ABSTRACT This study enhances the understanding of how heat release and magnetic fields influence shock propagation in turbulent flows, which is essential for advancements in aerospace, fluid dynamics, and magnetohydrodynamics systems. The findings provide insights into shock stability, entropy production, and turbulence dynamics, improving magnetohydrodynamics flow control. The objective is to investigate how heat release and magnetic fields influence normal shock propagation in turbulent compressible flows through analytical modeling and artificial neural network‐based predictions. Governing equations for turbulent compressible flow are derived and integrated with Rankine–Hugoniot relations, incorporating heat release and magnetic flux effects. An artificial neural network using the Levenberg–Marquardt algorithm is trained to predict shock parameters, analyzing shock strength, entropy production, and turbulence dynamics. The results show that heat release strengthens normal shocks, increasing entropy production and post‐shock pressure, while magnetic fields enhance shock compression by raising magnetic pressure and reducing the density ratio. The interaction between heat energy and magnetic forces significantly influences shock stability, turbulence dynamics and entropy generation in turbulent magnetohydrodynamics flows. This study introduces an artificial neural network‐based predictive framework for analyzing shock behavior in turbulent magnetohydrodynamics flows, providing new insights into energy interactions.
Srivastava et al. (2026) studied this question.