High-Performance Computing of Big Data for Turbulence and Combustion
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High-Performance Computing of Big Data for Turbulence and Combustion

High-Performance Computing of Big Data for Turbulence and Combustion


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About the Book

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.

About the Author:

Sergio Pirozzoli is professor of Fluid Dynamics at Sapienza University of Rome, Italy. His research interests focus on the numerical simulation of incompressible and compressible turbulent flows, having carried out the first DNS of shock/boundary layer interactions in 2004. His expertise includes the development of energy-conserving discretizations for the compressible Navier-Stokes equations. He has been involved as leader of research units in several research projects funded by the European Union focusing of the understanding of unsteady effects in turbulent and transitional SBLI.

Tapan K. Sengupta is head of the High Performance Computing Laboratory at IIT Kanpur, India. His research interests focus on developing high accuracy computing methods to aid in bridging the gap between theoretical and computational fluid dynamics and heat transfer. He has published over 100 papers in international refereed journals covering diverse topics such as transition and turbulence, unsteady aerodynamics, flow control, CFD and numerical methods, mixed convection and compressible flows.


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Product Details
  • ISBN-13: 9783030170110
  • Publisher: Springer Nature Switzerland AG
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Spine Width: 16 mm
  • Width: 156 mm
  • ISBN-10: 303017011X
  • Publisher Date: 11 Jun 2019
  • Height: 234 mm
  • No of Pages: 250
  • Series Title: CISM International Centre for Mechanical Sciences
  • Weight: 594 gr


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High-Performance Computing of Big Data for Turbulence and Combustion
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High-Performance Computing of Big Data for Turbulence and Combustion
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