Efficient Parallel Algorithms for Unsteady Incompressible Flows

Jean-Luc Guermond; Peter D. Minev

doi:10.1007/978-1-4614-7172-1_10

Efficient Parallel Algorithms for Unsteady Incompressible Flows

Jean-Luc Guermond, Peter D. Minev

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

The objective of this paper is to give an overview of recent developments on splitting schemes for solving the time-dependent incompressible Navier–Stokes equations and to discuss possible extensions to the variable density/viscosity case. A particular attention is given to algorithms that can be implemented efficiently on large parallel clusters.

Original language	English (US)
Title of host publication	Numerical Solution of Partial Differential Equations: Theory, Algorithms, and Their Applications
Publisher	Springer Nature
Pages	185-201
Number of pages	17
ISBN (Print)	9781461471714
DOIs	https://doi.org/10.1007/978-1-4614-7172-1_10
State	Published - May 12 2013
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This material is based upon work supported by the National ScienceFoundation grants DMS-0713829, by the Air Force Office of Scientific Research, USAF, undergrant/contract number FA9550-09-1-0424, and a discovery grant of the National Science andEngineering Research Council of Canada. This publication is also partially based on worksupported by Award No. KUS-C1-016-04, made by King Abdullah University of Science andTechnology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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10.1007/978-1-4614-7172-1_10

Cite this

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title = "Efficient Parallel Algorithms for Unsteady Incompressible Flows",

abstract = "The objective of this paper is to give an overview of recent developments on splitting schemes for solving the time-dependent incompressible Navier–Stokes equations and to discuss possible extensions to the variable density/viscosity case. A particular attention is given to algorithms that can be implemented efficiently on large parallel clusters.",

author = "Jean-Luc Guermond and Minev, {Peter D.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: This material is based upon work supported by the National ScienceFoundation grants DMS-0713829, by the Air Force Office of Scientific Research, USAF, undergrant/contract number FA9550-09-1-0424, and a discovery grant of the National Science andEngineering Research Council of Canada. This publication is also partially based on worksupported by Award No. KUS-C1-016-04, made by King Abdullah University of Science andTechnology (KAUST). This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

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doi = "10.1007/978-1-4614-7172-1_10",

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AU - Minev, Peter D.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: This material is based upon work supported by the National ScienceFoundation grants DMS-0713829, by the Air Force Office of Scientific Research, USAF, undergrant/contract number FA9550-09-1-0424, and a discovery grant of the National Science andEngineering Research Council of Canada. This publication is also partially based on worksupported by Award No. KUS-C1-016-04, made by King Abdullah University of Science andTechnology (KAUST). This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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AB - The objective of this paper is to give an overview of recent developments on splitting schemes for solving the time-dependent incompressible Navier–Stokes equations and to discuss possible extensions to the variable density/viscosity case. A particular attention is given to algorithms that can be implemented efficiently on large parallel clusters.

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BT - Numerical Solution of Partial Differential Equations: Theory, Algorithms, and Their Applications

PB - Springer Nature

ER -

Efficient Parallel Algorithms for Unsteady Incompressible Flows

Abstract

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