Numerical simulations of multiple vehicle crashes and multidisciplinary crashworthiness optimization

Hongbing Fang, K. Solanki, M. F. Horstemeyer

Research output: Contribution to journalArticlepeer-review

64 Scopus citations


In this study, a full-scale finite element vehicle model of a 1996 Dodge Neon is used in simulating two types of vehicle crashes, offset-frontal and side impacts. Based on an analysis of the vehicle's internal energy absorption under both impacts, twenty-one components are selected and represented by thirteen design variables for the multidisciplinary optimization including the weight, intrusion distance, and energy absorptions. The second-order polynomials are used in creating the metamodels for the objective and constraint functions. The optimization results show that the weight can be significantly reduced while decreasing the intrusion distance and keeping the original level of energy absorption. With the successfully implemented optimization scheme, a set of non-dominated (tradeoff) solutions is obtained and the final design can be selected based on the designer's preference. A simulation of 100 ms offset-frontal impact using LS-DYNA MPP v970 takes approximately 17 hours with 36 processors on an IBM Linux Cluster with Intel Pentium III 1.266 GHz processors and 607.5 GB RAM. A simulation of 100 ms side impact takes approximately 29 hours with the same condition as that of the offset-frontal simulation.

Original languageEnglish (US)
Pages (from-to)161-172
Number of pages12
JournalInternational Journal of Crashworthiness
Issue number2
StatePublished - Jan 1 2005
Externally publishedYes


  • Crashworthiness
  • Finite element analysis
  • Impact
  • Metamodeling
  • Multidisciplinary
  • Non-dominated
  • Optimization
  • Response surface methodology

ASJC Scopus subject areas

  • Transportation
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Numerical simulations of multiple vehicle crashes and multidisciplinary crashworthiness optimization'. Together they form a unique fingerprint.

Cite this