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

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, horizontal ellipsis etc.) have significant effects on the shock wave characteristics.

Automated summary

The features of dust ion acoustic shock waves in magnetized dusty plasma were investigated using the Cellular Neural Network (CNN) method, deriving the Zakharov Kuznetsov Burger (ZKB) equation with the reductive perturbation method and simulating it with high accuracy through integration with the Finite Difference Method. The plasma parameters were found to have significant effects on the shock wave characteristics, with the obtained solution closely matching the analytical solution. The accuracy of the CNN method was assessed by employing an algorithm to solve the ZKB equation using the Finite Difference Method.