The performance of two popular turbulence models, the Spalart-Allmaras model and Menter's SST model, and one relatively new model, Olsen & Coakley's Lag model, are evaluated using the OVERFLOW code. Turbulent shock-boundary layer interaction predictions are evaluated with three different experimental datasets: a series of 2D compression ramps at Mach 2.87, a series of 2D compression ramps at Mach 2.94, and an axisymmetric coneflare at Mach 11. The experimental datasets include flows with no separation, moderate separation, and significant separation, and use several different experimental measurement techniques (including laser doppler velocimetry (LDV), pitot-probe measurement, inclined hot-wire probe measurement, preston tube skin friction measurement, and surface pressure measurement). Additionally, the OVERFLOW solutions are compared to the solutions of a second CFD code, DPLR. The predictions for weak shock-boundary layer interactions are in reasonable agreement with the experimental data. For strong shock-boundary layer interactions, all of the turbulence models overpredict the separation size and fail to predict the correct skin friction recovery distribution. In most cases, surface pressure predictions show too much upstream influence, however including the tunnel side-wall boundary layers in the computation improves the separation predictions.
This study presents a new class of turbulence model designed for wall bounded, high Reynolds number flows with separation. The model addresses deficiencies seen in the modeling of nonequilibrium turbulent flows. These flows generally have variable adverse pressure gradients which cause the turbulent quantities to react at a finite rate to changes in the mean flow quantities. This "lag" in the response of the turbulent quantities can't be modeled by most standard turbulence models, which are designed to model equilibrium turbulent boundary layers. The model presented uses a standard 2-equation model as the baseline for turbulent equilibrium calculations, but adds transport equations to account directly for non-equilibrium effects in the Reynolds Stress Tensor (RST) that are seen in large pressure gradients involving shock waves and separation. Comparisons are made to several standard turbulence modeling validation cases, including an incompressible boundary layer (both neutral and adverse pressure gradients), an incompressible mixing layer and a transonic bump flow. In addition, a hypersonic Shock Wave Turbulent Boundary Layer Interaction with separation is assessed along with a transonic capsule flow. Results show a substantial improvement over the baseline models for transonic separated flows. The results are mixed for the SWTBLI flows assessed. Separation predictions are not as good as the baseline models, but the over prediction of the peak heat flux downstream of the reattachment shock that plagues many models is reduced. Greek Symbols
JSC Engineering, Technology, and Science (JETS): Jacobs Technology and HX5, LLC An overview of the capabilities of the CHarring Ablator Response (CHAR) code is presented. CHAR is a one-, two-, and three-dimensional unstructured continuous Galerkin finite-element heat conduction and ablation solver with both direct and inverse modes. Additionally, CHAR includes a coupled linear thermoelastic solver for determination of internal stresses induced from the temperature field and surface loading. Background on the development process, governing equations, material models, discretization techniques, and numerical methods is provided. Special focus is put on the available boundary conditions including thermochemical ablation and contact interfaces, and example simulations are included. Finally, a discussion of ongoing development efforts is presented. Nomenclature α thermal diffusivity ( m 2 /sec) or coefficient of thermal expansion (K −1 ) or absorptivity α t , β t , γ t temporal finite-difference weights (sec −1 ) β extent of reaction also referred to as degree of char for volume fraction in virgin material γ P parameters for defining gas flow contact model stability ( m 2 /sec) and (sec) n unit normal vector κ permeability (m 2 ) λ Lamé's first parameter (Pa) or blowing reduction parameter R residual µ dynamic viscosity (Pa · sec) or Lamé's second parameters (Pa) ν Poisson's ratio Ω domain volume (m 3 ) φ porosity * Applied Aeroscience and CFD Branch 1 of 37 American Institute of Aeronautics and Astronautics Downloaded by MONASH UNIVERSITY on June 23, 2016 | http://arc.aiaa.org | This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AIAA Aviation ψ basis function ρ density ( kg /m 3 ) ρ e u e C H heat transfer film coefficient ( kg /m 2 ·sec) σ Stefan-Boltzmann constant ( W /m 2 ·K 4 ) or stress (Pa) σ P gas flow contact model stability parameter ( m /sec) and ( sec /m) σ T thermal contact model stability parameter ( W /m 2 ·K) τ shear stress (Pa) α coefficient of thermal expansion tensor (K −1 ) σ shear stress tensor (Pa) ε mechanical strain tensor C rotation matrix to shift coordinate reference framẽ K stiffness tensor (Pa) κ permeability tensor (m 2 ) k thermal conductivity tensor ( W /m·K) ε emissivity A element face area (m 2 ) b Klinkenberg parameter (Pa) B non-dimensional mass flux C specific heat ( J /kg·K) C H convective heat transfer Stanton number DE discretization error E activation energy ( J /kg) or Young's modulus (Pa) e o total internal energy ( J /kg) H convective heat transfer coefficient ( Wpre-exponential factor (sec −1 ) or roughness height (m) or thermal conductivity ( W /m·K) k + non-dimensional roughness height m reaction order N number of nodes nc number of components P pressure (Pa) p temporal order of accuracy q spatial order of accuracyfunction y effective mass fractions in solid DoF acronym for degree of freedom PDE acronym for partial differential equation TC abbreviation for thermocouple TPS acronym for thermal protection...
Reconstruction of flight aerothermal environments often requires the solution of an inverse heat transfer problem, which is an ill-posed problem of determining boundary conditions from discrete measurements in the interior of the domain. This paper will present the algorithms implemented in the CHAR code for use in reconstruction of EFT-1 flight data and future testing activities. Implementation details will be discussed, and alternative hybrid-methods that are permitted by the implementation will be described. Results will be presented for a number of problems.
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