An asymptotic stochastic response surface approach to reliability assessment under multi-source heterogeneous uncertainties

Jingjing He, Min Huang, Wei Wang, Shaohua Wang and Xuefei Guan

Reliability Engineering and System Safety, 2021, vol. 215, issue C

Abstract: An asymptotic stochastic response surface method for structural reliability assessment under multi-source heterogeneous uncertainties is proposed in this study. The fatigue reliability of an aeroengine Curvic coupling under the uncertainties from material, contact surface state, and part dimension deviation is used to motivate the methodology development. A randomized block design of experiments for numerical models are employed to obtain the fatigue strain responses under different variations and combinations of the uncertain variables. A stochastic response surface model is constructed, and the analytical forms of the mean and bound predictions of the fatigue strain are derived. By further coupling the fatigue strain into the low-cycle fatigue model, the fatigue life under multi-source uncertainties is obtained. The stochastic moment approximation is used to derive the asymptotic approximation of the fatigue life distribution, allowing for efficient reliability and risk-informed lifetime prediction. The variance of the fatigue life contributed by individual uncertain sources is naturally resolved in the method, providing a quantitative measure for uncertainty control and management. The adequacy of the stochastic response surface model and the accuracy of the asymptotic reliability assessment result are verified using statistical testing and the Monte Carlo method, respectively.

Keywords: Multi-source heterogeneous uncertainties; Stochastic response surface; Asymptotic reliability assessment; Variance decomposition; Curvic coupling (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021003276

DOI: 10.1016/j.ress.2021.107804

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