Improvements in Analyzing High-Speed Fuel/Air Mixing Problems Using Scalar Fluctuation Modeling

Mattick, Stephen J.; Brinckman, Kevin; Dash, S. M.; Liu, Z.

doi:10.2514/6.2008-768

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…The work of Mattick et al [16] employs variable turbulent Prandtl and Schmidt numbers formulation and allows for compressibility effects but not turbulence/chemistry interaction. The variable turbulent number formulation is based on the incompressible energy equation.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Supersonic Combustion in Three-Dimensional Configurations

Keistler

Hassan

Xiao

2009

Journal of Propulsion and Power

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A turbulence model that calculates the turbulent Prandtl and Schmidt numbers as part of the solution, addresses turbulence/chemistry interactions, and accounts for compressibility effects is used to simulate supersonic combustion in two three-dimensional experiments: the SCHOLAR experiment, which employs vitiated air and hydrogen fuel, and the HyShot experiment, which employs air and hydrogen fuel. Two chemical kinetic models are employed: one employs reaction rates that are functions of temperature, whereas the other employs rates that are both functions of pressure and temperature. In general, fair to good agreement is indicated with available measurements.

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Section: Introductionmentioning

confidence: 99%

Simulation of Supersonic Combustion in Three-Dimensional Configurations

Keistler

Hassan

Xiao

2009

Journal of Propulsion and Power

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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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