Analysis of the heat transfer in a Maxwell-based non-Newtonian ternary hybrid nanofluid flow over a wedge surface with an advanced soft computing method
摘要
The wedge surface in ternary hybrid nanofluid research is important because it facilitates the design of more effective systems, including heat and mass transfer, enhances the understanding of the flow and thermal properties in complicated geometries and aids in simulating real-world engineering problems. This work examines the two-dimensional steady incompressible non-Newtonian flow of a ternary hybrid nanofluid comprising Al2O3, graphene and CNT nanoparticles with the base fluid engine oil across a wedge surface. This work analyses the flow behaviour under the effect of suction/injection, magnetic field, radiation parameter, natural convection, Hartree pressure gradient parameter and the Falkner–Skan parameter. The Maxwell fluid model is considered under the impact of natural convection and thermal radiation on the flow over the wedge surface. To solve the mathematical model, the authors used the function of bvp4 in MATLAB software. Also, soft computing techniques, fuzzy particle swarm optimisation and artificial neural networks are used to improve the analysis of predicting the Nusselt number. From the results, it is seen that the radiation parameter has a significant influence on the heat transfer rate. Across all studies, for both artificial neural networks (ANN) and fuzzy particle swarm optimization (FPSO), the mean squared error (MSE) values are very close to 0 and the coefficient of correlation (R) values are very close to 1, indicating a low error in the forecasts.