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Risk-based design optimization under hybrid uncertainties

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Abstract

The rapidly changing requirements of engineering optimization problems require unprecedented levels of compatibility to integrate diverse uncertainty information to search optimum among design region. The sophisticated optimization methods tackling uncertainty involve reliability-based design optimization and robust design optimization. In this paper, a novel alternative approach called risk-based design optimization (RiDO) has been proposed to counterpoise design results and costs under hybrid uncertainties. In this approach, the conditional value at risk (CVaR) is adopted for quantification of the hybrid uncertainties. Then, a CVaR estimation method based on Monte Carlo simulation (MCS) scenario generation approach is derived to measure the risk levels of the objective and constraint functions. The RiDO under hybrid uncertainties is established and leveraged to determine the optimal scheme which satisfies the risk requirement. Three examples with different calculation complexity are provided to verify the developed approach.

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Acknowledgements

This work was supported by the National Key R&D Program of China [Grant number 2019YFB1706103], the National Natural Science Foundation of China [Grant number 51975075], the Chongqing Technology Innovation and application development special general project [Grant number cstc2020jscx-msxm1193].

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Authors and Affiliations

  1. College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China

    Wei Li & Congbo Li

  2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

    Congbo Li

  3. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Liang Gao & Mi Xiao

  4. No. 55 Research Institute of China North Industries Group Corporation, Changchun, 130012, China

    Mi Xiao

  5. Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China

    Mi Xiao

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  1. Wei Li
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  2. Congbo Li
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  3. Liang Gao
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  4. Mi Xiao
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Correspondence to Congbo Li.

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Li, W., Li, C., Gao, L. et al. Risk-based design optimization under hybrid uncertainties. Engineering with Computers 38, 2037–2049 (2022). https://doi.org/10.1007/s00366-020-01196-4

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