Towards a Unified K-Epsilon Turbulence Model for the Practical Analysis of Aeropropulsive Flows

Brinckman, Kevin; Rodebaugh, Gregory; Dash, S. M.

doi:10.2514/6.2013-392

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…We should note that several of these cases were also run using RANS modeling, as described in another paper at this meeting. 24 The DDES results were used to improve the RANS model behavior by providing detailed turbulent statistics that helped guide model refinement.…”

Section: Discussionmentioning

confidence: 99%

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DDES of Aeropropulsive Flows Based on an Extended k-? RANS Model

Rodebaugh

Brinckman

Dash

2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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Aeropropulsive flows may contain localized separated flow regions whose analysis using RANS methodology may be inadequate, particularly so for massively separated flows having unsteady/oscillatory features. DES-type methods provide for the improved analysis of these local regions, and can often demarcate the zones to be analyzed using RANS or LES methodology in an automated fashion. The DDES implementation described utilizes an extended k-ε model in RANS regions containing a number of unique features catered to analyzing aeropropulsive flows. Several aeropropulsive cases of interest are considered for which test data is available. Cases include: a supersonic base flow calculation; a transverse supersonic air jet interacting with a subsonic airtstream; and two angled fuel simulant jets (M j = 2.5 helium jet and M j = 1.0 ethylene jet) injected into a M ∞ = 2.0 airstream. The results obtained for the supersonic base flow case as well for the transverse air jet in a crossflow case, both compare favorably with the experimental measurements. The predictions for the helium injection case also agree reasonably well with the experimental data. The ethylene case case was not as well predicted which may be due to the RANS representation of the approach boundary layer for this low momentum ratio case (as also pointed out by other investigators).

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Section: Discussionmentioning

confidence: 99%

“…al. presented at this meeting 24 , involving the inclusion of a recalibrated baroclininc torque correction term). …”

Section: Supersonic Fuel-air Mixing 1 Helium Injectionmentioning

confidence: 99%

DDES of Aeropropulsive Flows Based on an Extended k-? RANS Model

Rodebaugh

Brinckman

Dash

2013

51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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“…Numerous near wall correction terms, different model constants, 14 or in the case of the SST model, a completely different length scale equation is required to get good wall boundary layer predictions with the k- model.…”

Section: Use Of the K-kl Model For Jet Flowsmentioning

confidence: 99%

Modification of the k-kl Two Equation Turbulence Model for Improved Jet Flow Predictions (Invited)

Smith

2015

22nd AIAA Computational Fluid Dynamics Conference

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Reynolds averaged Navier-Stokes (RANS) computational fluid dynamics (CFD) methods are relied on for design and analysis of both conventional and STOVL tactical military aircraft. Application of one and two transport equation turbulence models is the standard approach used to account for the effects of turbulence. While the commonly used turbulence models predict flows in wall boundary layers and wakes with reasonable accuracy, they typically do not predict jet flows well. As a result, predictions of jet effects and jet environments as part of vehicle computations are inaccurate when these models are used. In this work, modifications that improve jet flow predictions for the k-kl two transport equation turbulence model are presented. The improved model gives accurate predictions of wall turbulent boundary layers and 2-D shear layers consistent with the baseline k-kl model. Results for the modified model are presented for a series of subsonic, near sonic and hot axisymmetric jet flows. While the baseline k-kl model gives results that are comparable in accuracy to other commonly used turbulence models, the modified model gives much more accurate results for all of the jet flows considered while maintaining accuracy for a wall turbulent boundary layer.

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“…A default value of 2 has been used in the calculations presented in this paper with progress towards quantifying the functional dependence on the turbulent Mach number discussed in Ref. [38].…”

Section: Turbulence Model Equations 21 K-ε Turbulence Modelmentioning

confidence: 99%

Turbulence Modeling Advances and Validation for High Speed Aeropropulsive Flows

Dash

Brinckman

Ott

2012

International Journal of Aeroacoustics

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Advances made in extending a unified k-ε based RANS turbulence model to "more reliably" analyze high-speed aero-propulsive flows are discussed. The unified model solves additional scalar fluctuation model (SFM) equations to predict variations in turbulent Prandtl and Schmidt numbers, which can be quite substantial for these types of flows. The discussion includes a historical perspective of the developmental work performed as well as an overview of the current modeling status. RANS modeling still plays a dominant role in the "practical" analysis of aeropropulsive flows. It is used for preliminary design and design-optimization studies, where a large matrix of calculations must be performed, and, in the application of hybrid RANS/LES methodology, where the use of LES is often restricted to massively separated or jet/free shear regions of the flow. In extending the model, we have adhered to a "building-block" philosophy using data sets of increasing complexity and emphasizing high-speed applications where compressibility effects can play a dominant role. Fundamental laboratory data sets as well as benchmark LES/DNS solutions have been used for model calibration and validation, with several key comparisons presented in this article. Recent extensions described in this article include: low Re extensions to the SFM model improving near wall predictions; a compressibility/density gradient correction to the species fluctuation equation improving mixing predictions; and, a baroclinic torque correction to the kinetic energy equation improving predictions for angled jets. This article shows how these extensions serve to improve comparisons with data for a number of basic aeropropulsive flows, and it also discusses utilization of the unified RANS model in a hybrid RAN/LES DES modeling framework. NOMENCLATURE CC Compressibility correction nomenclature CVS Compressible vortex stretching nomenclature DES Detached Eddy Simulation DNS Direct Numerical Simulation EASM Explicit Algebraic Stress Model F d Density correction term in k f equation F m , f 1 , f 2 Near wall damping terms in SSGZ model G b Baroclinic torque correction term in k equation k Turbulent kinetic energy (TKE) k e Internal energy (or temperature) variance k f Species variance L et Turbulent Lewis number LES Large Eddy Simulation M Mach number M c Convective Mach number M t Turbulent Mach number PDE Partial differential equation P k Turbulent production term Pr t Turbulent Prandtl Number RANS Reynolds averaged Navier-Stokes SSGZ So, Sarkar, Gerodimos, and Zhang Sc t Turbulent Schmidt Number SFM Scalar fluctuation model SS ε Round jet vortex stretching correction SS k Compressibility correction term SWBLI Shock wave boundary layer interaction ε Dissipation rate of turbulent kinetic energy ε e Dissipation rate of internal energy fluctuations ε f Dissipation rate of scalar fluctuations λ b Near wall coefficient blending function ξ εT Near wall damping term in SFM model µ t Turbulent viscosity 814 Turbulence modeling advances and validation for high speed aeropropul...

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Towards a Unified K-Epsilon Turbulence Model for the Practical Analysis of Aeropropulsive Flows

Cited by 3 publications

References 16 publications

DDES of Aeropropulsive Flows Based on an Extended k-? RANS Model

DDES of Aeropropulsive Flows Based on an Extended k-? RANS Model

Modification of the k-kl Two Equation Turbulence Model for Improved Jet Flow Predictions (Invited)

Turbulence Modeling Advances and Validation for High Speed Aeropropulsive Flows

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