Sequential optimization method based on the adaptive Kriging model for the possibility-based design optimization

doi:10.1016/j.ast.2022.107939

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Sequential optimization method based on the adaptive Kriging model for the possibility-based design optimization

2022-11-01

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Ning Wei

Zhenzhou Lü *

DOI：10.1016/j.ast.2022.107939

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Abstract

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The possibility-based design optimization (PBDO) model under the fuzzy uncertainty can provide the optimal design parameters by taking a trade-off between the performance and security. In order to efficiently solve the PBDO model with implicit failure possibility constraints in engineering, a Sequential Optimization Method based on the Adaptive Kriging model (AK-SOM) is proposed in this paper. The AK-SOM firstly constructs the Kriging model of the performance function corresponding to each failure possibility constraint. The optimized design parameters of the PBDO based on the current Kriging model (It is called the current K-PBDO) can be obtained without any additional model evaluations. Then, the effective possibility constraints in the current K-PBDO can be identified by the effective constraint criterion proposed in this paper, and only the Kriging models of the effective constraints are updated. The sequential optimization process continues until both the relative error stopping criterion and the U -function stopping criterion are satisfied simultaneously. The effectiveness and superiority of the AK-SOM are verified by five examples, and the obtained experimental results indicate that the proposed AK-SOM has no restrictions on the expression of the performance function, and it can effectively improve the efficiency of solving the PBDO model while ensuring the accuracy. Additionally, the proposed method is employed in the PBDO of a GH4169 aero-engine turbine disk, and the optimal scheme of PBDO shows that the failure possibility of the disk is reduced and the maximum stress in the dangerous part is also greatly reduced.(c) 2022 Elsevier Masson SAS. All rights reserved.

Keywords:

Fuzzy uncertainty

Failure possibility

Kriging model

Possibility-based design optimization

Performance measure approach

Aero-engine turbine disk