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Parametric Model

Structure Representation

Exterior Representation

Fenyes4/14/04

Parametric Geometry Changes

Parametric animation

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Challenges in Parametric Representation

• Must combine geometric, non-geometric data• Robust parameterization of points, curves, surfaces, solids

– Maintain robust associativity across parts and assemblies• Must be able to flexibly modify

– Relationships (independent, dependent) - constraint management– Geometry and parts (add / remove / modify)

• Manage coarse-to-fine strategy• Share parametrics with

– Other CAD systems - STEP/IGES are inadequate– CAE applications, MDO systems

Structures

Aerodynamics

Solar Load

OccupantDynamics

Ride & Handling

FuelEconomy

Crashworthiness

OtherAnalyses*

Representation

Fenyes4/14/04

Multidisciplinary Analysis and Optimization

Required Functionality

•Automatically run analysis and optimization from the shared representation

• Coordinate the analyses

•Dataflow

•Distributed, multiplatform

• Share design variables and responses

•Database is the common repository

• Improve the design

•Optimization, DOE, manual, …

Our Goal: Multidisciplinary analysis and optimization for early vehicle development – coarse balance and integration

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Key MAO Framework Concepts

– Modular system• Easily add, modify, replace, analysis tools, modify framework

– Flexible, parametric design representation and database– Discipline analysis tools tightly coupled to design representation

• Automated generation of inputs• Automated capture of results (responses, sensitivities, histories, etc)

– Analysis and design shell• Coordinate and execute discipline analyses• Iterative design improvement

Fenyes4/14/04

Commercial and University MAO Systems

• DOME – MIT• VADOR – Waterloo

• iSIGHT/FIPER – Engineous• Model Center - Phoenix Integration• AML/TIE - TecnoSoft• Optimus – LMS

And growing ….

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Approaches to Multidisciplinary Design and Improvement

• Flow down, Target Cascading

• Hierarchical methods – COO, coordination methods

• Multi Objective methods – Rankings, Pareto optimality

• Preference specification via combined objective

• “Natural” objective – Formulate a objective – e.g. profit – that combines the discipline interactions

in a natural way– This is the approach presented

Fenyes4/14/04

GM’s Framework for Architecture Design

DesignRepresentation(Unigraphics)

Database(MS Access)

MultidisciplinaryDesign

(iSIGHT)

StructuralOptimization(NASTRAN)

AerodynamicsInterior

Roominess(Excel)

Business

Summary ofResults(Excel)

Energy

(Custom)

(Custom)

(Custom)

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Discipline Analysis to Support Tradeoffs

• Focus on key disciplines• Provide consistent information to all discipline

analyses– Tight coupling to representation– Automated discipline modeling

• Balance analysis detail against design knowledge and required analysis speed Structures

Aerodynamics

Solar Load

OccupantDynamics

Ride & Handling

FuelEconomy

Crashworthiness

OtherAnalyses*

Representation

Fenyes4/14/04

Vehicle Design – Many DisciplinesExamples of key drivers and responses

Parameters: p1,..pnBOMBOP

WBL

FO RO

GC

FH RH

LA

LF

LB

LR

LD

LH

h

b

Database/ParametricArchitecture

Representation

Engineering

Business

Decision Process

Packaging

Structures (frequency)drivers: overall length, width, component massresponses: bending, torsion frequencyFuel Economydrivers: Cd, powertrain, 0-60 performance, massresponses: city & highway economyAerodynamics drivers: backlight angle, tumblehomeresponses: frontal area, Cd

Profitabilitydrivers: sales, components, assembly, physical plantresponses: component cost, investment, revenue

Packagingdrivers: b-pillar size,overall width, height, tumblehome responses: aero, fuel economy, structures, piece cost

Geometrydrivers: topology, layout, proportionsresponses: load paths, aesthetics

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Example Tools to Support Early Vehicle Design

• Analysis/Communication Framework– iSIGHT (Engineous)

• Database– MS/Access

• Geometry engine, parametric CAD & CAE model creation– Unigraphics

• Discipline Analysis– Structures: NASTRAN (MSC)– Fuel Economy: Proprietary (GM R&D)– Financial - Piece Cost: Technical Cost Modeling (J. Clark, MIT)– Manufacturing - Investment Cost - Proprietary (GM R&D)– Packaging: UG, Spreadsheet, – Aerodynamics: Proprietary (GM R&D)– Safety: Proprietary (GM R&D)– Business (marketing, revenue): Proprietary (GM R&D)

• Decision Engine– iSIGHT (Engineous) - Optimization, DOE

Simple, fast-running analyses – run in minutes!

Fenyes4/14/04

Database/Architecture Representation

• Single consistent representation for architecture and derivatives– Must comprehend data used by all disciplines

• Combine geometric and non-geometric; combine inputs and responses – UG parametric data– BOM data - available/allowable components– Marketing data– Responses - analysis results….