arametric Model Structure Representation GM RD Parametric Geometry Changes Parametric animation
8 4/14/04 Fenyes Parametric Model Structure Representation Exterior Representation 4/14/04 Fenyes Parametric Geometry Changes Parametric animation
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/GES are inadequate Multidisciplinary Analysis and Optimization Our Goal: Multidisciplinary analysis and optimization for early vehicle development- coarse balance and integration Required Functionality Automatically run analysis and optimization from the shared representation Distributed, multiplatform .Database is the common repository Optimization, DOE, manual
9 4/14/04 Fenyes 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 Aero dynamics Solar Load Occupant Dynamics Ride & Handling Fuel Economy Crash worthiness Other Analyses* Representation 4/14/04 Fenyes 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
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 GM RD Commercial and University MAO Systems DOME-MIT VADOR- Waterloo SIGHT/FIPER-Engineous Model Center-Phoenix Integrati AMUTIE- TecnoSoft Optimus-LMS
10 4/14/04 Fenyes 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 4/14/04 Fenyes Commercial and University MAO Systems • DOME – MIT • VADOR – Waterloo • iSIGHT/FIPER – Engineous • Model Center - Phoenix Integration • AML/TIE - TecnoSoft • Optimus – LMS And growing …
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 obiective Formulate a objective -e.g. profit-that combines the discipline interactions in a natural way This is the approach presented GM RD GMs Framework for Architecture Design Representation (Unigraphics Results Aerodynami Multidisciplinary (SIGHT) Business (Custom) Database (MS Access
11 4/14/04 Fenyes 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 4/14/04 Fenyes GM’s Framework for Architecture Design Design Representation (Unigraphics) Database (MS Access) Multidisciplinary Design (iSIGHT) Structural Optimization (NASTRAN) Aerodynamics Interior Roominess (Excel) Business Summary of Results (Excel) Energy (Custom) (Custom) (Custom)
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 equired analysis speed GM RD Vehicle Design-Many Disciplines Examples of key drivers and responses drivers: overall length, width, component mass responses:bending, torsion frequency s: city highway economy drivers: backlight angle, tumblehome nses frontal area, Ce Decision Process 三 drivers: sales, components, assembly, physical pla topology layout, proportions es: load paths, ae
12 4/14/04 Fenyes 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 Aero dynamics Solar Load Occupant Dynamics Ride & Handling Fuel Economy Crash worthiness Other Analyses* Representation 4/14/04 Fenyes Vehicle Design – Many Disciplines Examples of key drivers and responses Parameters: p1,..pn BOM BOP WB L FO RO GC FH RH LA LF LB LR LD LH h b Database/ Parametric Architecture Representation Engineering Business Decision Process Packaging Structures (frequency) drivers: overall length, width, component mass responses: bending, torsion frequency Fuel Economy drivers: Cd, powertrain, 0-60 performance, mass responses: city & highway economy Aerodynamics drivers: backlight angle, tumblehome responses: frontal area, Cd Profitability drivers: sales, components, assembly, physical plant responses: component cost, investment, revenue Packaging drivers: b-pillar size, overall width, height, tumblehome responses: aero, fuel economy, structures, piece cost Geometry drivers: topology, layout, proportions responses: load paths, aesthetics
Example Tools to Support Early Vehicle Design Analysis/Communication Framework Database MS/Access Geometry engine, parametric CAd CAE model creation Unigraphics Discipline Analysis Structures:NASTRAN(MSC) Economy. Proprietary(( Financial -Piece Cost Technical Cost Modeling (J Clark, MIT) Manufacturing- Investment Cost- Proprietary (GM R&D ackaging: 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 GM RD 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-availablelallowable components Marketing data Responses-analysis results
13 4/14/04 Fenyes 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! 4/14/04 Fenyes 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 …
