Stefanie M. Hirt

Glenn Research Center, Cleveland, Ohio

David B. Reich

University of Florida, Gainesville, Florida

Michael B. O’Connor

University of Notre Dame, Notre Dame, Indiana

Micro-Ramp Flow Control for Oblique Shock 

Interactions: Comparisons of Computational 

and Experimental Data

NASA/TM—2012-217242

August 2012

AIAA–2010–4973
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Stefanie M. Hirt

Glenn Research Center, Cleveland, Ohio

David B. Reich

University of Florida, Gainesville, Florida

Michael B. O’Connor

University of Notre Dame, Notre Dame, Indiana

Micro-Ramp Flow Control for Oblique Shock 

Interactions: Comparisons of Computational 

and Experimental Data

NASA/TM—2012-217242

August 2012

AIAA–2010–4973

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

40th Fluid Dynamics Conference and Exhibit

sponsored by the American Institute of Aeronautics and Astronautics

Chicago, Illinois, June 28–July 1, 2010
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supported by the Supersonics project under NASA’s Fundamental Aeronautics Program.

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at the NASA Glenn Research Center.

Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2012-217242 1

Micro-Ramp Flow Control for Oblique Shock Interactions: 

Comparisons of Computational and Experimental Data

Stefanie M. Hirt

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

David B. Reich

University of Florida

Gainesville, Florida 32611

Michael B. O’Connor

University of Notre Dame

Notre Dame, Indiana 46556

Abstract

Computational fluid dynamics was used to study the effectiveness of micro-ramp vortex generators to 

control oblique shock boundary layer interactions. Simulations were based on experiments previously 

conducted in the 15- by 15-cm supersonic wind tunnel at the NASA Glenn Research Center. Four microramp geometries were tested at Mach 2.0 varying the height, chord length, and spanwise spacing between 

micro-ramps. The overall flow field was examined. Additionally, key parameters such as boundary-layer 

displacement thickness, momentum thickness and incompressible shape factor were also examined. The 

computational results predicted the effects of the micro-ramps well, including the trends for the impact 

that the devices had on the shock boundary layer interaction. However, computing the shock boundary 

layer interaction itself proved to be problematic since the calculations predicted more pronounced adverse 

effects on the boundary layer due to the shock than were seen in the experiment.

Nomenclature

Ap micro-ramp half angle

c micro-ramp chord length

H incompressible shape factor

h micro-ramp height

M Mach number

s micro-ramp spacing

u velocity component in the flow direction

x coordinate in the flow direction

y

+ distance from wall normalized by shear length scale

z coordinate in the spanwise direction

α shock generator angle of attack

δ∗ compressible boundary-layer displacement thickness

θ compressible boundary-layer momentum thickness

Introduction

In supersonic inlets, oblique shocks are used to compress and decelerate the airflow entering the 

engine. These shocks interact with the boundary layer and increase its thickness, resulting in large energy 
NASA/TM—2012-217242 2

losses, decreased system performance, and potentially separated flow. The traditional method to control 

shock boundary layer interactions (SBLIs) is bleed, which removes low-momentum flow from the 

boundary layer using suction through a porous surface on the inlet wall. Although bleed helps reduce the 

characteristic effects on the boundary layer, bleed systems are heavy and complex, decrease mass flow to 

the engine and introduce additional drag. Together these negative consequences of bleed lead to lower 

system efficiencies (Refs. 1 and 2). 

Many different types of vortex generators (VGs) have been proposed and investigated as possible 

replacements or supplements to bleed systems (Ref. 3). VGs are devices that redistribute the energy 

through the boundary layer. The velocity near the wall is increased to help maintain boundary-layer health 

through the SBLI at the cost of a velocity defect in the outer region of the boundary layer. They are 

attractive due to their low weight and mechanical simplicity, though they do impose a drag penalty on the 

system.

Micro-ramps are one such type of VG flow control device. However, unlike typical vane-type vortex 

generators which have heights matching the boundary-layer thickness, micro-ramps have heights ranging 

from 25 to 40 percent of the boundary-layer thickness (Ref. 4). Micro-ramp style VGs have gained 

interest due to their mechanical robustness.

Experiments studying the effectiveness of micro-ramp flow control were performed in the 15- by

15-cm supersonic wind tunnel at the NASA Glenn Research Center (GRC) by Hirt and Anderson (Ref. 5). 

Fifteen micro-ramp configurations of varying height, chord length, and spanwise spacing between microramps were investigated. A photograph showing three of the configurations from the experiment is shown in 

Figure 1. The 15 configurations represented a Design of Experiments (DOE) central composite design. An 

oblique shock was created using a shock generator plate at 8.5° angle of attack. Micro-ramps were placed 

upstream of where the shock reflects off of the tunnel floor. Boundary-layer profiles and properties and 

Mach number contours were examined for various micro-ramp configurations.

This paper presents results of a computational fluid dynamics (CFD) study of micro-ramp effects on 

an oblique SBLI based on the experiments of Hirt and Anderson (Ref. 5). In this effort, the baseline cases 

and four cases comprising the main effects design–which is embedded in the complete central composite 

design from the experiment–were simulated in order to determine the ability of Reynolds-averaged 

Navier-Stokes (RANS)-based computation methods to predict the effects of micro-ramps. This paper 

discusses the CFD results and compares them with the experimental data.

Figure 1.—Picture of three micro-ramp inserts from the 

experiment in the 15- by 15-cm supersonic wind 

tunnel.
NASA/TM—2012-217242 3

Modeling

Computational Methodology

Simulations were conducted using Wind-US version 2.0 (Ref. 6), a compressible 3-D RANS flow 

solver for multi-zone structured and unstructured grids. Wind-US includes several turbulence models, of 

which the Menter Shear Stress Transport (SST) model was chosen for this study. The SST turbulence 

model was chosen for its accuracy in wall boundary layers (Ref. 7). Structured grids were created using 

Pointwise Gridgen. A viscous grid was created using a wall grid spacing of 7.34×10–4 cm based on an 

average y

+ value of 2.0. A hyperbolic tangent distribution was used to distribute the grid points across the 

computational flow domain. A symmetry plane along the centerline of the tunnel was used to reduce the 

number of grid points by half, greatly reducing computational time. The grid was sequenced to reduce 

convergence time: the solution was first computed on a grid using every fourth point in each direction 

(coarse grid); then the solution was computed at every other point (medium grid); and finally the solution 

was computed using all the grid points (fine grid). Convergence was determined by examining differences 

in the parameters of interest–compressible displacement thickness, compressible momentum thickness, 

and incompressible shape factor–at the primary measurement plane, x = 4 cm. If the values changed less 

than 0.5 percent over 2500 iterations, the solution was deemed converged. Boundary layer velocities, skin 

friction coefficient, and vorticity profiles were also monitored as a qualitative check on convergence.

Configurations

The seven configurations modeled for this effort are divided into two groups: three baseline cases and 

four complete cases with micro-ramps and an oblique shock. The three baseline cases were modeled to 

match the experimental baseline configurations. The first baseline configuration was the empty 15- by

15-cm wind tunnel with a Mach 2.0 nozzle, which was simulated to verify all computational parameters 

were set up correctly (all subsequent simulations were also at M = 2.0). This grid contained 2.9 million 

grid points. For the second baseline configuration an oblique shock generator plate was added to the 

tunnel grid. The grid with the shock generator contained 5.9 million grid points. The final baseline 

configuration modeled the micro-ramps, but no shock generator, and contained 4.0 million grid points.

After the computations of the baseline cases were completed successfully, grids were built for the 

four micro-ramp with shock cases. The micro-ramp/shock generator grids contained about 15 million grid 

points at the fine grid level. The four micro-ramp configurations tested are shown in Table 1 with the 

referenced parameters from the table shown in Figure 2. The micro-ramp half angle, Ap, was 24° for all 

configurations. Micro-ramp centerlines (c/2) were placed at x = –13 cm, with the inviscid shock 

impingement location specified as x = 0 cm as shown in Figure 3. The four micro-ramp configurations 

tested were the main effects configurations from the designed experiment of Hirt and Anderson (Ref. 5). 

The main effects design was chosen due to the ability to study many variables with a limited number of 

test cases since it represents the sparsest DOE design.

TABLE 1.—TEST MATRIX FOR

MICRO-RAMP CONFIGURATIONS.

Configuration h,

mm

c,

mm

s,

mm

1 3 24 35

2 3 12 25

3 5 12 35

4 5 24 25
NASA/TM—2012-217242 4

Figure 2.—Micro-ramp configuration schematic.

Figure 3.—Tunnel layout. Dotted lines represent 

shocks and expansions.

Post Processing

The data presented a challenge when computing boundary-layer parameters. There were density 

variations in the tunnel due to imperfections in the expansion from the nozzle, presenting a more complex 

boundary layer analysis. But in the cases with a shock, density had to be taken into account since the 

region downstream of the shock had no clear boundary layer edge when looking at u-velocity alone. For 

consistency, the boundary layer edge was defined as the location where either the streamwise velocity or 

streamwise momentum reached a maximum.

Since the intent of the micro-ramps is to improve the boundary layer, standard boundary layer 

properties were examined to determine their effectiveness. Specifically, the compressible displacement 

thickness, compressible momentum thickness, and incompressible shape factor are considered in this 

paper. The displacement and momentum thicknesses give a measure of the flow blockage added by the 

devices, while the shape factor measures the boundary layer health. 
NASA/TM—2012-217242 5

The incompressible shape factor, H, was chosen primarily because it has been used to quantify 

improvements in boundary layer health for bleed flows (Ref. 1). The incompressible shape factor is 

typically used instead of the compressible form even in compressible flow because it is independent of 

Mach number. A flat plate turbulent boundary layer has an incompressible shape factor of approximately 

1.3 at all Mach numbers, and a boundary layer near separation has an incompressible shape factor of 

about 2.7 (Ref. 8).

Results

Baseline Cases

Empty Tunnel

The u-velocity contours of the empty tunnel baseline simulation are shown in Figure 4. The 

maximum velocity in the core flow is 519 m/s, which given the speed of sound of 258 m/s gives the 

expected Mach number of 2.0. Figure 5 shows a comparison of boundary-layer u-velocity profiles for the 

three grid sequences and the experimental data. All of the cases predict the correct velocity in the 

freestream. With grid refinement, the CFD solutions closely match the experiment throughout the 

boundary layer. Only subtle changes are observed between the medium and fine grid solutions, indicating 

grid convergence was obtained. Table 2 and Figure 6(a) and (b) show the baseline boundary-layer integral 

parameters compared with experiment. As expected, for the case with a simple converging-diverging 

nozzle and flat plate boundary layer, the agreement of the computed boundary layer parameters and 

growth rate to the measured values was very good.

TABLE 2.—SPAN-AVERAGED COMPRESSIBLE DISPLACEMENT THICKNESS, COMPRESSIBLE

MOMENTUM THICKNESS AND INCOMPRESSIBLE SHAPE FACTOR FOR THE BASELINE CASES

Computational results Experimental results

δ*, 

cm

θ, 

cm

H δ*, 

cm

θ, 

cm

H

x = -13 cm Empty tunnel 0.301 0.101 1.30 0.283 0.100 1.32

x = -8 cm Empty tunnel 0.321 0.104 1.30 0.292 0.104 1.31

Micro-ramps* 0.337 0.110 1.30 0.291 0.117 1.34

x = –4 cm Empty tunnel 0.324 0.112 1.31 0.301 0.106 1.32

Micro-ramps 0.347 0.111 1.29 0.324 0.108 1.30

x = 0 cm Empty tunnel 0.333 0.115 1.30 0.342 0.116 1.30

Micro-ramps 0.348 0.118 1.30 0.347 0.120 1.30

x = 4 cm Empty tunnel 0.347 0.115 1.30 0.326 0.118 1.32

Micro-ramps 0.356 0.118 1.29 0.337 0.124 1.32

Shock** 0.495 0.170 1.81 0.516 0.190 1.75 *

Micro-ramp configuration 2 with no oblique shock. **For the computational case the shock generator angle was 6.5°. For the experimental case the shock 

generator angle was 8.5°.

Figure 4.—Contours of u-velocity for the empty 15- by 15-cm supersonic wind tunnel 

configuration at z = 0 cm. 
NASA/TM—2012-217242 6

Figure 5.—Grid sensitivity and experimental data 

for empty tunnel.

Figure 6.—(a) Empty tunnel compressible displacement thickness, (b) empty tunnel compressible 

momentum thickness, (c) micro-ramps only compressible displacement thickness and (d) microramps only compressible momentum thickness along the streamwise direction.

Shock Only: Viscous Sidewalls

Attempts to run simulations for the 8.5° shock generator angle were unsuccessful in maintaining a 

supersonic flow within the vicinity of the shock generator. The pressure rise from the shock generator 

would move upstream of the shock generator, and the tunnel flow would unstart as in a supersonic inlet. 
NASA/TM—2012-217242 7

This was caused by boundary layer thickening along the tunnel floor coupled with an even stronger 

adverse flow response along the sidewall. Corrective measures such as starting the calculations with a 

higher inflow pressure (to increase the Reynolds number and reduce the effective blockage) always 

reverted to the same unstarted flow state when the pressures were reduced to the experimental conditions. 

In addition to the SST turbulence model this case was run using the Spalart-Allmaras (SA) turbulence 

model, which tends to predict weaker SBLIs. However, no significant improvement was noted when the 

SA turbulence model was used. In subsequent attempts, the shock generator angle was first reduced to 

7.5°, which still exhibited the undesirable unstart behavior, and then ultimately reduced to 6.5°, which 

eliminated the unstart characteristics, yet continued to produce a pressure rise upstream of the leading 

edge of the shock generator plate.

Even for the 6.5° shock generator case, when the micro-ramps were introduced, the effect of the 

sidewall boundary layer thickening interacted with the wake of the micro-ramps nearest the sidewall 

causing the flow to unstart. As a result, a further deviation from the exact experimental configuration was 

made to remove the viscous effect of the sidewall boundary layers to isolate the effects of the microramps on the SBLI. This was accomplished by changing the sidewall boundary condition from viscous 

wall to inviscid wall. While no longer accurately representing the experimental configuration, 

comparisons between CFD and experiment were restricted to a small region (17 to 23 percent of the test 

section width depending on the micro-ramp spacing) near the center of the tunnel where, in the 

experiment, the flow was observed to be free of tunnel sidewall effects.

The computational grid and contours of u-velocity are shown in Figure 7 for the tunnel with shock 

generator installed at 6.5° angle of attack. Figure 8 illustrates the interaction between the sidewall 

boundary layer and the tunnel floor boundary layer due to the shock. Measurements were not taken near 

the sidewalls in the experiment, but this sidewall boundary layer thickening was expected and has been 

observed in similar experiments.

(a) 

(b)

Figure 7.—(a) Grid and (b) u-velocity contours for tunnel with shock 

generator plate at 6.5° with viscous sidewalls at z = 0 cm.
NASA/TM—2012-217242 8

Figure 8.—Contours of u-velocity at various streamwise locations for 

shock-only case with viscous sidewalls. Symmetry plane normal to the 

z-direction.

Figure 9.—Contours of u-velocity for the shock-only case with inviscid tunnel 

sidewalls at z = 0 cm.

Shock Only: Inviscid Sidewalls

In order to mitigate the viscous wall issue and better observe the micro-ramp effectiveness, the tunnel 

sidewall boundary condition was changed from viscous wall to inviscid wall. All cases were simulated 

with this modified boundary condition, including the baseline cases. The tunnel floor and ceiling 

remained viscous to preserve the SBLI of interest. Figure 9 shows the shock-only baseline case with 

inviscid sidewall. It is apparent that the excessive sidewall boundary layer thickening is eliminated and no 

shocks are present upstream of the shock generator plate. 

From Figure 9 it is apparent that the boundary layer is significantly thickened in the region where the

oblique shock impacts the tunnel wall. This illustrates the SBLI that is to be mitigated by the microramps.

Micro-Ramps Only: Inviscid Sidewalls

This simulation showed the vortices generated by micro-ramp configuration 2 unaffected by any 

shocks. The grid in the vicinity of the micro-ramps is shown in Figure 10. Figure 11 illustrates the 

vortices formed by flow over the micro-ramps. As shown in Figure 11, vorticity magnitude is highest near 

the trailing-edge tip of the micro-ramps and decays downstream. Previous work has shown that vorticity 
NASA/TM—2012-217242 9

Figure 10.—Grid in the vicinity of the micro-ramps. Flow is 

from left to right. Symmetry plane normal to z-direction.

Figure 11.—Vorticity magnitude contours at various streamwise locations. Flow is 

from left to right. Symmetry plane normal to z-direction.

decay occurs at a higher rate in RANS-based CFD than is measured experimentally (Refs. 9 and 10). As 

shown in Table 2, the micro-ramps tend to increase compressible displacement thickness compared to the 

empty tunnel cases at the corresponding locations, but have little effect on incompressible shape factor 

when no shock is present. Figure 6 (c) and (d) show the micro-ramp only baseline CFD compared to the 

experimental data. Agreement is generally good expect for the first station downstream of the microramps at x = –8 cm. Because this station is so near the micro-ramps, it may be difficult to obtain good 

measurements in the experiment and calculate good velocities in the computational results.

Micro-Ramps With Shock

The inviscid wall boundary condition was applied to the tunnel sidewalls for all CFD simulations of 

the tunnel with the shock generator and micro-ramps. The viscous wall boundary condition continued to 

be applied to the upper and lower walls of the tunnel, as the SBLI on these surfaces was of primary 

interest in this study. Figures 12 and 13 show u-velocity contours of the four micro-ramp/shock 

configurations. Local separation occurs in the wake region of each micro-ramp, however it is unclear 

from contour plots alone whether flow is improved in the spanwise region between the micro-ramps. 
NASA/TM—2012-217242 10

(a)

(b)

(c)

(d)

Figure 12.—Contours of u-velocity at the tunnel centerline (z = 0) for the four micro-ramp 

configurations: (a) Configuration 1, (b) Configuration 2, (c) Configuration 3, and (d) Configuration 4.
NASA/TM—2012-217242 11

(a)

(b)

(c)

(d)

Figure 13.—Contours of u-velocity halfway between the micro-ramps (z = s/2) for the four microramp configurations: (a) Configuration 1, (b) Configuration 2, (c) Configuration 3, and 

(d) Configuration 4.
NASA/TM—2012-217242 12

Spanwise averaging of boundary-layer parameters was used to obtain a qualitative measure of microramp effectiveness. Table 3 shows compressible displacement thickness, compressible momentum 

thickness, and incompressible shape factor for all four micro-ramp with shock configurations as well as 

the shock only baseline case for comparison. Compared to the shock-only case, displacement thickness 

and momentum thickness increased with the addition of micro-ramps. The incompressible shape factor 

decreased for three of the four micro-ramp configurations.

TABLE 3.—SPAN-AVERAGED COMPRESSIBLE DISPLACEMENT

THICKNESS,COMPRESSIBLE MOMENTUM THICKNESS AND 

INCOMPRESSIBLE SHAPE FACTOR FOR MICRO-RAMPS 

WITH SHOCK CASES. x = 4 cm

Configuration δ*,

cm

θ, 

cm

H

Shock 0.495 0.170 1.81

1 0.520 0.183 1.75

2 0.541 0.184 1.81

3 0.546 0.192 1.77

4 0.514 0.194 1.66

Data were available from the 15- by 15-cm SWT test for one micro-ramp configuration with a shock 

generator angle of 6.5°. and was modeled as configuration 4 in the current study. The CFD and 

experimental data are presented in Table 4. Also presented are the experimental results for that 

configuration at 7.5° and 8.5° shock generator angle. Again the CFD predicts an effect of the shock 

interaction stronger than that observed in the experiment in terms of increased boundary-layer parameter 

thicknesses and increased shape factor. In fact, the computed boundary-layer parameters are closest to the 

experimental data at 8.5°. The experimental data seems to show a distinct break where the data at 6.5° and 

7.5° are quite similar with a slight increase in the displacement and momentum thicknesses, δ* and θ,

without a change in shape factor, H, but the values of all three parameters for the 8.5° case are 

significantly increased. It is possible that this is indicative of the flow being attached for the two small 

shock generator angles and separated at the highest angle. If this is the case, then it seems that the CFD is 

predicting an earlier separation limit in terms of shock strength than is evidenced in the experimental data.

TABLE 4.—SPAN-AVERAGED COMPRESSIBLE DISPLACEMENT THICKNESS, COMPRESSIBLE MOMENTUM THICKNESS 

AND INCOMPRESSIBLE SHAPE FACTOR FOR MICRO-RAMPS 

WITH SHOCK CONFIGURATION 4 (CFD) COMPARED 

TO EXPERIMENTAL DATA (SWT) FOR THE SAME 

MICRO-RAMP CONFIGURATION AT VARIOUS

SHOCK GENERATOR ANGLES. x = 4 cm

α, 

degrees

δ*, 

cm

θ, 

cm

H

6.5 CFD 0.514 0.194 1.66

6.5 SWT 0.400 0.129 1.29

7.5 SWT 0.445 0.140 1.29

8.5 SWT 0.556 0.226 1.62

To compare the trends between micro-ramp configurations, Figure 14 shows the span-averaged 

displacement and momentum thicknesses, δ* and θ, and shape factor, H, for the CFD compared to 

experimental data. Experimental data is shown in blue for all four micro-ramp configurations at a shock 

generator angle of 8.5° and in green for configuration 4 with a shock generator angle of 6.5°. The 

computational results are shown in red with squares marking the four configurations and the horizontal 

line representing the value for the 6.5° shock-only baseline. Despite the simulations not exactly modeling 
NASA/TM—2012-217242 13

(a)

(b) (c)

Figure 14.—Plots showing comparisons of span-averaged boundary-layer parameters at x = 4 cm by 

configuration numbers from Table 1 along the x-axis: (a) compressible displacement thickness, δ

*

, 

(b) compressible momentum thickness, θ, and (c) incompressible shape factor, H.

the experimental configuration, the flow affected by the micro-ramps away from the sidewalls was 

successfully simulated and captured the trends of the impact of the ramps on the shock system, especially 

in terms of the shape factor. It is important to mention, however, that the CFD predictions represent a 6.5°

shock generator angle, while the experimental data was obtained with a shock generator angle of 8.5°. We 

would expect that the CFD prediction for the 6.5° case should match the experimental data for the same 

angle. However, we observe that the values more closely match those for the 8.5° case, suggesting that 

the CFD predicts a greater adverse response to the SBLI than seen in the experiment.

Conclusions

The primary challenge encountered in the present study was that the CFD solutions showed 

limitations in replicating experimental results with the oblique shock, especially when the sidewalls were 

modeled as viscous walls. Across the board, the CFD predicted a greater adverse response to the shock 

interaction than was observed in the experiment. This was seen as a tunnel unstart with a plate angle of 

8.5° and also as a larger boundary-layer shape factor with a plate angle of 6.5°. Even when the sidewalls 

were modeled as inviscid walls, the response of the boundary layer to the shock interaction is not captured

well by currently available RANS-based CFD. 

Although there were difficulties modeling the exact experimental configuration, the modified 

simulations with inviscid sidewalls are a useful complement to the experimental data. The effect of the 

micro-ramps on the shock showed the same trends as for the experiment based on the limited data 

available for the 6.5° case. The addition of micro-ramps to a SBLI reduced the incompressible shape 

factor in three of the four configurations. However it is still unclear whether the micro-ramps are 
NASA/TM—2012-217242 14

beneficial since both the displacement thickness and momentum thickness increased with the addition of 

micro-ramps. This trade-off would need to be considered relative to a specific inlet system.

For the cases without a shock present, the CFD agreed reasonably well with the experiment. This was 

even true for the case with only the micro-ramps even though there are some questions as to whether a 

RANS solver is appropriate to use with vortical flows. Indeed, the RANS approximation needs to be more 

closely examined in its application to the study of vortical flow, micro-ramps in particular. All RANS 

models respond to vorticity by increasing turbulent viscosity, in turn decreasing vorticity, possibly too 

fast (vorticity is not preserved far enough downstream) to represent a real physical flow. Other 

approaches such as large-eddy simulation or direct numerical simulation should be investigated and may 

give more informative results.

References

1. Fukuda, M. K., Hingst, W. G., Reshotko, E., “Control of Shock Wave-Boundary Layer Interactions 

by Bleed in Supersonic Mixed Compression Inlets,” NASA CR2595, 1975.

2. Young, D. D., Jenkins, S. A., “An Investigation of Active Flowfield Control for Inlet 

Shock/Boundary Layer Interaction,” AIAA Paper 2005-4020, July 2005.

3. Lin, J. C., “Review of Research on Low-Profile Vortex Generators to Control Boundary-Layer 

Separation,” Progress in Aerospace Sciences, Vol. 38, 2002, pp. 389-420.

4. Anderson, B.H., Tinapple, J., Surber, L., “Optimal Control of Shock Wave Turbulent Boundary 

Layer Interactions Using Micro-Array Actuation,” AIAA Paper 2006-3197, June 2006.

5. Hirt, S.M., Anderson, B.H., “Experimental Investigation of the Application of Microramp Flow 

Control to an Oblique Shock Interaction,” AIAA Paper 2009-919, Jan 2009.

6. Towne, C. E., “Wind-US Users Guide, Version 2.0,” NASA/TM—2009-215804, 2009.

7. Georgiadis, N.J., Yoder, D.A., Towne, C.S., Engblom, W.A., Bhagwandin, V.A., Power, G.D., 

Lankford, D.W., Nelson, C.C., “Wind-US Code Physical Modeling Improvements to Complement 

Hypersonic Testing and Evaluation,” AIAA Paper 2009-193, Jan 2009.

8. Castillo, L., Wang, X., George, W. K., “Separation Criterion for Turbulent Boundary Layers Via 

Similarity Analysis”, Journal of Fluids Engineering, Vol. 126, 2004, pp 297-304.

9. Dippold, V., “CFD Analyses and Jet-Noise Predictions of Chevron Nozzles With Vortex 

Stabilization,” AIAA Paper 2008-37, Jan 2008.

10. Dudek, J. C., “Modeling Vortex Generators in the Wind-US Code,” AIAA Paper 2010-32, Jan 2010.

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1. REPORT DATE (DD-MM-YYYY)

01-08-2012 

2. REPORT TYPE

Technical Memorandum 

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Micro-Ramp Flow Control for Oblique Shock Interactions: Comparisons of Computational 

and Experimental Data 

5a. CONTRACT NUMBER

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Hirt, Stefanie, M.; Reich, David, B.; O'Connor, Michael, B. 

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

WBS 984754.02.07.03.13.02 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration 

John H. Glenn Research Center at Lewis Field 

Cleveland, Ohio 44135-3191 

8. PERFORMING ORGANIZATION

 REPORT NUMBER

E-17976-1 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration 

Washington, DC 20546-0001 

10. SPONSORING/MONITOR'S

 ACRONYM(S)

NASA 

11. SPONSORING/MONITORING

 REPORT NUMBER

NASA/TM-2012-217242 

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified-Unlimited 

Subject Category: 02 

Available electronically at http://www.sti.nasa.gov 

This publication is available from the NASA Center for AeroSpace Information, 443-757-5802

13. SUPPLEMENTARY NOTES

14. ABSTRACT

Computational fluid dynamics was used to study the effectiveness of micro-ramp vortex generators to control oblique shock boundary layer 

interactions. Simulations were based on experiments previously conducted in the 15- by 15-cm supersonic wind tunnel at the NASA Glenn 

Research Center. Four micro-ramp geometries were tested at Mach 2.0 varying the height, chord length, and spanwise spacing between 

micro-ramps. The overall flow field was examined. Additionally, key parameters such as boundary-layer displacement thickness, 

momentum thickness and incompressible shape factor were also examined. The computational results predicted the effects of the microramps well, including the trends for the impact that the devices had on the shock boundary layer interaction. However, computing the shock 

boundary layer interaction itself proved to be problematic since the calculations predicted more pronounced adverse effects on the boundary 

layer due to the shock than were seen in the experiment.

15. SUBJECT TERMS

Vortex generators; Oblique shock waves; Computational fluid dynamics 

16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF

 ABSTRACT

UU 

18. NUMBER

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 PAGES

22 

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email:help@sti.nasa.gov) 

a. REPORT

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b. ABSTRACT

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19b. TELEPHONE NUMBER (include area code)

443-757-5802 

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