Comprehensive study and scientific process to increase the accuracy in estimating the thermal conductivity of nanofluids containing SWCNTs and CuO nanoparticles using an artificial neural network
Mohammad Hemmat Esfe, Fatemeh Amoozad, Hossein Hatami, Davood Toghraie
Abstract
Abstract This investigation aimed to evaluate the thermal conductivity ratio (TCR) of SWCNT-CuO/Water nanofluid (NF) using experimental data in the T range of 28–50 ℃ and solid volume fraction range of SVF = 0.03 to 1.15% by an artificial neural network (ANN). MLP network with Lundberg-Marquardt algorithm (LMA) was utilized to predict data (TCR) by ANN. In the best case, from the set of various structures of ANN for this nanofluid, the optimal structure was chosen, which consists of 2 hidden layers, the first layer with the optimal structure consisting of 5 neurons and the second layer containing 7 neurons. Eventually, for the optimal structure, the R 2 coefficient and MSE are 0.9999029 and 6.33377E-06, respectively. Based on all ANN information, MOD is in a limited area of − 3% < MOD < + 3%. Comparison of test, correlation yield, and ANN yield display that ANN evaluates laboratory information more exactly.