A Machine Learning Pressure Emulator for Hydrogen Embrittlement
This work addresses pipeline safety for gas installation designers, but it is incremental as it builds on existing simulation data and methods.
The paper tackled predicting gas pressure on pipeline inner walls to address hydrogen embrittlement risks in hydrogen-natural gas blends, finding that a physics-informed machine learning model outperformed purely data-driven methods and satisfied physical constraints.
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.