Digital Twins for Cryogenic Hydrogen Safety: Integrating Computational Fluid Dynamics and Machine Learning

Abstract

The global transition toward low-carbon energy and transportation systems positions hydrogen as a key clean and versatile energy carrier. However, ensuring the safe handling and storage of hydrogen—particularly in its liquid form LH2)—remains a critical challenge to large-scale deployment. Accidental releases of LH2 can lead to rapid dispersion, cryogenic hazards, and increased risks of ignition or detonation due to hydrogen’s low ignition energy and wide flammability limits. This review synthesizes recent advances in the understanding and modelling of LH2 safety scenarios, emphasizing the complementary roles of Computational Fluid Dynamics (CFD) and Machine Learning (ML). The paper first outlines the fundamental physical processes governing cryogenic hydrogen leaks, spills, and jet releases, followed by an overview of current storage and sensing technologies. Special consideration is given to safety implications arising from the differences between open and enclosed environments and the fact that existent sensing technologies present deficiencies at low temperatures. CFD-based studies are reviewed to illustrate how these methods capture complex flow and dispersion dynamics under diverse operational and environmental conditions, supported by a summary of existing experimental investigations used for model validation. The emerging role of ML is then examined, focusing on its integration with CFD simulations and sensor networks for predictive risk assessment, real-time leak detection, and the development of digital twins. Finally, integrated CFD–ML-sensor systems are discussed as a pathway toward a physics-informed, data-driven framework for advancing hydrogen safety and reliability.