Evaluating Joule heating influence on heat transfer and entropy generation in MHD channel flow: A parametric study and ill-posed problem solution using PINNs

Abstract

In this study the effects of Joule heating parameter on entropy generation and heat transfer in MHD flow inside a channel is investigated by means of Physics-Informed Neural Networks (PINNs) in form of a parametric analysis in addition to exploring the solution to the ill-posed problem. All of the governing equations are reformulated in terms of first order derivatives and the dimensionless form of the governing equations has been employed to further lessen the number of parameters and achieve better compatibility with loss function terms. Dimensionless groups such as Reynolds number, Prandtl number, Hartmann number and Joule heating parameter have been designated as the input for the neural network in order to perform the parametric study. Besides achieving high accuracy for case of parameters confined in the predefined ranges, the generalization ability of the method is depicted by solving the problem for the cases where the dimensionless parameters were outside of the assumed ranges. Moreover, the ability of handling Neumann boundary conditions is also investigated in the present study despite being neglected prevalently in the literature concerning PINNs. The effects of Joule heating parameter on entropy generation are researched using a parametric approach utilizing PINNs which is another novel aspect of the article at hand. As a concluding remark, an ill-posed problem is also defined such that the Joule heating parameter is included in a learning process and the method has been able to determine Joule heating parameter as a parameter of interest alongside other learnable parameters of the neural network such as weights and biases