Informatics Publishing Limited

M. Auriemma ¹, A. Iazzetta ¹

Affiliations
1 Istituto Motori, C.N.R, IT

Abstract

A numerical study on variations of thermo-physical properties of Phase Change Material (PCM) due to dispersion of nanoparticles is presented in this article. Dispersed metal oxide nanoparticles in paraffin wax might be a solution to improve latent heat thermal storage performance. Thermo-physical properties such as thermal conductivity and latent heat could be changed for different concentration of dispersed nanoparticle. The paper will focus on numerical investigation of the melting of paraffin wax dispersed with three different metal oxide Alumina (Al₂O)₃, Copper Oxide (CuO) and Zinc Oxide (ZnO) that is heated from one side of rectangular enclosure of dimensions of 25 mm × 75 mm. The integrated simulation system ANSYS Workbench 15.0 for the numerical study was used including mesh generation tool ICEM and FLUENT software. In FLUENT, the melting model with Volume Of Fluid (VOF) that includes the physical model to disperse nanoparticles in the PCM and their interactions is applied. During melting process, the enhancement of heat transfer is considered. For each nanoparticle analyzed, three different volume fractions are considered and compared. Dispersed nanoparticles in smaller volumetric fractions show a rise the heat transfer rate. The thermal performances are slightly greater using Al2O3 respect both ZnO that CuO nanoparticles.

Keywords

Thermal Storage System; Phase Change Material; Paraffin Wax; Melting Process; Metal Oxide Nanoparticles

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M. Auriemma ¹, A. Iazzetta ¹

Affiliations
1 Istituto Motori, C.N.R, NA 80125, IT

Abstract

Objectives: To develop efficient models for the prediction of viscosity of nanofluids. Methods/Statistical Analysis: Artificial Neural Network (ANN) toolbox for Matlab and an experimental data set of effective viscosity of alumina waterbased nanofluids are used. ANN is mathematical model of artificial intelligence product, inspired by the structure and functioning of the human nervous system. Experimental data set are divided into two groups: train and test. The train instructed ANN and the results were compared with the test. Findings: ANN viscosity results were compared with the experimental data points. The expected values were in excellent agreement with the measured ones, viewing that the developed model is accurate and has the great ability for predicting the viscosity. 0.9994 and 0.9998 are the values of R2 linear regressions for training and testing data set, respectively and 2.7187*10-4 and 1.2461*10-4 are respective values of mean square errors. Applications: Artificial Neural Networks to model thermal characterization of nanofluids.

Keywords

Alumina Water-Based, Artificial Neural Network, Experimental Data, Nanofluids, Viscosity

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