Journal
ENERGY
Volume 243, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.123044
Keywords
Metal hydride hydrogen storage reactor; Hydrogen absorption; Fin-metal foam; Genetic algorithm; High thermal conductivity materials
Categories
Funding
- National Natural Science Foundation of China [52090063]
- National Key Research and Development Program of China [2018YFE0202000]
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This study investigates the coupled fin-metal foam metal hydride reactor using a 3D multi-physical model. The results show that the fin-metal foam reactor has lower thermal resistance and improved charging time and hydrogen absorption performance.
The optimal arrangement form of a certain amount of high thermal conductivity materials for getting the best heat transfer rate and uniformity in metal hydride reactor needs to be investigated. In this work, the coupled fin-metal foam metal hydride reactor was proposed and investigated using a 3D multi-physical model. Thermal resistance analysis showed that the thermal resistance of fin-metal foam reactor was below 0.0099K/W, which was lower than that of metal foam reactor of 0.01035K/W and fin reactor of 0.0235K/W. Furthermore, Genetic Algorithm was used to optimize the proportions of fin and metal foam in fin-metal foam reactors under a certain amount of high thermal conductivity materials. The comparison results showed that the charging time for 90% saturation of optimized fin-metal foam reactor was decreased by 6.9% and 38% compared with metal foam reactor and fin reactor, respectively. It was indicated that the optimal fin volume ratios of optimized fin-metal foam reactors maintained around 0.4 under different volumetric fractions of high thermal conductivity materials. Besides, considering the trade-off between hydrogen storage rate and hydrogen storage capacity, the optimal volumetric fraction of high thermal conductivity materials for obtaining the best comprehensive hydrogen absorption performance of optimized fin-metal foam reactor approximately equaled 0.08.(c) 2022 Elsevier Ltd. All rights reserved.
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