Artificial neural network modeling of MHD slip-flow over a permeable stretching surface

• Published in: Archive of applied mechanics (1991) Vol. 92; no. 7; pp. 2179 - 2189
• Main Authors: Soomro, Feroz Ahmed, Alamir, Mahmoud A., El-Sapa, Shreen, Haq, Rizwan Ul, Soomro, Muhammad Afzal
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022; Springer Nature B.V
• Subjects: Artificial neural networks; Boundary conditions; Classical Mechanics; Coefficient of friction; Engineering; Fluid flow; Heat transfer; Magnetohydrodynamics; Mathematical models; Neural networks; Nonlinear differential equations; Numerical methods; Numerical models; Numerical prediction; Ordinary differential equations; Original; Partial differential equations; Skin friction; Slip flow; Stretching; Theoretical and Applied Mechanics; Thermal conductivity; Slip flow; Artificial neural network; Magnetohydrodynamic; Heat transfer rate; bvp5c
• ISSN: 0939-1533; 1432-0681
• DOI: 10.1007/s00419-022-02168-4

In this work, we consider the flow of magnetohydrodynamic (MHD) fluid over a permeable surface due to continuous stretching. The stretching surface is subject to a constant magnetic field along normal direction and velocity-slip conditions. This flow is governed by nonlinear partial differential equations (PDEs) subject to associated boundary conditions. The similarity transformation technique was applied to obtain their non-dimensional form, coupled with nonlinear ordinary differential equations (ODEs). MATLAB-based program “bvp5c” was then used to obtain their numerical solution. Two artificial neural network models were also presented for predicting the coefficients of skin friction - f ″ 0 and heat transfer rate - θ ′ 0 . The present study revealed that heat transfer rate is decreased due to increases in first- and second-order slip parameters. Results also showed that neural network models can predict thermal conductivity with high accuracy. High R squared values of 0.99 were achieved for predicting coefficients of skin friction - f ″ 0 and heat transfer rate - θ ′ 0 . This shows the effectiveness of neural network models for predicting those characteristics and thus reducing the time required for numerical models for predicting MHD slip flow over a permeable stretching surface. Moreover, in comparison with the other numerical methods, the present ANN model can be applied to more complex mathematical models because it reduces the time and processing capacity required for solving the problem.