Experimental investigations on thermal properties of nano-SiO<sub>2</sub>/paraffin phase change material (PCM) for solar thermal energy storage applications

Kumar, P. Manoj; Mylsamy, K.; Saravanakumar, P.

doi:10.1080/15567036.2019.1607942

Cited by 110 publications

(21 citation statements)

References 40 publications

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“…67 Therefore, it can be concluded that the type of nanoparticle ( k p ), the amount of nanoparticles ( ϕ m ), and dependence of phonon behavior on temperature can affect the TC of nano‐PCM. It should be mentioned that the dependence of enhanced TC of nano‐PCM on nanoparticle TC, temperature, and nanoparticle loading has already been experimentally verified extensively 22‐33 . Consequently, it is also interpreted that the results predicted by the neural network influenced by the abovementioned parameters.…”

Section: Resultsmentioning

confidence: 67%

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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Section: Resultsmentioning

confidence: 67%

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 building integrated solar water heating system was investigated by incorporating the nano-SiO2 composite PCM for thermal management. The results reported that the nanocomposite PCM enhanced the system efficiency by 16.05% [30][31]. The paraffin wax was incorporated on the PV module to regulate the surface temperature.…”

Section: Methodsmentioning

confidence: 99%

Investigation of Inorganic Phase Change Material for a Semi-Transparent Photovoltaic (STPV) Module

et al. 2020

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The semi-transparent photovoltaic (STPV) module is an emerging technology to harness the solar energy in the building. Nowadays, buildings are turning from energy consumers to energy producers due to the integration of the STPV module on the building envelopes and facades. In this research, the STPV module was integrated on the rooftop window of the experimental room at Kovilpatti (9°10′0″ N, 77°52′0″ E), Tamil Nadu, India. The performance of the STPV modules varies with respect to the geographical location, incident solar radiation, and surface temperature of the module. The surface temperature of the STPV module was regulated by the introduction of the mixture of graphene oxide and sodium sulphate decahydrate (Na2SO4·10H2O). The various concentration of the graphene oxide was mixed together with the Na2SO4·10H2O to enhance the thermal conductivity. The thermal conductivity of the mixture 0.3 concentration was found to be optimum from the analysis. The instantaneous peak temperature of the semi-transparent photovoltaic phase change material (STPV-PCM) module was reduced to 9 °C during summer compared to the reference STPV. At the same time, the energy conversion efficiency was increased by up to 9.4% compared to the conventional STPV module. Due to the incorporation of the graphene oxide and Na2SO4·10H2O, the daily output power production of the STPV module was improved by 12.16%.

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“…In general, traditional SiO 2 -based composite PCMs have relatively low thermal conductivity ( Manoj Kumar et al., 2020 ). For instance, Ranjbar et al.…”

Section: Silica-based Composite Pcms For Thermal Energy Storagementioning

confidence: 99%

Toward Tailoring Chemistry of Silica-Based Phase Change Materials for Thermal Energy Storage

et al. 2020

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Summary Efficient thermal energy harvesting using phase change materials (PCMs) has great potential for thermal energy storage and thermal management applications. Benefiting from these merits of pore structure diversity, convenient controllability, and excellent thermophysical stability, SiO 2 -based composite PCMs have comparatively shown more promising prospect. In this regard, the microstructure-thermal property correlation of SiO 2 -based composite PCMs is still unclear despite the significant achievements in structural design. To enrich the fundamental understanding on the correlations between the microstructure and the thermal properties, we systematically summarize the state-of-the-art advances in SiO 2 -based composite PCMs for tuning thermal energy storage from the perspective of tailoring chemistry strategies. In this review, the tailoring chemistry influences of surface functional groups, pore sizes, dopants, single shell, and hybrid shells on the thermal properties of SiO 2 -based composite PCMs are systematically summarized and discussed. This review aims to provide in-depth insights into the correlation between structural designs and thermal properties, thus showing better guides on the tailor-made construction of high-performance SiO 2 -based composite PCMs. Finally, the current challenges and future recommendations for the tailoring chemistry are also highlighted.

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Experimental investigations on thermal properties of nano-SiO₂/paraffin phase change material (PCM) for solar thermal energy storage applications

Cited by 110 publications

References 40 publications

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

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

Investigation of Inorganic Phase Change Material for a Semi-Transparent Photovoltaic (STPV) Module

Toward Tailoring Chemistry of Silica-Based Phase Change Materials for Thermal Energy Storage

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Experimental investigations on thermal properties of nano-SiO2/paraffin phase change material (PCM) for solar thermal energy storage applications

Cited by 110 publications

References 40 publications

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

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

Investigation of Inorganic Phase Change Material for a Semi-Transparent Photovoltaic (STPV) Module

Toward Tailoring Chemistry of Silica-Based Phase Change Materials for Thermal Energy Storage

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Experimental investigations on thermal properties of nano-SiO₂/paraffin phase change material (PCM) for solar thermal energy storage applications