Experimental investigation and machine learning modeling of heat transfer characteristics for water based nanofluids containing magnetic Fe3O4 nanoparticles

The convective heat transfer coefficients of Fe3O4 magnetic nanofluids in both laminar and turbulent flow states inside a pipe were determined as base values and adequately simulated using machine learning techniques. Three selected machine learning methods for the simulations were Multiple Linear Regression Analysis (MLR), Radial Basis Function-Backpropagation (RBF-BP), and Least Squares-Support Vector Machines (LS-SVM). Initially, MLR was employed to fit the polynomial equation, followed by the selection of the best RBF-BP model with 6 hidden layer neurons using the grid search cross-validation method. For the LS-SVM model, a kernel function of 0.3 and a regularization parameter of 100 were used. By conducting a detailed comparison of the numerical patterns of the accuracy evaluation parameters, the RBF-BP and LS-SVM models were evaluated, with the LS-SVM model demonstrating superiority. The main parameters in the Reynolds number-mass fraction simulation were the mean square error (MSE) and regression coefficient (R2), which obtained specific values of MSE= 0.34 and R2 = 0.99994 under laminar flow conditions. Similarly, in the Reynolds number-magnetic field strength simplification, the best values for laminar flow conditions were MSE= 3.85 and R2 = 0.99993. The validity and accuracy of the model predictions were further demonstrated through visual comparisons of simulated and experimental values using Three-Dimensional smoothed surface plots. The groundbreaking discoveries hold the potential to catalyze advancements and foster significant progress in the fields of machine learning and nanotechnology. As a valuable resource, these results play a crucial role in propelling further research and development efforts, thus making noteworthy contributions to the continuous growth and innovation in these cutting-edge disciplines.