Computational Analysis of Hydrogen Leakage Using and Techniques

This chapter presents a comprehensive computational framework for analyzing hydrogen leakage and dispersion behavior in confined environments by integrating computational fluid dynamics (CFD) simulations with machine learning (ML) techniques. Focusing on an axisymmetric ceiling geometry, the study systematically investigates the effects of ceiling height and radial distance on hydrogen accumulation patterns. CFD simulations reveal critical insights into buoyancy-driven dispersion, ceiling layer formation, and the influence of spatial parameters on hydrogen concentration profiles. A structured database of hydrogen dispersion data is generated from the simulations and used to develop predictive ML models, including Decision Trees, Random Forests, and XGBoost . The XGBoost model achieved the highest predictive accuracy, demonstrating the potential of ML approaches for real-time hazard assessment. Feature importance analysis highlights the dominant role of spatial positioning and horizontal velocity in determining hydrogen concentration distributions. The integrated CFD-ML methodology offers practical guidance for hydrogen safety system design, particularly for sensor placement and rapid leak detection strategies. This work addresses existing research gaps by providing a structured approach to hydrogen dispersion modeling and establishes a foundation for future developments in predictive safety systems for the hydrogen economy.