Investigation of dust ion acoustic shock waves in dusty plasma using Cellular Neural Network

Abstract

The Cellular Neural Network (CNN) is implemented to investigate the features of dust ion acoustic shock waves in a two-fluid model of magnetized dusty plasma. The electrons in this model obey the hybrid Cairns-Tsallis distribution. The reductive perturbation method is used to derive the corresponding Zakharov Kuznetsov Burger (ZKB) equation. Then, the CNN algorithm is integrated with the Finite Difference Method to simulate the ZKB equation with a high accuracy. The obtained solution is approximately identical to the analytical solution which is obtained from the Tanh method. An algorithm to solve ZKB equation using the Finite Difference Method is employed to asses the accuracy of the CNN method. Moreover, it is found that the plasma parameters (viscosity coefficients, cyclotron frequency, nonextensive parameter,…etc.) have significant effects on the shock wave characteristics.