Packaging Geometric measures to check requirements Occupant model Inputs Shoulder room, knee room etc Roominess measure GM RD Trunk∨oume
14 4/14/04 Fenyes Packaging • Geometric measures to check requirements – Occupant model – Engine compartment packaging • Inputs – Occupant position - H-point, etc – Engine/transmission selection and position • Outputs – Shoulder room, knee room, etc – Roominess measure – Packaging feasibility 4/14/04 Fenyes Trunk Volume
Linear structures FE model associatively linked to UG parametric model - UG Scenario FE model is built once- automatically updated as geometry changes Beams/Springs/Shells/Masses Locations and properties associated to geometry (M, K. b, h, t, etc) Inputs Parametric geometry spe Component masses Initial structures model Outputs Vehicle mass Structural modes GM RD Tightly Coupling Representation to Analyses Analysis models remain synchronized with representation xample- CAD to CAE/structures: UG Modeling and UG Scenario Automatically update hybrid beam/spring/shell/mass model from CAD model Automatically update from CAD to CAE
15 4/14/04 Fenyes Linear Structures • FE model associatively linked to UG parametric model - UG Scenario – FE model is built once - automatically updated as geometry changes – Beams/Springs/Shells/Masses • Locations and properties associated to geometry (M, K, b, h, t, etc) • Inputs – Parametric geometry specification – Component masses – Initial structures model • Outputs – Vehicle mass – Structural modes 4/14/04 Fenyes Tightly Coupling Representation to Analyses • Analysis models remain synchronized with representation • Example – CAD to CAE/structures: UG Modeling and UG Scenario – Automatically update hybrid beam/spring/shell/mass model from CAD model Automatically update from CAD to CAE
Aerod Exterior aero surface linked to underlying structure CAD representation Aero drag is approximated Frontal area calculated in UG puts Outputs Frontal area GM RD Ener mpute fuel economy, acceleration based on spreadsheet model Depends on structures, marketing disciplines Frontal area Performance requirements Outputs Fuel economy (city, highway, combined)
16 4/14/04 Fenyes Aerodynamics • Exterior aero surface linked to underlying structure CAD representation – Aero drag is approximated – Frontal area calculated in UG • Inputs – Exterior shape • Outputs – Aero drag – Frontal area 4/14/04 Fenyes Energy • Compute fuel economy, acceleration based on spreadsheet model – Depends on structures, aero, marketing disciplines • Inputs – Cd – Drag – Frontal area – Powertrain, tires, etc. – Performance requirements • Outputs – Fuel economy • (city, highway, combined) – Acceleration
Business Estimate sales revenue costs Link performance to customer value Link customer value to sales/revenue Competitors Performance Forming and assembly technology Equipment, tooling costs BOM/Parts-size. mass. material Outputs Sales Revenue Net income, prof RD One Approach to Link Market Demand, Value, and Performance S-Model(Ref: H E. Cook, 1997) Customer-Perceived value Product Specifications Drive Drives market Demand Customer-Perceived value Increased Increased Improved Product spec Baseline value (e. g 0-60 Time, Turning Circle)
17 4/14/04 Fenyes Business • Estimate sales, revenue, costs – Link performance to customer value – Link customer value to sales/revenue • Inputs – Competitors – Performance – Forming and assembly technology – Equipment, tooling costs – BOM/Parts - size, mass, material • Outputs – Sales – Revenue – Cost – Net income, profit 4/14/04 Fenyes One Approach to Link Market Demand, Value, and Performance: S-Model (Ref: H. E. Cook, 1997) Customer-Perceived Value Drives Market Demand Price Sales Volume Baseline Value Competitive Advantage Increased Value Product Specifications Drive Customer-Perceived Value Product Value Product Spec Improved Function Increased Value (e.g. 0-60 Time, Turning Circle)
Example Problem Dimensional Flexibility for Vehicle Architecture atisfying performance requirements try, aero, fuel economy. packaging, business Body style Powertrain and components Nine high level, architectural design variables Vehicle width at rocker Front and rear track width+P Front and rear overhang Front and rear axe location- vertical and horizontal *N* Perturbed representation Computed change in Net Income(natural objective Generated sensitivities and optimized GM RD Example-Dimensional Flexibility Data Flow and Analysis esults (NASTRAN) (Excel) Aerodynamics Ene (SIGHT) Business
18 4/14/04 Fenyes Example Problem - Dimensional Flexibility for Vehicle Architecture • Maximize net income while satisfying performance requirements – Discipline analyses: geometry, aero, fuel economy. packaging, business – Discipline sub-optimization: structures, business • Specific vehicle configuration: – Body style – Powertrain and components • Nine high level, architectural design variables – Vehicle width at rocker – Front and rear track width – Front and rear overhang – Front and rear axle location – vertical and horizontal • Performed automated discipline analyses: – Perturbed representation – Generated analysis models – Exchanged data through database – Computed change in Net Income (“natural” objective) • Generated sensitivities and optimized 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 4/14/04 Fenyes Shoulder Room Design Representation (Unigraphics) Database (MS Access) Multidisciplinary Design (iSIGHT) Structural Optimization (NASTRAN) Aerodynamics Interior Roominess (Excel) Business Summary of Results (Excel) Vehicle Geometry Body Structure Mass Frontal Area, Cd Value of Roominess Optimized Gauges And Sections Architecture Configuration & Parameterization Net Income Exterior Width Gauges, Areas, Section Sizes Fuel Economy, Performance Energy Overall Width Example – Dimensional Flexibility Data Flow and Analysis Vehicle Width at Rocker
Framework Illustration: Analysis Outputs Sensitivity of Net Income Relative sensitivities for other o Rocker Location Architecture Parameters Rocco. Yo RR Track Front and Rear Axle Position -L H Front and Rear Overhang Front and Rear Track GM RD Iteration History -Dimensional Flexibility Sequential discipline analyses, sub-optimizations for structures and business 10 iterations for gradients, 6 iterations for convergence 11000% 10800% 10600% 10400% 10000% 9600%
19 4/14/04 Fenyes Framework Illustration: Analysis Outputs Sensitivity of Net Income to Rocker Location Relative Sensitivities for Other Architecture Parameters • Front and Rear Axle Position - L, H • Front and Rear Overhang • Front and Rear Track -8% -6% -4% -2% 0% 2% 4% 6% 8% -4% -2% 0% 2% 4% Change in Rocker Center Y Coordinate Change in Net Income -15% -10% -5% 0% 5% 10% 15% Rocker Ctr. Y Coord. - 5% + 5% RR Axle X Coord. + 5% - 5% FRT Overhang + 5% - 5% RR Overhang - 5% / + 5% 0% FRT Axle X Coord. + 5% - 5% RR Track - 5% + 5% RR Axle Z Coord. + 5% - 5% FRT Axle Z Coord. - 5% + 5% 4/14/04 Fenyes 96.00% 98.00% 100.00% 102.00% 104.00% 106.00% 108.00% 110.00% 112.00% 0 2 4 6 8 10 12 14 16 18 RunCounter Net Income % Series1 Iteration History - Dimensional Flexibility • Sequential discipline analyses, sub-optimizations for structures and business • 10 iterations for gradients, 6 iterations for convergence
Reporting Analysis and Optimization Results iew Database through Web Interface Iteration history may be reviewed GM RD Other Automotive MAO Applications Crash, linear analysis, robust design Aero and acoustics
20 4/14/04 Fenyes Reporting Analysis and Optimization Results: View Database through Web Interface • Iteration history may be reviewed 4/14/04 Fenyes Other Automotive MAO Applications • Crash, linear analysis, robust design • Aero and acoustics
General MAO Challenges Problem Formulation MDO formulation-"natural" objective, multi-objective, preference modeling, etc Consistent parametric representation Consistent information shared by all disciplines non-geometric data (BOM, configuration, material properties, .. analysis resuits history, gradients, approximations Discipline analysis to support tradeoffs Analysis tightly coupled to representation Key disciplines are support Balance analysis detail against design knowledge Support design and analysis strategies through quality, commercial software Design approaches OE, optimization, decision support, Pareto frontiers, GM RD Challenges to Widespread MAO Application Educational challenges Educating corporations on optimization, then MAO Educating the next generation of users and teachers Corporate cultural challenges Organizing work for MAO Software challenges to use. better GUl apable to handle distributed computation with broad range of es, database interaction, interactive data visualization, repo
21 4/14/04 Fenyes General MAO Challenges • Problem Formulation – Determine key drivers and responses – MDO formulation – “natural” objective, multi-objective, preference modeling, etc. • Consistent parametric representation – Consistent information shared by all disciplines • geometric data • non-geometric data (BOM, configuration, material properties, …) • analysis results history, gradients, approximations • Discipline analysis to support tradeoffs – Analysis tightly coupled to representation – Key disciplines are supported – Balance analysis detail against design knowledge • Support design and analysis strategies through quality, commercial software – Approximation strategies – Design approaches • DOE, optimization, decision support, Pareto frontiers, 4/14/04 Fenyes Challenges to Widespread MAO Application • Educational challenges – Educating corporations on optimization, then MAO – Educating the next generation of users and teachers • Corporate cultural challenges – Organizing work for MAO • Software challenges – Simpler to use, better GUI – More capable to handle distributed computation with broad range of analyses, database interaction, interactive data visualization, report generation, …
MAO Software Challenges Data Storage, Management, Communication Consistent information requires database storage and communication with disciplines Standardswillberequiredtodrivethis(e.g.http://www.omg.orgl Vehicle and Results databases used for- Storing analysis results, history, gradients, approximations Communication with commercial systems(ODBC, SQL)a must Full support for user defined design strategies, algorithms Approximation strategies DOE, neural net, e surfaces. etc Use gradients and Hessians as available Simplify use of proprietary or other algorithms within commercial frameworks Data Transformations Units, coordinate systems Geometric relationship Variable relationshi Parametric, DV linking GM RD Challenges in Automotive MA Level of detail and complexity When and how should MAo be used in the vehicle and component design Inclusion of more disciplines that have a strong linkage to the vehicle or component design problem Vehicle design Safety, reliability, aesthetics, vehicle dynamics
22 4/14/04 Fenyes MAO Software Challenges • Data Storage, Management, Communication – Consistent information requires database storage and communication with disciplines – Standards will be required to drive this (e.g. http://www.omg.org/ ) • Vehicle and Results databases used for: – Model building – Storing analysis results, history, gradients, approximations – Communication with commercial systems (ODBC, SQL) a must • Full support for user defined design strategies, algorithms – Approximation strategies • DOE, neural net, response surfaces, etc. • Use gradients and Hessians as available – Simplify use of proprietary or other algorithms within commercial frameworks • Data Transformations – Units, coordinate systems – Geometric relationships – Variable relationships • Parametric, DV linking 4/14/04 Fenyes Challenges in Automotive MAO • Level of detail and complexity – When and how should MAO be used in the vehicle and component design processes? • Inclusion of more disciplines that have a strong linkage to the vehicle or component design problem – Vehicle design • Safety, reliability, aesthetics, vehicle dynamics, …
Summary We have developed an MAO system for coarse balance and integration during he early vehicle development process which Enables use of math based decision tools for vehicle and architecture design Facilitates multidisciplinary analysis with consistent data Extends math based beyond engineering to manufacturing and business Provides consistent sharing of representation and analysis data through database Simplifies storage and access of analysis results through database and GUI Quantifies discipline consequences of design and architectural changes Much work remains Designing great GM Cars and Trucks ◎ 为—
23 4/14/04 Fenyes Summary We have developed an MAO system for coarse balance and integration during the early vehicle development process which – Enables use of math based decision tools for vehicle and architecture design – Facilitates multidisciplinary analysis with consistent data • Extends math based beyond engineering to manufacturing and business • Provides consistent sharing of representation and analysis data through database • Simplifies storage and access of analysis results through database and GUI – Quantifies discipline consequences of design and architectural changes Much work remains ! 4/14/04 Fenyes Designing Great GM Cars and Trucks !
