Projection and Quasi-Compressibility Methods for Solving the Incompressible Navier-Stokes Equations
Projection methods had been introduced in the late sixties by A. Chorin and R. Teman to decouple the computation of velocity and pressure within the time-stepping for solving the nonstationary Navier-Stokes equations. Despite the good performance of proje
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Andreas Prohl
Projection and QuasiCompressibility Methods for Solving the Incompressible NavierStokes Equations
Advances in Numerical Mathematics Andreas Prohl Projection and Quasi-Compressibility Methods for Solving the Incompressible Navier-Stokes Equations
Projection and Quasi-Compressibility Methods for Solving the Incompressible Navier-Stokes Equations Von Dr. rer. nat. Andreas Prohl University of Minnesota
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Springer Fachmedien Wiesbaden GmbH 1997
Dr. rer. nat. Andreas Prahl Geboren 1968 in Lübeck. Von 1988 bis 1993 Studium der Mathematik und Physik an der Ruprecht-Karls-Universität Heidelberg,1993 Diplom in Mathematik. Von 1993 bis 1996 Stipendiat des Graduiertenkollegs "ModelIierung und Wissenschaftliches Rechnen in Mathematik und Naturwissenschaften" am Interdisziplinären Zentrum für Wissenschaftliches Rechnen (IWR) in Heidelberg, 1996 Promotion. Seit 1996 Forschungsstipendiat der DFG am Institute for Mathematics and its Applications (IMA) der University of Minnesota, Minneapolis (USA).
Die Deutsche Bibliothek - CIP-Einheitsaufnahme Prohl, Andreas: Projection and quasi-compressibility methods for solving the incompressible Navier-Stokes equations / von Andreas Prohl. (Advances in numerical mathematics) ISBN 978-3-519-02723-2 ISBN 978-3-663-11171-9 (eBook) DOI 10.1007/978-3-663-11171-9
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Dedicated to my parents and my sister
Differentibus quaestionibus modi solvendi differentes
Preface The numerical treatment of the evolutionary incompressible Navier-Stokes equations, which determine many practicaIly relevant fluid flows, is an area of considerable interest for industrial as weIl as scientific applications. Important for drawing furt her conclusions for the behavior of certain flows in diverse disciplines such as (astro-)physics, engineering, meteorology, oceanography, or biology is a reliable, robust and efficient numerical model. The goal of computing highly complex flows requires the development of sophisticated algorithms. In general, numerical schemes which do not cause high computational cost, often suffer from stability or reliability problems and vice versa. So, it demands a numerical and physical a-priori knowledge from the user in order to select the "best fitting algorithm" for a particular problem under consideration. The use of knowledge about physical phenomena appearing in a specific problem aIlows the relaxation of some robustness-conditions that otherwise need to be imposed on the numerical scheme in order to ensure reliability with respect to the converg
