A library for wall-modelled large-eddy simulation based on OpenFOAM technology

Mukha, Timofey; Rezaeiravesh, Saleh; Liefvendahl, Mattias

doi:10.1016/j.cpc.2019.01.016

Cited by 64 publications

(26 citation statements)

References 54 publications

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“…A more detailed discussion on how the described wall modeling fits into the framework of finite volume discretization can be found in [34]. For the details of the open-source implementation of this and also several other wall models within the framework of OpenFOAM, see [44].…”

Section: Wall Modelingmentioning

confidence: 99%

“…An important question is whether the proposed guidelines, which are based on a study of a single flow at a single Re-number, are applicable for WMLES of other flow configurations. Currently, this has been shown to be the case for simulations of the zero-pressure-gradient (ZPG)-TBL flow [43] and of the flow over a backward-facing step [44]. Considering a wider range of flows is a subject for future work.…”

Section: Best Practice Guidelines For Wmlesmentioning

confidence: 99%

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Systematic study of accuracy of wall-modeled large eddy simulation using uncertainty quantification techniques

2019

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The predictive accuracy of wall-modeled large eddy simulation is studied by systematic simulation campaigns of turbulent channel flow. The effect of wall model, grid resolution and anisotropy, numerical convective scheme and subgrid-scale modeling is investigated. All of these factors affect the resulting accuracy, and their action is to a large extent intertwined. The wall model is of the wall-stress type, and its sensitivity to location of velocity sampling, as well as law of the wall's parameters is assessed. For efficient exploration of the model parameter space (anisotropic grid resolution and wall model parameter values), generalized polynomial chaos expansions are used to construct metamodels for the responses which are taken to be measures of the predictive error in quantities of interest (QoIs). The QoIs include the mean wall shear stress and profiles of the mean velocity, the turbulent kinetic energy, and the Reynolds shear stress. DNS data is used as reference. Within the tested framework, a particular second-order accurate CFD code (OpenFOAM), the results provide ample support for grid and method parameters recommendations which are proposed in the present paper, and which provide good results for the QoIs. Notably, good results are obtained with a grid with isotropic (cubic) hexahedral cells, with 15 000 cells per δ 3 , where δ is the channel half-height (or thickness of the turbulent boundary layer). The importance of providing enough numerical dissipation to obtain accurate QoIs is demonstrated. The main channel flow case investigated is Re τ = 5200, but extension to a wide range of Re-numbers is considered. Use of other numerical methods and software would likely modify these recommendations, at least slightly, but the proposed framework is fully applicable to investigate this as well. 1 density, respectively. In contrast, the size of the energetic structures in the outer layer of the TBL scales with the TBL thickness δ, where δ δ ν . The ratio of δ ν /δ decreases with the Reynolds (Re-)number. This results in the number of grid cells required for LES approximately scaling as Re 1.85 for a TBL affected by a weak pressure gradient, see [12,13,58].A possible strategy to circumvent this restrictive computational cost is to only aim for resolving the structures in the outer layer of TBL, and approximate the uncaptured effects in the near-wall region by some type of modeling. The resulting approach is referred to as wall-modeled (WM)LES, for which the required number of grid cells increases linearly with the Re-number [12,13], and hence leads to a large reduction in the computational cost as compared to classical LES, which is from hereon referred to as wall-resolving (WR)LES.Different methodologies have been proposed for wall modeling, see the reviews [7,29,42,52] and the references therein. A particular class referred to as wall-stress modeling is considered in the present study. The near-wall treatment in this type of modeling consists of accurately predicting the local value of the filtered wall sh...

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Section: Wall Modelingmentioning

confidence: 99%

Section: Best Practice Guidelines For Wmlesmentioning

confidence: 99%

Systematic study of accuracy of wall-modeled large eddy simulation using uncertainty quantification techniques

2019

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“…OpenFOAM uses the finite volume method to discretize the governing equations, supports arbitrary convex polyhedral cells, and offers a rich selection of numerical schemes [19]- [21]. For wall modelling, an additional publicly available library is used to enhance OpenFOAM's built-in capabilities [22]. A particularly important improvement (see [23]) is that the library allows sampling the LES solution used as the input to the wall model from an arbitrary distance from the wall, and not only from the wall-adjacent cell.…”

Section: Methods Of Computational Fluid Dynamicsmentioning

confidence: 99%

“…In OpenFOAM, the scheme using 25% upwinding is referred to as LUST (linear upwind stabilized transport). WMLES comparing LUST and linear interpolation with no upwinding have been conducted in previous studies [16], [22], with more favorable results for first-order statistics achieved using LUST. Here, the cases of 15% and 5% upwinding are additionally considered to get a clearer picture of how upwinding affects the results.…”

Section: Methods Of Computational Fluid Dynamicsmentioning

confidence: 99%

Effects of numerical dissipation on the predictive accuracy of wall-modelled large-eddy simulation

Mukha¹

2019

Proceedings of ISP RAS

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The effect of numerical dissipation on the predictive accuracy of wall-modelled large-eddy simulation is investigated via systematic simulations of fully-developed turbulent channel flow. A total of 16 simulations are conducted using the open-source computational fluid dynamics software OpenFOAM®. Four densities of the computational mesh are considered, with four simulations performed on each, in turn varying in the amount of numerical dissipation introduced by the numerical scheme used for interpolating the convective fluxes. The results are compared to publicly-available data from direct numerical simulation of the same flow. Computed error profiles of all the considered flow quantities are shown to vary monotonically with the amount of dissipation introduced by the numerical schemes. As expected, increased dissipation leads to damping of high-frequency motions, which is clearly observed in the computed energy spectra. But it also results in increased energy of the large-scale motions, and a significant over-prediction of the turbulent kinetic energy in the inner region of the boundary layer. On the other hand, dissipation benefits the accuracy of the mean velocity profile, which in turn improves the prediction of the wall shear stress given by the wall model. Thus, in the current framework, the optimal choice for the dissipation of the numerical schemes may depend on the primary quantity of interest for the conducted simulation. With respect to the resolution of the grid, the change in the accuracy is much less predictable, and the optimal resolution depends on the considered quantity and the amount of dissipation introduced by the numerical schemes.

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“…One of the LES methods compared is based on explicit asynchronous time-stepping scheme, which uses a hierarchy of local time steps depending on the local cell size [28]. The Navier-Stokes solver is based on the Compact Accurately Boundary-Adjusting high-REsolution Technique (CABARET) scheme [24], [25], [26], [27], [28], [30] which is implemented with a wall model [31], [32], [33] and a synthetic turbulence boundary condition 4 [34], [35]. Because of the computational stencil compactness, CABARET can utilse unstructured grids with patches of refined isotropic Cartesian meshes as required in the vicinity of viscous boundary and jet shear layers [21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Flow and Noise Predictions of Coaxial Jets

Markesteijn¹,

Gryazev²,

Karabasov³

et al. 2020

AIAA Journal

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Flow and noise solutions of the two Large Eddy Simulation (LES) approaches are evaluated for the jet flow conditions corresponding to a benchmark co-axial jet case from the EU CoJeN (Computation of Coaxial Jet Noise) experiment. The jet is heated and issues for a short-cowl axi-symmetric nozzle with a central body at a transonic speed. The first LES method is based on the Compact Accurately Boundary-Adjusting high-REsolution Technique (CABARET) scheme, for which implementation features include asynchronous time stepping at an optimal Courant-Friedrichs-Lewy (CFL) number, a wall model, and a synthetic turbulence inflow boundary condition. The CABARET LES is implemented on Graphics Processing Units (GPUs). The second LES approach is based on the hybrid 2 Reynolds Averaged Navier-Stokes (RANS)/ Implicit LES method that uses a mixture of high-order Roe and WENO scheme and a wall distance model of the Improved Delayed Detached Eddy Simulation (IDDES) type. The RANS/ILES method is run on an MPI cluster. Two grid generation approaches are considered: the unstructured grid using OpenFOAM utility "snappyHexMesh" (sHM) and the conventional structured multiblock body-fitted curvilinear grid. The LES flow solutions are compared with the experiment and also with solutions obtained from the standard axi-symmetric RANS method using the k- turbulence model. For noise predictions, The LES solutions are coupled with the penetrable surface formation of the Ffowcs Williams-Hawkings method. The results of noise predictions are compared with the experiment and the effect of different LES grids and acoustic integration surfaces is discussed.

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A library for wall-modelled large-eddy simulation based on OpenFOAM technology

Cited by 64 publications

References 54 publications

Systematic study of accuracy of wall-modeled large eddy simulation using uncertainty quantification techniques

Systematic study of accuracy of wall-modeled large eddy simulation using uncertainty quantification techniques

Effects of numerical dissipation on the predictive accuracy of wall-modelled large-eddy simulation

Flow and Noise Predictions of Coaxial Jets

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