of nodes
separated by a step h, it is possible, through direct or iterative methods, solve the
system of equations thus created (in which the unknown function f for us is the
temperature T) and obtain a solution to the problem. However, the solution is
approximated by its truncation in the Taylor series, and the error introduced is
unavoidable, as is inherent to the logic of discretization. Since the temporal and
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32
spatial coordinates of the problem switching from continuous domain to an
approximate one, a good solution will be given by a computational grid dense
enough to reduce the pace and to approach the real solution to the approximate
one. Of course, the program structure must be such as to ensure convergence
and stability of the calculation.
2.2.2 The finite element method
The finite volume method (FVM) consists in splitting the domain into “control
volumes” adjacent to one another and applying to them the balance equations in
their integral form. Nodes are placed within each volume, usually in the middle.
Unlike the previous method, the unknown quantities are not related to the nodes
but it is the grid to define the faces of control volumes. Because the variable
refers to each node, even in this case, a system composed of many algebraic
equations as there are volumes of discretization is obtained.
In order to numerically solve the problem of heat conduction in a solid, for
example, is necessary to introduce the concept of spatial discretization, in order
to define control volumes. The volume control (VC) are defined as the
application of a Cartesian grid computing with a pitch not necessarily constant:
if we consider, for example, the case of a multilayer wall, the procedure
involves first identifying of each layer (possibly divided into sub volumes), after
the awarding of a central node at each volume considered (using the "cell
centered" method). Figures 2.2 and 2.3 show the location of the nodes for given
control volume: the distance between them is a fundamental fact about writing
of the equations that solve the problem.
It is assumed that heat transfer occurs between node and node, and that:
the physical and thermal properties of the body are uniform and are not a function of temperature;
the heat capacities are concentrated in the nodes;
changes in temperature between the nodes are linear;
heat transfer takes place only between adjacent nodes.
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33
Figure 2.2. Discretization of a multi-component
Figure 2.3. Relative distance between nodes (a)
Figure 2.3. Relative distance between nodes (b)
Unlike the problems in steady state, where it is sufficient to refer only to the
spatial discretization, for dynamic problems the need for time integration arises:
the solution should be calculated referring to the individual intervals where the
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34
time domain is divided. Among the temporal discretization methods that require
only two moments of time for each equation we refer to the Euler method:
1. explicit: ttf nnnn ),(1 (2.6)
2. implicit: ttf nnnn
),( 111 (2.7)
There too, notice that OOMS tools natively support the time derivative as a
language primitive, so that only the spatial one remains as a burden for the
analyst.
2.3 A literature review
The US Department of Energy (DOE) has compiled an extensive summary of
building simulation tools [14], which describes more than 200 energy-related
software tools for buildings, with an emphasis on using renewable energy and
achieving energy efficiency and sustainability in buildings. In the following
paragraphs, a brief description of five of the most commonly used building
simulation tools is provided to show typical features of tools. A complete
comparison can be seen in [15] resumed in table 2.3.
DOE-2 is a tool that uses hourly weather data to simulate a building’s energy
use and energy cost for a given description of the building’s indoor climate,
architecture, materials, operating schedules, and HVAC equipment. Its
development has been funded by the U.S. Department of Energy. It is used for
building science research, teaching, designing energy-efficient buildings,
analyzing the impact of new technologies and developing energy conservation
standards [16].
EnergyPlus is a building simulation program that is currently being developed
by the Simulation Research Group at Lawrence Berkeley National Laboratory,
the Building Systems Laboratory at the University of Illinois, the U.S. Army
Construction Engineering Research Laboratory, and U.S. Department of Energy.
This program combines the best capabilities and features from BLAST and
DOE-2 along with new capabilities. It models heating, cooling, lighting,
ventilation, other energy flows, and water use. EnergyPlus includes many
innovative simulation capabilities: time-steps less than an hour, modular
systems and plant integrated with heat balance-based zone simulation, multi-
zone air flow, thermal comfort, water use, natural ventilation and photovoltaic
systems [16].
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35
ESP-r is a comprehensive simulation environment which can address problems
related to several domains. It has been developed at the Strathclyde University
in Scotland since 1974. ESP-r allows researchers and designers to assess the
manner in which actual weather patterns, occupant interactions, design
parameter changes and control systems affect energy requirements and
environmental states. It is used in many European universities and research
institutes, and in some private companies. Within ESP-r, it is possible to select
different approaches to domain solution – one, two or three dimensional
calculation, a mix of scheduled air flow, network or computational fluid
dynamics (CFD) for flow assessments, and a mix of ideal or explicit
representation of plant and control systems [17].
IDA is an advanced simulation environment for building and energy system
simulation. It has originally been developed at the Swedish Institute of Applied
mathematics in co-operation with the department of Building Services
Engineering at Royal university of Stockholm. This simulation package consists
of IDA Modeller, IDA Solver, and a Neutral Model Format (NMF) library. The
key idea is to separate the models, which are defined by free combinations of
algebraic and differential equations in NMF format, and the solver. By adopting
this approach, several practical problems with traditional monolithic simulation
tools can be avoided (Sahlin 1996). Namely, new building component models
can be described in NMF format, and they still can be solved by a differential-
algebraic equation (DAE) solver without any need to rewrite simulation and
solution source code [18].
TRNSYS (TRaNsient SYstem Simulation Program) was developed during the
early seventies at the Solar Energy Laboratory at the University of Wisconsin.
The primary application was initially solar energy systems. An important feature
of TRNSYS is that component models are pre-compiled. This means that end
users may compose system models with fixed components without access to a
compiler (Sahlin 1996). Historically, TRNSYS has been used for simulating
solar thermal systems, modern renewable energy systems including PV and
wind power, general HVAC systems, and buildings [19].
Each simulation tool has special features and some limitations. For example,
DOE-2 is based on a simplified modeling approach which makes it difficult to
include new systems and devices in the model. This had led to a whole new
development effort (i.e. EnergyPlus), which is still going on (Crawley 2000).
ESP-r is a very comprehensive simulation environment, but this simulation tool
is available only in special mainframe computers using UNIX operating system
(Hand 1998). IDA is a modern and promising simulation environment. It is
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36
becoming gradually more popular, but it has been reported some problems with
long execution times. The latest version of TRNSYS features many
improvements, and it has been utilized successfully in many cases. However,
TRNSYS also has some limitations due to adopted modeling methods.
Therefore, despite the fact that a great effort has been put in developing all the
existing building simulation tools, additional work is needed to rectify their
deficiencies.
Table 2.3. Comparison of E/E with ESP-r/DOE-2/BLAST Weather Data Formats
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37
2.4 Peculiar difficulties of dynamic modeling
Building simulation is an interdisciplinary subject, with elements from
numerical analysis, information technology, signal processing, as well as the
building sciences (Sahlin 1996). This makes it a fascinating field with the
endless challenge to estimate interacting energy flow paths encountered within
buildings with a meaningful level of accuracy. Further complexity comes from
the behavior of the heat and mass transfer mechanisms themselves, because they
are often highly non-linear, coupled and are dependent on design parameters
which, in turn, change with time (Clarke and Maver 1991).
A simulation of a building is a mathematical representation of the physical
behavior of each of its parts. However, it cannot precisely replicate a real
construction because all the simulations are based on a number of key
assumptions that affect the accuracy. The dynamic modeling approach tries to
preserve the integrity of the entire building-plant system, simultaneously
analyzing all the energy flows with a level of detail appropriate for the
objectives of the problem and the amount of data available. In this regard, a
building must be seen as systemic (entire system consists of many separate
parts), dynamic (the parts interact, have some memory of the past, and may
evolve at different rates), nonlinear (thermodynamic parameters depend on the
state) and, above all, complex (there are a myriad of interconnections and
iterations between the parties).
Assuming that the simulation has a theoretically perfect representation of the
operation of a building, it cannot perfectly replicate the real dynamics that
govern the behavior of energy. For example, the climate can drift apart from the
available meteorological data; the systems never work exactly as expected from
the curves of partial load operation; the performance may also change with the
age of the plant and the actual number of hours worked since the last cleaning or
maintenance. Consequently, particular care when interpreting the results, as they
constitute a representation on how it works, or can work, a building-plant
system.
2.4.1 The complexity of energy exchanges
As shown in figure 2.4, the internal environment conditions in buildings are
determined by different energy sources that evolve with different speeds and
characteristics. The main sources can be identified as:
external climate, whose main variables are: air temperature, radiant temperature, humidity, solar radiation, speed and direction of wind;
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38
occupants, causing an unpredictable energy supply because of their metabolism, the use electrical equipment and the adjustment of the
settings regulation;
auxiliary systems, which can provide heating, air conditioning or ventilation of the indoor environment.
In buildings, the air flows that result in an increase of heat transfer by
convection are infiltration, flows with the neighboring areas and forced
ventilation.
Infiltration means all air inlets from the outside; they can be divided into two
categories:
uncontrollable air infiltration through the seals of the windows and through the building envelope itself;
desired air inputs, implemented with the opening of doors and windows (natural ventilation).
Figure 2.5 shows the main factors influencing the distribution of air flow.
Random events, such as opening doors and windows, intermittent use of
ventilation systems have a strong influence on the assessment of the air flows
because they affect not only the areas directly affected but also the adjacent
rooms. So, modeling air is not easy and needs a particular accuracy.
Air movement is often computed on a mesh, where nodes represent volumes of
fluid, characterized by thermodynamic parameters such as temperature,
pressure, humidity; while nodal connections represent pathways, including loss,
which connect these volumes and through which air can flow. To determine the
parameters of each node in the network, techniques of numerical analysis are
commonly used. Many examples exist for the solution of the Navier-Stokes
equations with (continuity) ones of mass and energy balance.
Every air flow simulation model that uses the network approach must model
these phenomena.
The model of the air could be described by a three-dimensional model, using
advanced methods of calculation (i.e. CFD). Through an approach based on the
principle of finite elements, it could be possible to fully describe the motion of
the air in all conditions and provide information on speed, pressure and
temperature at each point in the simulated environment.
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Figure 2.4. Energy flows in a building (Hand 1998)
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Figure 2.5. Factors that influence the air flow distribution in buildings (Feustel and Dieris
1992)
2.4.2 Difficulty in writing the equation for building simulation
The problems associated with the thermodynamics of a building are complex for
a variety of reasons: for example the heat transfer processes are simultaneous
and have different characteristics; the heat input from air plants and solar
radiation are a function of time and space; the control volumes defined in the
discretization of the system may not always be homogeneous and isotropic; then
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the system geometry is multidimensional: the solution of these problems, therefore, can only be numeric. If the system is discretized with a grid arbitrarily
thick and conservation laws are written for each volume, unless discretization
error the results may be sufficiently "good".
The partial differential equations that govern the conductive heat transfer in
solids can be obtained from the energy conservation principle, which expresses
an energy balance for a volume V:
A VV
p dVqdAnqdVt
c 0
(2.8)
where the first term represents the change in internal energy, the second the heat
flux entering and the third generation of internal heat on the volume V, q is the
specific heat flux vector, n
is the unit vector normal to the surface and directed
outwards; finally, q is the internal heat generation over time.
The general equation of conduction in isotropic solids is:
qkt
cp
)(
(2.9)
Where the scalars θ, cp, and k are respectively the fluid temperature, constant-pressure specific heat capacity, density and thermal conductivity.
Fourier's postulate is obtained by replacing (2.9) in (2.8):
tkq (2.10)
We consider a control volume in thermodynamic equilibrium with the regions
surrounding it. The heat fluxes for this volume can result from energy
(mechanisms of convection, conduction, radiation) or mass exchanges. The
general problem must be placed in a dynamic context: assuming the presence in
Volume I of an internal heat generation, assumed, for convenience, independent
of temperature in the region, we obtain the numerical formulation corresponding
to (2.8):
ttIj
n
j
IjII
IIpI qkt
Vc )()(
)()()(1
,
0
, (2.11)
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Where I is the control volume, j adjacent regions; the term )(I is
characteristic density of volume I in ξ time [kgm-3
], )(, Ipc is characteristic
constant-pressure specific heat capacity [Jkg-1
°C-1
], )(V is cell volume [m3],
finally, n is the number of regions that exchange heat with I.
This type of equations refer to a time step, or two distinct moments in time: the
apex 0 indicates that the value is relative to known reference time. The
assessment of heat flow and the heat generation [W] referring to instant of
known time tt provides an explicit formulation, while at instant of
unknown time t an implicit formulation. Equation (2.8) written for each
node, provides a set of differential equations that can describe the exchange of
energy (in this case conductive) of the building-plant system. These equations
can then be collected to build the system and, therefore, the matrix for
computers.
So, the dynamic simulation of a building is not possible without a proper
modeling of systems installed in it. To make the mass and energy balances
describing the system, the components of a system are modeled by nodes and
inter-linked. The balance equations, again picked by a system, are written
through the matrix and solved simultaneously.
2.4.3 Different physical phenomena
The dynamic behavior of any "building-system" is influenced by:
- one or more walls (structure, roofing and cladding);
- fixed or mobile openings (such as windows, doors, gates, etc.);
- one or more energy conversion facilities that provides the necessary thermal energy for air conditioning of the building;
- one or more of the thermal energy distribution systems;
- one or more regulation systems of temperature, humidity, light;
- the influx of people;
- the usage factor.
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It is easy to see that the complexity of each part of the building - due to the
nonlinearity, the variability of the boundary conditions, etc. - means that its
dynamic behavior is difficult to predict. This is even more evident when
considering the overall system, where interactions between a component and the
others are not negligible.
2.4.4 Different disciplines
It is worth noticing that the contexts in which control problems arise, are very
varied, as well as technological realizations of controllers may be different. It is,
therefore, natural to ask how to deal with so heterogeneous equipment, ranging
from physics to hydraulics and electronics, in a unified way.
First, it is very convenient to recast the problem in purely mathematical terms.
Descriptions of the items that appear in the control system should be expressed
in mathematical form. As well as for the process, transducers and actuators,
appropriate mathematical models should be formulated. Facing the modeling of
a physical system is generally complex and the complexity grows exponentially
with the size of the initial problem.
2.4.5 Different time scales and spatial sizes
Model components are heterogeneous, and quite often their temporal dynamics
are different. Just think of the energy flow through a wall and along a conduit.
Another difficulty is represented by the fact that it is not easy to find a suitable
method to model a given physical situation. A resolution procedure, such as
finite difference approximation to solve a differential equation, might be perfect
(this includes mathematics and its computer coding to solve a particular model).
However, the model may be inadequate. For example, a method to model the
heat transfer through a wall is accomplished by using simplifying assumptions
such as the one-dimensional conduction. However, it can happen that one make
a mistake using a one-dimensional thermal conduction model to represent a
situation where the two-dimensional conduction is dominant.
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2.5 Current dynamic tools
There are different tools currently available (including MATLAB Simulink,
Energy Plus, CFD – related ones such as Fluent or Code-Saturne, and so forth).
Consequently, the approach of simulation is different among different language
codes, which create an ad-hoc model of a specific subsystem. The optimal
solution would be to exchange simulation models from different domains and
create collaboration among the various simulation languages. However, the lack
of separation between models, data and solvers makes it hard to integrate
models from different disciplines for co-simulation. In addition, any code
conducts specific system-level studies with a scalable detail level, based on the
particular simulation purpose. From an engineer’s perspective, it could be hard
to manage components in a modular manner and not convenient in terms of
timing of the simulation process. In addition, many essential elements of
building models are described in a distributed-parameter way, in one dimension
(piping) or even in three (air volume). It is the reason why simulation is difficult
to manage.
There is, therefore, a need to develop appropriate tools to carry out the
simulation of the entire system. A unique language is needed that is not aimed at
any particular branch of engineering (such as mechanics, thermodynamics,
electronics) but allows modeling of various systems, provided that described by
differential algebraic equations. In this way, reports from different backgrounds
can be treated in the same way. As a result, it will be immediate to combine
models coming from different fields of engineering, characterized by control
systems with continuous or discrete time. It is against this background that the
object-oriented modeling places.
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Chapter 3
An object-oriented solution based on
Modelica
After evidencing the major problems to be solved for efficient building
simulation, this chapter sketches out a solution based on OOMS.
3.1 Model library structuring
As anticipated, project-oriented system-level simulations need conducting at
different levels of detail. In the opinion of the author said levels have to play a
key role in structuring a model library, and for that purpose, adopting the
Object-Oriented Modeling and Simulation (OOMS) paradigm is highly
beneficial, allowing to cast the entire set of addressed problems into a single,
unitary framework. Doing so however requires a specific effort in a view to
suitably structuring the library, so that the inherent OOMS advantages are
exploited. Notice that such a structuring methodology, discussed in the
following, is novel with respect to similar works in the literature. The proposed
library structuring is composed of three steps [20].
3.1.1 Structuring step 1
The first step consists of defining and qualifying the already mentioned detail
levels. In this work four levels, are defined, corresponding to the basic questions
encountered along a building project. Of course the matter is more articulated,
and one could consider defining more levels, or further customizing them based
on the needs of some particular class of applications.
For each defined level, we point out (a) the purpose, i.e., what type of analysis it
is conceived for; (b) the hypotheses under which its models are valid; (c) the
analysis protocol, i.e., how the intended analysis is to be performed; (d) the
structural limitations, i.e., what facts the models are by construction unable of
capturing, and thus are implicitly considered negligible in the intended analysis;
(e) the practice-based limitations, i.e., for example, what the models could in
p