Results for “RANS modeling” — Page 8

NASA NTRS2004

Numerical Study of Flow Past a Circular Cylinder Using RANS, Hybrid RANS/LES and PANS Formulations

Two multiscale type turbulence models are implemented in the PAB3D solver. The models are based on modifying the Reynolds Averaged Navier-Stokes (RANS) equations. The first scheme is a hybrid RANS/LES model utilizing the two-equation (k(sub epsilon)) model with a RANS/LES transition function dependent on grid spacing and the computed turbulence length scale. The second scheme is a modified version of the partially averaged Navier-Stokes (PANS) model, where the unresolved kinetic energy parameter (f(sub k)) is allowed to vary as a function of grid spacing and the turbulence length scale. Solutions from these models are compared to RANS results and experimental data for a stationary and rotating cylinder. The parameter f(sub k) varies between zero and one and has the characteristic to be equal to one in the viscous sub layer, and when the RANS turbulent viscosity becomes smaller than the LES viscosity. The formulation, usage methodology, and validation example are presented to demonstrate the enhancement of PAB3D's time-accurate and turbulence modeling capabilities. The models are compared to RANS results and experimental data for turbulent separated flows (TS) and laminar separated flows (LS) around stationary and rotating cylinders. For a stationary cylinder, the TS case is accurately simulated using the general two-equation k(sub epsilon) turbulence model (eddy-viscosity model). PAB3D accurately predicts the drag coefficient (CD), lift coefficient (CL) and the Strouhal number (St). The LS case was a challenge for the RANS computation with an eddy-viscosity turbulence model. The RANS/LES and PANS performed well and showed marked improvements over the RANS solution. The modified PANS model was the most accurate. For the rotating cylinder, the spin ratio varied from zero to one, and the PANS results were in good agreement with published experimental data. RANS/LES and PANS capture both temporal and spatial fluctuations and produce large-scale structures that do not occur in the RANS simulation. The current results show promise for the capability of PANS in simulating unsteady and complex flow phenomena.

Elmiligui, Alaa A.↗

NASA NTRS2023

Implementation, Realization and an Effective Solver of Two-Equation Turbulence Models

Currently, when the Reynolds-Averaged Navier-Stokes (RANS) equations are solved using turbulence modeling, most often the one-equation model of Spalart and Allmaras is used. Then, it is only necessary to solve the RANS equations in conjunction with a single transport equation for modeling turbulence. For this model, considerable assessment and analysis has been performed, allowing the possibility of a reliable solution method for an eddy viscosity required to compute the Reynolds stresses in the RANS equations. Such evaluation along with analysis has not been achieved to realize similar performance with two-equation models of the k-w type. The primary objective of this paper is to present and discuss the components of an effective numerical algorithm for solving the RANS equations and the two transport equations of k-w type turbulence models. All the important details of the turbulence model as actually implemented are given, which is sometimes not done in various papers considering such modeling. The viability and effectiveness of this solution algorithm are demonstrated by solving both two-dimensional and three-dimensional aerodynamic flows. In all applications, a linear rate of convergence without oscillations or other evidence of unstable behavior is observed. This behavior is also particularly true when the proposed algorithm is applied to systematically renewed mesh sequences, which is generally not observed with algorithms solving more than one transport equation. Thus, numerical integration errors are systematically reduced, allowing for a significantly more reliable assessment of the effectiveness of the turbulence model. Additionally, in this paper, analysis of the solution algorithm, including linear stability, is also performed for a particular flow problem.

Turbulence↗

NASA NTRS2004

Minnowbrook IV: 2003 Workshop on Transition and Unsteady Aspects of Turbomachinery Flows

This Minnowbrook IV 2003 workshop on Transition and Unsteady Aspects of Turbomachinery Flows includes the following topics: 1) Current Issues in Unsteady Turbomachinery Flows; 2) Global Instability and Control of Low-Pressure Turbine Flows; 3) Influence of End Wall Leakage on Secondary Flow Development in Axial Turbines; 4) Active and Passive Flow Control on Low Pressure Turbine Airfoils; 5) Experimental and Numerical Investigation of Transitional Flows as Affected by Passing Wakes; 6) Effects of Freestream Turbulence on Turbine Blade Heat Transfer; 7) Bypass Transition Via Continuous Modes and Unsteady Effects on Film Cooling; 8) High Frequency Surface Heat Flux Imaging of Bypass Transition; 9) Skin Friction and Heat Flux Oscillations in Upstream Moving Wave Packets; 10) Transition Mechanisms and Use of Surface Roughness to Enhance the Benefits of Wake Passing in LP Turbines; 11) Transient Growth Approach to Roughness-Induced Transition; 12) Roughness- and Freestream-Turbulence-Induced Transient Growth as a Bypass Transition Mechanism; 13) Receptivity Calculations as a Means to Predicting Transition; 14) On Streamwise Vortices in a Curved Wall Jet and Their Effect on the Mean Flow; 15) Plasma Actuators for Separation Control of Low Pressure Turbine Blades; 16) Boundary-Layer Separation Control Under Low-Pressure-Turbine Conditions Using Glow-Discharge Plasma Actuators; 17) Control of Separation for Low Pressure Turbine Blades: Numerical Simulation; 18) Effects of Elevated Free-Stream Turbulence on Active Control of a Separation Bubble; 19) Wakes, Calming and Transition Under Strong Adverse Pressure Gradients; 20) Transitional Bubble in Periodic Flow Phase Shift; 21) Modelling Spots: The Calmed Region, Pressure Gradient Effects and Background; 22) Modeling of Unsteady Transitional Flow on Axial Compressor Blades; 23) Challenges in Predicting Component Efficiencies in Turbomachines With Low Reynolds Number Blading; 24) Observations on the Causal Relationship Between Blade Count and Developing Rotating Stall in a Four Stage Axial Compressor; 25) Experimental and Numerical Study of Non-Linear Interactions in Transonic Nozzle Flow; 26) Clocking Effects on a Modern Stage and One-Half Transonic Turbine; 27) DNS and LES of Transition on Turbine Blades; 28) The Use of Cellular Automata in Modeling the Transition; 29) Predicting Unsteady Buffet Onset Using RANS Solutions; 30) Transition Modelling With the SST Turbulence Model and an Intermittency Transport; and 31) Equation Workshop Summary Transcript

LaGraff, John E.↗

NASA NTRS1992

The hypersonic shock wave-turbulent boundary layer interaction generated by a sharp fin at Mach 8.2

A combined experimental and numerical study has been conducted on the hypersonic shock-wave turbulent-boundary layer interaction at Mach 8.2 generated by a single fin of angles alpha = 10 and 15 deg. Three models are considered: (1) the 3D compressible Reynolds-averaged Navier-Stokes (RANS) equations using the k-epsilon turbulence model, (2) the 3D RANS using the Rodi turbulence model, and (3) the conical RANS using the Baldwin-Lomax algebraic turbulence model. The computations are compared with various experimental data. The computations using models (1) and (2) show quantitatively very similar results and very good agreement with experimental data for surface pressure and skin friction. Comparison with boundary layer profiles of pitot pressure and yaw angle are also generally good, but the peak surface heat transfer is overestimated by up to 48 percent. The effect of the laminar boundary layer on the fin is restricted to the immediate vicinity of the fin surface. Conical calculations using model (3) show substantially poorer agreement with experiment.

Knight, Doyle D.↗

NASA NTRS2021

Pretest Computational Assessment of Boundary Layer Transition in the NASA Juncture Flow Model with an NACA 0015-Based Wing

The first two phases of the NASA Juncture Flow experiment were carried out on a DLR-F6 swept-wing model and were designed to provide “CFD validation-quality” data toward the assessment and improvement of existing CFD turbulence models in predicting onset and extent of three-dimensional separated flow near the wing-juncture trailing-edge region. The next phase of experiments will involve an NACA 0015-based swept wing, as prior risk reduction experiments had indicated that this wing shape resulted in reduced separation near the juncture region than the DLR-F6 wing, thus providing a better option to evaluate the ability of CFD models to predict incipient turbulent separation. The NACA 0015 measurements will also include IR thermography to infer the variation of transition front with respect to an increasing angle of attack. The primary objective of this work is to computationally make a preliminary assessment of the transition front on both surfaces of the NACA 0015 wing at a crank-chord-based Reynolds number of 2.4 x 106 for four different angles of attack, (0°, 2.5°, 5°, and 7.5°) and to determine the dominant mechanisms responsible for transition. This assessment includes both RANS-based transition models from NASA’s OVERFLOW 2.3b flow solver and linear parabolized stability equations (PSE) stability analysis based on the Langley Stability and Transition Analysis code, LASTRAC. Linear PSE results indicate that the upper surface of the wing is dominated by Tollmien- Schlichting (TS) instabilities, and that the laminar flow region shrinks from about 50% chord to a very small region just downstream of the attachment line as the angle of attack is increased from 0° to 7.5°. Consequently, the transition fronts predicted by the Spalart- Allmaras-based amplification factor transport (AFT-2017b) equation model (which accounts for the TS instabilities alone) and the Menter’s shear-stress transport equation (SST2003)- based Langtry-Menter transition model with ability to account for both TS and crossflow effects (LM2015) compare well with those predicted using linear PSE. On the lower surface of the wing, stationary crossflow (CF) instabilities begin to appear on the inboard portion of the wing in addition to the TS-instabilities for the larger angles of attack (5° and 7.5°), further reducing the laminar flow extent within the inboard region. The LM2015 model that accounts for CF effects is able to replicate this trend but appears to predict a slightly earlier transition. The outcome of this effort will inform the experiment and, when the actual experimental data become available, provide further opportunity to assess and improve the various transition models.

CFD modeling↗

NASA NTRS2021

Pretest Computational Assessment of Boundary Layer Transition in the NASA Juncture Flow Model with an NACA 0015-Based Wing

The first two phases of the NASA Juncture Flow experiment were carried out on a DLR-F6 swept-wing model and were designed to provide “CFD validation-quality” data toward the assessment and improvement of existing CFD turbulence models in predicting onset and extent of three-dimensional separated flow near the wing-juncture trailing-edge region. The next phase of experiments will involve an NACA 0015-based swept wing, as prior risk reduction experiments had indicated that this wing shape resulted in reduced separation near the juncture region than the DLR-F6 wing, thus providing a better option to evaluate the ability of CFD models to predict incipient turbulent separation. The NACA 0015 measurements will also include IR thermography to infer the variation of transition front with respect to an increasing angle of attack. The primary objective of this work is to computationally make a preliminary assessment of the transition front on both surfaces of the NACA 0015 wing at a crank-chord-based Reynolds number of 2.4 x 106 for four different angles of attack, (0°, 2.5°, 5°, and 7.5°) and to determine the dominant mechanisms responsible for transition. This assessment includes both RANS-based transition models from NASA’s OVERFLOW 2.3b flow solver and linear parabolized stability equations (PSE) stability analysis based on the Langley Stability and Transition Analysis code, LASTRAC. Linear PSE results indicate that the upper surface of the wing is dominated by Tollmien- Schlichting (TS) instabilities, and that the laminar flow region shrinks from about 50% chord to a very small region just downstream of the attachment line as the angle of attack is increased from 0° to 7.5°. Consequently, the transition fronts predicted by the Spalart- Allmaras-based amplification factor transport (AFT-2017b) equation model (which accounts for the TS instabilities alone) and the Menter’s shear-stress transport equation (SST2003)- based Langtry-Menter transition model with ability to account for both TS and crossflow effects (LM2015) compare well with those predicted using linear PSE. On the lower surface of the wing, stationary crossflow (CF) instabilities begin to appear on the inboard portion of the wing in addition to the TS-instabilities for the larger angles of attack (5° and 7.5°), further reducing the laminar flow extent within the inboard region. The LM2015 model that accounts for CF effects is able to replicate this trend but appears to predict a slightly earlier transition. The outcome of this effort will inform the experiment and, when the actual experimental data become available, provide further opportunity to assess and improve the various transition models.

CFD modeling↗

NASA NTRS2021

Transition Analysis for the CRM-NLF Wind Tunnel Configuration

This paper reports the results of a comprehensive linear stability analysis of the boundary layer flow over the common research model with natural laminar flow (CRM-NLF) aircraft configuration. The flow conditions match selected test conditions from a recent wind tunnel experiment in the National Transonic Facility at the NASA Langley Research Center. Previous work has shown that the measured onset of laminar-turbulent transition during the experiments can be correlated with the linear amplification of Tollmien-Schlichting (TS) and stationary crossflow (CF) instabilities in the swept wing boundary layer. However, a significant scatter ( N ∈ (4,9)) was observed in the values of the logarithmic amplification factor along the measured transition front. This previous analysis was based on an approximate basic state (based on a boundary layer code with conical flow approximation) and parallel stability computations without surface curvature effects. Here, we examine the effects of these various approximations with the goal of quantifying the resulting changes in the N-factor correlations. Specifically, both linear stability theory (LST) and the parabolized stability equations (PSE)are used in conjunction with an accurate definition of the laminar boundary layer flow as computed with a Navier-Stokes solver with a Reynolds-Averaged-Navier-Stokes (RANS) based turbulence model within the turbulent parts of the flow. Furthermore, the effects of instability wave propagation within a fully three-dimensional boundary layer are also evaluated by integrating the disturbance growth rates along suitably chosen, curvilinear (i.e., nonplanar) propagation trajectories. The results of this analysis are also used in an accompanying paper by Venkatachari et al. to develop improved, physics based transition predictions for the same CRM-NLF configuration.

Boundary layer transition↗

NASA NTRS2010

TPSAS-NF1676L-10980-DND

We plan to perform the following sets of computations on unadapted (fixed) grids: 1) Structured RANS set 1 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), 2) Structured RANS set 2 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 3) Structured RANS set 3 (time permitting) (Code: CFL3D, Grid: Str-OnetoOne-B-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 4) Unstructured RANS set 1 (Code: FUN3D, Grid: Unst-Mixed-FromTet-Nodecentered-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), and 5) Unstructured RANS set 2 (time permitting) (Code: FUN3D, Grid: Unst-Hex-FromOnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras),. Optional case 3 is not being computed. CFL3D is a structured upwind-biased cell-centered RANS code,1 and FUN3D is an unstructured upwind-biased node-centered RANS code

Elizabeth M Lee-Rausch↗

NASA NTRS2010

TPSAS-NF1676L-10454-DND

We plan to perform the following sets of computations on unadapted (fixed) grids: 1) Structured RANS set 1 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), 2) Structured RANS set 2 (Code: CFL3D, Grid: Str-OnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 3) Structured RANS set 3 (time permitting) (Code: CFL3D, Grid: Str-OnetoOne-B-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Menter SST), 4) Unstructured RANS set 1 (Code: FUN3D, Grid: Unst-Mixed-FromTet-Nodecentered-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras), and 5) Unstructured RANS set 2 (time permitting) (Code: FUN3D, Grid: Unst-Hex-FromOnetoOne-A-v1 (supplied by HiLiftPW-1 committee), Turbulence model: Spalart-Allmaras),. Optional case 3 is not being computed. CFL3D is a structured upwind-biased cell-centered RANS code,1 and FUN3D is an unstructured upwind-biased node-centered RANS code

C L Rumsey↗

NASA NTRS2007

Noise Generation in Hot Jets

A prediction method based on the generalized acoustic analogy is presented, and used to evaluate aerodynamic noise radiated from high speed hot jets. The set of Euler equations are split into their respective non-radiating and residual components. Under certain conditions, the residual equations are rearranged to form a wave equation. This equation consists of a third-order wave operator, plus a number of nonlinear terms that are identified with the equivalent sources of sound and their statistical characteristics are modeled. A specialized RANS solver provides the base flow as well as turbulence quantities and temperature fluctuations that determine the source strength. The main objective here is to evaluate the relative contribution from various source elements to the far-field spectra and to show the significance of temperature fluctuations as a source of aerodynamic noise in hot jets.

Khavaran, Abbas↗

NASA NTRS2011

Time Accurate CFD Simulations of the Orion Launch Abort Vehicle in the Transonic Regime

Significant asymmetries in the fluid dynamics were calculated for some cases in the CFD simulations of the Orion Launch Abort Vehicle through its abort trajectories. The CFD simulations were performed steady state and in three dimensions with symmetric geometries, no freestream sideslip angle, and motors firing. The trajectory points at issue were in the transonic regime, at 0 and +/- 5 angles of attack with the Abort Motors with and without the Attitude Control Motors (ACM) firing. In some of the cases the asymmetric fluid dynamics resulted in aerodynamic side forces that were large enough that would overcome the control authority of the ACMs. MSFC's Fluid Dynamics Group supported the investigation into the cause of the flow asymmetries with time accurate CFD simulations, utilizing a hybrid RANS-LES turbulence model. The results show that the flow over the vehicle and the subsequent interaction with the AB and ACM motor plumes were unsteady. The resulting instantaneous aerodynamic forces were oscillatory with fairly large magnitudes. Time averaged aerodynamic forces were essentially symmetric.

Rojahn, Josh↗

NASA NTRS2011

Time Accurate CFD Simulations of the Orion Launch Abort Vehicle in the Transonic Regime

Significant asymmetries in the fluid dynamics were calculated for some cases in the CFD simulations of the Orion Launch Abort Vehicle through its abort trajectories. The CFD simulations were performed steady state with symmetric boundary conditions and geometries. The trajectory points at issue were in the transonic regime, at 0 and 5 angles of attack with the Abort Motors with and without the Attitude Control Motors (ACM) firing. In some of the cases the asymmetric fluid dynamics resulted in aerodynamic side forces that were large enough that would overcome the control authority of the ACMs. MSFC s Fluid Dynamics Group supported the investigation into the cause of the flow asymmetries with time accurate CFD simulations, utilizing a hybrid RANS-LES turbulence model. The results show that the flow over the vehicle and the subsequent interaction with the AB and ACM motor plumes were unsteady. The resulting instantaneous aerodynamic forces were oscillatory with fairly large magnitudes. Time averaged aerodynamic forces were essentially symmetric.

Ruf, Joseph↗

NASA NTRSDate not supplied

TPSAS-NF1676L-31264-DND

Nonlinear unsteady aerodynamic models (TSD, Euler/Navier-Stokes (RANS), LES) are complex, expensive solutions. CFD used by industry mostly for steady/static loads cases. Limit Cycle Oscillation (LCO) is an equilibrium point of the nonlinear aeroelastic system induced by nonlinear unsteady aerodynamics (shock dynamics, separated flow) and/or nonlinear structural dynamics (geometric and material nonlinearities). While Euler solutions can generate LCOs, viscosity yields improved accuracy (shock strength, position). Most common aeroelastic CFD codes currently consist of nonlinear unsteady aerodynamics with linear structural dynamics. Van der Pol system is a simple example of LCOs.

Walter A Silva↗

NASA NTRSDate not supplied

Turbulence Simulations of Transonic Flows over an NACA-0012 Airfoil

Three different simulation approaches, namely unsteady Reynolds-averaged Navier-Stokes (URANS), delayed detached-eddy simulation (DDES), and wall-modeled large-eddy simulation (WMLES) are employed to simulate transonic flow over an NACA-0012 airfoil at different angles of attack covering pre- and post-buffet-onset regimes. The freestream Mach number is 0.75, and the Reynolds number based on the chord length is 10 million. These conditions are the same as the wind-tunnel experimental conditions of McDevitt and Okuno (1985). The NASA FUN3D solver is used for the simulations, which is an unstructured, compressible flow solver. The URANS simulations are performed using the Spalart-Allmaras (SA) model with the compressibility correction, the DDES predictions are based on the SA model, and the WMLES are performed using an equilibrium wall-model. The unsteady RANS simulations, only with the compressibility correction, predict the pre- and post- buffet characteristics, which compare well with the experimental results. DDES results predicted a lower buffet onset angle compared to experiment. The predicted shock locations are upstream of the locations predicted by URANS. Using a fine grid in the spanwise direction, WMLES predictions show buffeting consistent with the experiment.

Transonic Buffet↗

NASA NTRSDate not supplied

Turbulence Simulations of Transonic Flows over an NACA-0012 Airfoil

Three different simulation approaches, namely unsteady Reynolds-averaged Navier-Stokes (URANS), delayed detached-eddy simulation (DDES), and wall-modeled large-eddy simulation (WMLES) are employed to simulate transonic flow over an NACA-0012 airfoil at different angles of attack covering pre-and post-buffet-onset regimes. The freestream Mach number is 0.75,and the Reynolds number based on the chord length is 10million. These conditions are the same as the wind-tunnel experimental conditions of McDevitt and Okuno (1985). The NASA FUN3D solver is used for the simulations, which is an unstructured, compressible flow solver. The URANS simulations are performed using the Spalart-Allmaras (SA) model with the compressibility correction, the DDES predictions are based on the SA model, and the WMLES are performed using an equilibrium wall-model. The unsteady RANS simulations, only with the compressibility correction, predict the pre-and post-buffet characteristics, which compare well with the experimental results. DDES results predicted a lower buffet onset angle compared to experiment. The predicted shock locations are upstream of the locations predicted by URANS. Using a fine grid in the span wise direction, WMLES predictions show buffeting consistent with the experiment.

Transonic Buffet↗

NASA NTRSDate not supplied

Turbulence Simulations of Transonic Flows over NACA-0012 and OAT15A Airfoils

Three different simulation approaches, namely unsteady Reynolds-averaged Navier-Stokes (URANS), delayed detached-eddy simulation (DDES), and wall-modeled large-eddy simulation (WMLES) are employed to simulate transonic flow over an NACA-0012, OAT15A airfoils at different angles of attack covering pre- and post-buffet-onset regimes. The freestream Mach number is 0.75, and the Reynolds number based on the chord length is 10 and 3million, respectively. The NASA FUN3D solver is used for the simulations, which is an unstructured, compressible flow solver. The URANS simulations are performed using the Spalart-Allmaras (SA) model with the compressibility correction, the DDES predictions are based on the SA model, and the WMLES are performed using an equilibrium wall-model. The unsteady RANS simulations, only with the compressibility correction, predict the pre- and post- buffet characteristics, which compare well with the experimental results. DDES results predicted a lower buffet onset angle compared to experiment. The predicted shock locations are upstream of the locations predicted by URANS. Using a fine grid in the spanwise direction, WMLES predictions show buffeting consistent with the experiment.

Computational Fluid Dynamics↗

NASA NTRSDate not supplied

Turbulence Simulations of Transonic Flows over NACA-0012 and OAT15A Airfoils

Three different simulation approaches, namely unsteady Reynolds-averaged Navier-Stokes (URANS), delayed detached-eddy simulation (DDES), and wall-modeled large-eddy simulation (WMLES) are employed to simulate transonic flow over an NACA-0012, OAT15A airfoils at different angles of attack covering pre- and post-buffet-onset regimes. The freestream Mach number is 0.75, and the Reynolds number based on the chord length is 10 and 3million, respectively. The NASA FUN3D solver is used for the simulations, which is an unstructured, compressible flow solver. The URANS simulations are performed using the Spalart-Allmaras (SA) model with the compressibility correction, the DDES predictions are based on the SA model, and the WMLES are performed using an equilibrium wall-model. The unsteady RANS simulations, only with the compressibility correction, predict the pre- and post- buffet characteristics, which compare well with the experimental results. DDES results predicted a lower buffet onset angle compared to experiment. The predicted shock locations are upstream of the locations predicted by URANS. Using a fine grid in the spanwise direction, WMLES predictions show buffeting consistent with the experiment.

Computational Fluid Dynamics↗

NASA NTRSDate not supplied

Simulation and Modeling of Hypersonic Turbulent Boundary Layers Subject to Favorable Pressure Gradients due to Streamline Curvature

Direct numerical simulations (DNS) of favorable-pressure-gradient turbulent boundary layers are presented for a nominal freestream Mach number of 5, with the objective of assessing the limitations of the currently available Reynolds-averaged Navier-Stokes (RANS) models.The wall geometry and flow conditions of the DNS are representative of the experimental data for a Mach 4.9 turbulent boundary layer that was tested on a two-dimensional planar convexwall model in the high-speed blow-down wind tunnel located at the National Aerothermochemistry Laboratory at Texas A&M University. The favorable pressure gradient is induced bythe streamwise surface curvature. The DNS results will be compared with experimental measurements and RANS computations for the same flow conditions and wall geometry. The DNS datasets will also be analyzed to provide an improved physical understanding of hypersonic boundary-layer turbulence subject to mechanical nonequilibrium, including the validity of Morkovin’s hypothesis and the various available compressibility transformations, as well as to obtain turbulence statistics that are relevant to RANS modeling, including the distribution ofeddy viscosity, the budgets of turbulent kinetic energy and Reynolds stresses, and the velocity-temperature correlations. The RANS predictions will be obtained by using both the commonly used one- and two-equation models that assume a constant turbulent Prandtl number and an algebraic, variable Prandtl number model for the turbulent energy flux.

turbulent boundary layers↗