Using radial basis function network to model the heat transfer and pressure drop of water based nanofluids containing MgO nanoparticles. (December 2021) - Hemmat Esfe, Mohammad; Kamyab, Mohammad Hassan; Alirezaie, Ali; Toghraie, Davood
Abstract: Present study is focused on feasibility of predicting the thermal and fluid properties of MgO/water nanofluid using radial based function (RBF) type artificial neural networks (ANNs). To design the ANN, Reynolds number and volume fraction of nanoparticles ( ϕ ) were considered as ANN inputs and in principle independent parameters and on the other hand, the parameters of relative pressure drop and relative heat transfer coefficient were considered as the outputs of this ANN. One of the important innovations in the present study is the attempt to simultaneously predict the relative pressure drop as undesirable and the relative heat transfer coefficient as undesirable parameters. The designed ANN using the radial basis function (RBF) was able to predict the laboratory parameters of relative pressure drop and relative heat transfer coefficient (RHTC) with 99.3% and 99.5% accuracy, respectively. It can drastically reduce the time and financial costs of laboratory methods. Based on the obtained results in ϕ  = 0.125%, the unfavorable parameter (relative pressure drop) has a significant supremacy over the optimal parameter (RHTC) and therefore the ϕ is not suitable for use in thermal cycles. But on the other hand, with ϕ  = 0.5%, the optimal parameter (RHTC) has a significant supremacy over the undesirable parameter (relative pressure drop), and therefore for MgO/water nanofluid, ϕ >0.5% are suitable for using in thermal cycles.