Large eddy simulation (2D) using diffusion–velocity method and vortex‐in‐cell

Milane, R. E.

doi:10.1002/fld.673

Cited by 10 publications

(14 citation statements)

References 34 publications

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“…This is again consistent with the fact that the dynamic eddy viscosity model is less dissipative than Smagorinsky model. The negative cross-stream correlation (−u v = U 2 ) in Figure 6(c) is less a ected by SGS, consistent with being linked to the mean by the mean momentum equation and the mean ow is slightly a ected by SGS as found in Reference [22]. The peak rms!…”

Section: Sgs E Ectssupporting

confidence: 79%

“…its in uence extends to within two grids. Flow ÿeld results for the present mixing layer obtained using the area-weighting scheme have been compared with the ones obtained using the M 4 scheme in Reference [22]. Results suggest that the sensitivity to the smoothing function is quite small probably because the number of vortex blobs used is high.…”

Section: Vortex-in-cellmentioning

confidence: 93%

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Large eddy simulation (2D) of spatially developing mixing layer using vortex-in-cell for flow field and filtered probability density function for scalar field

Wang

Milane

2005

Int. J. Numer. Meth. Fluids

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SUMMARYA large eddy simulation based on ÿltered vorticity transport equation has been coupled with ÿltered probability density function transport equation for scalar ÿeld, to predict the velocity and passive scalar ÿelds. The ÿltered vorticity transport has been formulated using di usion-velocity method and then solved using the vortex method. The methodology has been tested on a spatially growing mixing layer using the two-dimensional vortex-in-cell method in conjunction with both Smagorinsky and dynamic eddy viscosity subgrid scale models for an anisotropic ow. The transport equation for ÿltered probability density function is solved using the Lagrangian Monte-Carlo method. The unresolved subgrid scale convective term in ÿltered density function transport is modelled using the gradient di usion model. The unresolved subgrid scale mixing term is modelled using the modiÿed Curl model. The e ects of subgrid scale models on the vorticity contours, mean streamwise velocity proÿles, root-mean-square velocity and vorticity uctuations proÿles and negative cross-stream correlations are discussed. Also the characteristics of the passive scalar, i.e. mean concentration proÿles, root-mean-square concentration uctuations proÿles and ÿltered probability density function are presented and compared with previous experimental and numerical works. The sensitivity of the results to the Schmidt number, constant in mixing frequency and in ow boundary conditions are discussed.

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Section: Sgs E Ectssupporting

confidence: 79%

Section: Vortex-in-cellmentioning

confidence: 93%

Large eddy simulation (2D) of spatially developing mixing layer using vortex-in-cell for flow field and filtered probability density function for scalar field

Wang

Milane

2005

Int. J. Numer. Meth. Fluids

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“…Usually, this term is treated together with molecular diffusion using Degond and Mas-Gallic's (Degond and Mas-Gallic 1989) Particle Strength Exchange (PSE) method (see, e.g., Mansfield et al (1996Mansfield et al ( , 1998; Winckelmans et al (2005); Pinon et al (2012)). Milane andNourazar (1995, 1997) used a diffusion velocity together with Leonard's (Leonard 1980) spreading core technique and Milane (2004) used a full scalar DVM method to treat LES terms in 2D computations. In the sequel, a vectorial formulation will be derived for treating 3D LES terms with DVM.…”

Section: Large Eddy Simulation (Les) Modellingmentioning

confidence: 99%

“…It was first introduced by Fronteau and Combis (1984) and popularized by ; Degond and Mustieles (1990), Ogami and Akamatsu (1991) and Kempka and Strickland (1993) in the early 1990s. This method was then largely analysed (Strickland et al 1996;Mas-Gallic 1999;Lacombe and Mas-Gallic 1999;Lacombe 1999;Lions and Mas-Gallic 2001), adapted to dispersion equations (Lions and Mas-Gallic 2001;Levy 2001, 2002), coupled with turbulence models (Milane 2004) and extended to the diffusion of a vector field and to axisymmetric flows (Rivoalen et al 1997;Grant and Marshall 2005;Rivoalen and Huberson 1999).…”

Section: Introductionmentioning

confidence: 99%

Formulation and analysis of a diffusion-velocity particle model for transport-dispersion equations

et al. 2014

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. Formulation and analysis of a diffusionvelocity particle model for transport-dispersion equations. Computational and Applied Mathematics, Springer Verlag, 2016, 35 (2), pp.447-473. 10.1007/s40314-014-0200-5. hal-01087854 Formulation and analysis of a diffusion-velocity particle model for transport-dispersion equations Paul Mycek · Grégory Pinon · Grégory Germain · Elie Rivoalen Abstract The modelling of diffusive terms in particle methods is a delicate matter and several models were proposed in the literature to take such terms into account. The Diffusion Velocity Method (DVM), originally designed for the diffusion of passive scalars, turns diffusive terms into convective ones by expressing them as a divergence involving a so-called diffusion velocity. In this paper, DVM is extended to the diffusion of vectorial quantities in the three-dimensional NavierStokes equations, in their incompressible, velocity-vorticity formulation. The integration of a Large Eddy Simulation (LES) turbulence model is investigated and a DVM general formulation is proposed. Either with or without LES, a novel expression of the diffusion velocity is derived, which makes it easier to approximate and which highlights the analogy with the original formulation for scalar transport. From this statement, DVM is then analysed in one dimension, both analytically and numerically on test cases to point out its good behaviour.

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“…Furthermore, Milane [7] developed a 2D large eddy simulation (LES) based on the VIC and on the diffusion velocity method using the Smagorinsky eddy viscosity SGS model. The results for the flow field of a mixing layer are close to the experimental results of Masutani and Bowman [8] (M&B).…”

Section: Introductionmentioning

confidence: 99%

Numerical prediction of isothermally reacting mixing layer using vortex‐in‐cell and filtered density function

Siklawi

Milane

2011

Numerical Methods in Fluids

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SUMMARYA large eddy simulation based on the filtered vorticity transport equation and the filtered density function (FDF) transport equation developed in an earlier study is extended to predict a chemically reacting flow with no heat release. The filtered vorticity transport equation is solved using the vortex-in-cell scheme in conjunction with the dynamic eddy viscosity subgrid-scale models. The transport equation for FDF is solved using the Lagrangian Monte-Carlo method. The methodology is tested on a chemically reacting spatially growing mixing layer with no heat release. The effects of Damköhler number (Da) on the concentration structure of the reacting mixing layer, the mean reactant and product concentrations and on the reactant FDF are investigated. It is shown that mixing has a greater effect on scalar field within the vortex structure as compared with the braid regions. Also for high Da, the reaction zones are mainly limited to the thin reacting interfacial zones, i.e. the contact zone between the reactants, whereas for low Da, the reacting zones are spread as reacting pockets within the vortex structure. The effects of Da on mean reactant and product concentrations, root-mean-square concentration fluctuations and probability density are discussed.

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Large eddy simulation (2D) using diffusion–velocity method and vortex‐in‐cell

Cited by 10 publications

References 34 publications

Large eddy simulation (2D) of spatially developing mixing layer using vortex-in-cell for flow field and filtered probability density function for scalar field

Large eddy simulation (2D) of spatially developing mixing layer using vortex-in-cell for flow field and filtered probability density function for scalar field

Formulation and analysis of a diffusion-velocity particle model for transport-dispersion equations

Numerical prediction of isothermally reacting mixing layer using vortex‐in‐cell and filtered density function

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