Numerical Simulation of Turbulent Flows

Moin, Parviz

doi:10.1146/annurev.fl.16.010184.000531

Cited by 774 publications

(327 citation statements)

References 59 publications

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“…At Mach 3.0 the empirical curve-fit yields a base pressure value of p/p, = 0.350, which compares well with the present prediction. The predicted value also compares well with the value given by simple supersonic theory for the pressure coefficient of base flow [54] C,= -$ (19 OCI Equation (19) yields a value of p/p, = 0.300. This theory is mainly valid at higher Mach numbers, but gives a valuable reference point for determining the accuracy of the numerical prediction.…”

Section: Numerical Predictions For Mach 30supporting

confidence: 70%

“…But current computer memory storage size and computational speeds prohibit analyzing turbulence at its fundamental scales for full-scale configurations. At present, even the largest computers can only simulate turbulence for simple flows with highly constrained flow parameters [19], and methods utilizing chaos theory were developed to enhance understanding of the fundamental processes of turbulence [ZO]. Due to these restraints, the full Navier-Stokes equations must be used in averaged form with some type of semi-empirical turbulence model to effect closure of the set of equations.…”

Section: Navier-stokes Equations For Compressible Flowmentioning

confidence: 99%

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Supersonic, turbulent flow computation and drag optimization for axisymmetric afterbodies

Cummings

Yang

1995

Computers & Fluids

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Abstract-The compressible, turbulent flow about an axisymmetric body was numerically studied using the MacCormack unsplit explicit algorithm applied to the mass-average Navier--Stokes equations solved in conjunction with the k+ turbulence model of Jones and Launder. Numerical predictions of total body drag (pressure drag, skin friction drag, and base drag) were made for an axisymmetric body six diameters in length, with and without a boattail. Surface pressures and viscous layer profiles are compared with available wind tunnel data and are found to be in good agreement for both geometries. The Golden Section optimization method was used to optimize the body boattail angle for minimum drag. The solution method can serve as a tool for preliminary design analysis where the relative merits of utilizing boattails on axisymmetric afterbodies is being considered.

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Section: Numerical Predictions For Mach 30supporting

confidence: 70%

Section: Navier-stokes Equations For Compressible Flowmentioning

confidence: 99%

Supersonic, turbulent flow computation and drag optimization for axisymmetric afterbodies

Cummings

Yang

1995

Computers & Fluids

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“…Next we turn to the group of eddy-viscosity models (e.g. Rogallo & Moin 1984). The anisotropic part of the turbulent stress, is modelled with The symbol u, represents the eddy viscosity and is the strain rate.…”

Section: Subgrid Modelsmentioning

confidence: 99%

“…In the filtering approach, which is the basis of the large-eddy simulation (LES) of turbulent flow (reviewed by e.g. Rogallo & Moin 1984), the averaging operator is a linear filtering operator, e.g. a local weighted average over a small volume of fluid.…”

Section: Introductionmentioning

confidence: 99%

Realizability conditions for the turbulent stress tensor in large-eddy simulation

1994

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The turbulent stress tensor in large-eddy simulation is examined from a theoretical point of view. Realizability conditions for the components of this tensor are derived, which hold if and only if the filter function is positive. The spectral cut-off, one of the filters frequently used in large-eddy simulation, is not positive. Consequently, the turbulent stress tensor based on spectrally filtered fields does not satisfy the realizability conditions, which leads to negative values of the generalized turbulent kinetic energy k. Positive filters, e.g. Gaussian or top-hat, always give rise to a positive k. For this reason, subgrid models which require positive values for k should be used in conjunction with e.g. the Gaussian or top-hat filter rather than with the spectral cutoff filter. If the turbulent stress tensor satisfies the realizability conditions, it is natural to require that the subgrid model for this tensor also satisfies these conditions. With respect to this point of view several subgrid models are discussed. For eddy-viscosity models a lower bound for the generalized turbulent kinetic energy follows as a necessary condition. This result provides an inequality for the model constants appearing in a ' Smagorinsky-type' subgrid model for compressible flows.

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“…Close to the bottom boundary, where the turbulent scales decrease in proportion to the distance to the surface, the energy-containing eddies are no longer resolved by the LES model. Rogallo and Moin [1984] mentioned the importance of the energy transfer between the resolved and parameterized motions within the LES approach, to assure a realistic generation of near surface turbulence. The numerical formulation of an appropriate SGS model is a matter of current research [e.g., Kumar et al, 2006;Chamecki et al, 2006].…”

Section: Introductionmentioning

confidence: 99%

Fine‐scale modeling of the boundary layer wind field over steep topography

Raderschall

Lehning

Schär

2008

Water Resources Research

105

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[1] This paper describes the adaptation of wind fields to steep and complex terrain using fine-scale numerical modeling. The work is motivated by the need of high-resolution flow fields to predict snow transport and snow cover development for avalanche warning purposes. Applying the nonhydrostatic and compressible atmospheric prediction model Advanced Regional Prediction System (ARPS) to steep alpine topography, the boundary layer flow was simulated and evaluated against measurements. The adaptation of the wind field to steep terrain for specific initial and boundary conditions was simulated. The topography used in our study has a length scale of 500 m, a typical height of 150 m, and maximum slopes of 45°. Numerical experiments with idealized triangular ridges were conducted to find an adequate model configuration. This analysis indicates that a highresolution grid with horizontal spacing of at least 25 m and vertical spacing of 3 m near the surface is necessary to reproduce small-scale flow features such as speed-up, separation, and recirculation. The onset of flow separation is highly sensitive to initial and boundary conditions, slope angle, and surface roughness. The results of the comparison between the model simulations and measurements on our experimental site show that typical wind field characteristics are well reproduced. The simulated wind fields have been used to drive a numerical three-dimensional snow drift model, which is presented in a companion paper by Lehning et al. (2008).

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Numerical Simulation of Turbulent Flows

Cited by 774 publications

References 59 publications

Supersonic, turbulent flow computation and drag optimization for axisymmetric afterbodies

Supersonic, turbulent flow computation and drag optimization for axisymmetric afterbodies

Realizability conditions for the turbulent stress tensor in large-eddy simulation

Fine‐scale modeling of the boundary layer wind field over steep topography

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