Analysis of the thermal distribution of a porous radial fin influenced by an inclined magnetic field with neural computing.

Fins and radial fins are versatile engineering components that significantly enhance heat transfer and thermal management in diverse applications, hence improving efficiency and performance across several sectors. This study examines the temperature distribution in a radial porous fin under steady-state conditions, evaluating the impact of several significant parameters by utilizing a novel methodology. We specifically introduce an inclined magnetic field and examine the effects of convection and internal heat generation on the thermal behavior of the fin. We employ the Levenberg Marquard Backpropagation Neural Network Algorithm. We initially obtain the data with the bvp4c solver. This novel methodology demonstrates commendable performance, by its mean squared error and its gradient which are mentioned in their figures along with absolute error. Furthermore, increase in the parameters of heat generation and ambient temperature, results in a tendency for the temperature profile to rise. In contrast, as convection-conduction parameter, porosity parameter and Hartmann number increase, the temperature profile decreases. This innovative approach offers a sophisticated solution for complex thermal models, improved prediction accuracy for nonlinear heat transfer, parameter-driven optimization in porous media heat transfer, and increased model efficiency for real-time thermal management.