A time-space Kriging-based sequential metamodeling approach for multi-objective crashworthiness optimization

F. L. Gao, Y. C. Bai*, C. Lin, I. Y. Kim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)
Plum Print visual indicator of research metrics
  • Citations
    • Citation Indexes: 25
  • Captures
    • Readers: 13
see details

Abstract

A time-space Kriging-based sequential metamodeling approach is proposed for multi-objective crashworthiness optimization (MOCO) in this paper. By defining the novel time-space design criteria, the constructed metamodels for the optimization objectives include the characteristic mechanical responses with respect to both the structural space domain and crash time domain, compared to standard metrics with the extremum of the time history of the entire structure. The adaptive addition of new samples is performed to gradually improve the approximation accuracy during the optimization with the guidance of an adaptive weighted sum method. The effectiveness of the proposed method is demonstrated by investigating a multi-cell thin-walled crashworthiness design problem. Finally, its effectiveness in practical engineering is validated by the crashworthiness design for a vehicle under full-overlap frontal crash loadcase.

Original languageEnglish
Pages (from-to)378-404
Number of pages27
JournalApplied Mathematical Modelling
Volume69
DOIs
Publication statusPublished - May 2019

Keywords

  • Adaptive weighted sum method
  • Intelligent sampling approach
  • Kriging model
  • Multi-objective crashworthiness optimization
  • Time-space metamodeling

Fingerprint

Dive into the research topics of 'A time-space Kriging-based sequential metamodeling approach for multi-objective crashworthiness optimization'. Together they form a unique fingerprint.

Cite this

Gao, F. L., Bai, Y. C., Lin, C., & Kim, I. Y. (2019). A time-space Kriging-based sequential metamodeling approach for multi-objective crashworthiness optimization. Applied Mathematical Modelling, 69, 378-404. https://doi.org/10.1016/j.apm.2018.12.011