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Automotive crashworthiness design using response surface‐based variable screening and optimization

Automotive crashworthiness design using response surface‐based variable screening and optimization Purpose – The purpose of this paper is to provide a methodology with which to perform variable screening and optimization in automotive crashworthiness design. Design/methodology/approach – The screening method is based on response surface methodology in which linear response surfaces are used to create approximations to the design response. The response surfaces are used to estimate the sensitivities of the responses with respect to the design variables while the variance is used to estimate the confidence interval of the regression coefficients. The sampling is based on the D ‐optimality criterion with over‐sampling to improve noise filtering and find the best estimate of the regression coefficients. The coefficients and their confidence intervals as determined using analysis of variance (ANOVA), are used to construct bar charts for the purpose of selecting the important variables. Findings – A known analytical function is first used to illustrate the effectiveness of screening. Using the finite element method (FEM), a complex vehicle occupant impact problem and a full vehicle multidisciplinary problem featuring frontal impact and torsional modal analysis of the vehicle body are modeled and parameterized. Two optimizations are conducted for each FEM example, one with the full variable set and one with a screened subset. An iterative, successive linear approximation method is used to achieve convergence. It is shown that, although significantly different final designs may be obtained, an appropriately selected subset of variables is effective while significantly reducing computational cost. Practical implications – The method illustrated provides a practical approach to the screening of variables in simulation‐based design optimization, especially in automotive crashworthiness applications with costly simulations. It is shown that the reduction of variables used in the optimization process significantly reduces the total cost of the optimization. Originality/value – Although variable screening has been used in other disciplines, the use of response surfaces to determine the variable screening information is novel in the crashworthiness field. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations: International Journal for Computer-Aided Engineering and Software Emerald Publishing

Automotive crashworthiness design using response surface‐based variable screening and optimization

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References (31)

Publisher
Emerald Publishing
Copyright
Copyright © 2005 Emerald Group Publishing Limited. All rights reserved.
ISSN
0264-4401
DOI
10.1108/02644400510572406
Publisher site
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Abstract

Purpose – The purpose of this paper is to provide a methodology with which to perform variable screening and optimization in automotive crashworthiness design. Design/methodology/approach – The screening method is based on response surface methodology in which linear response surfaces are used to create approximations to the design response. The response surfaces are used to estimate the sensitivities of the responses with respect to the design variables while the variance is used to estimate the confidence interval of the regression coefficients. The sampling is based on the D ‐optimality criterion with over‐sampling to improve noise filtering and find the best estimate of the regression coefficients. The coefficients and their confidence intervals as determined using analysis of variance (ANOVA), are used to construct bar charts for the purpose of selecting the important variables. Findings – A known analytical function is first used to illustrate the effectiveness of screening. Using the finite element method (FEM), a complex vehicle occupant impact problem and a full vehicle multidisciplinary problem featuring frontal impact and torsional modal analysis of the vehicle body are modeled and parameterized. Two optimizations are conducted for each FEM example, one with the full variable set and one with a screened subset. An iterative, successive linear approximation method is used to achieve convergence. It is shown that, although significantly different final designs may be obtained, an appropriately selected subset of variables is effective while significantly reducing computational cost. Practical implications – The method illustrated provides a practical approach to the screening of variables in simulation‐based design optimization, especially in automotive crashworthiness applications with costly simulations. It is shown that the reduction of variables used in the optimization process significantly reduces the total cost of the optimization. Originality/value – Although variable screening has been used in other disciplines, the use of response surfaces to determine the variable screening information is novel in the crashworthiness field.

Journal

Engineering Computations: International Journal for Computer-Aided Engineering and Software – Emerald Publishing

Published: Jan 1, 2005

Keywords: Statistical methods of analysis; Design for assembly; Road vehicle engineering; Optimization techniques

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