Benjamin Welle Stanford University Grant Soremekun Phoenix Integration

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

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)

Conceptual Phase – Model Based Design

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

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


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


Run-time: 0

Execute Main Input Macro File

Tool: EPMarcro


Run-time: 0

Execute Variables Input Macro File

Tool: EPMarcro


Run-time: 0

Create EnergyPlus Input File

Tool: EPMarcro


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

L

O

steel frame

column

beam

girder

Window to Wall Ratio

Orientation

DESIGNPROCESSOPTIMIZATIONINTEGRATION TRADES SIMULATION VISUALIZATION

www.phoenix-int.com© Copyright 2007 Phoenix Integration, Inc. All Rights Reserved



• 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





Phoenix Value Proposition

1. Improve your decision making capability Automate runs of existing

tools to quickly gather information

Apply intelligent algorithms to identify the best solutions

2. Manage design data Knowledge Capture, Search

and Reuse Collaboration and

Synchronization Data Pedigree/Traceability





AoA: Analysis of AlternativesCAIV: Cost As an Independent VariableSoS: Systems of SystemsDFSS: Design for Six SigmaMDO: Multi-Disciplinary Optimization

ModelCenter

Parameter Sweeps, DOE, Monte Carlo, Optimization, Add your own…

Run Matrix

Multi-Disciplinary Trade Studies

Process Results

Energy 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

http://clipart.usscouts.org/library/GSUSA_Clipart/General/compass.gif

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)

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

MDO Optimization of Structural vs. Energy Performance

Optimal Designs with Varying Objectives





Forest FlagerGrant Soremekun

Stadium Roof Structural Optimization

Studies

Stadium Roof Structural Optimization

Studies

x y

z

DESIGN LAYER

Scale: 1:727.8





ModelCenter

Web Browser

Compute ClusterMulti-processor Server

Spare Computers

Analysis LibraryAnalysis Execution

Trade Study ArchiveSoon: Bill of Analysis

Accelerating Design Studies





Preliminary CenterLink Results

• Load balance Energy Plus Trade Study90 Energy Plus Analyses

Single Machine – Run Time: 50 minutes

CenterLink– 4 Machines (Quad 4 processors)– Run Time: 7 minutes





Stanford Cluster

• 16 Blade Compute Cluster

• Dual Core / Quad 4 (128 nodes)

• Installed Jan / Feb - 09

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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Benjamin Welle Stanford University Grant Soremekun Phoenix Integration

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