Modeling and Multi-Objective Optimization of Thermophysical Properties for Thermal Conductivity and Reynolds number of CuO-Water Nanofluid using Artificial Neural Network.

Abstract

In nanofluids, due to the small size of the particles, they greatly reduce the problems caused by corrosion, impurities, and pressure drop, and the stability of fluids against sediment is significantly improved. Due to the high conductivity of nanoparticles, with the distribution in the base fluid, they increase the thermal conductivity of the fluid, which is one of the basic parameters of heat transfer. In this paper, properties using experimental data and artificial neural networks, to maximize thermal conductivity, temperature changes, and nanofluid volume fraction of NSGA-II optimization algorithm and also to obtain thermal conductivity values from 154 experimental data, artificial neural network modeling is used. Various indices including R-squared and Mean Square Error (MSE) have been used to evaluate the modeling accuracy in prediction, Reynolds number, and nanofluid thermal conductivity. The coefficient of determination of the relation (R-squared) is equal to 0.9988, which indicates the acceptable agreement of the proposed relationship with the experimental data. To optimize, the results are presented as a target function, the Parto-front, and its optimal points. Optimal results showed that the maximum thermal conductivity coefficient and the optimal Reynolds number occur in a volume fraction of 2%.