Development of a neural architecture to predict the thermal conductivity of nanofluids

The present study proposes a comprehensive and accurate artificial neural network (ANN) model for correctly estimating the thermal conductivity (K) of an extensive range of nanofluids. The ANN model was designed using the Levenberg-Marquardt (L-M) algorithm based on 800 experimental data containing spherical nanoparticles of TiO2, ZnO, CuO, Al2O3, ZrO2, Fe2O3, Fe3O4, SiO2, CeO2, MgO, Fe, Al, Cu, Ag, SiC and diamond in various fluids of oil, ethylene glycol, water, and radiator cooling. The nanoparticle and base fluid thermal conductivity, volume fraction (0.4-0.4%), and particle diameter (4-150 nm) of the nanoparticles, and temperature (10-80 degrees C) were considered as effective input variables, while the thermal conductivity of nanofluid was defined as the target variable. According to the results, R and MSE using 5-13-1 topology for all data, and training sub-set were founded to be about 0.9975 and 0.000238, and 0.9976 and 0.000229, respectively, indicating the proper ability of the designed ANN model. In addition, the developed model showed an excellent ability for predicting the thermal conductivity for oil and radiator cooling-based nanofluids with MSE of 0.000037 and 0.000042, respectively. The validation of the ANN model was successfully confirmed by achieving a low error between experimental and predicted data. These findings prove the comprehensive and accurate function of the developed ANN model.Graphical AbstractAn artificial neural network model using 5-13-1 topology for predicting thermal conductivity of nanofluids