Artificial neural network modeling of MHD stagnation point flow and heat transfer towards a porous stretching sheet

• Main Authors: Elayarani, M., Shanmugapriya, M.
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial neural networks; Back propagation networks; Boundary layers; Computational fluid dynamics; Fluid flow; Heat generation; Heat transfer; Magnetic properties; Magnetohydrodynamics; Neural networks; Nonlinear equations; Numerical prediction; Ordinary differential equations; Parameters; Partial differential equations; Porosity; Prandtl number; Runge-Kutta method; Stagnation point; Stretching; Viscosity
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/1.5127634

In this work, the prediction of the magnetohydrodynamic (MHD) stagnation point flow past a porous stretching surface with heat generation is investigated and analyzed by using some mathematical technique namely shooting method and artificial neural network (ANN). The fluid flow and heat transfer phenomena have been studied numerically to observe the effects of various parameters such as magnetic parameter, porosity parameter, stretching parameter, Prandtl number, Eckert number and heat generation coefficient. Using similarity transformation, the governing boundary layer partial differential equations are converted into a system of coupled non-linear ordinary differential equations and solved numerically using shooting technique together with Runge-Kutta fourth order integration scheme and then ANN is applied to them. Back propagation neural network is used for predicting the desired outputs. The obtained numerical values and the ANN results are in good agreement than the existing numerical results.