2015
DOI: 10.1016/j.jcp.2015.04.035
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A new class of finite element variational multiscale turbulence models for incompressible magnetohydrodynamics

Abstract: New large eddy simulation (LES) turbulence models for incompressible magnetohydrodynamics (MHD) derived from the variational multiscale (VMS) formulation for finite element simulations are introduced. The new models include the variational multiscale formulation, a residual-based eddy viscosity model, and a mixed model that combines both of these component models. Each model contains terms that are proportional to the residual of the incompressible MHD equations and is therefore numerically consistent. Moreove… Show more

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Cited by 19 publications
(16 citation statements)
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References 70 publications
(142 reference statements)
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“…The first transient MHD test case is a Taylor‐Green vortex generalized to MHD as described in the work of Pouquet et al The Taylor‐Green MHD vortex problem is useful for studying characteristics of the turbulent total energy decay spectrum, and the interchange of kinetic and magnetic energy for the system (see, eg, the work of Pouquet et al and the references contained therein). We employ the same domain and initial conditions as described in the work of Sondak et al This MHD flow is initialized with a large fluid vortex and associated magnetic field structure that decays into a fine scale turbulent MHD flow. This problem begins with smooth initial conditions for the velocity field and magnetic induction alignleftalign-1u(x,t=0)=u0sinfalse(xfalse)cosfalse(yfalse)cosfalse(zfalse)cosfalse(xfalse)sinfalse(yfalse)cosfalse(zfalse)0,align-2align-1B(x,t=0)=B0cosfalse(xfalse)sinfalse(yfalse)sinfalse(zfalse)sinfalse(xfalse)cosfalse(yfalse)sinfalse(zfalse)2sinfalse(xfalse)sinfalse(yfalse)cosfalse(zfalse).align-2 The domain is a periodic box of size [− π , π ] 3 with Reynolds number Re =1800 and magnetic Reynolds number Re m =1800.…”
Section: Resultsmentioning
confidence: 99%
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“…The first transient MHD test case is a Taylor‐Green vortex generalized to MHD as described in the work of Pouquet et al The Taylor‐Green MHD vortex problem is useful for studying characteristics of the turbulent total energy decay spectrum, and the interchange of kinetic and magnetic energy for the system (see, eg, the work of Pouquet et al and the references contained therein). We employ the same domain and initial conditions as described in the work of Sondak et al This MHD flow is initialized with a large fluid vortex and associated magnetic field structure that decays into a fine scale turbulent MHD flow. This problem begins with smooth initial conditions for the velocity field and magnetic induction alignleftalign-1u(x,t=0)=u0sinfalse(xfalse)cosfalse(yfalse)cosfalse(zfalse)cosfalse(xfalse)sinfalse(yfalse)cosfalse(zfalse)0,align-2align-1B(x,t=0)=B0cosfalse(xfalse)sinfalse(yfalse)sinfalse(zfalse)sinfalse(xfalse)cosfalse(yfalse)sinfalse(zfalse)2sinfalse(xfalse)sinfalse(yfalse)cosfalse(zfalse).align-2 The domain is a periodic box of size [− π , π ] 3 with Reynolds number Re =1800 and magnetic Reynolds number Re m =1800.…”
Section: Resultsmentioning
confidence: 99%
“…This construction implies that the unresolved scales are driven by the resolved scale residuals and therefore the exact continuous solution satisfies the system of partial differential equations (PDEs) and is for this reason consistent in this sense . The benefit of this representation is that VMS methods provide stabilization of the Galerkin FE weak form for convective‐type operators, the stabilization of the saddle‐point coupling between ( u , P ) and ( B , ψ ) that allows the use of trilinear hexahedral FE basis functions for all of the variables, and in the context of turbulence modeling provides a means of dynamically modeling subgrid scale interaction terms …”
Section: Resistive Mhd Model Equations and Discretizationmentioning
confidence: 99%
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“…Our recent work has developed scalable simulations for resistive MHD models . To incorporate such simulations into generator design, a designer must understand the sensitivities of model predictions to changes in model input parameters.…”
Section: Introductionmentioning
confidence: 99%