High-performance computing of big data for turbulence and combustion
Contributor(s): Pirozzoli, Sergio [ed.] | Sengupta, Tapan K. [ed.].
Series: CISM International Centre for Mechanical Sciences: courses and lectures. / edited by Elisabeth Guazzelli; v.592.Publisher: Switzerland Springer 2019Description: ix, 250p.ISBN: 9783030170110.Subject(s): Turbulence | Jets -- Fluid dynamicsDDC classification: 629.1323 | H537 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.Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|
Books | PK Kelkar Library, IIT Kanpur | General Stacks | 629.1323 H537 (Browse shelf) | Available | A185023 |
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629.1323 G95f Fundamentals of modern unsteady aerodynamics | 629.1323 H191I INTRODUCTION TO THE FLIGHT DYNAMICS OF RIGID AEROPLANES | 629.1323 H537 HIGH-SPEED FLIGHT PROPULSION SYSTEMS | 629.1323 H537 High-performance computing of big data for turbulence and combustion | 629.1323 H814 FURTHER AERODYNAMICS FOR ENGINEERING STUDENTS | 629.1323 H814a3 Aerodynamics for engineering students [3rd ed.] | 629.1323 H814a3 cop.1 Aerodynamics for engineering students [3rd ed.] |
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.
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