A hybrid physics–Bayesian framework for fatigue design curves under cryogenic conditions with consideration of load ratio and residual stress

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Lin, Yu Yao and Kim, Yun-Jae and Cho, Nak-Kyun and Hwang, Jin Ha and Park, Kyu-Sik and Mehmanparast, Ali and Kim, Do Kyun (2026) A hybrid physics–Bayesian framework for fatigue design curves under cryogenic conditions with consideration of load ratio and residual stress. Ocean Engineering, 345. 123912. ISSN 0029-8018 (https://doi.org/10.1016/j.oceaneng.2025.123912)

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Abstract

Fatigue performance is a critical design consideration for cryogenic structures used in the storage and transport of alternative fuels such as liquefied natural gas (LNG), ammonia, and captured CO2. However, fatigue crack growth rate (FCGR) testing at cryogenic temperatures is expensive and prone to uncertainty due to complex experimental conditions. This study proposes a physics-informed Bayesian framework to improve the prediction and design of FCGR behaviour without extensive cryogenic testing. Four probabilistic models are developed: two Gaussian process (GP) regressions, a physics-informed Bayesian neural network (PIBNN), and a hybrid physics–GP fusion model. The framework explicitly incorporates temperature-dependent material properties, residual stress, load ratio, and crack closure mechanisms while utilising Bayesian inference to quantify epistemic and aleatory uncertainties. The physics-informed components constrain the model to physically admissible trends, improving extrapolation beyond the training data. Based on these models, Bayesian design curves are constructed to replace the traditional “mean + 2SD” rule, achieving a balanced level of conservatism with quantified confidence intervals. The proposed approach demonstrates reliable prediction of fatigue behaviour under untested cryogenic conditions, offering a data-efficient and mechanistically consistent tool for the design and integrity assessment of cryogenic structures.

ORCID iDs

Lin, Yu Yao, Kim, Yun-Jae, Cho, Nak-Kyun, Hwang, Jin Ha, Park, Kyu-Sik, Mehmanparast, Ali ORCID logoORCID: https://orcid.org/0000-0002-7099-7956 and Kim, Do Kyun;
CORE (COnnecting REpositories)