Compressible Boundary-Layer Predictions at High Reynolds Number Using Hybrid LES/RANS Methods

Choi, Jung Il; Edwards, Jack R.; Baurle, Robert A.

doi:10.2514/1.41598

Cited by 65 publications

(44 citation statements)

References 35 publications

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“…The hybrid RAS/LES methodology used in this effort is based on the framework originally developed in [2], with subsequent variants described in [24,25]. This framework is designed to enforce a RAS behavior near solid surfaces and switch to an LES behavior in the outer portion of the boundary layer and free shear regions.…”

Section: Hybrid Reynolds-averaged/large-eddy Simulation Settingsmentioning

confidence: 99%

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Hybrid Reynolds-Averaged/Large-Eddy Simulation of a Scramjet Cavity Flameholder

Baurle

2017

AIAA Journal

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.J055339Steady-state and scale-resolving simulations have been performed for flow in and around a model scramjet combustor flameholder. Experimental data available for this configuration include velocity statistics obtained from particle image velocimetry as well as a limited number of scalar measurements using laser-induced breakdown spectroscopy. Several turbulence models were used for the steady-state Reynolds-averaged simulations, which included both linear and nonlinear eddy viscosity models. The scale-resolving simulations used a hybrid Reynoldsaveraged/large-eddy simulation strategy that is designed to be a large-eddy simulation everywhere except in the inner portion (log layer and below) of the boundary layer. Hence, this formulation can be regarded as a wall-modeled largeeddy simulation. This effort was undertaken to not only assess the performance of the hybrid Reynolds-averaged/ large-eddy simulation modeling approach in a flowfield of interest to the scramjet research community but also to begin to understand how this capability can best be used to augment standard Reynolds-averaged simulations. The numerical errors were quantified for the steady-state simulations and at least qualitatively assessed for the scaleresolving simulations before making any claims of predictive accuracy relative to the measurements. The steady-state Reynolds-averaged results displayed a high degree of variability when comparing the flameholder fuel distributions obtained from each turbulence model. This prompted the consideration of applying the higher-fidelity scale-resolving simulations as a surrogate "truth" model to calibrate the Reynolds-averaged closures in a nonreacting setting before their use for the combusting simulations. In general, the Reynolds-averaged velocity profile predictions at the lowest fueling level matched the particle imaging measurements almost as well as was observed for the nonreacting condition. However, the velocity field predictions proved to be more sensitive to the flameholder fueling rate than was indicated in the measurements.

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Section: Hybrid Reynolds-averaged/large-eddy Simulation Settingsmentioning

confidence: 99%

“…where F is a blending function [24] that varies between 0 and 1. Note that the transport equation for the RAS specific dissipation rate ω does not have an SGS counterpart.…”

Section: Hybrid Reynolds-averaged/large-eddy Simulation Settingsmentioning

confidence: 99%

Hybrid Reynolds-Averaged/Large-Eddy Simulation of a Scramjet Cavity Flameholder

Baurle

2017

AIAA Journal

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“…The constant is chosen to enforce the average LES to RANS transition ( 0:99 position) for equilibrium boundary layers at the point where the wake law starts to deviate from the log law. The procedure for doing this is presented in Edwards et al [16] and Choi et al [23]. When applied to the entire development of the boundary layer in the computational domain considered in this study, this procedure leads to a spatially varying form for the model constant that is fit as x 53:90 11:04x, with x being the distance from the leading edge of the computational domain ( 0:3181 m).…”

Section: A Rans and Hybrid Les/rans Methodologymentioning

confidence: 99%

Simulation of Shock/Boundary-Layer Interactions with Bleed Using Immersed-Boundary Methods

Ghosh

Choi

Edwards

2010

Journal of Propulsion and Power

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This work uses an immersed-boundary method to simulate the effects of arrays of discrete bleed holes in controlling shock-wave/turbulent-boundary-layer interactions. Both Reynolds-averaged Navier-Stokes and hybrid large-eddy/Reynolds-averaged Navier-Stokes turbulence closures are used with the immersed-boundary technique. The approach is validated by conducting simulations of Mach 2.5 flow over a perforated plate containing 18 individual bleed holes. Computed values of discharge coefficient as a function of bleed plenum pressure are compared to experimental data. Simulations of an impinging-oblique-shock/boundary-layer interaction at Mach 2.45 with and without bleed control are also performed. For the studies with bleed, two different bleed rates are employed. The 68 hole bleed plate is rendered as an immersed object in the computational domain. Wall pressure predictions show that, in general, the large-eddy/Reynolds-averaged Navier-Stokes technique underestimates the upstream extent of axial separation that occurs in the absence of bleed. Good agreement with pitot pressure surveys throughout the interaction region is obtained, however. Flow control at the maximum-bleed rate completely removes the separation region and induces local disturbances in the wall pressure distributions that are associated with the expansion of the boundary-layer fluid into the bleed port and its subsequent recompression. Computed pitot pressure distributions are in good agreement with experiment for the cases with bleed. Swirl-strength probability density distributions are used to estimate the evolution of turbulent length scales throughout the interaction. These, along with Reynolds-stress predictions, indicate that an effect of strong bleed rates is to accelerate the recovery of the boundary layer toward a new equilibrium state downstream of the interaction region. Nomenclature a 1 = model constant for Menter baseline model C f = skin-friction coefficient C M = model constant for mixed-scale model C = model constant for Menter baseline model D = diameter of bleed hole d = distance from nearest-wall/immersed-surface point d ; = normalized distance F 2 = model constant for Menter baseline model f, g = scaling functions G = Heaviside step function based on signed distance function k = turbulent kinetic energy/power law L = length of bleed hole M = Mach number n = coordinate normal to immersed surface P = static pressure P t = pitot pressure Q = sonic flow coefficient or discharge coefficient q 2 = estimate of subgrid kinetic energy R = universal gas constant Re = Reynolds number R n1;l i = residual vector at cell i for time step n 1 and subiteration l r = recovery factor S = characteristic filtered rate of strain T = temperature u = velocity vector, x component of velocity u = friction velocity V = primitive variable vector v = component of velocity in y direction w = component of velocity in z direction X = streamwise distance from leading edge of bleed-plate insert x = streamwise distance from leading edge of computational domain Y = ...

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“…The model for the blending function is based on Gieseking et al's [28] reformulation of an earlier model [29] to avoid a problem-specific calibration required in the earlier version. The intent of both models is to target the location where an equilibrium boundary layer shifts from its logarithmic form to its wake-like structure as the effective location for the transition.…”

Section: Methodsmentioning

confidence: 99%

Mach 6 Wake Flow Simulations Using a Large-Eddy Simulation/Reynolds-Averaged Navier–Stokes Model

Salazar

Edwards

2014

Journal of Spacecraft and Rockets

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A hybrid large-eddy simulation/Reynolds-averaged Navier-Stokes turbulence model is used to simulate the Mach 6 flow around a scaled model similar to NASA's Orion multipurpose crew vehicle. The results for surface pressure and heat transfer are compared with experimental data from previous base flow experiments conducted at the CalspanUniversity at Buffalo Research Center. Using the highest Reynolds number test case (11 × 10 6 based on capsule diameter), different numerical aspects of the hybrid approach are addressed, such as use of a low-dissipation scheme, a modification to the eddy-viscosity blending function, time-averaging results, filtering computational results, and sensitivity to grid resolution. In addition, results are compared with Reynolds-Averaged Navier-Stokes using Menter's two-equation baseline model and to detached-eddy simulation predictions. By introducing a new modification to the blending from Reynolds-averaged Navier-Stokes to large-eddy simulation within boundary layers, very good agreement with the experiment is obtained in regions where the boundary-layer grid spacing is too coarse for large-eddy simulation. The findings show that the high-fidelity schemes produce results that agree much better with the experimental data than Reynolds-averaged Navier-Stokes methods, which tend to underpredict base pressures and overpredict heat fluxes. The overall accuracy of each scheme is evaluated using a normalized root mean square error in different regions of the flow, and the analysis shows that, in separated regions, the integrated error is over 120% using a Reynolds-averaged Navier-Stokes model, and 30-50% using the higher fidelity schemes. Additionally, the hybrid methodology presented is further validated by considering a lower Reynolds number (6 × 10 6 ), where the flow is nominally transitional, and very accurate heat transfer predictions are also obtained. NomenclatureC M = hybrid large-eddy simulation/Reynolds-averaged Navier-Stokes model constant, 0.06 D = capsule diameter, m d = distance from the nearest wall, m gl out = blending correction function k, k R = turbulence, resolved kinetic energy, m 2 ∕s 2 l inn , l out = inner, outer boundary-layer length scale, m M ∞ = freestream Mach number Re D = Reynolds number based on capsule diameter S = vorticity tensor, 1∕s T wall = wall temperature, K T ∞ = freestream temperature, K t = time, s U ∞ = freestream velocity, m∕s u i = velocity component, m∕ŝũ = resolvable-scale Favre-averaged velocity component, m∕s u = Favre-averaged velocity component, m∕s Γ = large-eddy simulation/Reynolds-averaged NavierStokes blending function Δ = cell volume, m 3 Δ max = maximum grid spacing over the three coordinate directions, m ϵ = turbulent kinetic energy dissipation rate, m 2 ∕s 3 κ = von Kármán constant λ = length scale ratio for blending function μ μ T= molecular, turbulent viscosity, Pa · s ν = kinematic viscosity, m 2 ∕s ν T = kinematic eddy viscosity, m 2 ∕s ν T;SGS = subgrid kinematic eddy viscosity, m 2 ∕s ρ = density, kg∕m 3 ρ ∞ = freestream density...

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Compressible Boundary-Layer Predictions at High Reynolds Number Using Hybrid LES/RANS Methods

Cited by 65 publications

References 35 publications

Hybrid Reynolds-Averaged/Large-Eddy Simulation of a Scramjet Cavity Flameholder

Hybrid Reynolds-Averaged/Large-Eddy Simulation of a Scramjet Cavity Flameholder

Simulation of Shock/Boundary-Layer Interactions with Bleed Using Immersed-Boundary Methods

Mach 6 Wake Flow Simulations Using a Large-Eddy Simulation/Reynolds-Averaged Navier–Stokes Model

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