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At least 325 records · Page 18

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↗

Testing of Mars Retropropulsion Concepts in the Langley Unitary Plan Wind Tunnel

Future Mars human lander missions will require using retrorockets beginning at supersonic conditions, something which has never been done before at Mars. Significant aerosciences challenges exist due to interactions between the retrorocket exhaust plumes, the freestream flow, and the vehicle that alter the aerodynamic behavior. Historical retropropulsion ground and flight data do not provide a basis for calibrating the computational uncertainties for aerodynamic interference forces and moments on proposed Mars descent vehicles, either because of insufficient data or dissimilar vehicles geometries and/or conditions. A new retropropulsion wind tunnel test was conducted in the NASA Langley Unitary Plan Wind Tunnel in order to address some of the deficiencies of previous tests and to provide a basis for computational model comparisons on relevant geometries. The test included improvements in model design and measured data. Two sub-scale model geometries derived from full-scale vehicle concepts were tested: a blunt low lift-to-drag vehicle and a more slender geometry. The first model includes variations in nozzle parameters that were estimated to have first-order effects on the measurements: location, pointing direction, size, and exit-to-throat area ratio. Both models were instrumented with discrete steady and unsteady pressure sensors on the aerodynamic and interior flow path surfaces. The heats shields of both models also were covered with pressure sensitive paint that effectively measures a steady pressure distribution in an otherwise unsteady flow field. This pressure distribution was integrated over the surface geometry in order to estimate the steady aerodynamic force and moment coefficient contribution from the painted surfaces; this approach was used as a replacement for using a flow-through balance. This paper will cover the test objectives, model design, test matrix, and available test results for each model configuration at different freestream Mach numbers, model attitude, and non-dimensional thrust magnitude. A subsequent publication will cover uncertainty quantification of the test data and comparisons to computational fluid dynamics model results.

Powered Descent↗

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 favorable pressure gradient is induced by the streamwise curvature of the two-dimensional, planar, convex measurement surface used during experiments at the Texas A&M University. The DNS data shows good comparison with the measured velocity profiles, strain rates, and some, but not all, of the Reynolds-stress components. The discrepancies between the predicted and the measured wall-normal as well as shear stress components are primarily attributed to the lower than actual values inferred from typical PIV measurements of turbulent boundary layers. The DNS data shows a zero or slightly negative Reynolds shear stress in the outer part of the boundary-layer, which is indicative of the decaying turbulent motion under a strong favorable pressure gradient. The DNS data is also compared with the results of RANS computations based on commonly used zero, one, and two equation eddy-viscosity models. The RANS models yield reasonable comparisons with the DNS-based skin friction under zero and weak pressure gradients, but significant discrepancies under a strong pressure gradient. The k-w SST model provided the best overall predictions of skin friction, except in the region where the flow transitions from a favorable to an adverse pressure gradient. While the RANS models examined herein also give good predictions of the Reynolds shear stress under a sufficiently weak pressure gradient, none of those models are able to appropriately capture the reduction in the Reynolds stresses when the flow was subjected to a strong pressure gradient. An a priori assessment of the turbulent heat-flux prediction based on the assumption of a constant turbulent Prandtl number with the DNS data shows that while the constant turbulent Prandtl number model is effective in predicting the wall-normal component of turbulent heat flux, it does not capture the turbulent heat transfer in the streamwise direction for all the pressure gradient cases. The failure of the constant turbulent Prandtl number model highlights a requirement for more advanced models of the turbulent heat flux.

turbulent boundary layers↗

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

Direct numerical simulations (DNS) of adverse-pressure-gradient turbulent boundary layers over a planar concave wall 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 concave wall model in the high-speed blow-down wind tunnel located at the National Aerothermochemistry Laboratory at Texas A&M University (TAMU). The DNS was validated against the experimental results of TAMU for the same flow conditions and wall geometry. An analysis of the DNS datasets was also conducted to provide an assessment of the validity of Morkovin’s hypothesis and the strong Reynolds analog for turbulence subject to mechanical nonequilibrium. In addition to the DNS results, RANS predictions are obtained by using the Baldwin-Lomax (BL), Spalart-Allmaras (SA), and the k - w SST turbulence models. The comparisons between RANS and DNS showed little impact of an adverse pressure gradient on the accuracy of these models, at least up to an incompressible Clauser pressure gradient parameter of beta(sub inc) 1.22. While the Boussinesq assumption provided reasonable predictions for the Reynolds shear stress, it failed to adequately predict the normal components of the Reynolds stress.

turbulent boundary layers↗

Integral Velocity Sampling for Unsteady Rotor Models on Cartesian Meshes

An unsteady body-force rotor model utilizing integral velocity sampling is developed for Cartesian meshes with a focus on accurate performance predictions. In this approach, the rotors are modeled as body-force source terms, and integral sampling is used to define the local freestream velocity vector. The method is verified in two dimensions for airfoils, providing accurate angle of attack and freestream Mach number estimates even on relatively coarse meshes, and demonstrating excellent mesh convergence across a variety of source region thicknesses. Extensions of the model to three dimensions for both fixed and rotating wings are considered, with numerical experiments demonstrating the need for a spanwise-continuous representation of the induced velocity or angle of attack. The model provides reasonable spanwise lift distributions without any explicit tip-loss model. Comparisons to high-fidelity simulations and experimental data show excellent agreement for predictions of an isolated DJI Phantom 3 propeller in hover. Finally, simulations of the full aircraft in edgewise forward flight also compare well with wind tunnel data across a variety of pitch angles and propeller speeds.

ARMD↗

Overview of Dragonfly Entry Aerosciences Measurements (DrEAM)

NASA Ames Research Center (ARC) leads the Dragonfly Entry Aerosciences Measurements (DrEAM) project, which is an aeroshell instrumentation suite on the Dragonfly mission that fulfills the Engineering Science Investigation requirement for the New Frontiers mission. NASA ARC is partnering with NASA Langley Research Center (LaRC) and the German Aerospace Center (DLR) to provide a comprehensive sensor suite, including a DLR-provided Data Acquisition System (DAS). DrEAM will provide key aerothermodynamic data and performance analysis for Dragonfly’s forebody and backshell Thermal Protection System (TPS). Titan’s atmosphere predominantly consists of ni-trogen (~98% by mole) with small amounts of me-thane (~2% by mole) and other trace gases. CN is a strong radiator, and is found in nonequilibrium con-centrations for Titan entry. The accurate modeling of nonequilibrium CN radiation has proven to be a diffi-cult task. Prompted by the Huygens mission, many experimental campaigns and analyses were performed to better understand the aerothermal environments experienced by the probe during Titan entry [1]. How-ever, the Huygens probe carried no heatshield instru-mentation. Therefore, the DrEAM sensor suite will sig-nificantly advance the state-of-the-art not only by documenting the environment and performance of Dragonfly’s entry system but also by making key in situ measurements in Titan’s atmosphere for the first time. Aerothermal environments and TPS response will be measured using sensors whose flight heritage is tak-en from the Mars Entry, Descent, and Landing In-strumentation 2 (MEDLI2) thermocouple plugs and the COMbined Aerothermal and Radiometer Sensor (COMARS) suite [2], with the latter supplied by DLR. The MEDLI2 project used embedded thermocouples to directly measure the in-depth TPS temperature-time history at several locations on the heat shield and backshell of the Mars 2020 entry vehicle. These tem-perature measurements, in turn, can be used to infer surface environments via an inverse analysis proce-dure analogous to that used for MEDLI. For DrEAM, the thermocouple plug subsystem will be known as Dragonfly Sensors for Aero-Thermal Reconstruction (DragSTR). On Schiaparelli, the COMARS suite in-cluded three total surface-mounted heat flux sensors, three pressure sensors, and one radiometer. For DrEAM, the COMARS package will be known as the COmbined Sensor System for Titan Atmosphere (COSSTA). Since the methane concentration in the Titan atmos-phere is directly proportional to the radiative heat flux, the COSSTA measurements will be used to reduce the current uncertainty in the methane volume fraction. Atmospheric density measurements and capsule aero-dynamic data will be obtained through the onboard Inertial Measurement Unit (IMU), supplemented by pressure transducers similar to those used by the MEDLI and MEDLI2 projects. The DrEAM pressure sensors will be known as the Dragonfly Atmospheric Flight Transducers. (DrAFT). The pressure measure-ments, when combined with data from the on-board IMU, will allow for reconstruction of such quantities as vehicle Mach number, freestream density, and atmos-pheric winds. DrAFT measurements will enhance Dragonfly trajectory reconstruction and enable a sepa-ration of the aerodynamics from the atmosphere, as was done for MEDLI [3] and is currently in process for the MEDLI2 flight data set.

EDL↗

Structure and Dynamics of Bleed-Controlled Impinging Shock/Turbulent-Boundary-Layer Interactions

Porous boundary-layer bleed is often employed to alleviate the detrimental effects of shock/turbulent-boundary-layer interactions (STBLI) in high-speed vehicles. However, outstanding questions remain on the underlying mechanisms through which bleed influences STBLI dynamics. This work addresses this knowledge gap by performing a scale-resolved Large-Eddy Simulation of an impinging STBLI controlled by discrete bleed holes. The simulation has a freestream Mach number of 2.5, an oblique shock generator angle of 8 degrees, and a Reynolds number based on the incoming momentum thickness of 2800. Bleed substantially reduces the separation size and upstream influence of the STBLI compared to the canonical, uncontrolled case. Consequently, the characteristic, low-frequency motion of STBLI-induced separation is mitigated, as revealed by wall-pressure spectra. Energy is shifted from the low frequency separation behavior to a higher-frequency signature caused by the bleed shocks and expansions. The scale-resolved simulations also reveal that these small, localized structures severely limit the turbulence production traditionally seen in the uncontrolled interaction.

computational fluid dynamics↗

Semispan Test Results of a Conventional High-Lift Common Research Model in Landing Configuration

A 10%-scale high-lift version of the Common Research Model (CRM-HL) was tested in the 14- by 22-Foot Subsonic Tunnel at the NASA Langley Research Center. This research was aimed at providing a representative reference case for comparison with an Active Flow Control (AFC) enabled version of the CRM-HL and to increase the existing experimental database for CFD high-lift prediction. The test was conducted mostly at a freestream Mach number of 0.20. The effects of the engine nacelle, nacelle chine, tufts, small variations in Mach number, hysteresis-associated increasing/decreasing angle of attack, and incoming floor boundary-layer thickness (i.e., thinning by activation of the floor boundary layer removal system) were examined. A prestall lift performance degradation for the CRM-HL configuration was resolved with a properly placed nacelle chine. Surface pressure results are presented in detail for three key variants of CRM-HL — baseline (nacelle on), nacelle off, and nacelle with the most effective chine installation. The presented aerodynamic forces and surface pressures include both with and without the wall correction using the Transonic Wall Interference Correction System (TWICS) method. A limited set of photogrammetry results is also presented to document the model deformation under test conditions.

high-lift↗

Semispan Test Results of an Active Flow Control Enabled High-Lift Common Research Model in Landing Configuration

A 10%-scale semispan, Active Flow Control (AFC) enabled, simplified high-lift version of the Common Research Model (CRM-SHL-AFC) was tested in the 14- by 22-Foot Subsonic Tunnel at the NASA Langley Research Center. The main objective of the test was to develop an AFC system that can provide the necessary lift recovery on a simple-hinged flap high-lift system while minimizing its pneumatic power requirement. Three new types of AFC approaches were examined: Double-Row Sweeping Jets (DRSWJ), Alternating Pulsed Jets (APJ), and High Efficiency Low Power (HELP) actuators. The DRSWJ and the APJ actuators used two rows of unsteady jets, whereas the HELP actuators used an upstream row of sweeping jets combined with a downstream row of steady jets to overcome strong adverse pressure gradients. The test was conducted mostly at a freestream Mach number of 0.20. For exploration purposes, a limited number of runs were made at lower Mach numbers or using vortex generators (VGs). Minimal sensitivity to Mach number or VGs, for the cases evaluated, were observed. The AFC-induced lift coefficient increment was maintained over the AFC-off case for most flow-control cases examined. The CRM-SHL-AFC configuration equipped with HELP actuation was the only actuator configuration able to match or exceed the targeted lift performance of a reference conventional high-lift configuration. The presented aerodynamic data include lift, drag, and pitching moment coefficients as a function of angle of attack, with and without the Transonic Wall Interference Correction System (TWICS) method applied. Lift increments as a function of AFC pneumatic power usage (i.e., nozzle pressure ratio, mass flow, momentum coefficient, and power coefficient) are also presented at a lower angle of attack (α = 8.9°) and at maximum lift (α= 17.1°). At the two angles of attack, the surface pressure distributions and autospectral densities from the unsteady pressure transducers for the AFC-off case and the best HELP actuation case are compared.

high-lift↗

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↗

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↗

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).1The 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↗

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↗

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↗

Some characteristics of airfoil-jet interaction with Mach number nonuniformity

The image method is used to examine the upper-surface-blowing jet-airfoil interaction with Mach number nonuniformity. The formulation represents an extension of the classical incompressible results (Ting and Liu, 1969; Koning, 1963). Some characteristics of the interaction are discussed. The main assumptions are (1) inviscid linear theory, (2) two-dimensional jet, (3) no turbulent mixing, and (4) no airfoil thickness effect. A plane jet with Mach number M sub 2 is assumed to be imbedded in a freestream of Mach number M sub 1. A thin airfoil is placed at a distance h below the lower jet surface. For h = 0, this may represent an idealized configuration with an upper-surface blowing jet.

Lan, C. E.↗

Jet to freestream velocity ratio computations for a jet in a crossflow

The flowfield induced by a single, subsonic jet exhausting perpendicularly from a flat plate into a subsonic crossflow has been numerically investigated. Time-averaged solutions were obtained using the thin-layer Navier-Stokes equations and two overlapping grids. Test cases were chosen to match available experimental data where the jet Mach number was 0.78 and the freestream Mach number was varied to represent effective velocity ratios R from 4 to 12. Comparisons of the pressures induced on the flat plate are presented. The results show that the best agreement is obtained for R of 4 and 6, R of 8 is more difficult to resolve, and the R of 12 solution was not satisfactory. It is anticipated that the resolution could be improved by using either smaller time steps or finer grid spacing as R increases.

Margason, Richard J.↗

Fluidic Fence Flow Control on A 30° Swept Wing in Compressible Freestreams

A 30° swept wing with a NACA 64(3)-618 airfoil cross-section was evaluated in four different freestream conditions with Mach numbers increasing from 0.05 to 0.7 using computational fluid dynamics simulations. Active flow control (AFC) through a streamwise row of vortex generating jets was applied at 70% span, blowing inboard to obstruct the development of spanwise flow and increase lift outboard of the control location. The effectiveness of the AFC was evaluated at α = 5° and 10° as freestream Mach number increased and the AFC mass flow coefficient was held constant. Increases to lift coefficient were reduced at higher Mach numbers, but the ratio of change in lift coefficient and the AFC momentum coefficient remained roughly constant. The fluidic fence caused decreased pressure coefficients on the outboard wing sections, but this region of decreased pressure coefficient moved aft on the chord with increasing Mach number. Mach 0.7 is higher than the critical Mach number of the wing and produced interesting interplay with the AFC, as the suction surface shock moved forward on the inboard wing section but moved aft on the outboard wing section.

Evan J McFadden↗

Fluidic Fence Flow Control on A 30° Swept Wing in Compressible Freestreams

A 30° swept wing with a NACA 64(3)-618 airfoil cross-section was evaluated in four different freestream conditions with Mach numbers increasing from 0.05 to 0.7 using computational fluid dynamics simulations. Active flow control (AFC) through a streamwise row of vortex generating jets was applied at 70% span, blowing inboard to obstruct the development of spanwise flow and increase lift outboard of the control location. The effectiveness of the AFC was evaluated at α = 5° and 10° as freestream Mach number increased and the AFC mass flow coefficient was held constant. Increases to lift coefficient were reduced at higher Mach numbers, but the ratio of change in lift coefficient and the AFC momentum coefficient remained roughly constant. The fluidic fence caused decreased pressure coefficients on the outboard wing sections, but this region of decreased pressure coefficient moved aft on the chord with increasing Mach number. Mach 0.7 is higher than the critical Mach number of the wing and produced interesting interplay with the AFC, as the suction surface shock moved forward on the inboard wing section but moved aft on the outboard wing section.

Evan J McFadden↗