Optimization of vehicle protection components based on reliability

WU Kai; WANG Xianhui; ZHOU Yunbo; BI Zheng; LI Mingxing

doi:10.11883/bzycj-2020-0126

Volume 41 Issue 3

Mar. 2021

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Article Navigation > Explosion And Shock Waves > 2021 > 41(3): 035101

WU Kai, WANG Xianhui, ZHOU Yunbo, BI Zheng, LI Mingxing. Optimization of vehicle protection components based on reliability[J]. Explosion And Shock Waves, 2021, 41(3): 035101. doi: 10.11883/bzycj-2020-0126

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WU Kai, WANG Xianhui, ZHOU Yunbo, BI Zheng, LI Mingxing. Optimization of vehicle protection components based on reliability[J]. Explosion And Shock Waves, 2021, 41(3): 035101. doi: 10.11883/bzycj-2020-0126

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Optimization of vehicle protection components based on reliability

doi: 10.11883/bzycj-2020-0126

School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China

Received Date: 2020-04-30
Rev Recd Date: 2020-07-16

Available Online: 2021-03-05

Publish Date: 2021-03-10

Abstract

Abstract

In order to improve the bottom protection capability of military vehicles, a reliable optimization method was proposed for the design of vehicle protection components. In the traditional optimization process, if the uncertainty of design variables is not considered, the performance of design objectives will fluctuate or even fail. In this paper, multi-objective reliability optimization was introduced into the optimization of explosion protection. The surrogate model with the highest accuracy was constructed and chosen after the design variables were selected through experimental design and sensitivity analysis. The reliability optimization of the protection components was achieved by the multi-objective genetic algorithm. Eventually, through experiment and simulation verification, the optimized protective components met the requirements of protection and lightweight, and the reliability was improved, which can provide reference for the subsequent design and production of protective components.
- protection components,
- protection performance,
- lightweight,
- multi-objective reliable optimization

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References(17)

References

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