A Machine Learning Pressure Emulator for Hydrogen Embrittlement

TL;DR

This paper introduces a physics-informed machine learning model to efficiently predict internal pipeline pressure for hydrogen-natural gas blends, aiding in preventing embrittlement-related failures.

Contribution

It presents a novel physics-informed ML approach that outperforms purely data-driven models in predicting pipeline pressure, reducing computational costs.

Findings

Physics-informed ML outperforms purely data-driven models.

Model satisfies physical constraints of gas flow.

Potential for pipeline system surveillance and safety.

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.