1. Benjamin Welle Stanford University Grant Soremekun Phoenix Integration
Improving Multi-Disciplinary Building Design
Geometry, Structural, Thermal, and Cost Trade-Off Studies using
Process Integration and Design Optimization
Benjamin Welle Stanford University Grant Soremekun Phoenix Integration
Good morning everyone. My name is Benjamin Welle (intro), and this is Grant Soremekun (intro), and today we are going to discuss some joint research we are conducting in multi-disciplinary building design. This research considers the application of Process Integration and Design Optimization of building geometry, structural performance, energy and daylighting performance, and cost in support of providing design teams with better information earlier in the design process.

2. Overview Introduction to CIFE Research Objectives
Case Study: Classroom MDO
Future Work / Q&A
Phoenix Integration/ModelCenter

3. Introduction to the Center for Integrated Facility Engineering (CIFE)
An academic research center within the Civil and Environmental
Engineering 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 3

4. Overview of CIFE Research Projects
Conceptual Phase Model-Based Design
Integrated Concurrent Engineering
Collective Decision Assistance
4D Construction Planning
Design-Fabrication-Integration
Building Performance Monitoring 4

5. Problem Statement and Project Objectives
Overview
The time required for model-based structural and energy performance analysis feedback means few (if any) alternatives are evaluated before a decision is made.
Objective
Develop/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
Intuition
Providing 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.
In a recent survey conducted by CIFE of some leading architectural and MEP firms, it was confirmed that on average, fewer than 3 design alternatives are considered for a building project.
Software automation (automating execution of simulation, including variable input)
Software Integration (data exchange between tools)
Simplification of running of trade studies
Data Visualization
So, we wanted to find a methodology and develop the functionality to allow designers to better understand performance trade-offs between different disciplines in support of an Integrated Design Process. 5 5

6. Multidisciplinary Optimization Process
The MDO process we wanted to support is shown in this diagram. We wanted to be able to be able to extract a parametric model from Digital Project, by Gehry Technologies, and feed that geometric model downstream to a performance analysis tool for energy and structure. In this case we chose GSA and EnergyPlus. The results of these simulation were to then be considered in unison in determining how one should navigate through the design space. 6 6

7. Structural Steel Section Optimization Process
In the structural process, the structural design including beams columns and girders was to be extracted from DP, assigned section types from a separate database, run through the FEA, the design checked for code compliance, then finally processed by the optimizer. 7 7

8. Energy and Daylighting Optimization Process

9. 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
beam
steel frame
girder
To run a constraint-based optimization, you need to define your……Explain Design Variables vs. Constraints vs. Objectives
Explain length/width parametric reasoning
Daylighting explanation-DetailedDaylighting, not Delight
Orientation
Length O
column
Window to Wall Ratio 9 9

10. Structural Model
After investigating various development strategies and software platforms, we decided upon a software program called Phoenix Integration, which is a platform that supports MDO through process integration and design optimization, or what many simply refer to as PIDO. We’ll go into more detail later on this particular interface and process of setting up this model, but for now I’ll just describe what the model does.

11. Impact of Steel Section Sizes on Structure Cost
Values for section types / building length that yield best designs
Each line represents a single design
Each point represents a single design
For the structural steel optimization, 4 variables that were considered: Length, and Steel section types for Beam, Column, Girder
What are the best combinations of length and steel section type to minimize cost while meeting structural code requirements
The two swaths on the left represent different beam depths, and the design points within each swath represent different weights within each beam depth category. Max DC Ratio- Steel Utilization Factor
Thickness of columns irrelevant to the steel weight as do Beams and Girders. Top two building lengths are where all the best designs reside.
As the length gets longer, the span gets shorter, making the beams more efficient.
Total Cost
Beam
Sections
Column
Sections
Girder
Sections
Building Length
DC Ratio
Max
Cost
Beam Section Type

12. Impact of Building Geometry on Structure Cost
Steel Cost vs. Building Length and Number of Columns
total cost of steel structure
building length (L)
number of columns
along length

13. Thermal Model

14. Impact of Design Variables on Energy Performance
Total Lifecycle Operating Costs vs. Orientation and Length
Less Efficient
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
Total Lifecycle Operating Costs ($/ 30 years)
Surface plots are giving you a general idea of the performance trends and sweet spots in the design space.
Length (mm)
Most Efficient
Orientation (deg)

15. Impact of Design Variables on Energy Performance (cont’d)
Total Lifecycle Operating Costs vs. Total Wall Area and Total Window Area
Total Operating Cost
Total Window Area
Total Wall Area

16. Optimization vs. DoE Results for Energy and Daylighting Performance
The correlation between the optimum designs using DOE and the optimizer was extremely high. Simulation time to achieve optimum designs was reduced by 95%.
Total Life-cycle Operating Costs vs. Orientation and Length
Optimum areas of design space
Total Life-cycle Costs
($/ 30 years)
Surface plots show general trends, then use GA to find the exact designs that are best. Can use both the DoE and GA in parallel to check your results.
Length (mm)
Orientation (deg)
DoE simulations
Optimization-93 simulations

17. Optimization vs. DOE Results for Energy and Daylighting Performance
Comment on 3 design issue.

18. Multi-Disciplinary Model
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
Size of Design Space: 55,000,000
MDO Run: 5600 (0.01%)
Time: 34 hours

19. Pareto Optimal Designs for Classroom MDO
Structural First Cost vs. Energy Lifecycle Cost
Structural Cost vs. Energy Cost with Pareto Front
Lifecycle Energy Cost ($/ 30 years)
This is example of the type of information that this type of analysis can provided. This graph shows the design space from the perspective of structural first cost vs. energy lifecycle cost. Blue designs are the best.
Down at the bottom you can see these little black x’s. These are pareto optimal designs and this curve here is the Pareto front. A pareto optimal design is one where you cannot improve in one performance objective without becoming worse in another. If one of these designs had a better structural cost and energy cost than another one, that other design would not be pareto optimal.
Information like this provides the design with the ability to consider the design space from a myriad of perspectives. Obviously, preferences are a major variable on building projects. Preference for structure vs. energy. vs first cost.
More time needs to be spend on analyzing absolute interpretations of the design space
Structural Cost ($)

20. Pareto Optimal Designs for Classroom MDO
Building Length vs. Energy Lifecycle Cost

21. Pareto Optimal Designs for Classroom MDO
Building Length vs. Structural Cost
Comment contrast between energy and structural objectives. One favors longer lengths, one favors shorter lengths. This type of objective conflict is representative of what design team encounter in practice.

22. Pareto Optimal Designs for Classroom MDO
Window to Wall Ratio vs. Energy Lifecycle Cost

23. Structural vs. Energy Performance
MDO Optimization of
Structural vs. Energy Performance
Optimal Designs with Varying Objectives
Let me re-emphasize that an MDO of this nature isn’t meant to tell what per se what the best design is, rather it’s meant to provide you a set of useful information to make a more informed decision on what the best design is to meet your particular project goals.
Importance: Objective functions weighted equally. If the weighting changes, the color coding will change. If energy performance is weighted higher, there will be more blue designs, though the Pareto Front will still remain the same.
Genetic algorithms are one of the few optimization strategies suited to multi-objective optimizations.

24. 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

25. Industry Collaborators:
Project Team Members
Research Team:
Forest Flager, Structural Engineer
Benjamin Welle, Mechanical Engineer
Prasun Bansal, Aerospace Engineer
Kranthi Kode, Structural Engineer
Victor Gane, Architect
Industry Collaborators:
Grant Soremekun, Phoenix Integration
Gehry Technologies
Supervised By:
Professor John Haymaker 25 25

26. Questions and Answers Benjamin Welle bwelle@stanford.edu
Grant Soremekun