A comparative analysis of magnetohydrodynamic jeffery-hamel blood flow: solutions from HPM to ANN

Studying magnetic effects on blood flow through geometrically varying arteries reveals how vessel shape influences circulation. However, it is difficult to find accurate and efficient solutions for these complex flow patterns. In this study, we look at magnetohydrodynamic Jeffery-Hamel blood flow in an arterial channel by combining analytical solutions, numerical checks, and neural network predictions. We solve the main equation using the Homotopy Perturbation Method (HPM) and compare these results with numerical solutions from the BVP5c method, finding that they match well. Using more HPM iterations makes the results more accurate and stable. We then use the data to train a neural network time-series model with the Levenberg-Marquardt algorithm. Bayesian Regularization gives the best predictions, with low error and strong agreement with the reference data. This combined approach offers an efficient way to analyze and predict complex blood flow in arteries under magnetic fields.