Computational Modeling of Latent Heat Thermal Energy Storage in a Shell-Tube Unit: Using Neural Networks and Anisotropic Metal Foam

Jana Shafi (), Mehdi Ghalambaz, Mehdi Fteiti, Muneer Ismael and Mohammad Ghalambaz
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Jana Shafi: Department of Computer Science, College of Arts and Science, Prince Sattam Bin Abdul Aziz University, Wadi Ad-Dawasir 11991, Saudi Arabia
Mehdi Ghalambaz: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Mehdi Fteiti: Physics Department, Faculty of Applied Sciences, Umm Al-Qura University, Makkah 24381, Saudi Arabia
Muneer Ismael: Mechanical Engineering Department, Engineering College, University of Basrah, Basrah 61004, Iraq
Mohammad Ghalambaz: Laboratory on Convective Heat and Mass Transfer, Tomsk State University, 634045 Tomsk, Russia

Mathematics, 2022, vol. 10, issue 24, 1-26

Abstract: Latent heat storage in a shell-tube is a promising method to store excessive solar heat for later use. The shell-tube unit is filled with a phase change material PCM combined with a high porosity anisotropic copper metal foam (FM) of high thermal conductivity. The PCM-MF composite was modeled as an anisotropic porous medium. Then, a two-heat equation mathematical model, a local thermal non-equilibrium approach LTNE, was adopted to consider the effects of the difference between the thermal conductivities of the PCM and the copper foam. The Darcy–Brinkman–Forchheimer formulation was employed to model the natural convection circulations in the molten PCM region. The thermal conductivity and the permeability of the porous medium were a function of an anisotropic angle. The finite element method was employed to integrate the governing equations. A neural network model was successfully applied to learn the transient physical behavior of the storage unit. The neural network was trained using 4998 sample data. Then, the trained neural network was utilized to map the relationship between control parameters and melting behavior to optimize the storage design. The impact of the anisotropic angle and the inlet pressure of heat transfer fluid (HTF) was addressed on the thermal energy storage of the storage unit. Moreover, an artificial neural network was successfully utilized to learn the transient behavior of the thermal storage unit for various combinations of control parameters and map the storage behavior. The results showed that the anisotropy angle significantly affects the energy storage time. The melting volume fraction MVF was maximum for a zero anisotropic angle where the local thermal conductivity was maximum perpendicular to the heated tube. An optimum storage rate could be obtained for an anisotropic angle smaller than 45°. Compared to a uniform MF, utilizing an optimum anisotropic angle could reduce the melting time by about 7% without impacting the unit’s thermal energy storage capacity or adding weight.

Keywords: computational modeling; finite element method; neural networks; anisotropic metal foam; thermal energy storage (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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