A machine learning-based model to estimate the density of nanofluids of nitrides in ethylene glycol

Abstract

The density of nanofluids is an important thermophysical property whose value is required to evaluate various heat-transfer parameters such as the Reynolds number, the Nusselt number, pressure loss, and the Darcy friction factor. The determination of these parameters is central to the design of many heat-transfer applications. Notably, the density of nanofluids has received relatively little research attention compared with other thermophysical properties. The present study thus focuses on the development of a support vector regression model to estimate the densities of aluminum nitride, titanium nitride, and silicon nitride nanoparticles dispersed in ethylene glycol solution. As inputs, the proposed model uses the mass fraction, temperature, nanoparticle size, and the molecular weight of the nanoparticles. The proposed model predicts the nanofluid densities with high accuracy, as determined by a correlation coefficient of 99.87% and an absolute average relative deviation of 0.0701. To further highlight the accuracy of the proposed model, we compare its results with those of the model of Pak and Cho. The Pak and Cho results deviate considerably from the experimental data except at 298 K. Overall, the proposed support vector regression model is much more accurate than the Pak and Cho model. We thus conclude that the machine learning approach is more reliable for obtaining rapid estimates of the density of nanofluids.

A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods

“…The results obtained by the BPN-GA model are in good agreement with the experimental data, with an absolute deviation of 0.13%. Sahaluddin et al 180 used the development of a SVR model to estimate the density of aluminum nitride, titanium nitride, and silicon nitride nanoparticles dispersed in a glycol solution. Mass fraction, temperature, nanoparticle size, and nanoparticle molecular weight was taken as input.…”

Thermal Conductivity of Nanofluids: A Review on Prediction Models, Controversies and Challenges

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Influence of Support Vector Regression (SVR) on Cryogenic Face Milling

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