copyright 2013 using mdo to support sequential design decisions cife tac 2013 1 using mdo to...
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Copyright 2013
Using MDO to Support Sequential Design Decisions
CIFE TAC 2013 1
Using MDO to Support Sequential Design Decisions
Michael Lepech (PI)
Martin Fischer (co-PI)
Forest Flager
John Basbagill
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Using MDO to Support Sequential Design Decisions
CIFE TAC 2013 2
Case Study: Multi-family Residential
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Conceptual Design Problem
SCOPE
Housing buildings:substructure ● shell ● interiors ● services
OBJECTIVES
(1) Minimize life-cycle cost
(2) Minimize carbon footprint
VARIABLES
(1) Number of buildings: 3 - 4
(2) Number of stories: 5 - 8
(3) Building footprint: H-shape
(4) Building orientation: 0-360°
CONSTRAINTS
(1) Gross Floor Area (GFA): 15,000m2
(2) Building width (a, b and f): >15m
(3) Distance to site perimeter: >20m
(4) Distance between building: >20m
a bcd
ef
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CURRENT INDUSTRY PRACTICE
• 3 design alternatives evaluated
• 162 man hours required
Design Exploration Today:
Using MDO to Support Sequential Design Decisions
Is this sufficient?
DESIGN SPACE
• 1.46E11 possible design alternatives
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Introduction to MDO
CONSTRUCTION COST
DProfiler
GEOMETRYMODEL
DProfiler
OPERATIONAL COST
eQUEST, CostLab
EMBODIEDIMPACT
SimaPro
OPERATIONALIMPACT
eQUEST, SimaPro
OPTIMIZER
Genetic Algorithm
“Multidisciplinary Design Optimization (MDO) involves the formalization of design iteration and coordination to leverage computer processing power to systematically search the design space.”
- MDO Technical Committee, AIAA (1991)
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MDO Case Study Results
275 280 290 295285
Carbon Footprint (met kilotons CO2e)
Life
-Cyc
le C
ost
(US
D, M
illio
ns)
165
195
210
225
180
+KEY
Baseline
Selected Designs
3 Buildings, 5 Stories
3 Buildings, 6 Stories
3 Buildings, 7 Stories
3 Buildings, 8 Stories
4 Buildings, 5 Stories
4 Buildings, 6 Stories
4 Buildings, 7 Stories
4 Buildings, 8 Stories
Infeasible Solution
X
+
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MDO Case Study Results
275 280 290 295285
Carbon Footprint (met kilotons CO2e)
Life
-Cyc
le C
ost
(US
D, M
illio
ns)
165
195
210
225
180
+KEY
Baseline
Selected Designs
3 Buildings, 5 Stories
3 Buildings, 6 Stories
3 Buildings, 7 Stories
3 Buildings, 8 Stories
4 Buildings, 5 Stories
4 Buildings, 6 Stories
4 Buildings, 7 Stories
4 Buildings, 8 Stories
Infeasible Solution
X
1
3
2
+
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CIFE MDO Research
KBZ NATIONAL STADIUM
Scope: Steel roof structure
Savings: USD $4M (19%)
Support:2009 CIFE Seed, Autodesk
PASTEDUCATIONAL BUILDING
Scope: Steel superstructure
Savings: USD $570K (21%)
Support: Skanska
GOVERNMENT OFFICE BUILDING
Scope: Structural system
Savings: USD $1.7M (7%)
Support:2012 CIFE Seed, Beck
MULTI-FAMILY RESIDENTIAL
Scope: Structural system
Savings: USD $4M (19%)
Support:2009 CIFE Seed, Autodesk
CURRENT
x
y
z
ANALYSIS LAYERScale: 1:290.3
• MDO class under development• Ongoing MDO research
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Conventional vs. MDO Design Process
CONVENTIONAL
MDO
- building massing- orientation
- system type- system topology
- component sizing
- building massing- orientation- system type- system topology- component sizing
CONCEPT DEVELOPMENT
new information
DETAIL
new information
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Research Question
Using MDO to Support Sequential Design Decisions
10
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How can automated design exploration be
leveraged to support sequential design decisions?
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Research Goals
Q1: What decisions are most important?
Develop a decision support methodology to provide stakeholders with
clear and quantitative information about:
Infl
uen
ce (
%)
Design Decision
0
10
GlazingThickness
CladdingMaterial
WWR WallR-value
Height
5
A: Analyze relative influence that different decisions have on performance criteria of interest.
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Research Goals
Q2: What is the impact of the decision on product performance?
A: Analyze how a given decision affects the distribution of possible solutions.
No decisions made
1. Six floors
1. Six floors2. Orientation = 323°
1. Six floors2. Orientation = 323°3. Steel cladding
Impact (kg CO2e)
Pro
bab
ility
of
Occ
urr
ence
(%
)
2
6
10
14
18
150k 250k 350k 450k
KEY
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Research Goals
Q3: What are the synergies between design decisions?
A: Analyze the relationship between coupled decisions.
84 30 30 30 30 50 360 2.34E82.97E5
53 10 10 0 0 10 0 1.67E82.75E5
Num
ber
ofB
uild
ings
Num
ber
ofF
loor
s
Leng
th a
(m)
Car
bon
Foo
tprin
t (k
g C
O2e
)
Life
Cyc
leC
ost
($)
Bui
ldin
g O
rient
atio
n (d
eg)
Leng
th b
(m)
Leng
th d
(m)
Leng
th c
(m)
Leng
th e
(m)
Preference Shading: Worst Best
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Research Methods
1) Formalize design problem and process
2) Develop design space analysis methods
- Design of Experiments (DoE)
- Optimization
3) Develop data visualization methods
4) Implement decision support methodology
- Massively parallel (cloud) computing environment
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Research Validation
1) Parallel design charrettes / case studies
- Subjects: students and practitioners
- Groups: conventional (control) / decision support (intervention)
2) Sample metrics
- solution quality / feasibility
- design cycle duration
- total design cycles
- use of data visualization methods
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Schedule
Task Timeline
1: DEVELOP DESIGN SPACE ANALYSIS METHODS
Summer 2013
2: DEVELOP DATA VISUALIZATION METHODS Fall 2013
3: IMPLEMENT DECISION SUPPORT METHOD Fall 2013
4: DESIGN CHARETTES Winter 2014
5. JOURNAL PUBLICATIONS Spring 2014
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Research Impact
Schedule Performance
Cost Conformance / Sustainability
dramatically reduced design cycle time
many alternatives evaluated quicklybetter performing designs identified
IMPROVED TIME TO MARKET
COST CONFORMING, SUSTAINABLE DESIGNS
early quantitative assessment of quality risks
CIFE 2015 Goals
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Opportunities for Industry Participation
Software Providers
Owners /Designers /Contractors access to software / training
partner on case studies
IDENTIFY NEW TECHNOLOGY / METHODS FOR PRODUCT DEVELOPMENT
USE OF METHODS / TECHNOLOGY TO SUPPORT BUSINESS OBJECTIVES
partner to incorporate your softwareinto MDO environment
participate in design charrettes
Learn from new methods developed and user testing
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Questions?
19
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Budget
Item Description Cost
Post‐Doctorate Researcher
Flager, Forest $26,179
Graduate Student Research Assistant
TBD $45,626
TOTAL AMOUNT REQUESTED $71,805