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Response Surface Methodology (RSM) and Design of Experiments (DOE)Applied to Racecar Vehicle Dynamics Simulation and Development
Lynn BishopMotorsports Technical Specialist
Mechanical Dynamics Inc.2301 Commonwealth Blvd.
Ann Arbor , MI 48105Phone: +1 (734) 887 2505, Fax: +1 (734) 994 6418, E-Mail: [email protected]
Vehicle dynamics simulation and testing have historically proven able to produce large amountsof static information (data) that is very powerful and useful in the vehicle development process.However, this data is often difficult to access in order to answer specific questions about theeffects of vehicle design parameter changes on system level, vehicle dynamics and handlingresponse. This is complicated by the fact that the information is needed in very short periods oftime by a larger engineering organization outside of the simulation and testing experts.
A process has been developed that combines two disciplines together with powerful and accuratecomputer vehicle handling simulation technology to create live information (data) that can bepublished through standard spreadsheet technology and utilized by an enterprise-wideengineering organization. This process has been proven in the most time critical environment ofNASCAR racing by race engineers with little or no vehicle dynamics simulation expertise.
This paper describes the approach of applying Response Surface Methodology (RSM) withDesign of Experiments (DOE) to assist race engineers in the optimization of on-track vehicle
dynamics performance.
Through the use of the ADAMS/RaceCar mechanical system simulation environment, engineersperform Design of Experiments (DOE) on full vehicle handling models to determine vehicleresponse sensitivity and effects. Screening DOE's are performed to eliminate factors insignificantto vehicle response and higher order factors are investigated using RSM methodology to optimizevehicle behavior. Once the DOE is performed and Response Surface Equations are generated,the response equations are published through a standard spreadsheet environment wherevehicle parameter influences and interactions can be quickly evaluated, trackside, in real-time,
with the accuracy of the complex full vehicle simulation model.
A racing application example will be part of this paper. Pending the approval of the collaboratingrace team, the paper will discuss specific examples and success of this technique relative to anactual vehicle and driver participating in the Winston Cup NASCAR Championship. If approval isnot obtained, the paper will use a generic NASCAR vehicle model to demonstrate the processand approach outlined in the paper. The racing example is used in this paper because it is anaccelerated version of an OEM vehicle development process with the race engineer serving as a
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Response Surface Methodology (RSM)Response Surface Methodology (RSM)and Design of Experiments (DOE)and Design of Experiments (DOE)
Applied to Racecar Vehicle DynamicsApplied to Racecar Vehicle Dynamics
Simulation and DevelopmentSimulation and Development
Lynn Bishop - MDI
David Fredriksson - MDI
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AbstractAbstract
Vehicle dynamics simulation and testing have historically proven
able to produce large amounts of static information (data) that is
very powerful and useful in the vehicle development process.
However, this data is often difficult to access in order to answer
specific questions about the effects of vehicle design parameter
changes on system level, vehicle dynamics and handling response.
This is complicated by the fact that the information is needed in very
short periods of time by a larger engineering organization outside of
the simulation and testing experts
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7/31/2019 Response Surface Methodology (Rsm) and Design of Experiments (Doe) Applied to Racecar Vehicle Dynamics Simulation and Development
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A process has been developed that combines two disciplines
together with powerful and accurate computer vehicle handling
simulation technology to create live information (data) that can be
published through standard spreadsheet technology and utilized by
an enterprise-wide engineering organization. This process has beenproven in the most time critical environment of NASCAR, CART and
Formula One racing by race engineers with little or no vehicle
dynamics simulation expertise
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DOE ProcessDOE Process
Define Factors(control, noise, discrete, continuous)
Define Responses Define Event
Screening DOEDetermine Significant Factors
Design DOEQuadratic, D-Optimal
Fit Model(Residuals, correlation coeff.)
Interpret Model(Response Surface Equations)
Optimize Model
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Factor GenerationFactor Generation
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Event/Response GenerationEvent/Response Generation
Multi-Event DOE
SVC - Static Vehicle Characteristics
Roll Center Heights
Ride Frequencies
Percent Anti-Dive/Lift/Squat
Constant Speed Cornering with Banking
Understeer Gradients
Steering Sensitivity
Roll-Couple Distribution
Front/Rear Cornering Compliance
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DOE Model & DesignDOE Model & Design
Quadratic Model for non-linear response equations
D-Optimal Design
Discrete and Continuous Factors
Multiple Factor Levels
500 runs for embedded SVC simulation in CSC event
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Interpret Response ModelsInterpret Response Models
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Interpret Response ModelsInterpret Response Models
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Interpret Response ModelsInterpret Response Models
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Interpret Response ModelsInterpret Response Models
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Response Equation SpreadsheetResponse Equation Spreadsheet
Export response surface equations to Excel
32,000 lines of quadratic model terms Link multi-event response equations to master setup
Excel Solver functions to Optimize
Session Trend Analysis and Review
VBA Pareto generation
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Excel Setup PageExcel Setup Page
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Through the use of the ADAMS/RaceCar mechanical system
simulation environment, engineers can perform Design ofExperiments (DOE) on full vehicle handling models to determine
vehicle response sensitivity and effects. Screening DOE's are
performed to eliminate factors insignificant to vehicle response and
higher order factors are investigated using RSM methodology to
optimize vehicle behavior. Once the DOE is performed and Response
Surface Equations are generated, the response equations are
published through a standard spreadsheet environment wherevehicle parameter influences and interactions can be quickly
evaluated, trackside, in real-time, with the accuracy of the complex
full vehicle simulation model
ConclusionsConclusions