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<p>This study investigates the behavior of normal shock waves in high-temperature, compressible turbulent flows influenced by chemical reactions. Understanding such reacting shocks is essential for the design of hypersonic vehicles, propulsion systems, and magnetohydrodynamic energy devices, where heat release strongly alters shock strength. A modified Rankine–Hugoniot framework is developed by incorporating temperature-dependent chemical heat addition, enabling unified analysis of exothermic and endothermic effects on shock jumps. Artificial Neural Network methods are applied in combination with an optimised back-propagation algorithm employing the Levenberg–Marquardt approach to forecast shock intensity and Mach number to improve predictive performance. The combined consideration of turbulence and chemical heat-release effects is the unique aspect of this work. This study also determines entropy generation and shock parameters like Mach number, velocity variation and ultimate compression. In contrast to exothermic processes, shock jumps for endothermic reactions are observed to increase as the Arrhenius number rises. This paper compares analytical and numerical results and concludes that the linearized model is valid up to <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\text {Ar}=5\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mtext>Ar</mtext> <mo>=</mo> <mn>5</mn> </mrow> </math></EquationSource> </InlineEquation> for exothermic reactions and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\text {Ar}=7\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mtext>Ar</mtext> <mo>=</mo> <mn>7</mn> </mrow> </math></EquationSource> </InlineEquation> for endothermic reactions with maximum errors of 27% and 14% respectively. The findings have wide implications for propulsion systems and high-speed aerodynamics.</p>

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Propagation of normal shock waves in chemically reactive turbulent ideal gas using analytical and neural models

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Tanya Srivastava,
  • Nilam Venkata Koteswararao

摘要

This study investigates the behavior of normal shock waves in high-temperature, compressible turbulent flows influenced by chemical reactions. Understanding such reacting shocks is essential for the design of hypersonic vehicles, propulsion systems, and magnetohydrodynamic energy devices, where heat release strongly alters shock strength. A modified Rankine–Hugoniot framework is developed by incorporating temperature-dependent chemical heat addition, enabling unified analysis of exothermic and endothermic effects on shock jumps. Artificial Neural Network methods are applied in combination with an optimised back-propagation algorithm employing the Levenberg–Marquardt approach to forecast shock intensity and Mach number to improve predictive performance. The combined consideration of turbulence and chemical heat-release effects is the unique aspect of this work. This study also determines entropy generation and shock parameters like Mach number, velocity variation and ultimate compression. In contrast to exothermic processes, shock jumps for endothermic reactions are observed to increase as the Arrhenius number rises. This paper compares analytical and numerical results and concludes that the linearized model is valid up to \(\text {Ar}=5\) Ar = 5 for exothermic reactions and \(\text {Ar}=7\) Ar = 7 for endothermic reactions with maximum errors of 27% and 14% respectively. The findings have wide implications for propulsion systems and high-speed aerodynamics.