benjamin welle stanford university grant soremekun phoenix integration geometry, structural,...
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Benjamin WelleStanford University
Grant SoremekunPhoenix Integration
Geometry, Structural, Thermal, and Cost Trade-Off Studies using Process Integration and Design Optimization
Improving Multi-Disciplinary
Building Design
Improving Multi-Disciplinary
Building Design
• Introduction to CIFE
• Research Objectives
• Case Study: Classroom MDO
• Future Work / Q&A
• Phoenix Integration/ModelCenter
Overview
An academic research center within the Civil and EnvironmentalEngineering department at Stanford University: Research focus is on the Virtual Design and Construction (VDC) of
Architecture – Engineering – Construction (AEC) projects in collaboration with our industry partners
Introduction to theCenter for Integrated Facility Engineering
(CIFE)
Overview of CIFE Research Projects
Building Performance Monitoring
4D Construction Planning
Collective Decision Assistance
Conceptual Phase Model-Based Design
Design-Fabrication-Integration
Integrated Concurrent Engineering
Problem Statement and Project Objectives
OverviewThe time required for model-based structural and energy performance analysis feedback means few (if any) alternatives are evaluated before a decision is made.
ObjectiveDevelop/utilize a platform to integrate CAD and analysis tools for design exploration and optimization that:
Can interface with commonly used design tools in AEC industry Can support the following:
Software automation Software integration Data visualization Simplification of running of trade studies
Provides a robust, flexible and extensible environment
IntuitionProviding designers with this platform will allow them to systematically explore larger design space more efficiently and better understand those design spaces, resulting in higher performance and cost-effective design solutions.
Multidisciplinary Optimization Process
Structural Steel Section Optimization Process
Structural Geometry
Tool: Catia / DP
Actor: Architect / Structural Engineer
Run-time: 0
Structural Analysis
Tool: GSA
Actor: Structural Engineer
Run-time: 0
Structural Code Checker
Tool: VB Code
Actor: Structural Engineer
Run-time: 0
Structural Section Database
Tool: Access / Excel
Actor: Structural Engineer
Run-time: 0
Structural Section Optimizer
Tool: VB Code
Actor: Structural Engineer
Run-time: 0
loading tributary areas
node coordinates
steel section info
steel section info
member section
member strength D/C ratio
section groups
total steel cost
member info
element forces
total steel cost
GSA model
Catia model
4Pin
5Enc
43
11
23
2
xy
z
ANALYSIS LAYER
Scale: 1:36.73
Labels:
Node No.s
Elem. No.s
Deformation magnification: 2.500
Node Loads, Force: 25.00 kip/pic.cm
Beam Point Loads, Force: 20.00 kip/pic.cmBeam Loads, Force: 10.00 kip/ft/pic.cm
Case: C3 "1.2DL+1.2LL+1.2E"
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
4500000
5000000
0 5 10 15 20 25 30 35 40
Iteration Number
Mas
s
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
% o
f ele
men
ts o
ver u
tilis
ed
Model Mass Percentage Over Utilised
xy
z
ANALYSIS LAYER
Scale: 1:36.73
Info 2
info 1
Energy and Daylighting Optimization Process
geometry definition parameters
Energy Analysis
Tool: EnergyPlus
Actor: Mechanical Engineer
Run-time: 0
Energy Analysis Results:
Energy Consumption: MJ/m2/yearSolar Heat Gains: MJ/m2/yearLighting Intensity: MJ/m2/yearLighting Multiplier: 0-1Cooling Intensity: MJ/m2/yearHeating Intensity: MJ/m2/yearElectricity Costs: $/yearGas Costs: $/yearTotal Costs: $/year
Adjust building geometry to minimize annual energy cost while meeting energy and daylighting constraints
Load Batch File
Tool: RunEPlus
Actor: Mechanical Engineer
Run-time: 0
Architectural Geometry from DP
Wall surface coordinates: X1, Y1, Z1, X2, Y2, Z2, X3, Y3, Z3, X4, Y4, Z4
Roof surface coordinates: X1, Y1, Z1, X2, Y2, Z2, X3, Y3, Z3, X4, Y4, Z4
Floor surface coordinates: X1, Y1, Z1, X2, Y2, Z2, X3, Y3, Z3, X4, Y4, Z4
Window surface coordinates: X1, Y1, Z1, X2, Y2, Z2, X3, Y3, Z3, X4, Y4, Z4
Create Variables Input Macro File
Tool: J-Script
Actor: Mechanical Engineer
Run-time: 0
Execute Main Input Macro File
Tool: EPMarcro
Actor: Mechanical Engineer
Run-time: 0
Execute Variables Input Macro File
Tool: EPMarcro
Actor: Mechanical Engineer
Run-time: 0
Create EnergyPlus Input File
Tool: EPMarcro
Actor: Mechanical Engineer
Run-time: 0
Proof of Concept Case Study: Classroom
Design Variables Building orientation (0) Building length (L) Window to wall ratio (W) Structural steel sections
Constraints Fixed floor area Structural safety Daylighting performance
Objectives Minimize first cost for structural steel Minimize lifecycle operating costs for
energy
Length
O
steel frame
column
beam
girder
Window to Wall Ratio
Orientation
Structural Model
Impact of Steel Section Sizes on Structure Cost
Beam
S
ecti
on
s
Gir
der
Secti
on
s
Colu
mn
S
ecti
on
s
Bu
ild
ing
Len
gth
Max
DC
R
ati
o
Cost
Beam Section Type
Tota
l C
ost
Each point represents a single design
Each line represents a single design
Values for section types / building length that yield
best designs
Impact of Building Geometry on Structure Cost
730872691764652655613547574438535330496221457113418004378896
Plot Variable: response (Model.class_cost_2.total_cost)
bldg_length 6059.55958.55857.55756.55655.55554.55453.55352.55251.55150.55049.54948.54847.54746.54645.54544.54443.54342.54241.54140.54039.53938.53837.53736.53635.53534.53433.53332.53231.53130.53029.52928.52827.52726.52625.52524.52423.52322.52221.52120.520
tota
l_co
st
$720,000
$710,000
$700,000
$690,000
$680,000
$670,000
$660,000
$650,000
$640,000
$630,000
$620,000
$610,000
$600,000
$590,000
$580,000
$570,000
$560,000
$550,000
$540,000
$530,000
$520,000
$510,000
$500,000
$490,000
$480,000
$470,000
$460,000
$450,000
$440,000
$430,000
$420,000
$410,000
$400,000
$390,000
$380,000
$370,000
num_columns_along_length
9.69.49.298.88.68.48.287.87.67.47.276.86.66.46.265.85.65.45.254.84.64.44.243.83.63.43.232.8
Steel Cost vs. Building Length and Number of Columns
tota
l co
st o
f st
ee
l str
uct
ure
building length (L) number of columnsalong length
Thermal Model
Impact of Design Variables on Energy Performance
Design of Experiments (DoE) allow for the visualization of the design space and an understanding of variable sensitivity and performance trends.
The design space can be explored from a wide range of perspectives, including general trends using surface plots, actual data points using glyphs, and sensitivity data using bar charts
Orientation (deg)
To
tal
Lif
ec
yc
le O
pe
rati
ng
Co
sts
($
/ 3
0 y
ea
rs)
Most Efficient
Less Efficient
Length (mm)
Total Lifecycle Operating Costs vs. Orientation and Length
Total Window Area
Total Operating Cost
Total Wall Area
Impact of Design Variables on Energy Performance (cont’d)
Total Lifecycle Operating Costs vs. Total Wall Area and Total Window Area
Optimization vs. DoE Results for Energy and Daylighting Performance
DoE- 1882 simulations Optimization-93 simulations
Optimum areas of design space
The correlation between the optimum designs using DOE and the optimizer was extremely high. Simulation time to achieve optimum designs was reduced by 95%.
To
tal L
ife-c
ycle
Co
sts
($
/ 3
0 y
ea
rs)
Total Life-cycle Operating Costs vs. Orientation and Length
Orientation (deg)
Length (mm)
Optimization vs. DOE Results for Energy and Daylighting Performance
Multi-Disciplinary Model
Size of Design Space: 55,000,000
MDO Run: 5600 (0.01%)
Time: 34 hours
Design Variables• Building orientation
• 0-180 deg, 10 deg inc • Building length
•4-14m, 1m inc• Window to wall ratio
•0.1 to 0.9, 0.1 inc• Structural steel sections
•Girders (65 types)•Columns (7 types)•Beams (65 Types
Structural Cost vs. Energy Cost with Pareto Front
Pareto Optimal Designs for Classroom MDOStructural First Cost vs. Energy Lifecycle Cost
Structural Cost ($)
Life
cycl
e E
nerg
y C
ost
($/
30
years
)
Pareto Optimal Designs for Classroom MDOBuilding Length vs. Energy Lifecycle Cost
Pareto Optimal Designs for Classroom MDOBuilding Length vs. Structural Cost
Pareto Optimal Designs for Classroom MDOWindow to Wall Ratio vs. Energy Lifecycle Cost
MDO Optimization of Structural vs. Energy Performance
Optimal Designs with Varying Objectives
Next Steps / Future Work
General: Make software wrappers more robust / flexible More complex building types Topology changes Parallel computing to reduce trade study run times
Structural: Consider life cycle costs (embodied energy) Consider alternative structural materials
Mechanical / Energy: Consider different constructions, HVAC equipment, internal loads, etc. Integrate the lighting simulation engine Radiance for daylighting performance Integrate the computational fluid dynamics (CFD) simulation program FLUENT for
space temperature stratification, air speed, and mean radiant temperature
Project Team Members
Research Team:Forest Flager, Structural Engineer
Benjamin Welle, Mechanical EngineerPrasun Bansal, Aerospace EngineerKranthi Kode, Structural Engineer
Victor Gane, Architect
Industry Collaborators:Grant Soremekun, Phoenix Integration
Gehry Technologies
Supervised By: Professor John Haymaker
