An Artificial Neural Network Solution for the Casson Fluid Flow Past a Radially Stretching Sheet with Magnetic and Radiation Effect
D. Srinivasacharya, Ramesh Kumar
Abstract
Abstract The Casson fluid flow past a radially stretching sheet in the presence of an applied magnetic field and thermal radiation is investigated. Artificial neural networks are used to compute the solution of the flow. A multilayer perceptron neural network with adjustable parameters is used in the trial functions. The Adams optimization (adaptive moment estimation) algorithm is used to determine adjustable parameters of the trial solution. The results of the current method are validated utilizing shooting method in conjunction with the Runge–Kutta fourth-order method. The conclusions demonstrate that the artificial neural network-based method provides significant accuracy and that the effectiveness of the solution improves as the number of neurons in the neural network grows. The impact of relevant parameters on the physical quantities is displayed through graphs. As per the present research, enhancing the radiation parameter causes an increase in skin friction and Nusselt number, while boosting the magnetic parameter causes decrease in skin friction and Nusselt number.