Comprehensive analysis of normal shock wave propagation in turbulent non-ideal gas flows with analytical and neural network methods

Abstract

Shock wave propagation in gases through turbulent flow has wide-reaching implications for both theoretical research and practical applications, including aerospace engineering, propulsion systems, and industrial gas processes. The study of normal shock propagation in turbulent flow over non-ideal gas investigates the changes in pressure, density, and flow velocity across the shock wave. The Mach number is derived for the system and explored across various gas molecule quantities and turbulence intensities. This study analytically investigated the normal shock wave propagation in turbulent flow of adiabatic gases with modified Rankine–Hugoniot conditions. Artificial neural network (ANN) techniques are used to estimate the solutions for shock strength and Mach number training validation phases of back-propagated neural networks with the Levenberg–Marquardt algorithm. The results reveal that pressure ratio with density ratio increase for higher values of increase in the turbulence level as well as intermolecular forces. A reverse trend is observed in velocity coefficient after shock in the presence of adiabatic gas. The regression coefficient values obtained using the network model ranged from 0.999 99 to 1, indicating an almost perfect correlation. These findings demonstrate that the ANN can predict the Mach number with high accuracy.