High-performance computing of big data for turbulence and combustion / Sergio Pirozzoli, Tapan K. Sengupta, editors.
2019
TL574.T8 H55 2019
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Details
Title
High-performance computing of big data for turbulence and combustion / Sergio Pirozzoli, Tapan K. Sengupta, editors.
ISBN
3030170128 (electronic book)
9783030170127 (electronic book)
9783030170110
9783030170127 (electronic book)
9783030170110
Published
Cham, Switzerland : Springer, [2019]
Copyright
©2019
Language
English
Description
1 online resource (257 pages) : illustrations.
Other Standard Identifiers
10.1007/978-3-030-17 doi
Call Number
TL574.T8 H55 2019
Dewey Decimal Classification
629.1323
532.0527
532.0527
Summary
This book provides state-of-art information on high-accuracy scientific computing and its future prospects, as applicable to the broad areas of fluid mechanics and combustion, and across all speed regimes. Beginning with the concepts of space-time discretization and dispersion relation in numerical computing, the foundations are laid for the efficient solution of the Navier-Stokes equations, with special reference to prominent approaches such as LES, DES and DNS. The basis of high-accuracy computing is rooted in the concept of stability, dispersion and phase errors, which require the comprehensive analysis of discrete computing by rigorously applying error dynamics. In this context, high-order finite-difference and finite-volume methods are presented. Naturally, the coverage also includes fundamental notions of high-performance computing and advanced concepts on parallel computing, including their implementation in prospective hexascale computers. Moreover, the book seeks to raise the bar beyond the pedagogical use of high-accuracy computing by addressing more complex physical scenarios, including turbulent combustion. Tools like proper orthogonal decomposition (POD), proper generalized decomposition (PGD), singular value decomposition (SVD), recursive POD, and high-order SVD in multi-parameter spaces are presented. Special attention is paid to bivariate and multivariate datasets in connection with various canonical flow and heat transfer cases. The book mainly addresses the needs of researchers and doctoral students in mechanical engineering, aerospace engineering, and all applied disciplines including applied mathematics, offering these readers a unique resource.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Description based on online resource; title from digital title page (viewed on June 18, 2019).
Series
Courses and lectures ; no. 592.
Available in Other Form
High-Performance Computing of Big Data for Turbulence and Combustion
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Table of Contents
Focusing Phenomenon in Numerical Solution of Two-Dimensional Navier-Stokes Equation
Space-Time Resolution for Transitional and Turbulent Flows
Finite Difference Methods for Incompressible and Compressible Turbulence
Physical and Numerical Instabilities in Simulations of Re-acting and non-Reacting Flows
Low-Rank Approximation of Multidimensional Data.
Space-Time Resolution for Transitional and Turbulent Flows
Finite Difference Methods for Incompressible and Compressible Turbulence
Physical and Numerical Instabilities in Simulations of Re-acting and non-Reacting Flows
Low-Rank Approximation of Multidimensional Data.