Lightweight design of vehicle parameters under crashworthiness using conservative surrogates

Ping Zhu*, Feng Pan, Wei Chen, Felipe A.C. Viana

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


Lightweight design of vehicle structures parameters under crashworthiness is hard to accomplish because of the complexity of simulations required in crash analysis. To reduce the computation demand, surrogates (metamodels) are often used in place of the actual simulation models in design optimization to fit the mathematical relationship between design variables and responses. Each optimization cycle consists of analyzing a number of designs, fitting surrogates for the responses, performing optimization based on the surrogates for a candidate optimum, and finally analyzing that candidate. Even so, optimization using crash analysis codes is often allowed to run only for very few cycles. While traditional surrogate is unbiased which means prediction values at half region is lower than actual values, predicted candidate optimum usually is not feasible after validating by crash simulation. This paper explores the use of conservative surrogates for safe estimations of crashworthiness responses (e.g., intrusion and peak acceleration). We use safety margins to conservatively compensate for fitting errors associated with surrogates. Conservative surrogates minimize the risks associated with underestimation of the responses, which helps push optimization toward the feasible region of the design. We also propose an approach for sequential relaxation of the safety margins allowing for further weight minimization. The approach was tested on the lightweight design of a vehicle subjected to the full-overlap frontal crash. We compare this approach with the traditional use of unbiased surrogates (that is, without adding any safety margin). We find that conservative surrogates successfully drive optimization toward the feasible region of a design space, while that is not always the case with unbiased surrogates.

Original languageEnglish (US)
Pages (from-to)280-289
Number of pages10
JournalComputers in Industry
Issue number3
StatePublished - Apr 2013


  • Conservative surrogates
  • Design for crashworthiness
  • Design of vehicle structures
  • Surrogate-based optimization

ASJC Scopus subject areas

  • General Computer Science
  • General Engineering


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