Experimental evaluation and artificial neural network modeling of thermal conductivity of water based nanofluid containing magnetic copper nanoparticles

Abstract Thermal conductivity of nanofluids performs as a crucial role in heat transfer capacity of fluids. Nanoparticles’ addition to a base fluid results in enhancing thermal conductivity ratio. In this investigation, the thermal conductivity ratio of modified copper nanoparticles/water nanofluid is modeled by applying artificial neural network approaches utilizing experimental data. The nanofluids’ thermal conductivity at various fluid temperatures, nanoparticle concentration and diameter was measured experimentally using KD2Pro. Additionally, in order to model the nanofluid’s thermal conductivity with respect to temperature, solid volume fraction, and diameter, a correlation is proposed by applying artificial neural networks and considering the experimental data. According to statistical accuracy analysis, the best structure to model this task is a two-layer feed-forward ANN model with 6 hidden neurons. This model predicted the experimental data with Mean absolute percentage error (MAPE) of 1.09%, mean square errors (MSE) of 2.5  ×  10 − 4 , and coefficient of determination ( R 2) of 0.99. Based on the comparison results, the ANN model is able to predict the enhancement in the thermal conductivity of nanofluids favorably.