Enrichment of heat transfer in a latent heat storage unit using longitudinal fins

Mehta, Digant S.; Vaghela, Bhavesh; Rathod, Manish K.; Banerjee, Jyotirmay

doi:10.1002/htj.21739

Cited by 12 publications

(8 citation statements)

References 76 publications

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“…To address this, various approaches have been proposed to enhance the LTES rate and accelerate the phase change process. These techniques include incorporating fins, [14][15][16] using porous matrices, 17,18 and dispersing high-conductive nanoparticles in PCMs. [19][20][21][22][23] Apart from these additive techniques, some researchers have proposed innovative designs of LTES systems that enhance the heat exchange surface between PCMs and heat transfer fluid (HTF).…”

Section: Introductionmentioning

confidence: 99%

Thermal analysis of a cascaded latent thermal storage tank in a solar domestic water heating system

Elbahjaoui,

Hanchi

2023

Energy Storage

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To enhance the thermal characteristics of a solar collector storage system, this study investigates the performance of a rectangular thermal energy storage (TES) tank by incorporating cascade phase change materials (cascade‐PCMs). Three different commercially available PCMs (RT44HC, RT54HC, and RT62HC) with distinct melting temperatures are employed. These cascade‐PCMs are utilized as slabs in vertical rectangular modules, which are integrated into the water TES tank. The heat transfer fluid (HTF) in the flat‐plate solar collector captures solar energy and transfers it to the TES tank, where it is stored as latent thermal energy. A two‐dimensional (2D) numerical model, utilizing the enthalpy‐porosity approach and conservation equations, is developed to analyze the melting and heat transfer processes within the TES tank. The model is validated against previous experimental and numerical data. Performance evaluation of the cascade‐PCMs tank is conducted during a 9‐h charging period (from 8:00 am to 5:00 pm) under Marrakesh weather conditions in Morocco. An optimization study is carried out to determine the optimal height of each cascade‐PCM. The results suggest that an optimal design with heights of 23, 16, and 11 cm for RT44HC, RT54HC, and RT62HC respectively, offers improved melting and storage quality. The thermal characteristics of the TES tank incorporating cascade‐PCMs are compared to those of a TES tank filled with a single phase change material (single‐PCM) during the charging period. The findings indicate that the cascade‐PCMs achieve complete melting, while the single‐PCM only reaches a melting fraction of 0.903 at the end of the charging process. Furthermore, the optimal configuration of the cascade‐PCMs storage tank exhibits slightly higher sensible and latent thermal energy storage capacity compared to the single‐PCM tank. The combination of the solar collector with the cascade‐PCMs storage tank results in a 3.47% higher average collection efficiency when compared to the single‐PCM tank.

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Section: Introductionmentioning

confidence: 99%

Thermal analysis of a cascaded latent thermal storage tank in a solar domestic water heating system

Elbahjaoui,

Hanchi

2023

Energy Storage

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“…To improve the thermal storage of PCMs, their thermal conductance must be increased. As a result, many improvement techniques have been suggested, including incorporating highly thermally conductive materials, including metallic and graphene porous foams [6][7][8][9] into the PCM, microencapsulation of the PCM [10,11], high-TC ns [12], and integrated heat pipes PCM [13].…”

Section: Introductionmentioning

confidence: 99%

Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method

Chen

Majdi²,

Ismael

et al. 2022

International Journal of Chemical Engineering

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Thermal conductivity (TC) of a phase change material (PCM) may be enhanced by distributing nanostructured materials (NSMs) termed nano-PCM. It is critical to accurately estimate the TC of nano-PCM to assess heat transfer during phase transition processes, namely, solidification and melting. Here, we propose Gaussian process regression (GPR) strategies involving four various kernel functions (KFs) (including exponential (E), squared exponential (SE), rational quadratic (RQ), and matern (M)) to predict TC of n-octadecane as a PCM. The accessible computational techniques indicate the accuracy of our proposed GPR model compared to the previously proposed methods. In this research, the foremost forecasting strategy has been considered as a GPR method. This model consists of the matern KF whose R2 values of training and testing phases are 1 and 1, respectively. In the following, a sensitivity analysis (SA) is used to explore the effectiveness of variables in terms of outputs and shows that the temperature (T) of nanofluid (NF) is the most efficient input parameter. The work describes the physical properties of NFs and the parameters that should be determined to optimize their efficiency.

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“…Hence, various enhancement methods have been proposed, including adding high thermally-conductive materials to the PCM, such as graphene 4,5 and metallic porous foams, 6,7 microencapsulation of PCM, 8,9 integrated heat pipes-PCM, 10 and the use of fins with high TC. 11 In the past decades, with nanotechnology advancement, nanostructured materials with high TC have been produced and commercialized. These nanostructures comprise a wide range of metallic, non-metallic, and carbon-based materials.…”

Section: Introductionmentioning

confidence: 99%

“…In order to make PCMs suitable for thermal storage applications, it is necessary to enhance their thermal conductance. Hence, various enhancement methods have been proposed, including adding high thermally‐conductive materials to the PCM, such as graphene 4,5 and metallic porous foams, 6,7 microencapsulation of PCM, 8,9 integrated heat pipes‐PCM, 10 and the use of fins with high TC 11 …”

Section: Introductionmentioning

confidence: 99%

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Motahar

Sadri

2021

Intl J of Energy Research

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The weak thermal conductance of a phase change material (PCM) can be intensified by dispersing nanostructured materials called nano-PCM. Accurate thermal conductivity (TC) prediction of nano-PCM is essential to evaluate heat transport during phase change processes, namely, melting and solidification. The present study develops an artificial neural network (ANN) to forecast the TC of n-octadecane as a PCM with dispersed oxide nanoparticles. A total of 122 experimental datasets from existing literature with a wide range of temperatures (5-60 C), nanoparticles (CuO, Al 2 O 3 , TiO 2 , and mesoporous SiO 2 ), nanoparticle mass fractions (0.5-12 wt%) are compiled to train a multi-layered feed-forward ANN with Levenberg-Marquardt back-propagation algorithm.An optimal architecture of the neural network is acquired by varying the number of network hidden layers, the number of neurons in each layer, and the transfer function of layers. The minimum mean square error (MSE) of 1.3512 Â 10 À5 is obtained for the best developed ANN. Results show that average absolute deviation (AAD) of 0.002458, mean absolute percentage error (MAPE) of 0.8260%, and correlation coefficient (R) of 0.999964948 are achieved for training data. Moreover, MAPE, AAD, and R values are, respectively, 0.9478, 0.002167, and 0.9999715861 for testing data. The maximum percentage errors of ANN computed values are 2.31%, and 0.812% for liquid and solid phases, respectively. This indicates that the ANN model accurately predicts the enhanced TC of nano-PCM across various oxide nanoparticles, temperatures, and nanoparticle loadings.

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Enrichment of heat transfer in a latent heat storage unit using longitudinal fins

Cited by 12 publications

References 76 publications

Thermal analysis of a cascaded latent thermal storage tank in a solar domestic water heating system

Thermal analysis of a cascaded latent thermal storage tank in a solar domestic water heating system

Insights into the Estimation of the Enhanced Thermal Conductivity of Phase Change Material-Containing Oxide Nanoparticles using Gaussian Process Regression Method

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

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