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Multidisciplinary robust design optimization considering parameter and metamodeling uncertainties

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Abstract

Multidisciplinary robust design optimization (MRDO) is a useful tool to improve the stability of the performance of complex engineering systems involving uncertainty. However, the majority of existing MRDO studies only consider the parameter uncertainty. Metamodeling uncertainty, defined as the discrepancy between the computer model and metamodel at un-sampled locations, is often overlooked in MRDO. To solve the multidisciplinary problems under parameter and metamodeling uncertainties, this paper proposes a new framework called MRDO under parameter and metamodeling uncertainties (MRDO-UPM). The collaboration model is used to select the samples which satisfy coupled state equations. The selected samples are employed to construct the Gaussian process metamodels of the objective, constraint, and multidisciplinary coupled functions. Monte Carlo simulation is adopted to quantify the compound impact of parameter and metamodeling uncertainties. The MRDO-UPM framework is employed to explore the optimum. The proposed framework is verified through a numerical example, and the design of a speed reducer and a liquid cooling battery thermal management system.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant Numbers 51675196 and 51721092], Natural Science Foundation of Hubei Province [Grant Number 2019CFA059], the Program for HUST Academic Frontier Youth Team [Grant Number 2017QYTD04] and the Program for HUST Graduate Innovation and Entrepreneurship Fund [Grant Number 2019YGSCXCY037].

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Authors and Affiliations

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

    Wei Li, Liang Gao, Akhil Garg & Mi Xiao

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

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Appendix

Appendix

See Tables

Table 8 Fifty samples for Test1
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Table 9 Robust optimization for parameter uncertainty with 10 samples
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Table 10 Robust optimization for parameter and metamodeling uncertainties with 10 samples
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Table 11 Robust optimization for parameter uncertainty with 30 samples
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Table 12 Robust optimization for parameter and metamodeling uncertainties with 30 samples
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Table 13 Forty samples for speed reducer problem
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Table 14 LHS method for generation of 50 data samples
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Li, W., Gao, L., Garg, A. et al. Multidisciplinary robust design optimization considering parameter and metamodeling uncertainties. Engineering with Computers 38, 191–208 (2022). https://doi.org/10.1007/s00366-020-01046-3

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