Modeling of TES Tanks by Means of CFD Simulation Using Neural Networks

Edgar F. Rojas Cala, Ramón Béjar, Carles Mateu, Emiliano Borri, Alessandro Romagnoli and Luisa F. Cabeza ()
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Edgar F. Rojas Cala: GREiA Research Group, Universitat de Lleida, Pere de Cabrera 3, 25001 Lleida, Spain
Ramón Béjar: GREiA Research Group, Universitat de Lleida, Pere de Cabrera 3, 25001 Lleida, Spain
Carles Mateu: GREiA Research Group, Universitat de Lleida, Pere de Cabrera 3, 25001 Lleida, Spain
Emiliano Borri: GREiA Research Group, Universitat de Lleida, Pere de Cabrera 3, 25001 Lleida, Spain
Alessandro Romagnoli: School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Luisa F. Cabeza: GREiA Research Group, Universitat de Lleida, Pere de Cabrera 3, 25001 Lleida, Spain

Energies, 2025, vol. 18, issue 3, 1-20

Abstract: Modeling of thermal energy storage (TES) tanks with computational fluid dynamics (CFD) tools exhibits limitations that hinder the time, scalability, and standardization of the procedure. In this study, an innovative technique is proposed to overcome the challenges in CFD modeling of TES tanks. This study assessed the feasibility of employing neural networks for TES tank modeling, evaluating the similarities in terms of structure and signal-to-noise ratio by comparing images generated by neural networks with those produced through CFD simulations. The results regarding the structural similarity index indicate that around 94% of the images obtained have a similarity index above 0.9. For the signal-to-noise ratio, the results indicate a mean value of 25 dB, which can be considered acceptable, although indicating room for improvement. Additional results show that our neural network model obtains the best performance when working with initial states close to the stable phase of the TES tank. The results obtained in this study are promising, laying the groundwork for a future pathway that could potentially replace the current methods used for TES tank modeling.

Keywords: CFD; neural network; machine learning; TES tank; simulation; modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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