Differential Reynolds Stress Modeling for Aeronautics

Cécora, René-Daniel; Eisfeld, Bernhard; Probst, Axel; Crippa, Simone; Radespiel, Rolf

doi:10.2514/6.2012-465

Cited by 64 publications

(61 citation statements)

References 15 publications

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“…A Reynolds stress model (RSM) is used both as a turbulence model in the RANS mode and as a subgridscale model in the LES mode. The Speziale-Sarkar-Gatski/ Launder-Reece-Rodi (SSG/LRR) [31] model is applied to estimate the redistribution of turbulent energy by the pressure-strain correlations. Whereas the dissipation tensor in the Reynolds stress transport equations can be taken to be isotropic under high-Reynoldsnumber conditions, the simple gradient-diffusion model suggested by Shir [32] is used for the pressure transport and convective fluxes.…”

Section: A Flow Solver and Turbulence Modelsmentioning

confidence: 99%

Prediction and Measurement of the Common Research Model Wake at Stall Conditions

Lutz

Gansel

Waldmann

et al. 2016

Journal of Aircraft

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The development of the unsteady wake of transport aircraft under stall conditions and its impact on the empennage are challenging to predict, and the flow physics is far from being understood. In the European Strategic Wind Tunnels Improved Research Potential project, time-resolved particle image velocimetry measurements on the separated wake of the NASA Common Research Model were performed in the cryogenic European Transonic Windtunnel for flight Reynolds number conditions supplemented by aerodynamic measurements for a great variety of inflow conditions. In the time-resolved particle image velocimetry measurements, both low-speed stall (M ∞ 0.25, Re 11.6∕17 million) and high-speed stall conditions (M ∞ 0.85, Re 19.8∕30 million) were considered and the frequency resolution was as high as 1 kHz, enabling a sufficient characterization of the turbulent wake spectrum. For selected high-and low-speed stall conditions, hybrid Reynolds-averaged Navier-Stokes/large-eddy simulations were performed. In the present paper, the numerical results are discussed and compared to the experiments and first evaluations of the particle image velocimetry data are presented. NomenclatureYoung modulus M ∞ = freestream Mach number q = dynamic pressure Re = Reynolds number based on the reference chord length S = wing reference area Sr = Strouhal number based on the reference chord length and freestream velocity t = time U = velocity U ∞ = freestream velocity x, y, z = Cartesian coordinates y = wall distance normalized by the viscous length scale α = angle of attack Δ = filter width in large-eddy simulations η = spanwise position normalized by the semispan of the wing ω = vorticity

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Section: A Flow Solver and Turbulence Modelsmentioning

confidence: 99%

Prediction and Measurement of the Common Research Model Wake at Stall Conditions

Lutz

Gansel

Waldmann

et al. 2016

Journal of Aircraft

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“…8,[22][23][24] The new adaptive mesh refinement capability of OVERFLOW, 20, 21 seemed a perfect means to provide grid systems tailored to each turbulence model, and get an accurate solutions to this problem efficiently. Figure 1 shows the wing inside the test section, with the mean velocity measurement locations shown by the symbols, with the profiles taken as vertical traverses, normal to the page.…”

Section: Resultsmentioning

confidence: 99%

LagRST Model Predictions of a Wingtip Vortex Flowﬁeld (Invited)

Olsen

Lillard

Coakley

2015

22nd AIAA Computational Fluid Dynamics Conference

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The performance of the new LagRST models is compared with curvature corrected eddy viscosity RANS models on the Chow-Zilliac wingtip flowfield. The Reynolds-stress models provide very good predictions of this flow without any additional curvature corrections. Along with comparison of predictions of the vortex itself, some of the other measurements included in the flowfield are compared with the predictions in the wake and over the wing. Differences in predictions between the various RANS models in the wake region are smallnot substantial, and all predictions are in line with experimental measurements from 0.005c to 0.67c downstream of the trailing edge, at odds with what self-similar wake analysis would predict.

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“…This reasonable added cost was also roughly estimated in other studies. 3,10 The key idea of the UPC scheme is the design of the implicit operator to form a special M-matrix. 20 Among its properties, the inverse of an M-matrix is a positive matrix and an M-matrix has a convergent regular splitting.…”

Section: Turbulence Model Equations Time Integration Methodsmentioning

confidence: 99%

Turbulent Flow Simulations on Unstructured Grids using a Reynolds Stress Model

Mor-Yossef¹

2015

22nd AIAA Computational Fluid Dynamics Conference

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Progress toward a stable and efficient numerical treatment for the Reynolds-averaged Navier-Stokes equations with a Reynolds-stress model on unstructured grids is presented. The unconditionally stable time marching scheme for Reynolds stress models, originally developed by the author for structured grids, is extended for unstructured grids using a finite volume method. The scheme guarantees the convergence of the fixed point iteration on the linearized problem. Moreover, the scheme is a positivity-preserving, regardless of the time step. Thanks to the scheme characteristics, a spatially second-order discretization method for the Reynolds stress model equations (exactly as applied to the mean-flow equations) can be used, free of stability difficulties within the fixed point iterations. Another key to the overall flow solver stability is a simple and robust procedure that is proposed to explicitly enforce all the realizability conditions of the Reynolds stress tensor. Moreover, it is shown that the limiter has a dramatic influence on the convergence characteristics. Specifically, the limiter applied to the turbulence model variables alone found to significantly influence the overall convergence behavior. Results obtained from two-and three-dimensional numerical simulations demonstrate the robustness of the overall flow solver for industrial applications.

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Differential Reynolds Stress Modeling for Aeronautics

Cited by 64 publications

References 15 publications

Prediction and Measurement of the Common Research Model Wake at Stall Conditions

Prediction and Measurement of the Common Research Model Wake at Stall Conditions

LagRST Model Predictions of a Wingtip Vortex Flowﬁeld (Invited)

Turbulent Flow Simulations on Unstructured Grids using a Reynolds Stress Model

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