Neural-accelerated numerical model for packed bed latent heat storage system

Developing accurate and computationally efficient dynamic models for packed-bed latent-heat storages (PBLHS) is crucial for reliably predicting their performance across different operating scenarios and enabling their use in planning and real-time control. In this study, a novel neural-accelerated n...

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Autors principals:	Dessie Tadele Embiale, Shri Balaji Padmanabhan, Mohamed Tahar Mabrouk, Stéphane Grieu, Bruno Lacarrière
Format:	Artigo
Idioma:	Inglês
Publicat:	Elsevier 2025-12-01
Col·lecció:	Energy and AI
Matèries:	Thermal storage Latent heat Phase change material Machine learning Neural networks Numerical models
Accés en línia:	http://www.sciencedirect.com/science/article/pii/S266654682500134X
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