Parameterized coefficient fine-tuning-based polynomial chaos expansion method for sphere-biconic reentry vehicle reliability analysis and design

Xiaohu Zheng, Wen Yao, Xiaoya Zhang, Weiqi Qian and Hairui Zhang

Reliability Engineering and System Safety, 2023, vol. 240, issue C

Abstract: Polynomial chaos expansion (PCE) is an efficient surrogate modeling method that can be used for reliability analysis. However, the existing methods generally require sufficient labeled data to build a high-precision surrogate model and cannot use much-unlabeled data. Thus, this paper proposes a parameterized coefficient fine-tuning-based PCE (PCFT-PCE) method. Based on limited labeled data, the PCFT-PCE method first uses the traditional PCE method to initialize the parameterized expansion coefficients of a single-layer neural network. Then based on abundant unlabeled data, the single-layer neural network parameters are fine-tuned by adopting two properties of PCE to construct an unsupervised loss function. Based on the PCFT-PCE method, this paper builds a sphere-biconic reentry vehicle reliability-based design optimization (SBRV-RBDO) framework to minimize the mass and the highest temperature considering geometric and material parameters’ uncertainties, where a combination penalty method is proposed to adjust and balance the mass and the highest temperature constraints. Finally, a numerical example and an engineering case validate the proposed methods. The results show that the proposed PCFT-PCE method builds a more accurate PCE model with a little extra calculation time. The SBRV-RBDO framework helps engineers to find reliable optimization schemes for SBRV conceptual design.

Keywords: Fine-tuning; Polynomial chaos expansion; Parameterized coefficient; Neural network; Sphere-biconic reentry vehicle; Reliability analysis; Design optimization (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023004829

DOI: 10.1016/j.ress.2023.109568

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