Predicting heat transfer performance of Fe3O4-Cu/water hybrid nanofluid under constant magnetic field using ANN

Abstract

In this study, the experimental results using mono (Fe3O4/water and Cu/water) and hybrid (Fe3O4-Cu/water) type nanofluid with nanoparticle volume concentrations of (0<_& phi;<_0.02) under laminar flow conditions (994<_Re<_2337) were compared with the results obtained by ANN. While the Reynolds number (Re), hydraulic diameter (Dh), thermal conductivity (k) of working fluid, and volume concentration of the nanoparticles (& phi;) were selected as input layers, the Nusselt number (Nu) were considered as output layers. The %75 of the findings obtained from experiments were used to train Artificial Neural Network (ANN). The estimated data by ANN is in perfect agreement with the experimental data. The success of ANN was determined by comparing it with SVM, Dec Tree, and their variations. Mean square error (MSE), root mean square error (RMSE), R-sq (R2), and mean absolute error (MEA) were considered in evaluating the results obtained. According to findings, MAE 0.00088274, MSE 1.4106e-06, RMSE 0.0011877 and R2 1.00 were measured. These findings show that the use of ANN is a feasible way to predict the convective heat transfer performance of hybrid nanofluid under a magnetic field (MF).