Prediction of specific heat of hybrid nanofluids using artificial neural networks

Abstract

Determination of thermophysical and rheological properties of nanofluids with high accuracy to be used in the experimental and numerical analysis of nanofluid-based engineering systems has a significant effect on the accuracy of results. The aim of this study is to develop an Artificial Neural Networks (ANN) based estimator that can be used to predict the specific heat of deionized water-based CuO + MWCNT, MgO + MWCNT, and SnO2 + MWCNT hybrid nanofluids and to investigate the usability of the ANN-based estimators instead of the commonly used correlations available in the literature. Experimentally obtained data found in the literature on the specific heat of deionized water-based CuO + MWCNT, MgO + MWCNT and SnO2 + MWCNT hybrid nanofluids measured for various temperature T (25 – 50℃), volume concentration φ (0.25% – 1.50%), and particle diameter dp (20 – 50 nm) were used in the present study. The training algorithm's and the network's hyperparameters such as neuron number, hidden layer number, transfer function, epoch number, and learning rate, and the best training algorithm for the problem addressed among various training algorithms were determined by employing the Bayes optimization. K-fold cross-validation was applied as a precaution against overfitting. It was concluded as a result of the study that estimation with higher accuracy can be made with the ANN-based estimator compared to classical correlations and ANN is a powerful tool that can be used in determining the specific heat of nanofluids.