article

Local maximum-entropy based surrogate model and its application to structural reliability analysis

Authors:

Bo LiAuthors Info & Claims

Structural and Multidisciplinary Optimization, Volume 57, Issue 1

Pages 373 - 392

https://doi.org/10.1007/s00158-017-1760-y

Published: 01 January 2018 Publication History

Abstract

A novel surrogate model based on the Local Maximum-Entropy (LME) approximation is proposed in this paper. By varying the degrees of locality, the LME-based surrogate model is constructed according to the local behavior of the response function at the prediction points. The proposed method combines the advantages of both local and global approximation schemes. The robustness and effectiveness of the model are systematically investigated by comparing with the conventional surrogate models (such as Polynomial regression, Radial basis function, and Kriging model) in three types of test problems. In addition, the performance of the LME-based surrogate model is evaluated by an industry case of turbine disk reliability analysis (TDRA) involving random geometric parameters. In TDRA, two LME-based surrogate models are built including a 1st surrogate model employed in the sensitivity analysis to determine the key random variables and a 2nd surrogate model utilized in Monte-Carlo Simulations (MCS) to predict the Low Cycle Fatigue (LCF) life of turbine disks. Finally, a model-based Uncertainty Quantification (UQ) analysis is performed to rigorously quantify the uncertainties of the physical system and fidelity of surrogate model predictions simultaneously. Results show that the LME-based surrogate model can achieve a desirable level of accuracy and robustness with reduced number of sample points, which indicates the proposed method possess the potential for approximating highly nonlinear limit state functions and applicable for structural reliability analysis.

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Cited By

Huang ZWang HChen LGomez HLi BCao C(2024)A meshfree phase-field model for simulating the sintering process of metallic particles for printed electronicsEngineering with Computers10.1007/s00366-023-01909-540:4(2241-2257)Online publication date: 1-Aug-2024
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Li HLu ZFeng K(2023)An efficient method for analyzing local reliability sensitivity by moment method and extended failure probabilityStructural and Multidisciplinary Optimization10.1007/s00158-022-03478-566:2Online publication date: 4-Feb-2023
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Chen GFan JXu HLi B(2021)Calculation of hybrid reliability of turbine disk based on self-evolutionary game model with few shot learningStructural and Multidisciplinary Optimization10.1007/s00158-020-02734-w63:2(807-819)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s00158-020-02734-w
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cover image Structural and Multidisciplinary Optimization

Structural and Multidisciplinary Optimization Volume 57, Issue 1

January 2018

458 pages

ISSN:1615-147X

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Copyright © Copyright © 2018 Springer-Verlag GmbH Germany, part of Springer Nature.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2018

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Cited By

Huang ZWang HChen LGomez HLi BCao C(2024)A meshfree phase-field model for simulating the sintering process of metallic particles for printed electronicsEngineering with Computers10.1007/s00366-023-01909-540:4(2241-2257)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00366-023-01909-5
Li HLu ZFeng K(2023)An efficient method for analyzing local reliability sensitivity by moment method and extended failure probabilityStructural and Multidisciplinary Optimization10.1007/s00158-022-03478-566:2Online publication date: 4-Feb-2023
https://dl.acm.org/doi/10.1007/s00158-022-03478-5
Chen GFan JXu HLi B(2021)Calculation of hybrid reliability of turbine disk based on self-evolutionary game model with few shot learningStructural and Multidisciplinary Optimization10.1007/s00158-020-02734-w63:2(807-819)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s00158-020-02734-w
Li YZhang FYan YZhou JLi Y(2019)Multi-source uncertainty considered assembly process quality control based on surrogate model and information entropyStructural and Multidisciplinary Optimization10.1007/s00158-018-2154-559:5(1685-1701)Online publication date: 1-May-2019
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Guo JZhao JZeng S(2018)Structural reliability analysis based on analytical maximum entropy method using polynomial chaos expansionStructural and Multidisciplinary Optimization10.1007/s00158-018-1961-z58:3(1187-1203)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s00158-018-1961-z

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