Fr. 89.00

Finite Difference Methods for Compressible Two-Fluid Dynamics

English, German · Hardback

Will be released 22.05.2025

Description

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Finite Difference Methods for Compressible Two-Fluid Dynamics provides the essentials of high-order numerical methods for compressible single-fluid and two-fluid transport phenomena. This book can serve as a first course on the numerical methods for transport phenomena or fluid dynamics for students in mechanical, aerospace, and chemical engineering, applied mathematics, and physics at the senior level of an undergraduate or graduate degree. It also provides foundations and algorithmic details for implementing the most recent numerical schemes for compressible flows and extending them to include other physics, such as elasticity, reaction, and magnetohydrodynamics.
The book's presented schemes enable computations for broad applications, including shock-induced interfacial instability and turbulence, shock-bubble interactions, and detonation, to name a few. For a broad reach and impact, the numerical schemes satisfy the simultaneous requirements of simplicity, extendibility, and efficiency on serial and parallel computers. The physics of the compressible single- and two-fluid system also guide the design and analysis of the numerical methods. The enabled direct numerical simulations also help obtain accurate data for tuning the emerging physics-based neuromorphic algorithms.

List of contents

Introduction and Basic Dynamics of Compressible Medium.- Interpolation and High-Order Non-Linear Finite Difference Schemes.-Approximation On and Near Boundary.- High-Order Time Integration Methods.- High-order Nonlinear Schemes for Nonlinear Conservation Laws.- Schemes for Compressible Two-Fluid Model.- Schemes for Compressible Two-Fluid Navier-Stokes Equations.-Thermodynamic Property Relations for Two-Phase Modeling.- Mixture Theory Modeling of Compressible Two-Phase Systems.

About the author

Khosro Shahbazi received his Ph.D. in Mechanical Engineering from the University of Toronto. He held postdoctoral positions in the Division of Applied Mathematics at Brown University (Providence, Rhode Island) and Mechanical Engineering at the University of Wyoming. He is a tenured associate professor of Mechanical Engineering at the South Dakota School of Mines and Technology and a fellow of the Japan Society for Science Promotion.
His research has appeared in the Journal of Computational Physics, SIAM Journal on Scientific Computing, Computer and Fluids, Numerical Methods for Partial Differential Equations, and International Journal of Heat Transfer. He has has chaired or co-chairs sessions at APS Fluid Dynamics Meetings, the AIAA conference, and served on two NSF review panels on Data-enabled Computational Science and Engineering and Fluid Dynamics.

Summary

Finite Difference Methods for Compressible Two-Fluid Dynamics provides the essentials of high-order numerical methods for compressible single-fluid and two-fluid transport phenomena. This book can serve as a first course on the numerical methods for transport phenomena or fluid dynamics for students in mechanical, aerospace, and chemical engineering, applied mathematics, and physics at the senior level of an undergraduate or graduate degree. It also provides foundations and algorithmic details for implementing the most recent numerical schemes for compressible flows and extending them to include other physics, such as elasticity, reaction, and magnetohydrodynamics.
The book's presented schemes enable computations for broad applications, including shock-induced interfacial instability and turbulence, shock-bubble interactions, and detonation, to name a few. For a broad reach and impact, the numerical schemes satisfy the simultaneous requirements of simplicity, extendibility, and efficiency on serial and parallel computers. The physics of the compressible single- and two-fluid system also guide the design and analysis of the numerical methods. The enabled direct numerical simulations also help obtain accurate data for tuning the emerging physics-based neuromorphic algorithms.

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