A Practical Guide to Large Scale Computational Fluid Dynamics

Ian Eames,Christian Klettner,Andre Nicolle

ISBN 10: 1119212111 / ISBN 13: 9781119212119

Editore: Wiley, 2021

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Codice articolo S_312290291

Dati bibliografici

• Titolo: A Practical Guide to Large Scale Computational Fluid Dynamics
• Editore: Wiley
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 1119212111
• ISBN 13: 9781119212119
• Rilegatura: digital
• Condizione: Good

Descrizione articolo

Riassunto

A practical guide to large scale computational fluid dynamics

This book is a practical guide to large scale computational fluid dynamics which covers the main elements in writing large scale efficient fluid dynamics codes before considering the applications of these codes.

A Practical Guide to Large Scale Computational Fluid Dynamics begins with an overview of fluid mechanics and the different methods (experimental, analytical and numerical) of analyzing fluid problems. It provides an introduction to the finite element method and the computational challenges encountered when writing largescale code and handling large data sets. The qualitative and quantitative diagnostics, which are essential to gaining physical insight, are presented and given in the fields of turbulence, fluid-structure interaction and free-surface flows. Finally, future trends are considered.

Key

Review of programming paradigms and open source high performance libraries which can be used to cut code development time. Extensive presentation of diagnostics which will help both numerical and experimental researchers. Provides validation cases which include a comprehensive list of common benchmark examples. Conceptual challenges from turbulent flows, fluid structure interaction and free surface flows are covered. Current state of the art research is described. Accompanied by a website hosting software and tutorials. The book is essential reading for postgraduate students, post-doctoral researchers and principal investigators who are writing large scale fluid mechanics codes and working with large datasets.