Modelling activated carbon hydrogen storage tanks using machine learning models Klepp, Georg Heinrich Hydrogen storage Adsorption Activated carbon Machine learning Simulation Computational fluid dynamics The application of hydrogen for energy storage and as a vehicle fuel necessitates efficient and effective storage technologies. In addition to traditional cryogenic and high-pressure tanks, an alternative approach involves utilizing porous materials such as activated carbons within the storage tank. The adsorption behaviour of hydrogen in porous structures is described using the Dubinin-Astakhov isotherm. To model the flow of hydrogen within the tank, we rely on the equations of mass conservation, the Navier-Stokes equations, and the equation of energy conservation, which are implemented in a computational fluid dynamics code and additional terms account for the amount of hydrogen involved in sorption and the corresponding heat release. While physical models are valuable, data-driven models often offer computational advantages. Based on the data from the physical adsorption model, a data-driven model is derived using various machine learning techniques. This model is then incorporated as source terms in the governing conservation equations, resulting in a novel hybrid formulation which is computationally more efficient. Consequently, a new method is presented to compute the temperature and concentration distribution during the charging and discharging of hydrogen tanks and identifying any limiting phenomena more easily. Elsevier BV 2024 info:eu-repo/semantics/other doc-type:other text http://purl.org/coar/resource_type/c_1843 https://www.th-owl.de/elsa/record/11808 Klepp GH. Modelling activated carbon hydrogen storage tanks using machine learning models. <i>Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy</i>. 2024;306. doi:<a href="https://doi.org/10.1016/j.energy.2024.132318">10.1016/j.energy.2024.132318</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.energy.2024.132318 info:eu-repo/semantics/altIdentifier/issn/0360-5442 info:eu-repo/semantics/altIdentifier/issn/1873-6785 info:eu-repo/semantics/closedAccess