Singh / Zeidan | Shock Wave Dynamics | Buch | 978-3-032-42801-1 | www.sack.de

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Singh / Zeidan

Shock Wave Dynamics

From Classical Models to Machine Learning Applications
Erscheinungsjahr 2027
ISBN: 978-3-032-42801-1
Verlag: Springer

From Classical Models to Machine Learning Applications

Buch, Englisch, Format (B × H): 155 mm x 235 mm

ISBN: 978-3-032-42801-1
Verlag: Springer


This book charts a path for next-generation high-speed flow physics and AI-enhanced computational science. It offers a forward-looking exploration of shock wave phenomena, combining rigorous theory, high-fidelity computation, and modern artificial intelligence. Across five parts, it begins with the mathematical foundations of shock mechanics, including continuum and non-equilibrium formulations and characteristic shock dynamics in multiphase flows such as rotational dusty gas. It presents advanced computational methods: high-order WENO schemes, discontinuous Galerkin methods, moment-based DSMC, and the Unified Gas-Kinetic Scheme as well as shock–interface interactions, shock–vortex dynamics, and instability development. It also shows how data-driven modeling and physics-informed neural networks improve predictive accuracy, with applications to curved converging channels and Fanno flow. The book is written for researchers, graduate students, and industry professionals in aerospace engineering, CFD, applied mathematics, and gas dynamics.

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Introduction to Shock Wave Dynamics.- Mathematical Foundations of Shock Wave Propagation.- Characteristic Shock Dynamics and Singular Surface Interaction in Rotational Dusty Gas Flow.- Multi Scale Simulation of Shock Aerospike Flow Interactions Using the Regularized 13 Moment Equations.- High Order WENO Type Schemes for Compressible Shock Dynamics.


Satyvir Singh is a Research Associate Fellow at the Institute of Applied and Computational Mathematics, RWTH Aachen University, Germany. He holds a Ph.D. in Computational Fluid Mechanics from Gyeongsang National University, South Korea, an M.Tech. in Industrial Mathematics & Scientific Computing from the Indian Institute of Technology Madras, and an M.Sc. in Mathematics from CCS University Meerut, India. He has previously held research positions at Nanyang Technological University, Singapore, and Gyeongsang National University, South Korea. His research expertise spans computational fluid dynamics, high-order numerical methods, shock-driven hydrodynamic instabilities, non-equilibrium gas dynamics, and physics-informed machine learning — all of which are central themes of this volume. He has authored more than 65 peer-reviewed articles with over 950 citations in leading journals including Physics of Fluids, Journal of Computational Physics, Physical Review Fluids, and Computers & Fluids. He is recognized among the top 2% most influential researchers worldwide according to the Stanford University/Scopus ranking for 2025. His academic profiles are accessible via ORCID (0000-0001-6669-5296), Google Scholar, and ResearchGate.

Dia Zeidan is a Professor of Applied and Computational Mathematics at the German Jordanian University, Amman, Jordan. He is Elected Fellow of the European Society of Computational Methods in Sciences and Engineering. An active researcher in developing mathematical and numerical tools for multiphase fluid flow problems for several years, he is recognized for research contributions in applied and computational mathematics with multiphase flows including his creative approaches to teaching and research. His work has been highly interdisciplinary, involving international collaborations with applied and computational researchers. Among various institutional obligations, he has been a visitor to several important international research groups bridging national research infrastructure gaps in Jordan. He serves on several expert review panels, as Technical Editor and Reviewer for several peer-reviewed journals, and as Member of several program committees of technical conferences around the world.



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