Numerical study of dynamic melting enhancement in a latent heat thermal energy storage system

Gasia, Jaume; Groulx, Dominic; Tay, N.H.S.; Cabeza, Luisa F.

doi:10.1016/j.est.2020.101664

Cited by 26 publications

(1 citation statement)

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“…Voller and Prakash [108] described this technique as one that does not require explicitly tracking the phase's interface. The enthalpy-porosity method has been used in recent research to improve the accuracy of phase change simulations [109][110][111][112].…”

Section: Governing Equations and Numerical Proceduresmentioning

confidence: 99%

Optimization of Nano-Additive Characteristics to Improve the Efficiency of a Shell and Tube Thermal Energy Storage System Using a Hybrid Procedure: DOE, ANN, MCDM, MOO, and CFD Modeling

2021

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Using nano-enhanced phase change material (NePCM) rather than pure PCM significantly affects the melting/solidification duration and the stored energy, which are two critical design parameters for latent heat thermal energy storage (LHTES) systems. The present article employs a hybrid procedure based on the design of experiments (DOE), computational fluid dynamics (CFD), artificial neural networks (ANNs), multi-objective optimization (MOO), and multi-criteria decision making (MCDM) to optimize the properties of nano-additives dispersed in a shell and tube LHTES system containing paraffin wax as a phase change material (PCM). Four important properties of nano-additives were considered as optimization variables: volume fraction and thermophysical properties, precisely, specific heat, density, and thermal conductivity. The primary objective was to simultaneously reduce the melting duration and increase the total stored energy. To this end, a five-step hybrid optimization process is presented in this paper. In the first step, the DOE technique is used to design the required simulations for the optimal search of the design space. The second step simulates the melting process through a CFD approach. The third step, which utilizes ANNs, presents polynomial models for objective functions in terms of optimization variables. MOO is used in the fourth step to generate a set of optimal Pareto points. Finally, in the fifth step, selected optimal points with various features are provided using various MCDM methods. The results indicate that nearly 97% of the Pareto points in the considered shell and tube LHTES system had a nano-additive thermal conductivity greater than 180 Wm−1K−1. Furthermore, the density of nano-additives was observed to be greater than 9950 kgm−3 for approximately 86% of the optimal solutions. Additionally, approximately 95% of optimal points had a nano-additive specific heat of greater than 795 Jkg−1K−1.

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