parametric MDO lecture MIT...
Transcript of parametric MDO lecture MIT...
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Parametric Model
Structure Representation
Exterior Representation
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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
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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
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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
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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
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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!
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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….