A Machine Learning Pressure Emulator for Hydrogen Embrittlement

• URL: http://arxiv.org/abs/2306.13116v1
• Date: Thu, 22 Jun 2023 13:42:35 GMT
• Title: A Machine Learning Pressure Emulator for Hydrogen Embrittlement
• Authors: Minh Triet Chau and Jo\~ao Lucas de Sousa Almeida and Elie Alhajjar and Alberto Costa Nogueira Junior
• Abstract summary: Hydrogen embrittlement is a major concern for scientists and gas installation designers to avoid process failures. We propose a physics-informed machine learning model to predict the gas pressure on the pipes' inner wall.
• Score: 1.7205106391379026
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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