Crashworthiness design of vehicle by using multiobjective robust optimization

  • Authors:

  • Guangyong Sun;Guangyao Li;Shiwei Zhou;Hongzhou Li;Shujuan Hou;Qing Li

  • Affiliations:

  • The State Key Laboratory of Advanced Technology for Vehicle Design & Manufacture, Hunan University, Changsha, China 410082 and School of Aerospace, Mechanical and Mechatronic Engineering, The Univ ...;The State Key Laboratory of Advanced Technology for Vehicle Design & Manufacture, Hunan University, Changsha, China 410082;School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, Australia 2006;The State Key Laboratory of Advanced Technology for Vehicle Design & Manufacture, Hunan University, Changsha, China 410082;The State Key Laboratory of Advanced Technology for Vehicle Design & Manufacture, Hunan University, Changsha, China 410082;School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, Australia 2006

  • Venue:
  • Structural and Multidisciplinary Optimization
  • Year:
  • 2011

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Abstract

Although deterministic optimization has to a considerable extent been successfully applied in various crashworthiness designs to improve passenger safety and reduce vehicle cost, the design could become less meaningful or even unacceptable when considering the perturbations of design variables and noises of system parameters. To overcome this drawback, we present a multiobjective robust optimization methodology to address the effects of parametric uncertainties on multiple crashworthiness criteria, where several different sigma criteria are adopted to measure the variations. As an example, a full front impact of vehicle is considered with increase in energy absorption and reduction of structural weight as the design objectives, and peak deceleration as the constraint. A multiobjective particle swarm optimization is applied to generate robust Pareto solution, which no longer requires formulating a single cost function by using weighting factors or other means. From the example, a clear compromise between the Pareto deterministic and robust designs can be observed. The results demonstrate the advantages of using multiobjective robust optimization, with not only the increase in the energy absorption and decrease in structural weight from a baseline design, but also a significant improvement in the robustness of optimum.