A neural network-based predictive model for the thermal conductivity of hybrid nanofluids

Nanofluids are known to have immense potential for heat transfer applications because of their unique thermophysical properties when compared to the conventional heat transfer fluid. Predicting the thermophysical features like thermal conductivity has posed a challenge to their application. This article addresses some of the challenges posed in their prediction by using data sets from several experimental research on various hybrid nanofluids to train an intelligent neural network. The thermal conductivity of hybrid nanofluids is predicted using seven different input variables namely, volume concentration, temperature, the acentric factor of the base fluid, nanoparticle bulk density, mixture ratio of particles, the thermal conductivity, and size of nanoparticles. 715 experimental data points from studies using different hybrid nanoparticles are used in developing a multilayer perceptron artificial neural network (ANN) and support vector regression (SVR) models. The performance validation of the models is computed using the mean square error (MSE) and the coefficient of determination (R-2). The performance result showed an R-2 value of 0.99997 and 0.99788 in the validation phase of the ANN and SVR model, respectively. This indicates that the models are capable of accurately predicting the thermal conductivity of hybrid nanofluids over a wide range of hybrid nanoparticle combinations. Finally, a universal formula using MLP-ANN for predicting the thermal conductivity of hybrid nanofluids is presented.