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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. An academic research center within the Civil and Environmental

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Benjamin Welle Stanford University Grant Soremekun Phoenix Integration

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  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. 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)

  3. Conceptual Phase – Model Based Design • 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.

  4. Multidisciplinary Optimization Process

  5. Energy and Daylighting Optimization Process

  6. 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 Orientation L O beam steel frame girder Window to Wall Ratio column

  7. 13 year history Provide process integration and design optimization (PIDO) software and services to customers in aerospace, defense, civil, oil and gas, financial Evolved out of a research program at Virginia Tech Office locations Philadelphia, PA (Corporate) Blacksburg, VA (R&D) California (Sales) North East (Sales) World-wide sales in North America, Europe, and Asia Phoenix Integration

  8. Phoenix Value Proposition Improve your decision making capability Automate runs of existing tools to quickly gather information Apply intelligent algorithms to identify the best solutions Manage design data Knowledge Capture, Search and Reuse Collaboration and Synchronization Data Pedigree/Traceability

  9. Multi-Disciplinary Trade Studies Run Matrix Parameter Sweeps, DOE, Monte Carlo, Optimization, Add your own… Process Results ModelCenter AoA: Analysis of Alternatives CAIV: Cost As an Independent Variable SoS: Systems of Systems DFSS: Design for Six Sigma MDO: Multi-Disciplinary Optimization

  10. Energy Model

  11. 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)

  12. 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

  13. 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

  14. 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

  15. Pareto Optimal Designs for Classroom MDO Structural First Cost vs. Energy Lifecycle Cost Structural Cost vs. Energy Cost with Pareto Front Lifecycle Energy Cost ($/ 30 years) Structural Cost ($)

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

  17. Pareto Optimal Designs for Classroom MDO Building Length vs. Structural Cost

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

  19. Stadium Roof Structural Optimization Studies Forest FlagerGrant Soremekun

  20. Accelerating Design Studies ModelCenter Compute Cluster Multi-processor Server Spare Computers Analysis Library Analysis Execution Trade Study Archive Soon: Bill of Analysis Web Browser

  21. Preliminary CenterLink Results • Load balance Energy Plus Trade Study • 90 Energy Plus Analyses • Single Machine • Run Time: 50 minutes • CenterLink • 4 Machines (Quad 4 processors) • Run Time: 7 minutes

  22. Stanford Cluster • 16 Blade Compute Cluster • Dual Core / Quad 4 (128 nodes) • Installed Jan / Feb - 09

  23. Current and Future Work General: Make software wrappers more robust / flexible More complex building types, Case Studies (ARUP, SOM, Gensler, Burro Happold, AKT) Topology changes Parallel computing to reduce trade study run times Energy: Variable constructions, locations, HVAC equipment, internal loads, schedules, etc. Developing a scriptwrapper to handle any DP geometry (or from any other BIM tool) and convert it to EP syntax (no macros) Daylighting: Developing a Radiance wrapper with support from Zack Rogers Combine SPOT and DAYSIM engines to calculate dynamic daylighting metrics Automatic sensor grid generation, using construction data from EP Each room will become a separate Radiance run, and an include file will be generated for EP Developing methodology using translucent windows to reduce simulation time CFD: Developing a Fluent wrapper with auto-meshing using Gambit for space temperature stratification, air velocity distribution, and mean radiant temperature Construction properties and surface temperatures taken from EP Variable diffuser locations

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