Abstract
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.
Highlights
latent heat thermal energy storage (LHTES) systems containing phase change material (PCM) have gained popularity in the field of thermal energy storage (TES); PCM is used in a wide variety of engineering applications, including energy conservation in buildings [5,6,7], solar collector systems [8,9,10], solar building [11,12,13,14], and photovoltaic systems [15,16,17]
One of the primary concerns of heat transfer engineers has been the application of phase change materials containing nano-additives with optimal properties that increase stored energy and decrease phase change time in TES systems
The present paper employs a combined procedure based on the design of experiments (DOE), computational fluid dynamics (CFD), artificial neural networks (ANN), 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)
Summary
Solar energy is one of the most attractive energy resources for investment in this regard as its annual production capacity is several times that of the world’s energy consumption [1,2]. Solar energy is a time-dependent resource that cannot operate continuously throughout the day. Thermal energy storage (TES) systems are required to bridge the gap between energy supply and demand and ensure the performance stability of solar energy systems. There are three types of thermal energy storage: thermochemical, latent, and sensible [3]. Latent heat TES has a greater energy storage capacity than sensible heat TES and is more stable than thermochemical TES [4]. LHTES systems containing PCM have gained popularity in the field of TES; PCM is used in a wide variety of engineering applications, including energy conservation in buildings [5,6,7], solar collector systems [8,9,10], solar building [11,12,13,14], and photovoltaic systems [15,16,17]
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