Benjamin Welle Stanford University Grant Soremekun Phoenix Integration

1. Improving Multi-Disciplinary Building Design Geometry, Structural, Thermal, and Cost Trade-Off Studies using Process Integration and Design Optimization Benjamin WelleStanford UniversityGrant SoremekunPhoenix Integration

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

3. 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 Introduction to theCenter for Integrated Facility Engineering (CIFE)

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

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.

6. Multidisciplinary Optimization Process

7. Structural Steel Section Optimization Process

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 Orientation Length O column Window to Wall Ratio

10. Structural Model

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 Total Cost Cost Beam Sections Column Sections Girder Sections Building Length Max DC Ratio 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) 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) Length (mm) Orientation (deg) DoE- 1882 simulations Optimization-93 simulations

17. Optimization vs. DOE Results for Energy and Daylighting Performance

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 ($/ 30 years) Structural Cost ($)

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

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

23. MDO Optimization of Structural vs. Energy Performance Optimal Designs with Varying Objectives

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. 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

26. Questions and Answers Benjamin Welle bwelle@stanford.edu Grant Soremekun grant@phoenix-int.com