Shock Wave Dynamics
From Classical Models to Machine Learning Applications
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Beschreibung
From Classical Models to Machine Learning Applications offers a comprehensive and forward-looking exploration of shock wave phenomena by integrating rigorous theoretical foundations, high-fidelity computational modeling, and modern artificial intelligence techniques. Designed to bridge classical shock wave physics with emerging data-driven methodologies, this volume provides a unified framework for understanding, analyzing, and predicting complex shock-driven processes across a broad range of contemporary scientific and engineering applications.
Structured into five cohesive parts, the book begins by establishing the theoretical and mathematical foundations of shock wave mechanics, covering continuum and non-equilibrium formulations alongside characteristic shock dynamics in multi-phase flows such as rotational dusty gas. It then advances to computational and experimental advancements, presenting high-order WENO-type schemes, high-resolution Discontinuous Galerkin methods, moment-based DSMC simulations, and the Unified Gas-Kinetic Scheme — tools that collectively push the boundaries of shock wave modeling from continuum to rarefied flow regimes. Practical engineering relevance is demonstrated through detailed numerical investigations of shock structure in supersonic intakes for ramjet-assisted artillery systems.
The third part examines shock interactions and instabilities, exploring shock-driven hydrodynamic evolution of complex interfaces and shock–vortex interaction dynamics across varying shock intensities, providing deep physical insight into vorticity generation, energy redistribution, and instability development in compressible flows. A dedicated section then explores the transformative role of artificial intelligence in shock wave research, demonstrating how data-driven modeling and Physics-Informed Neural Networks (PINNs) enhance predictive accuracy and enable the effective resolution of complex compressible flow phenomena, including shock wave dynamics in curved converging channels and Fanno flow systems. The final part offers a visionary outlook on emerging trends and open challenges, identifying future research directions at the frontier of shock wave science, advanced numerics, and scientific machine learning.
By combining rigorous mathematical formulations, state-of-the-art computational strategies, and AI-driven innovations, this book delivers deep insights into multi-scale shock dynamics — spanning rarefied
gas effects, shock-induced instabilities, kinetic non-equilibrium phenomena, and high-speed engineering applications. It serves as an essential resource for researchers, graduate students, and industry professionals in aerospace engineering, computational fluid dynamics, applied mathematics, gas dynamics, and related interdisciplinary fields.
Published by Springer Nature, this volume not only consolidates current advances in shock wave research but also charts a clear and inspiring course for next-generation developments in high-speed flow physics, predictive modeling, and AI-enhanced computational science
From Classical Models to Machine Learning Applications offers a comprehensive and forward-looking exploration of shock wave phenomena by integrating rigorous theoretical foundations, high-fidelity computational modeling, and modern artificial intelligence techniques. Designed to bridge classical shock wave physics with emerging data-driven methodologies, this volume provides a unified framework for understanding, analyzing, and predicting complex shock-driven processes across a broad range of contemporary scientific and engineering applications.
Structured into five cohesive parts, the book begins by establishing the theoretical and mathematical foundations of shock wave mechanics, covering continuum and non-equilibrium formulations alongside characteristic shock dynamics in multi-phase flows such as rotational dusty gas. It then advances to computational and experimental advancements, presenting high-order WENO-type schemes, high-resolution Discontinuous Galerkin methods, moment-based DSMC simulations, and the Unified Gas-Kinetic Scheme — tools that collectively push the boundaries of shock wave modeling from continuum to rarefied flow regimes. Practical engineering relevance is demonstrated through detailed numerical investigations of shock structure in supersonic intakes for ramjet-assisted artillery systems.
The third part examines shock interactions and instabilities, exploring shock-driven hydrodynamic evolution of complex interfaces and shock–vortex interaction dynamics across varying shock intensities, providing deep physical insight into vorticity generation, energy redistribution, and instability development in compressible flows. A dedicated section then explores the transformative role of artificial intelligence in shock wave research, demonstrating how data-driven modeling and Physics-Informed Neural Networks (PINNs) enhance predictive accuracy and enable the effective resolution of complex compressible flow phenomena, including shock wave dynamics in curved converging channels and Fanno flow systems. The final part offers a visionary outlook on emerging trends and open challenges, identifying future research directions at the frontier of shock wave science, advanced numerics, and scientific machine learning.
By combining rigorous mathematical formulations, state-of-the-art computational strategies, and AI-driven innovations, this book delivers deep insights into multi-scale shock dynamics — spanning rarefied
gas effects, shock-induced instabilities, kinetic non-equilibrium phenomena, and high-speed engineering applications. It serves as an essential resource for researchers, graduate students, and industry professionals in aerospace engineering, computational fluid dynamics, applied mathematics, gas dynamics, and related interdisciplinary fields.
Published by Springer Nature, this volume not only consolidates current advances in shock wave research but also charts a clear and inspiring course for next-generation developments in high-speed flow physics, predictive modeling, and AI-enhanced computational science
Bridges theory and application for tackling complex shock wave problems with case studies and computational exercises Highlights emerging trends like quantum computing, AI modeling, and next-gen experiments for cutting-edge research Explores machine learning methods like deep learning, PINNs, and reinforcement learning for improved shock wave modeling
Autor*in
Satyvir Singh
Themen in »Shock Wave Dynamics«
Computational Fluid Dynamics Machine Learning in Fluid Dynamics Physics-Informed Neural Networks Non-Equilibrium Thermodynamics Turbulence Modeling Rarefied Gas Dynamics Multi-Scale Shock Interactions High-Fidelity Simulations Quantum Computing for Shock Wave Analysis Multi-Phase Shock Dynamics Data-Driven Approaches in Shock Wave Science