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arXiv · 2306.13116

A Machine Learning Pressure Emulator for Hydrogen Embrittlement

Minh Triet Chau·João Lucas de Sousa Almeida·Elie Alhajjar·Alberto Costa Nogueira Junior
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Abstract

A recent alternative for hydrogen transportation as a mixture with natural gas is blending it into natural gas pipelines. However, hydrogen embrittlement of material is a major concern for scientists and gas installation designers to avoid process failures. In this paper, we propose a physics-informed machine learning model to predict the gas pressure on the pipes' inner wall. Despite its high-fidelity results, the current PDE-based simulators are time- and computationally-demanding. Using simulation data, we train an ML model to predict the pressure on the pipelines' inner walls, which is a first step for pipeline system surveillance. We found that the physics-based method outperformed the purely data-driven method and satisfy the physical constraints of the gas flow system.

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Minh Triet Chau, João Lucas de Sousa Almeida, Elie Alhajjar, Alberto Costa Nogueira Junior. 2023-06-22. A Machine Learning Pressure Emulator for Hydrogen Embrittlement. https://arxiv.org/abs/2306.13116

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