Decision-oriented probabilistic framework for maintenance and repair of hydrogen-pipelines with fatigue cracks using Dynamic Bayesian Networks

Kurzfassung

In the transition to a sustainable energy future, hydrogen pipelines play an important role in transporting and distributing gaseous hydrogen in an economically efficient way. While ensuring safe and reliable operation of hydrogen pipelines, coupled effect of hydrogen embrittlement and fatigue crack growth is not negligible, as both of them introduce significant uncertainties in structural integrity assessments. To identify the threat of hydrogen-assisted crack growth, experimental and numerical studies were carried out in the past, focusing on the effect of hydrogen on pipeline material and developing empirical crack growth models [1,2]. Probabilistic methods have also been explored to account for material variability; however, limited attention has been given to integrating with inspection data for reliability updating and decision support [3,4]. To address the gap, this study proposes a probabilistic framework based on Dynamic Bayesian networks (DBNs) for modelling the degradation and integrity of hydrogen pipelines, including the interaction between hydrogen embrittlement and fatigue crack growth. The proposed framework incorporates physics-informed hydrogen-assisted crack growth model in air and in hydrogen environments for different crack geometries under cyclic pressure loadings. It takes into account of uncertainties in material properties and measurement errors during inspections. The DBNs enable the sequential updating of the pipeline condition as new inspection data become available, supporting improved assessment of structural integrity over time. A representative hydrogen pipeline is used as a case study to demonstrate the capability of the proposed method. The results highlight the potential of the proposed framework to enhance the structural integrity assessment and provide a basis for decision-making to improve maintenance timing and inspection strategies. References [1] A. Rajabipour, R.E. Melchers, Application of Paris’ law for estimation of hydrogen-assisted fatigue crack growth, International Journal of Fatigue 80 (2015) 357–363. https://doi.org/10.1016/j.ijfatigue.2015.06.027. [2] R.L. Amaro, R.M. White, C.P. Looney, E.S. Drexler, A.J. Slifka, 2018. Development of a Model for Hydrogen-Assisted Fatigue Crack Growth of Pipeline Steel1. Journal of Pressure Vessel Technology 140, 021403. https://doi.org/10.1115/1.4038824. [3] K. Kethamukkala, S. Potts, Y. Liu, Probabilistic hydrogen-assisted fatigue crack growth under random pressure fluctuations in pipeline steels, International Journal of Pressure Vessels and Piping 211 (2024) 105293. https://doi.org/10.1016/j.ijpvp.2024.105293. [4] S. Jallouf, J. Capelle, G. Pluvinage, Probabilistic fatigue initiation assessment diagram pipe steel ×52: influence of hydrogen, Fatigue Fract Eng Mat Struct 40 (2017) 1260–1266. https://doi.org/10.1111/ffe.12636.