AMG for Linear Systems in Engine Flow Simulations

Emans, Maximilian

doi:10.1007/978-3-642-14403-5_37

Cited by 10 publications

(13 citation statements)

References 10 publications

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“…Stand wall function was used to describe heat transfer of wall. Piso 168 algorithm [10,29,30] was adopted here to solve the highly unsteady state flow of combustion 169…”

Section: Simulation Model 165mentioning

confidence: 99%

Comparison and combination of NLPQL and MOGA algorithms for a marine medium-speed diesel engine optimisation

Zhou

Yang

2017

Energy Conversion and Management

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This version is available at https://strathprints.strath.ac.uk/59209/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the

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“…Stand wall function was used to describe heat transfer of wall. Piso 168 algorithm [10,29,30] was adopted here to solve the highly unsteady state flow of combustion 169…”

Section: Simulation Model 165mentioning

confidence: 99%

Comparison and combination of NLPQL and MOGA algorithms for a marine medium-speed diesel engine optimisation

Zhou

Yang

2017

Energy Conversion and Management

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“…The components of the vector (that correspond to cells) are defined as j D 1=% j ; again carries the index of the phase. Whereas system (10) is symmetric, the density scaling in system (11) destroys the symmetry. Problem N2 represents a two-phase flow addressed by this technique.…”

Section: N2-multiphase Flowmentioning

confidence: 99%

“…Roughly speaking, multigrid becomes more effective in these methods because the defect equation is solved by a Krylov-accelerated AMG, that is, by a Krylov method with AMG preconditioning (with a variable number of iterations), instead of being approximated by a recursive application of the multigrid method alone. This adaptive, recursive scheme is referred as the k-cycle AMG.For simulations of weakly compressible flows (with symmetric positive definite matrix of the pressure-correction equation), the performance of this new method is not significantly better than that of the conventional algorithms, see Emans [11,12]. In the present paper, we will examine k-cycle methods for other important classes of flow problems.The treatment of incompressible flows with SIMPLE leads to systems with semi-definite matrices.…”

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confidence: 99%

Krylov‐accelerated algebraic multigrid for semi‐definite and nonsymmetric systems in computational fluid dynamics

Emans

2012

Numerical Linear Algebra App

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SUMMARY This article reports on experiences with aggregation algebraic multigrid relying on Krylov acceleration on each level of the grid hierarchy as preconditioners for linear systems in general purpose fluid flow simulation software. In benchmarks that reflect the requirements of industrial simulations, it is demonstrated that for semi‐definite problems, the performance of recently published algorithms of this type is very attractive but that proposed variants of these algorithms occasionally fail for nonsymmetric problems. A modification leading to reliable solvers is suggested. Copyright © 2012 John Wiley & Sons, Ltd.

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“…Priesching et al [23]. Further information about the setup can be found in Emans [12]. The computations were done with eight parallel processes located on one node of the cluster.…”

Section: Gasoline Engine -Example Imentioning

confidence: 99%

“…The computing times of the whole calculation and that of the solver part only, the iteration count, and the parallel efficiency (defined as e.g. in Emans [12] or Starikovičius et al [26]) of the solver part are shown in Figure 4. The short amv1gm stands for the solver we use for the other calculations in this article; the solver amf1gm is a similar AMG algorithm which uses the same coarsegrid construction method as amv1gm, but employs a f-cycle, see Trottenberg et al [27] for the definition, instead of a v-cycle; algorithm ichpgm is a much simpler solver: It has an ILU(0) preconditioner, see Saad [24], instead of a multigrid method while the solver is still GMRES; this preconditioner is used e.g.…”

Section: Gasoline Engine -Example Imentioning

confidence: 99%

A Novel Simple-Based Pressure-Enthalpy Coupling Scheme for Engine Flow Problems

Emans

Žunič

Basara

et al. 2012

Mathematical Modelling and Analysis

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A novel method in CFD derived from the SIMPLE algorithm is presented. Instead of solving the linear equations for each variable and the pressurecorrection equation separately in a so-called segregated manner, it relies on the solution of a linear system that comprises the discretisation of enthalpy and pressurecorrection equation which are linked through physical coupling terms. These coupling terms reflect a more accurate approximation of the density update with respect to thermodynamics (compared to standard SIMPLE method). We show that the novel method is a reasonable extension of existing CFD techniques for variable density flows based on SIMPLE. The novel method leads to a reduction of the number of iterations of SIMPLE which translates in many – but not in all – cases to a reduction in computing time. We will therefore demonstrate practical advantages and restrictions in terms of computational efficiency for industrial CFD applications in the field of piston engine simulations.

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AMG for Linear Systems in Engine Flow Simulations

Cited by 10 publications

References 10 publications

Comparison and combination of NLPQL and MOGA algorithms for a marine medium-speed diesel engine optimisation

Comparison and combination of NLPQL and MOGA algorithms for a marine medium-speed diesel engine optimisation

Krylov‐accelerated algebraic multigrid for semi‐definite and nonsymmetric systems in computational fluid dynamics

A Novel Simple-Based Pressure-Enthalpy Coupling Scheme for Engine Flow Problems

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