Analysis method of time-varying reliability for complex aerostructure under mixed uncertainty

By employing a multiple Kriging proxy model and an adaptive update strategy, the uncertainty in the regression term setting in traditional methods is resolved, enabling efficient time-varying reliability analysis of complex aerospace structures under mixed uncertainties.

CN115495965BActive Publication Date: 2026-07-24UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
UNIV OF ELECTRONICS SCI & TECH OF CHINA
Filing Date
2022-10-27
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Traditional methods based on the Kriging surrogate model require manual setting of regression terms, which leads to subjective uncertainty in the final reliability analysis results, and the computational efficiency of time-varying reliability analysis of complex aerospace structures under mixed uncertainties is low.

Method used

A multiple Kriging surrogate model is adopted. By constructing the response function of complex aerospace structures and quantifying the input uncertainty variables, a multiple Kriging surrogate model is established. The optimal regression term is determined by KL divergence and local accuracy index, and time-varying reliability analysis is carried out by combining an adaptive update strategy.

Benefits of technology

It effectively avoids the shortcomings of manually setting regression terms, improves computational efficiency and accuracy, and provides time-varying reliability analysis results under mixed uncertainties.

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Abstract

The application discloses a kind of complex aviation structure under mixed uncertainty Time-varying reliability analysis method, and its basic flow is as follows: the finite element simulation model of complex aviation structure is established, the examination site is selected and the failure criterion is determined, the finite element simulation model is parameterized, and the response function of complex aviation structure is constructed;Generate training sample set, and establish the multiple Kriging surrogate model of complex aviation structure response function according to training sample set and corresponding structure response, and the KL divergence is combined with limit state surface projection profile method to construct adaptive learning function under mixed uncertainty, and the new training sample is screened to update and enhance Kriging surrogate model;Global accuracy index of multiple Kriging surrogate model is used to adjust multiple surrogate models in the proxy model set, and local accuracy index is used to solve the time-varying reliability result of complex aviation structure under mixed uncertainty.
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