A Machine Learning Pressure Emulator for Hydrogen Embrittlement
Minh Triet Chau João Lucas de Sousa Almeida Elie Alhajjar Alberto Costa Nogueira Junior
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.