Michelle L. McMillan

SynGenics Corporation, St. Louis, Missouri

Abe Gissen and Bojan Vukasinovic

Georgia Institute of Technology, Atlanta, Georgia

Matthew T. Lakebrink

The Boeing Company, St. Louis, Missouri

Ari Glezer

Georgia Institute of Technology, Atlanta, Georgia

Mori Mani and James Mace

The Boeing Company, St. Louis, Missouri

Inlet Flow Control and Prediction Technologies for 

Embedded Propulsion Systems 

Summary Report for Fiscal Year 2009

NASA/CR—2010-216779

October 2010
NASA STI Program . . . in Profi le

Since its founding, NASA has been dedicated to the 

advancement of aeronautics and space science. The 

NASA Scientifi c and Technical Information (STI) 

program plays a key part in helping NASA maintain 

this important role.

The NASA STI Program operates under the auspices 

of the Agency Chief Information Offi cer. It collects, 

organizes, provides for archiving, and disseminates 

NASA’s STI. The NASA STI program provides access 

to the NASA Aeronautics and Space Database and 

its public interface, the NASA Technical Reports 

Server, thus providing one of the largest collections 

of aeronautical and space science STI in the world. 

Results are published in both non-NASA channels 

and by NASA in the NASA STI Report Series, which 

includes the following report types:

• TECHNICAL PUBLICATION. Reports of 

completed research or a major signifi cant phase 

of research that present the results of NASA 

programs and include extensive data or theoretical 

analysis. Includes compilations of signifi cant 

scientifi c and technical data and information 

deemed to be of continuing reference value. 

NASA counterpart of peer-reviewed formal 

professional papers but has less stringent 

limitations on manuscript length and extent of 

graphic presentations.

• TECHNICAL MEMORANDUM. Scientifi c 

and technical fi ndings that are preliminary or 

of specialized interest, e.g., quick release 

reports, working papers, and bibliographies that 

contain minimal annotation. Does not contain 

extensive analysis.

• CONTRACTOR REPORT. Scientifi c and 

technical fi ndings by NASA-sponsored 

contractors and grantees.

• CONFERENCE PUBLICATION. Collected 

papers from scientifi c and technical 

conferences, symposia, seminars, or other 

meetings sponsored or cosponsored by NASA.

• SPECIAL PUBLICATION. Scientifi c, 

technical, or historical information from 

NASA programs, projects, and missions, often 

concerned with subjects having substantial 

public interest.

• TECHNICAL TRANSLATION. Englishlanguage translations of foreign scientifi c and 

technical material pertinent to NASA’s mission.

Specialized services also include creating custom 

thesauri, building customized databases, organizing 

and publishing research results.

For more information about the NASA STI 

program, see the following:

• Access the NASA STI program home page at 

http://www.sti.nasa.gov

• E-mail your question via the Internet to help@

sti.nasa.gov

• Fax your question to the NASA STI Help Desk 

at 443–757–5803

• Telephone the NASA STI Help Desk at

 443–757–5802

• Write to:

 NASA Center for AeroSpace Information (CASI)

 7115 Standard Drive

 Hanover, MD 21076–1320
Michelle L. McMillan

SynGenics Corporation, St. Louis, Missouri

Abe Gissen and Bojan Vukasinovic

Georgia Institute of Technology, Atlanta, Georgia

Matthew T. Lakebrink

The Boeing Company, St. Louis, Missouri

Ari Glezer

Georgia Institute of Technology, Atlanta, Georgia

Mori Mani and James Mace

The Boeing Company, St. Louis, Missouri

Inlet Flow Control and Prediction Technologies for 

Embedded Propulsion Systems 

Summary Report for Fiscal Year 2009

NASA/CR—2010-216779

October 2010

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared under Contract NNC07CB76C
Acknowledgments

This work was sponsored by the Fundamental Aeronautics Program at the NASA Glenn Research Center.

Available from

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076–1320

National Technical Information Service

5301 Shawnee Road

Alexandria, VA 22312

Available electronically at http://gltrs.grc.nasa.gov

Trade names and trademarks are used in this report for identifi cation 

only. Their usage does not constitute an offi cial endorsement, 

either expressed or implied, by the National Aeronautics and 

Space Administration.

This work was sponsored by the Fundamental Aeronautics Program 

at the NASA Glenn Research Center.

Level of Review: This material has been technically reviewed by NASA technical management.

This report contains preliminary fi ndings, 

subject to revision as analysis proceeds.
NASA/CR—2010-216779 iii

Preface

This report documents the fundamental aeronautics research performed by The Boeing Company 

under NASA Contract No. NNC07CB76C during the Fiscal Year 2009. Subcontractors supporting The 

Boeing Company in this Contract were SynGenics Corporation and Georgia Institute of Technology. 

Ms. Julie Dudek of NASA served as the Contract Officer’s Technical Representative.

NASA/CR—2010-216779 v 

Executive Summary

Fail-safe, hybrid flow control (HFC) may be an enabling technology for meeting high-speed cruise 

efficiency, low noise signature, and reduced fuel burn goals for future HWB aircraft with embedded 

engines. The objectives of the Boeing IFCPT program are to develop flow-control technologies for highly 

integrated, offset inlets, as well as, to develop and improve novel test methods and validated tools for 

predicting active HFC effectiveness in managing inlet pressure distortion. The approach to accomplish the 

objectives encompasses experimental investigations of flow-control devices conducted in combination 

with numerical simulations incorporating robust flow-control-device modeling and advanced 

Computational Fluid Dynamics (CFD) tools. 

In support of the program objective to develop flow-control technologies, the interaction of surfacemounted, passive and active flow-control devices with a Mach 0.5 cross flow were examined in a smallscale wind tunnel. The evolution of streamwise vortices induced by the flow control was investigated in 

an adverse pressure gradient consistent with the pressure gradient within a diffuser compatible with future 

HWB vehicles. Counter-rotating-vortex pairs and single-sense vortices were formed and characterized 

using passive microramps and microvanes, respectively. Similar streamwise vortices were also generated 

using synthetic jet actuators. Finally, hybrid actuation approaches were demonstrated where a passive 

microvane and active synthetic jet were designed and operated in a tandem arrangement, such that the 

induced vorticity from the active device enhanced the control of the passive device, improving the overall 

control effectiveness.

In support of the program objective to improve and validate tools and test methods for predicting 

active, hybrid flow-control performance, simulations of isolated devices in the Georgia Institute of 

Technology FMRL tunnel test section with the profiled wall were conducted. Results of the numerical 

simulations were validated against experimental data. The sensitivity of numeric results to grid resolution, 

turbulence model, and viscous-flux-calculation technique was examined. A grid refinement study was 

conducted to establish the grid size required for grid-independent solutions. Multiple turbulence models 

were used in simulations, and results were validated against experimental data. Comparisons of numerical 

simulations against experimental data showed that the Spalart-Allmaras (SA) turbulence model most 

accurately captured the microvane-induced vorticity in an adverse pressure gradient in Mach 0.5 flow. In 

general, the simulations over-predicted vortex strength, especially the downwash component. However, 

the qualitative shape of the vortex and its general influence on the flowfield were captured well by 

simulation. 

Also in support of the objective to improve test techniques, numerical simulations of a BoundaryLayer-Ingesting (BLI) offset inlet duct consistent with that in the HWB vehicle were conducted during 

the current reporting period. These simulations were used to support the development of the test technique 

for simulating BLI inlet flow in an isolated diffuser. Specifically, a boundary-layer profile at the duct 

entrance that produced AIP distortion levels and patterns consistent with BLI inlets was defined. This 

profile could be replicated experimentally with the use of a fence installed near the duct entrance. 

Following the establishment of test and analysis techniques, microvane and microramp, passive, flowcontrol devices were simulated in the offset BLI inlet duct to assess the predicted performance benefits of 

these devices at the Aerodynamic Interface Plane (AIP). Benefits were quantified in terms of recovery 

and distortion reduction from the baseline, non-actuated flow. Microvane arrays were found to be 

significantly more effective than microramp arrays at improving recovery and distortion in BLI inlet 

ducts, as they produced the large-scale, vortical structures necessary to redistribute the ingested, lowenergy boundary layer fluid at the AIP into more favorable engine-face patterns. 

Finally, SA and Shear-Stress-Transport (SST) turbulence models were employed with a hybrid 

Reynolds-Averaged Navier-Stokes / Large Eddie Simulation (RANS/LES) model to improve prediction 

capabilities by developing the tools necessary to simulate the time-dependent flow features and attempt to 

predict dynamic distortion accurately in an offset, BLI inlet duct. This work will continue into the final 

year of this program. As experimental data is obtained for the duct test configurations, numerical 
NASA/CR—2010-216779 vi

simulations will be validated against the data. Using this data, the prediction tool will be enhanced to 

improve dynamic distortion prediction capabilities in offset diffusers.

Future work on this effort includes detailed experimental measurements of active, passive, and hybrid 

flow control in a BLI inlet diffuser. Benefits of flow control will be quantified in terms of recovery, as 

well as, steady-state and dynamic distortion. Experimental data will be used to validate and tailor hybrid 

RANS/LES turbulence models for accurate, numerical prediction of dynamic distortion. Finally, systemlevel payoffs and penalties of flow-control in BLI ducts will be assessed in terms of TOGW, performance, 

and risk. 
NASA/CR—2010-216779 vii

Contents

Preface ......................................................................................................................................................... iii

Executive Summary ...................................................................................................................................... v

Introduction ................................................................................................................................................... 1

Background .......................................................................................................................................... 1

Scope .................................................................................................................................................... 1

Objectives ............................................................................................................................................. 3

Approach ....................................................................................................................................................... 3

Experimental Setup and Procedures ..................................................................................................... 4

Profiled, 2–D Duct ..................................................................................................................... 4

Offset Diffuser ........................................................................................................................... 7

Numerical Simulation Technique and Flow Conditions ...................................................................... 8

Results and Discussion ................................................................................................................................. 9

Experimental Characterization of Flow-Control-Device Physics ........................................................ 9

Passive Flow-Control Devices ................................................................................................... 9

Active Flow-Control Devices................................................................................................... 12

Hybrid Flow-Control Devices .................................................................................................. 15

Numerical Simulations of Flow-Control-Device Physics .................................................................. 19

Baseline Wind-Tunnel Simulations ......................................................................................... 19

Passive Flow-Control-Device Simulations .............................................................................. 20

Numerical Simulations of a BLI Inlet Offset Diffuser ....................................................................... 25

Numerical Simulations of BLI Offset Diffuser ........................................................................ 25

Numerical Simulations of Flow Control in a BLI Offset Diffuser ........................................... 27

Dynamic Distortion Simulations ........................................................................................................ 31

Conclusions ................................................................................................................................................. 31

Future Work ................................................................................................................................................ 33

Appendix.—Nomenclature ......................................................................................................................... 35

References ................................................................................................................................................... 37

List of Figures

Figure 1.—“N+2” next generation HWB aircraft. ........................................................................................ 1

Figure 2.—Plan to improve and validate numerical analysis tools and develop advanced hybrid 

active flow control system for improved performance, reduced weight, and reduced 

emissions. .................................................................................................................................. 2

Figure 3.—Boeing’s microvane/microjet hybrid flow control concept. ....................................................... 2

Figure 4.—The Transonic Wind Tunnel (a), test section and PIV optical setup (b), and Profiled 

upper wall of the test section (c)................................................................................................ 5

Figure 5.—Flow-control element configurations: (a) microvane, (b) microramp, (c) slanted, (d) 

streamwise. (The distance of each element from the surface apex is denoted in mm.) ............. 5

Figure 6.—Schematics of the PIV measurement domains for (a) hybrid flow control and (b) 

slanted-jet active flow control. .................................................................................................. 6

Figure 7.—(a) Synthetic jet and (b) microvane dimensions and (c) relative spacing. (d) location 

and orientation of PIV measuring station. (Flow from top to bottom of page.) ........................ 6

Figure 8.—BLI offset diffuser model. .......................................................................................................... 7

Figure 9.—AIP total-pressure rake assembly. .............................................................................................. 8

Figure 10.—(a) Surface oil-flow visualization of the streamwise single-sign and (b) vortex pair 

formation. ................................................................................................................................ 10
NASA/CR—2010-216779 viii

Figure 11.—Color, raster, composite, time-averaged contour plots at x/δapex= 42 downstream of 

the microramp of (a) streamwise velocity, U, (b) cross stream velocity, V, and (c) 

streamwise velocity difference from baseline. ........................................................................ 10

Figure 12.—Spanwise distribution of the boundary-layer shape factor, h, downstream of the 

microramp at x/δapex=42 normalized by the baseline shape factor, h0. .................................... 11

Figure 13.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream of 

the microvane of (a) streamwise velocity, U, (b) cross stream velocity, V, and (c) 

streamwise velocity difference ................................................................................................ 11

Figure 14.—Spanwise distribution of the boundary-layer shape factor, h, downstream of the 

microvane x/δapex = 42 normalized by the baseline shape factor, h0. ....................................... 12

Figure 15.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream of 

the streamwise synthetic jet of (a) streamwise velocity, U, (b) cross stream velocity, 

V, and (c) streamwise velocity difference. .............................................................................. 13

Figure 16.—Spanwise distribution of the boundary-layer shape factor, h, downstream of the 

streamwise synthetic jet x/δapex = 42 normalized by the baseline shape factor, h0. ................. 13

Figure 17.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream of 

the slanted, synthetic jet of (a) streamwise velocity, U, (b) cross-stream velocity, V, 

and (c) streamwise velocity difference. ................................................................................... 14

Figure 18.—Composite, upstream view, 3–D, time-averaged velocity field downstream of the 

slanted, synthetic jet showing surfaces of (a) V and (b) ∆U. ................................................... 14

Figure 19.—Spanwise distribution of the boundary-layer shape factor, h, downstream of the 

slanted, synthetic jet x/δapex=42 normalized by the baseline shape factor, h0. ......................... 14

Figure 20.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream. (a) 

streamwise velocity of isolated microramp, (b) cross-stream velocity, V of isolated 

microramp, (c) streamwise velocity of hybrid microramp/synthetic jet, (d) crossstream velocity, V of hybrid, microramp/synthetic jet ............................................................ 15

Figure 21.—The shape-factor distribution for the hybrid control (h) relative to the passive (h0) 

control across the span in the far-field domain (Figure 6(a)). ................................................. 15

Figure 22.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream of 

the skewed, slanted, synthetic jet of (a) streamwise velocity, U, (b) cross-stream 

velocity, V, and (c) streamwise velocity difference................................................................. 16

Figure 23.—Contours of the streamwise velocity difference (in the presence and absence of the 

synthetic jet) are shown for slant angles of: (a) 8, (b) 0, (c) –8, (d) –16, (e) –24. ................... 17

Figure 24.—Contours of the streamwise velocity difference in the presence and absence of the 

synthetic-jet flow control for various relative spanwise locations of the synthetic jet 

and microvane. ........................................................................................................................ 18

Figure 25.—Contours of the streamwise (a), (b) and cross-stream (c),(d) velocity difference (in 

the presence and absence of the active flow-control device) are shown for the case 

where two microvanes are paired with inactive (a), (c) and active (b), (d) synthetic 

jet. ............................................................................................................................................ 19

Figure 26.—Baseline comparison of experimental and numerical results.................................................. 20

Figure 27.—Microvane dimensions. ........................................................................................................... 20

Figure 28.—Side view of computational domain with microvane (red) and data measurement 

plane (green). ........................................................................................................................... 21

Figure 29.—Wake refinement downstream of the microvane. ................................................................... 21

Figure 30.—Velocity contours depicting vortex for coarse grid. ............................................................... 22

Figure 31.—Velocity contours depicting vortex for level 1 refined grid.................................................... 22

Figure 32.—Velocity contours depicting vortex for level 2 refined grid.................................................... 22

Figure 33.—Velocity contours depicting vortex for level 3 refined grid.................................................... 22

Figure 34.—Velocity profiles depicting grid convergence. ........................................................................ 23
NASA/CR—2010-216779 ix

Figure 35.—Comparison of measured versus predicted vortex downstream of a microvane. ................... 23

Figure 36.—Boundary layer profile sensitivity to turbulence model variations. ........................................ 24

Figure 37.—BLI diffuser AIP total-pressure-recovery contours. ............................................................... 25

Figure 38.—Numerical model of the FMRL S-duct test configuration. ..................................................... 26

Figure 39.—AIP total-pressure contours resulting from unperturbed model inflow conditions ................ 26

Figure 40.—BLI inlet simulation velocity profile boundary condition. ..................................................... 27

Figure 41.—Centerline Mach and AIP total-pressure contours (interpolated onto a 40-probe rake) 

resulting from an imposed boundary-layer flow condition ..................................................... 27

Figure 42.—Location at which flow control was simulated in the S-duct. ................................................. 28

Figure 43.—Microramp flow-control device dimensions. .......................................................................... 28

Figure 44.—AIP recovery and distortion for baseline and microramp flow control. ................................. 28

Figure 45.—Geometry and dimensions of the straight and tapered microvanes. ....................................... 29

Figure 46.—Baseline distortion and recovery compared to a straight microvane array. ............................ 29

Figure 47.—Straight microvane recovery and distortion compared to a tapered microvane array. ........... 30

Figure 48.—Comparison of recovery and distortion between three microvane configurations. ................ 30

Figure 49.—Dynamic distortion simulations with SA and SST turbulence models. .................................. 31

Figure 50.—BLI inlet offset diffuser with flow control. ............................................................................ 33

NASA/CR—2010-216779 1 

Introduction

This document reports on the progress accomplished during Fiscal Year 2009 for an element of the 

NASA AMRD Fundamental Aeronautics program effort, Appendix A.2, Topic A.2.4.2—Integrated 

Embedded Propulsion Systems. This work focuses on developing prediction tools for innovative 

aeronautical technologies for HWB aircraft configurations. 

Background 

Major technology extensions beyond current activities are needed to obtain high-speed cruise 

efficiency and low noise while reducing field lengths for HWB aircraft. The use of embedded turbofan 

engines in this wing / body aircraft, Figure 1, is being studied as a means to attain both high-speed cruise 

efficiency and low noise signature and fuel burn. Boeing is advancing both inlet technologies and 

prediction tools specifically focused at lowering and managing flow distortion using fail-safe, hybrid 

flow-control technologies. The investigations are to advance prediction methods for inlet flows by 

applying designed experiments using integrated Computational Fluid Dynamics (CFD) and tests to 

establish a database in a relevant environment and validate the methods.

This program supports the NASA Fundamental Aeronautics Program goal to pursue long-term, 

cutting-edge research in all flight regimes to produce data, knowledge, and design tools that are 

applicable across a broad range of vehicles by applying fail-safe hybrid flow-control technologies to 

manage flow distortion, thereby attaining both high-speed cruise efficiency and low noise signature.

Furthermore, The Boeing Company is working in collaboration with the NASA Glenn Research Center 

(GRC), Georgia Institute of Technology, and SynGenics Corporation to extend the current state-of-the-art 

in active, hybrid flow control analytical design tools. Hybrid flow control analytical design tools are being 

validated through component-level testing, in an environment relative to embedded inlets of futuregeneration, HWB aircraft designs. This not only supports the aforementioned strategic goal, but leads 

directly to the strategic outcome of developing multidisciplinary design, analysis, and optimization 

capabilities.

Scope

The Boeing program, as depicted in Figure 2, combines technology advances made at the GRC in 

robust modeling of flow-control systems with advances in flow-control device modeling and testing at 

Boeing and Georgia Institute of Technology. The combination of testing and numerical simulation, 

integrated with the robust design methods from SynGenics, makes it possible to improve and validate 

current, state-of-the-art prediction tools necessary to quantify the benefits and optimize the design of 

flow-control systems in advanced inlets. To arrive at a demonstration of the hybrid system in a laboratory 

environment (TRL of 4) at the completion of the 3-year program, in collaboration with GRC, we will 

design experiments where data from test and CFD are used to produce response surfaces representing 

performance of inlet flow-control-system-design features. This technology base forms the foundation on 

which the hybrid system will be designed. 

Figure 1.—“N+2” next generation HWB aircraft.
NASA/CR—2010-216779 2 

Figure 2.—Plan to improve and validate numerical analysis tools and develop advanced hybrid active flow control 

system for improved performance, reduced weight, and reduced emissions.

Figure 3.—Boeing’s microvane/microjet hybrid flow control concept.

Boeing has been conducting research on an earlier variation of a hybrid flow-control system for an 

inlet. This earlier system combines passive microvanes and active microjets into an integrated hybrid 

system, Figure 3. The passive microvanes ensure flight-critical engine operability, while the active 

microjets provide flight-mission operability. With this earlier hybrid flow-control technology, a failure in 

the active flow control elements of the system places no operability demands on the engine’s compression 

system because of the presence of the passive flow control element. Hence, the risk of compromising a 

mission or a vehicle is substantially reduced using a hybrid flow-control system in comparison to relying 

on an active, inlet-flow-control system with microjets alone. As a result, the development time and risk 

Microvane

Flow Control

CFD

Test

DOE

Response

Surfaces / 

Numerical 

Simulations

Selected 

DOE Test 

Data Runs

Selected 

DOE CFD 

Data Runs

Validation

Synthetic Jet

Flow Control

Hybrid Flow Control in Offset Duct

[ Microvanes + Synthetic Jets]
NASA/CR—2010-216779 3 

for introducing this new technology into an operational vehicle is significantly reduced compared to that 

of a system relying solely on active flow control to provide flight-critical operation.

This earlier hybrid flow-control system adapts the inlet flow to tailor the performance of a propulsion 

system throughout its flight/operational envelope to optimize installed thrust, fuel consumption, 

operability, or life. Boeing’s in-house testing with this system has clarified the flow physics of the 

elements and demonstrated the potential of a hybrid system. This earlier hybrid flow-control system has 

undergone wind tunnel testing to verify its performance. The technology has been transitioned to 

Boeing’s advanced projects.

The hybrid flow-control system under development in this program goes substantially beyond the 

earlier system. Here the hybrid system is composed of integrated microvanes and synthetic jets. 

Microvanes were selected for this study based on their proven effectiveness in controlling secondary 

flows in offset diffuser designs required for HWB aircraft with embedded engines. Furthermore, the use 

of synthetic jets, in lieu of microjets, will result in less fuel burn. No bleed-air extractions will be used 

with the synthetic jets and bleed air ducting and control systems will not be needed. This approach 

facilitates major system level benefits.

Objectives

This program has two key objectives. The first is to develop fail-safe flow-control technologies for 

highly integrated offset inlets to move towards “N+2” project goals. The second is to develop and 

improve novel test methods and validate tools for predicting active hybrid flow-control effectiveness in 

managing inlet pressure distortion. From this program, validated computational tools will be available to 

improve system trades for advanced inlet concepts. The prediction capabilities will be applicable to a 

range of subsonic, fixed-wing aircraft, and validated for advanced HWB vehicles. The tools will enable 

accurate prediction of active hybrid flow-control-system effectiveness in controlling inlet total-pressure 

distortion at conditions relevant to flight.

The objectives are being accomplished in two phases. In Phase 1, passive, active, and hybrid flowcontrol devices were used to alter boundary-layer characteristics for improved resistance to separation in 

the presence of an adverse pressure gradient. CFD was employed in combination with experimental 

testing to characterize the flow physics of flow-control devices. CFD-based analysis tools for the 

simulation of active hybrid flow-control devices were developed and validated against test data. The 

Phase 1 work was completed in Fiscal Year 2008 and detailed in a previous report. 

The Phase 2 work is being conducted during Fiscal Years 2009 and 2010. During Phase 2, the flow 

physics that govern the interaction between passive and active components of hybrid flow-control devices 

are being investigated. The influence of flow control on inlet performance in an offset duct is being 

quantified. CFD-based tools developed in Phase 1 are being expanded to simulate active, hybrid flow 

control in an offset duct and validated against test data. 

Approach

Experimental investigations, robust modeling, and numerical simulations comprise the approach 

employed to accomplish the program objectives. During Phase 1, experimental investigations were 

conducted in a contoured, 2–D-duct test section of a wind tunnel where the contoured surface was 

designed to mimic the adverse pressure gradient in an offset diffuser of an advanced hybrid-wing / body 

vehicle. Flow-control hardware was integrated into the duct wall and used to develop test techniques and 

evaluate the effectiveness of passive, active, and hybrid flow-control devices in improving boundary-layer 

characteristics. Detailed flow diagnostics, including high-magnification particle image velocimetry (PIV), 

were employed to develop an understanding of the flow physics associated with the flow-control 

actuators. These measurements were used to identify flow control configurations that would likely be 

effective for controlling secondary flows and flow separation in an offset diffuser, as well as, to validate 

advanced, numerical-modeling tools. Analytical and response surface modeling approaches were 
NASA/CR—2010-216779 4 

developed and applied, in conjunction with CFD methods, to simulate the effects of synthetic-jet actuators 

on a flowfield commensurate with that in the offset diffuser of an advanced hybrid-wing / body vehicle. 

Phase 2 builds on the accomplishments of Phase 1 by characterizing the interaction between passive 

and active flow-control devices, demonstrating diffuser performance benefits of hybrid flow-control 

devices in an offset diffuser, and developing and validating CFD-based analysis tools for the simulation 

of hybrid flow-control devices. Benefits of second generation, hybrid-inlet-flow control are being 

assessed in terms of both inlet performance improvements and vehicle system integration. Response 

surfaces derived from the flow-control performance databases generated in this program may be used to 

develop and optimize design guidelines for fail-safe, second generation, hybrid flow-control actuators in 

an offset diffuser. In addition, the experimental database generated in Phase 2 of this program will 

provide details necessary to understand the complex physics of applied inlet-flow control and support 

development of numerical modeling techniques, including hybrid Reynolds-Averaged NavierStokes/Large Eddie Simulation (RANS/LES) CFD methods for predicting dynamic pressure distortion. 

During the current reporting period, experimental techniques were developed and applied to 

characterize the complex physics resulting from flow control actuation in an adverse pressure gradient.

Results of the experiments were used to validate and improve CFD predictive capabilities. Validated CFD 

predictive tools were employed to assess the baseline and actuated flow in a Boundary-Layer-Ingesting 

(BLI) inlet diffuser. In addition, dynamic CFD simulations of an offset BLI-inlet diffuser were conducted 

in order to assess the capability of using Hybrid-RANS/LES-CFD for predicting dynamic, pressure 

distortion. Finally, during the current reporting period, the design and fabrication of an offset diffuser was 

completed in preparation for measuring the effects of flow-control technologies on inlet distortion and 

recovery in a BLI-inlet diffuser at the Georgia Institute of Technology Fluid Mechanics Research 

Laboratory (FMRL) transonic facility during year three of this program. 

Experimental Setup and Procedures

Experimental tests during this program are being performed in the Georgia Institute of Technology 

FMRL transonic facility shown schematically in Figure 4(a). This facility is an open return, pull-down 

wind tunnel. The tunnel uses a 150 hp inverter duty motor capable of pulling 85 in. of water to reach test 

section speeds of Mach 0.73. The motor is controllable to ~ 0.005 percent speed. Ambient temperatures 

are maintained by a 20-ton, air-cooled, hermetic-scroll, liquid chiller coupled with an ultra low-pressuredrop heat exchanger. Flow-control investigations are carried out with the tunnel operated in one of two 

configurations. The flow-control devices are integrated into a profiled wall in the 2–D test section of the 

tunnel. Following those tests, the 2–D duct will be replaced with an offset diffuser. Flow-control devices 

will be integrated into the diffuser surface and inlet performance and operability will be assessed. 

Profiled, 2–D Duct

The 2–D test section used for the initial flow-control investigations measures 5-in. high by 5-in. wide 

with a length of 24-in. The modular, upper wall of the test section was modified for the present 

experiments. Specifically, the flat tunnel wall was replaced with a CD (CD) wall, as seen in Figure 4(c). 

The adverse pressure gradient on the profiled wall was designed such that the pressure gradient 

d(ps/pinf)/dx ≈ 0.38, which is consistent with the pressure gradients in diffusers of HWB vehicles such as 

the Blended Wing Body (BWB). 

During the current reporting period, combinations of passive (microvane or microramp) and active 

(synthetic jet) flow-control elements were surface mounted near the apex of the CD test-section. 

Schematic descriptions of the devices are shown in Figure 5, where the streamwise position is measured 

relative to the wall apex. Diagnostics of the resulting flowfield are done using high-resolution PIV 

measurements at multiple spanwise (z-direction) “cuts” through the flowfields. The PIV optical setup, 

Figure 4(b), includes synchronized, computer-controlled, motorized motion of the all of the optics, 

including the laser, along the test section axial direction and additional motorized motion of the PIV 

camera in the cross-stream direction.
NASA/CR—2010-216779 5 

Figure 4.—The Transonic Wind Tunnel (a), test section and PIV optical setup (b), and Profiled upper 

wall of the test section (c).

(a) (b) (c) (d) 

Figure 5.—Flow-control element configurations: (a) microvane, (b) microramp, (c) slanted, 

(d) streamwise. (The distance of each element from the surface apex is denoted in mm.) 

The characteristic scaling of the passive devices employed in the present investigation was defined in 

terms of the local, boundary-layer thickness, δ. Specifically the sizing guidelines employed were based on 

design guidelines presented in the statistically designed studies by Anderson et al. (2004, 2006). For a 

freestream Mach of 0.5, δ measured 0.20 in. (5 mm) at the wall apex. The microvane, Figure 5(a), was 

designed with a rectangular planform measuring 0.25 δ and 2.56 δ in the cross-stream and streamwise 

directions, respectively. The microvane was oriented at an angle of 8° relative to the freestream. The 

microramp, Figure 5(b), measures 0.51 δ high, 3 δ wide, and 3.4 δ long, and has a half-angle of 24°. 
NASA/CR—2010-216779 6 

In addition to passive devices, active and hybrid devices were examined. The active, synthetic-jet 

orifice measured 0.98 in. (24.9 mm) long by 0.02 in. (0.5 mm) wide and could operate within the range of 

1 to 2.5 kHz. The isolated jet was tested in two orientations, aligned with the freestream, Figure 5(d), and 

slanted, Figure 5(c). The slanted jet was installed at an angle of 24° to the freestream, which is equivalent 

to the half angle of the microramps. Both jet configurations blew normal to the surface. The hybrid 

configurations were comprised of passive devices closely coupled to active devices. Two types of hybrid 

configurations were examined, those that included microramps as the passive actuator and those that 

included microvanes. In the microramp hybrid devices, the streamwise-aligned jet was located along the 

microramp centerline, 0.31 in. (8 mm) upstream of the microramp, Figure 6. In the microvane hybrid 

configurations, the synthetic jet was slanted at the same angle as the microvane and skewed to blow at an 

angle in the spanwise direction, which will henceforth be referred to as the skew angle, Figure 7. 

Diagnostics employed in the present investigation include high-resolution, high-speed PIV 

measurements at multiple cross-stream planes of the flowfield. The PIV field of view measured 0.67 in. 

(17 mm) on the side and the magnification was 17 μm/pixel. The outlines of the PIV measurement 

stations are shown in Figure 6 (referenced to a hybrid configuration). Near-field measurements were 

taken at centerspan, marked “1”, where the PIV view is comprised of four partially overlapping 

streamwise fields. Far-field measurements were taken at x/δapex = 42 downstream of the downstream edge 

of the microramp, at cross-stream planes 0.039 in. (1 mm) apart.

Figure 6.—Schematics of the PIV measurement domains for 

(a) hybrid flow control and (b) slanted-jet active flow control.

Figure 7.—(a) Synthetic jet and (b) microvane dimensions and (c) relative spacing. (d) location and orientation of PIV 

measuring station. (Flow from top to bottom of page.)

a

Jet

X

Z

FLOW

b

Jet

X

Z

FLOW

PIV Measurements

x/δapex = 42

Apex

2

1

d PIV Measuring Plane

CL

~

Apex 210 mm

L = 24.9

W = .5

L = 13.2

W = .5

JET

VANE

Δy

Δz

L = 24.9

W = .5

L = 13.2

W = .5

JET

VANE

Δy

Δz

a

b c

downstream v4 37.2 2.8

downstream v3 37.2 -8.6

downstream v2 37.2 7.5

downstream v1 37.2 -0.5

location Δy Δz

downstream v4 37.2 2.8

downstream v3 37.2 -8.6

downstream v2 37.2 7.5

downstream v1 37.2 -0.5

mm location Δy Δz

mm

mm

[mm] [mm]

mm

FLOW
NASA/CR—2010-216779 7 

Offset Diffuser

Following the conclusion of the flow-control device assessments in the 2–D test section, the test 

section will be removed and replaced with an offset duct that was designed to be consistent with future 

HWB vehicles. Design and fabrication of this duct was conducted during the current reporting period. 

The replacement hardware utilizes the existing contraction, diffuser, and drive system of the Georgia 

Institute of Technology FMRL transonic wind tunnel to provide the airflow requirements for the duct 

system. This facility was designed to be suspended from an overhead beam system, allowing for vibration 

isolation and a limited adjustability of the component locations. In order to account for the diffuser offset 

and slightly shorter overall length of the IFCPT assembly, the contraction was moved approximately 

2.5 in. downstream, and approximately 5.2 in. to one side. This, along with the adjustability of the support 

struts suspending the facility, were sufficient to mate the new hardware to the existing hardware.

The offset-duct-model apparatus consists of a forward adapter, BLI S-duct, AIP total pressure rake 

assembly, and the aft adapter, Figure 8. The Forward Adapter serves as the transition between the existing 

facility contraction and the offset duct. It also contains a provision for mounting a boundary-layer fence.

The diffuser-assembly moldline was defined based on the current BWB diffuser moldline. The duct 

model was designed with a removable insert in the lower surface to provide for installing and testing 

flow-control devices. Two sets of contoured windows were incorporated into the design to facilitate the 

acquisition of PIV data. Three hot-wire installation locations were also included near the duct throat. 

Finally, the aft adapter serves to mate the AIP to the existing facility expansion section.

Figure 8.—BLI offset diffuser model.

GT Contraction Adapter Adapter GT Expansion

BLI 

Inlet AIP

 

2 nd Gen Hybrid Flow

Control Devices

BLI

S-Duct

Inlet

SteadyState 5”

AIP

Bottom View

Flow

BLI

S-Duct
NASA/CR—2010-216779 8 

Figure 9.—AIP total-pressure rake assembly.

Instrumentation on the model includes twenty-one static-pressure taps on the inner-moldline surface. 

In addition to the static taps, the model includes a steady-state total-pressure rake assembly, Figure 9, 

which consists of eight equiangularly spaced rakes around the circumference of the AIP. Each rake 

contains five total pressure probes located at the centers of equal areas. The rake assembly was designed 

such that the eight steady-state rakes would be interchangeable with existing, Boeing-owned, dynamictotal-pressure rakes for the acquisition of dynamic distortion data. 

Numerical Simulation Technique and Flow Conditions

In addition to experimental facilities and hardware, state-of-the-art numerical methods were 

employed to develop and validate prediction tools for inlet flow-control-technology development. To 

accomplish this, steady and unsteady numerical simulations of flow in the Georgia Institute of 

Technology FMRL transonic tunnel test section, as well as, in offset diffusers were generated. The 

Boeing Computational Fluid Dynamics (BCFD) code was used for the numerical simulations. In all cases, 

the 3–D viscous Navier-Stokes equations were solved in the domain of interest. Both structured and 

unstructured grids were used to represent the computational domains. The Boeing Modular Aerodynamic 

Design Computational Analysis Process (MADCAP) tool was used to generate the surface mesh. For the 

unstructured grids, Advancing-Front/Local-Reconnection (AFLR) was used to generate the interior 

volume mesh. Mesh spacing were taken from a grid resolution study of BLI-inlet simulations conducted 

in Phase 1 of this program, as well as, additional grid-resolution studies of the FMRL tunnel test section. 

The Roe flux-differencing numerical discretization scheme was used on the governing equations in the 

structured grid zones, while the Harten-Lax-van Leer-Einfeldt (HLLE) flux vector-splitting scheme was 

applied in the unstructured zones. Two turbulence models were used in the simulations. The one-equation 

Spalart-Allmaras (SA) model was used in a limited number of dynamic distortion simulations, while the 

two-equation Shear-Stress-Transport (SST) model was used for the majority of the simulations. 

For simulations in the offset duct, constant-pressure boundary conditions were prescribed 

downstream of the AIP in order to control the duct mass flow. The Mach number at the throat for all cases 

comparing flow control and dynamic distortion was maintained at approximately 0.7, which corresponds 

to a corrected mass flow of approximately 5.5 lbm/s at the AIP. 
NASA/CR—2010-216779 9 

Results and Discussion

Experimental Characterization of Flow-Control-Device Physics

In support of the program objective to develop flow-control technologies, the interaction of surfacemounted, passive and active flow-control devices with the cross flow was examined both experimentally 

and numerically during the current reporting period. The experiments were conducted in a small-scale 

wind tunnel at high subsonic speeds of up to Mach 0.5. Numerical simulations were carried out using the 

3–D, viscous, Navier-Stokes CFD code, BCFD. The evolution of streamwise vortices induced by the flow 

control was investigated in an adverse pressure gradient that mimics the pressure gradient within a 

diffuser compatible with future hybrid-wing/body vehicles. Counter-rotating vortex pairs and single-sense 

vortices were formed and characterized using passive microramps and microvanes, respectively. Similar 

streamwise vortices were also generated using synthetic jet actuators. The jets had rectangular orifices 

that were either slanted and/or skewed to produce single-sense vortices or streamwise aligned to produce 

vortex pairs. Finally, hybrid actuation approaches were characterized by combining a passive microvane 

and synthetic jet in a tandem arrangement.

Passive Flow-Control Devices

The single-sense and counter-rotating, streamwise vortices induced by microvanes and microramps 

were characterized in the adverse pressure gradient domain downstream of the test-section-wall apex, 

Figure 4(c). The initial vortex formation from the passive elements was investigated through surface oil 

visualization using a mixture of linseed oil and titanium-dioxide paint. The oil traces around the 

microvane, Figure 10(a), indicate stagnation points upstream and downstream of the microvane leading 

and trailing edges, respectively. The pressure differential across the microvane surfaces resulted in the 

rollup of a “tip vortex”, which rolls to form a single-sense, streamwise vortex. The oil-streak 

accumulation downstream from the trailing edge of the microvane indicates roughly an upwash region 

across the boundary layer. Figure 10(b) shows the near-wall topology of the flow over the microramp and 

initial vortex formation. A symmetric split of the oncoming flow over the microramp is visible, and the 

footprint of the initial streamwise roll of the flow is evident from the wall traces on each side of the 

microramp. As more fluid rolls into each streamwise vortex that forms along the microramp edge, its 

footprint on the microramp sidewall intensifies. The evolving counter-clockwise (CCW) and clockwise 

(CW) vortices, in the downstream view, that form along the left and right edges of the microramp, 

respectively, merge at its tip and are advected downstream within the boundary layer This is evident from 

the two narrow traces about the microramp axis. These traces are not a normal projection of the vortex 

cores. They delineate the upwash due to the vortex-induced, spanwise flow. The vortex pair self-advects 

away from the surface as is evidenced by the streamwise thinning of the upwash traces. Nevertheless, the 

streamwise flow clearly dominates the way in which the vortex pair is convected downstream. 

The far-field effect of the microramp on the boundary layer at x/δapex = 42 is elucidated from a 

sequence of planar PIV measurements in cross-stream planes, at a number of spanwise stations, 

Figure 6(a). A color raster plot with contours of composite, time-averaged, streamwise and cross-stream 

velocity distributions in the y-z plane are shown in Figure 11(a) [U(y,z;x)] and Figure 11(b) [V(y,z;x)], 

respectively. In addition, contours of the streamwise velocity difference, in the presence and absence of 

the microvane, are shown in Figure 11(c). The raster plots illustrate the effects of the flow-control device 

with contours of composite, time-averaged, streamwise and cross-stream velocity distributions in the 

cross-stream (y-z) plane at x/δapex = 42. Note that the cross-stream (z-y) plane in the raster plots is viewed 

in the upstream direction. The distributions in Figure 11 indicate a clear upwash effect at center span (the 

centerline of the microramp) which is accompanied by the downwash domains with peaks at z/δapex = +1 

and –1 on both sides of the upwash. These effects are a direct consequence of the counter-rotating-vortex 

pair induced by the microramp. To better isolate the effect of the streamwise vortex, the streamwise 

velocity increment and decrement relative to the baseline, non-actuated flow, ΔU(y,z;x) is shown in 
NASA/CR—2010-216779 10

Figure 10.—(a) Surface oil-flow visualization of 

the streamwise single-sign and (b) vortex pair 

formation.

Figure 11.—Color, raster, composite, time-averaged contour plots at x/δapex= 42 downstream of the microramp of 

(a) streamwise velocity, U, (b) cross stream velocity, V, and (c) streamwise velocity difference from baseline. 

Figure 11(c). Inspection of Figure 11(c) indicates that the deficit caused by the common upwash of 

the vortices is advected upward introducing higher speed flow in the near wall region. The slight bias in 

the PIV measurements, in which the boundary layer appears slightly inclined in the absence of the 

microramp is caused by a slight misalignment between the PIV camera horizon and the wall contour.

A cross-stream integral effect of the microramp on the boundary layer flow was assessed from the 

relative spanwise changes in the shape factor, h, of the cross-stream velocity distribution in the absence 

and presence of the microramp, Figure 12. These data show that the spanwise extent of the microramp is 

almost 5δapex, while the microramp width is about 3δapex. The most prominent feature of the boundarylayer shape factor is that the induced, streamwise vortices lower the shape factor through most of the 

affected spanwise domain. Even though the upwash along the centerline of the microramp increases the 

boundary-layer-velocity deficit, Figure 11, the vortex pair is sufficiently far from its source, such that its 

lift off the wall actually leads to a decrease of the velocity deficit near the wall. In the downwash region, 

the transport of high-momentum fluid towards the surface leads to an increase in the velocity deficit near 

the wall. The combination of off-centerline downwash and displaced upwash in between is what makes 

the microramp attractive (when properly scaled) for boundary layer separation delay (Lin, 2002). 

b

a

(b) -2 -1 0 1 2 0 -2 -1 0 1 2 0 0 

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

a b

0

.4

0.8

1.2

1.6

2

2.4

2.8

c

-2 -1 0 1 2

.06

0

-.06

.94

.75

.5

.02

0

-.03

U V ΔU
NASA/CR—2010-216779 11

Figure 12.—Spanwise distribution of the boundary-layer shape factor, h, 

downstream of the microramp at x/δapex=42 normalized by the baseline 

shape factor, h0. 

Figure 13.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream of the microvane of 

(a) streamwise velocity, U, (b) cross stream velocity, V, and (c) streamwise velocity difference.

Similar to Figure 11, the changes in the flowfield that are induced by a microvane were measured at 

x/δapex=42 and are shown in color raster plots, Figure 13(a) and Figure 13(b). The time-averaged 

distribution of the streamwise velocity U(y,z;x), Figure 13(a), shows the upwash (0.5 < z/δapex < 2) and 

downwash (2 < z/δapex < 3) domains, indicating the presence of a CW streamwise vortex. This is further 

supported by the distribution of the cross-stream velocity, Figure 13(b), that includes two adjacent zones 

of fluid motion either away (upwash) or towards (downwash) the wall. As might be expected, owing 

to the presence of the wall, the cross-stream elevations of the peaks (positive and negative) of ΔU, 

Figure 13(c), are different. The deficit owing to the upward advection of low-momentum fluid at 

y/δapex ≈ 1.2 is farther away from the surface than the high-momentum fluid at y/δapex ≈ 0.4. In fact, the 

transported high-momentum concentration appears to spread in the spanwise direction along the surface. 

It is noteworthy that the upwash in absence of an opposite-sense vortex is not sufficient to displace the 

low momentum fluid away from the surface indicating that the effectiveness of the microvane in terms 

of overcoming flow separation may be lower than that of the microramp. 

This is further confirmed by the distribution of the shape factor h(z) across the span, Figure 14. In 

comparison to the corresponding distribution for the microramp, the domain in which h(z) increases is 

considerably broader. However, considering that the spanwise projection of the microvane is 

approximately 0.4δapex, compared to 3δapexfor the microramp, perhaps comparable effects can be achieved 

by increasing the packing density of the microvanes.

-2 1 0 1 2

.06

0

-.06

.94

.75

.5

.02

0

-.03

(a) (b) (c) y (mm) y (mm)

0 1 2 3 4 0 0 1 2 3 4 0 0 1 2 3 4 00

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

a b c

U V ΔU
NASA/CR—2010-216779 12

Figure 14.—Spanwise distribution of the boundary-layer shape factor, h, 

downstream of the microvane x/δapex = 42 normalized by the baseline 

shape factor, h0. 

Active Flow-Control Devices

In addition to passive devices, active synthetic-jet flow-control devices were tested to compare the 

effectiveness of the different types of actuation in forming controlled counter-rotating or single-sense, 

streamwise-vorticity concentrations. Isolated, synthetic jets were tested in two orientations, streamwise 

aligned and slanted, relative to the freestream flow. The streamwise aligned configuration was designed to 

produce vortical structures consistent with microramp actuation, while the slanted jet was designed with 

the intent of producing structures consistent with microvane actuation.

As expected, aligning the long dimension of the rectangular, jet orifice with the direction of the free 

stream, as illustrated in Figure 5, produced a pair of counter-rotating, streamwise vortices. In still air, the 

counter-rotating vortices were generated along the orifice at each actuation cycle. In the presence of a 

cross flow, the vortices were augmented by the tilting and rollup, predominantly in the spanwise 

direction, of boundary-layer vorticity and often loosely connected at their downstream end, similar to a 

lambda vortex. The vortices, which were advected with the local cross flow, were interrupted and 

vanished during the suction stroke. Because the strength of the streamwise vortices that are formed by a 

synthetic-jet actuator is streamwise modulated with the periodicity of the actuation, the time-averaged 

vortex strength is considerably weaker than the instantaneous strength. A streamwise, vortex pair induces 

an upwash along its common axis and a downwash off centerline. The resulting changes in the baseline 

flow are shown in Figure 15 using color raster plots of the time-averaged streamwise and cross-stream 

velocity components. As expected, when the streamwise jet was active, Figure 15(a), there was a 

noticeable upwash near the centerline peak in boundary layer thickness. The peak is flanked on either side 

by weaker downwash where the high-speed flow was drawn closer to the wall. Analogous to Figure 11(c), 

the distributions of the streamwise velocity differences relative to the unforced flow, ΔU(y,z;x) are shown 

in Figure 15(c). These data indicate that the time-averaged flow induced by the synthetic jet was 

qualitatively similar to the far-field structure of the flow induced by the microramp. The corresponding 

shape factor, Figure 16, indicates that the streamwise jet led to an overall decrease in the shape factor, but 

the magnitude of the decrease was smaller than that induced by the microramp. Since the jet-orifice 

orientation is fixed, the jet momentum, or impulse per stroke, controlled the strength of the induced 

streamwise vortices. However, in this case, for a fixed jet velocity, the characteristic, spanwise scale of 

the jet also impacted the strength of the ensuing streamwise vortices. In the present configuration, the 

spanwise domain of influence of the jet was approximately 5δapex, which was similar to that of the 

microramp. However, the spanwise width of the jet was about 30 times smaller than that of the 

microramp or 0.1δapex compared to 3δapex. 

0 5 10 15 20

-10

-5

0

5

h/h0 [%]

z [mm]

0 1 2 3 4
NASA/CR—2010-216779 13

Figure 15.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream of the streamwise 

synthetic jet of (a) streamwise velocity, U, (b) cross stream velocity, V, and (c) streamwise velocity difference. 

Figure 16.—Spanwise distribution of the boundary-layer shape factor, 

h, downstream of the streamwise synthetic jet x/δapex = 42 

normalized by the baseline shape factor, h0. 

Unlike the streamwise-aligned jet, slanting the orifice of the jet, relative to the free stream, in the 

orientation shown in Figure 5(c) produced a single, streamwise vortex. The jet-slant angle was the same 

as the half angle of the microramp. The resulting flow in the far field, Figure 17, indicates the presence of 

a CCW-streamwise vortex. Unlike the streamwise jet, the low-speed flow that was pushed out away from 

the wall, was not convected upward quite as strongly, which led to areas of both increased and decreased 

velocity deficits in the near-wall region, as also illustrated by the raster plot of the velocity difference in 

Figure 17(c). Although the synthetic jet was slanted at the same direction as the microvane, as shown in

Figure 5, they generated single, streamwise vortices of opposite sense. Specifically, the vortex formed by 

a microvane is similar to a tip vortex of a lifting surface. However, the vortex that is formed by the jet 

appears to roll as a result of the bending of the jet by the cross flow, as shown by Peake et al (1999) for 

continuous, conventional jets. This result is also consistent with the measurements of Compton and 

Johnson (1992) for skewed jets. 

The near-field formation of the vortex that was generated by the slanted jet was measured in 16 crossstream (y-z) planes spaced 0.039 in. (1 mm) apart, where the field of view measured 0.669- by 0.669-in. 

(17- by 17-mm). The measurement region began at x/δapex = 2 downstream of the upstream orifice edge, 

Figure 6. The resulting rendition of a 3–D composite of near-field flow is shown in Figure 18. 

Figure 18(a) shows surfaces of the cross-stream velocity. The presence of the jet forces the oncoming 

flow up away from the surface along the jet orifice and induces a downward flow in a domain that is 

outboard and downstream from the jet orifice. The bending of the jet by the cross flow is evident in 

surfaces of the streamwise velocity difference, relative to the baseline flow, Figure 18(b). This 

phenomenon is accompanied by an increase in the streamwise velocity on the right, downstream of the 

.94

.75

.5

.02

0

-.03

.06

0

-.06 -5 -2 0 1 2 -5 -2 0 1 2 -5 -2 0 1 2 0

.4

0.8

1.2

1.6

2

2.4

0

.4

0.8

1.2

1.6

2

2.4

.94

.75

.5

.02

0

-.03

.06

0

-.06 2 0 1 2 2 0 1 2 2 0 1 2 0

.4

0.8

1.2

1.6

2

2.4

0

.4

0.8

1.2

1.6

2

2.4

0

.4

0.8

1.2

1.6

2

.94 2.4

.75

.5

.02

0

-.03

.06

0

-.06 -2 -1 0 1 2

2.4

2.0

1.6

1.2

0.8

0.4

0.0

2.4

2.0

1.6

1.2

0.8

0.4

0.0

2.4

2.0

1.6

1.2

0.8

0.4

0.0

U V ΔU

a -2 -1 0 1 2b -2 -1 0 1 2

c

-2 -1 0 1 2
NASA/CR—2010-216779 14

orifice, and a small decrease owing to the flow turning on the left. Clearly, the differences in the sense of 

the streamwise vortices that are formed by slanted, passive obstructions and by similar-slant, synthetic 

jets must be taken into consideration in the design of hybrid actuators that are comprised of both 

elements.

The spanwise distributions of the changes in the shape factor affected by the slanted jet are shown in 

Figure 19. The spanwise extent of the changes in shape factor that are induced by the slanted jet and by 

the microvane, Figure 14 were quite similar at approximately 3δapex in the far field. The streamwise 

projection of the microvane into the spanwise plane was 0.4δapex and the streamwise projection of the jet 

into the spanwise plane was 2δapex. Furthermore, the magnitude of the effect of the jet was only about 

15 percent lower based on the averaged change in shape factor.

Figure 17.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream of the slanted, synthetic 

jet of (a) streamwise velocity, U, (b) cross-stream velocity, V, and (c) streamwise velocity difference. 

Figure 18.—Composite, upstream view, 3–D, time-averaged velocity field downstream of the slanted, synthetic jet 

showing surfaces of (a) V and (b) ∆U. 

Figure 19.—Spanwise distribution of the boundary-layer shape 

factor, h, downstream of the slanted, synthetic jet x/δapex=42 

normalized by the baseline shape factor, h0. 

.06

0

-.06

(a)

.94

.75

.5 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

a b c

.02

0

-.03

U V ΔU

X

a b

.02

-.02

∆V/U0

X

5 10 15 20 25 30

-10

-5

0

5

h/h0 [%]

z [mm]

1 2 3 4 5 6
NASA/CR—2010-216779 15

Hybrid Flow-Control Devices

Following the exploration and documentation of the governing physics of isolated, passive and active 

devices operating in a Mach 0.5, adverse pressure gradient flow, the performance benefits attained by 

adding an active component to a passive flow-control approach was investigated. Hybrid devices were 

tested to characterize the interaction between closely coupled, passive and active devices. Tests of a 

hybrid actuator, comprising a vortex generator and synthetic jet, were conducted using a streamwise jet, 

placed upstream and along the centerline of a microramp, Figure 6(a). This configuration was selected 

based on the successful integration and purported effectiveness of a hybrid device made up of a 

conventional, continuous jet and a microramp by Anderson et al. (2009). In addition, hybrid 

configurations whose components included microvanes and synthetic jets were examined, and 

performance sensitivities to variations in the device geometric parameters, such as the relative placement 

and orientation of the microvane and jet were measured and documented. 

The flow physics resulting from actuating the flow with a microvane/synthetic-jet hybrid device 

were investigated. The far-field effects of an isolated microramp were discussed in a previous section, 

Figure 11. Performance enhancements due to the incorporation of a synthetic jet with a passive microvane 

were assessed by comparing distributions of the time-averaged, streamwise and cross-stream velocities

downstream of the microramp, in the absence and presence of the jet, Figure 20. These data clearly show 

the central upwash domain and two downwash regions on either side. In addition, these data indicate that 

the time-averaged effect of the jet in this configuration was somewhat limited in that there was only a 

slight enhancement of both the upwash and downwash. The spanwise effect of the hybrid actuation was 

assessed based on spanwise distributions of the shape factor, Figure 21. These data show that the percent 

of additional changes in the shape factor, relative to the shape factor in the presence of an isolated 

microramp, are somewhat smaller than the percent changes induced by the jet alone, Figure 12. 

Figure 20.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream. (a) streamwise velocity 

of isolated microramp, (b) cross-stream velocity, V of isolated microramp, (c) streamwise velocity of hybrid 

microramp/synthetic jet, (d) cross-stream velocity, V of hybrid, microramp/synthetic jet.

Figure 21.—The shape-factor distribution for the hybrid control (h) 

relative to the passive (h0) control across the span in the far-field 

domain (Figure 6(a)). 

.94

.75

.5

.02

0

-.03

.94

.75

.5

.02

0

-.03

(b) (c) (d) -2 -1 0 1 2 0 -2 -1 0 1 2 0 -2 -1 0 1 2 0 -2 -1 0 1 2 0 0 

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

a b c d

.94

.75

.5

.02

0

-.03

.94

.75

.5

.02

0

-.03

(b) (c) (d) -2 -1 0 1 2 0 -2 -1 0 1 2 0 -2 -1 0 1 2 0 -2 -1 0 1 2 0 0 

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

0

.4

0.8

1.2

1.6

2

2.4

2.8

a b c d

U V U V

-10 -5 0 5 10

-10

-5

0

5

h/h0 [%]

z [mm]

-2 -1 0 1 2
NASA/CR—2010-216779 16

In addition to hybrid devices comprised of synthetic jets and microramps, synthetic jet/microvane 

hybrid devices were examined. In the preceding section, it was illustrated that a synthetic jet emerging 

normal to a surface and slanted at the same direction as the microvane generates single, streamwise 

vortices of a sense opposite to the microvane, Figure 17. Therefore, in order to facilitate development of a 

fully integrated hybrid device, the jet orifice was modified such that the jet emanated at a non-zero skew 

angle, meaning at an angle in the spanwise direction rather than normal to the surface. Various relative 

positions and orientations of the microvane and synthetic jet were tested to assess performance 

sensitivities to these parameters. Schematic descriptions of the various devices used in this study are 

shown in Figure 7, where the streamwise position is measured relative to the wall apex. A nominal 

microvane at an angle of 8°, Figure 7(a), has a rectangular planform measuring 0.059- by 0.602-in. (1.5- 

by 15.3-mm) in the cross-stream and streamwise directions, respectively, relative to the free stream. The 

resulting skewed jet is also slanted with respect to the flow.

The slanted, skewed jet produced a single-sign vortex causing a disturbance in the streamwise 

component of the velocity, as seen in the far-field composite raster plots, Figure 22. Coherent regions 

of upwash and downwash are visible in Figure 22(a) and Figure 22(b), respectively. Such a disturbance 

induces a decrease in the velocity deficit near wall, as visible in Figure 22(c), which shows the 

difference between the resulting flow and the baseline, uncontrolled flow. The effect of the skewed, 

slanted jet on the baseline flow was comparable to that of an isolated microvane in a cross flow, 

Figure 13. Determination that a skewed and slanted jet orifice can generate the same-sense, streamwise 

vorticity as a parallel microvane enables utilization of a synthetic jet to enhance the performance benefits 

of a microvane. 

The sensitivity of the strength of the single-sense vorticity that results from skewed and slanted-jet 

actuation was studied by varying the slant angle for a constant skew angle, Figure 23. When the syntheticjet orifice was aligned with the flow, Figure 23(b), a single-sign vortex was formed. The sign of the 

vortex was determined by the direction of the skew angle of the orifice. As the jet is slanted at a nonzero 

angle, the spanwise projection of the orifice increases, and the induced vorticity gives rise to its spanwise 

component. A small, positive, slant angle slightly increases the far-field area of influence and somewhat 

enhances the decrease in velocity deficit, Figure 23(a). As the orifice slant angle was increased in the 

opposite direction, a clear trend of increased area of influence and decreased effect magnitude was 

measured, Figure 23(c) to (e). Based on this study, the jet orifice, slanted at the same angle as the 

microvane, was selected for further studies, due to the significant, far-field effect and convenience for any 

interlaced integration of the jets and the microvanes in a hybrid-control element.

Figure 22.—Color, raster, composite, time-averaged contour plots at x/δapex = 42 downstream of the skewed, slanted, 

synthetic jet of (a) streamwise velocity, U, (b) cross-stream velocity, V, and (c) streamwise velocity difference.

0

.4

0.8

1.2

1.6

2

2.4

2.8

-3 -2 -1 0 1 2 3

.94

.75

.5 0

.4

0.8

1.2

1.6

2

2.4

2.8

-3 -2 -1 0 1 2 3

.06

0

-.06

.02

0

-.03 0

.4

0.8

1.2

1.6

2

2.4

2.8

-3 -2 -1 0 1 2 3

a b c

U V ΔU
NASA/CR—2010-216779 17

Figure 23.—Contours of the streamwise velocity difference (in the presence and absence of the synthetic jet) are 

shown for slant angles of: (a) 8, (b) 0, (c) –8, (d) –16, (e) –24.

Following selection of the microvane hybrid device fluidic component configuration, the skewed, 

slanted, synthetic jet, the passive and active devices were installed in tandem, as a hybrid device, to 

examine the resulting flow physics in detail. Experiments were performed with the microvane located 

both upstream and downstream of the jet and in various, relative spanwise locations to provide insight 

into the nonlinear superposition of the two, distinct, resulting flowfields. Distributions of the timeaveraged, streamwise and cross-stream velocities, U* and V*, downstream of the microvane, in the 

absence and presence of the jet, for various upstream spatial locations of the microvane, Figure 24, were 

used to assess the performance of the hybrid devices. As the microvane was moved in the positive 

spanwise direction, the resulting vortex that formed off the microvane shifted within the measured field of 

view, Figure 24. Analyses of the vortices induced by actuation from hybrid configurations at various 

spanwise spacing between the microvane and jet also showed that the maximum vortex strength was 

attained when the spanwise spacing was minimized, Figure 24(a) and c. Aligning the jet with the 

microvane resulted in slightly reduced vortex strength, Figure 24(b). The least favorable configuration 

was one in which the spanwise distance between the microvane and jet was maximized, Figure 24(d), 

where two weakly interacting but distinct vortices were seen under superposition of the jet and the

microvane.

One strategy previously shown to counteract flow distortion resulting from significant boundary-layer 

ingestion is to apply flow control that redistributes the concentrated, low total pressure, boundary-layer 

flow around the perimeter of the AIP, thus creating a distortion pattern to which engines are typically 

more tolerant. This result may be achieved by using flow control to generate two, large-scale, counterrotating vortices that sweep the concentrated boundary layer flow up and around the outer perimeter of 

the duct. In previous studies (Anabtawi et al. 1999), it has been shown that an array of streamwise 

vortices must be generated such that they merge into a large-scale vortex downstream of the duct, which 

requires consideration of the vortex sources, sizes and spacing. It is this prerequisite that often dictates the 

packing density and number of devices. The use of a hybrid system, in place of a passive system offers 

the potential of reducing the required number of microvanes, thereby potentially reducing total-pressure 

losses, maintainability, and supportability issues. To this end, two microvanes were placed in the flow 

such that the pair of resulting vortices exhibited weak interaction at the downstream measuring plane, 

Figure 25(a) and (c). This microvane pair was then mated with a synthetic jet, analogous to the 

configuration illustrated in Figure 22 and Figure 24(a). When the jet was activated the microvanegenerated vortices merged into one larger, coherent structure, as indicated by the large domain of 

influence of diminished velocity deficit, Figure 25(b), and by the cross-stream-velocity component, 

Figure 25(d), which indicated only one zone of upwash and one zone of downwash. 

a b c d e

0

.4

0.8

1.2

1.6

2

2.4

2.8

-3 -2 -1 0 1 2 3

.12

0

-.12

-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 FLOW

ΔU
NASA/CR—2010-216779 18

Figure 24.—Contours of the streamwise velocity difference in the presence and absence of 

the synthetic-jet flow control for various relative spanwise locations of the synthetic jet and 

microvane. 

The integration of a synthetic jet with a pair of microvanes was proven to enhance the weakly 

interactive vortices resulting from passive microvane actuation. Specifically, hybrid, syntheticjet/microvane flow control resulted in vortices that merged into one large, coherent structure. These 

results suggest that implementation of a row of synthetic jets followed by a row of microvanes could be 

made more efficient by implementing this concept of merging vortices. Furthermore, hybrid 

microvane/jet devices offer the benefit of reducing distortion with a reduced size and/or number of 

microvanes, thus offering potential improvements in inlet performance, as well as, system-level 

requirements such as supportability and maintainability.

Jet OFF Jet ON .12

0

-.12

.12

0

-.12

.12

0

-.12

.12

0

-.12 0

.4

0.8

1.2

1.6

2

2.4

2.8

-3 -2 -1 0 1 2 3

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

-3 -2 -1 0 1 2 3

vane

jetFLOW

a

b

c

d

ΔU
NASA/CR—2010-216779 19

Figure 25.—Contours of the streamwise (a), (b) and cross-stream (c),(d) velocity 

difference (in the presence and absence of the active flow-control device) are 

shown for the case where two microvanes are paired with inactive (a), (c) and 

active (b), (d) synthetic jet.

Numerical Simulations of Flow-Control-Device Physics

As previously stated, a primary objective of this program is to validate tools for predicting active, hybrid 

flow-control performance. As a step toward meeting that objective, simulations of isolated devices in the 

Georgia Institute of Technology FMRL tunnel test section with a profiled wall were conducted. Results of 

the numerical simulations were validated against experimental data detailed in the previous sections. Several 

factors were varied to explore the sensitivity of numeric results to simulation methodology. These factors 

included grid resolution, turbulence model, and viscous-flux-calculation technique. 

Baseline Wind-Tunnel Simulations

The accuracy of numerical simulations of the baseline wind tunnel flow, in the absence of flowcontrol devices, was assessed. Specifically, measured and predicted boundary-layer velocity profiles on 

the profiled wall near the test-section apex were compared, Figure 26. For all figures containing boundary 

layer profiles, Ymax was ~0.69 in. and Umax was ~550 ft/s. For all figures containing velocity contours, Uinf

was ~550 ft/s. The flow approaching the converging/diverging section was at Mach 0.5, standard-day 

conditions corresponding to the altitude of Atlanta, Georgia. The measured profiles are denoted “GT” in 

the legend and are plotted at spanwise locations from 1- to 11-mm offset from the centerline. Based on 

the experimental profiles, the velocity deficit increased monotonically with increasing spanwise offset. 

This was due to secondary flow, resulting from the sharp corners in the duct imposing an upwash on the 

otherwise uniform boundary layer. 

The numerical simulations were conducted with both SA and SST turbulence models. Both models 

compared very closely with one another, but the SA model predicted the centerline experimental results 

slightly better. Away from the centerline, however, the numeric results did not exhibit the same upwash 

or velocity deficit effect that was realized in the experiment. In fact, the simulated profiles at 1-, 5-, and 

11-mm offset fell on top of one another. This suggests that the simulation is not capturing any influence 

the duct corners have on the flow. It is believed that a significant increase in global grid resolution would 

be required to capture this effect. Because the offset diffuser does not contain sharp corners, no further 

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3

.12

0

-.12

.02

0

-.03

FLOW

2.9

36.5

6.8

∆U* ∆U*

∆V* ∆V*

a b

c d

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

2.8

0

.4

0.8

1.2

1.6

2

2.4

0

.4

0.8

1.2

1.6

2

2.4

2.8

-3 3 -2 2 -1 0 1 2 3 1 0 1 2 3 -3 3 -2 2 -1 0 1 2 3 1 0 1 2 3

.12

0

-.12

.02

0

-.03

FLOW

2.9

36.5

6.8

∆U* ∆U*

∆V* ∆V*

a b

c d
NASA/CR—2010-216779 20

investigation into this was conducted. Nevertheless, the discrepancy was noted for consideration in future 

simulations. 

Passive Flow-Control-Device Simulations

Simulations of the microvane, Figure 27, mounted near the throat of the converging/diverging section 

of a 5 in. by 5 in. by 30 in. duct representing the test section of the Georgia Institute of Technology 

FMRL tunnel, Figure 28, were generated and validated against experimental data.

Prior to making comparisons with experimental data, a grid resolution study was conducted to ensure 

that the grid was sufficiently refined to capture the flow physics. Specifically, the goal was to verify that 

the simulation accurately predicted the vortex shed by a microvane in an adverse pressure gradient. For 

each grid refinement level, boundary layer profiles and velocity contours were taken at a station 

downstream of the throat, Figure 28. These profiles were then compared between refinement levels to 

determine if the solution was grid independent. A coarse grid consisting of 4.3 million cells and a 

characteristic surface element size of 0.1 in. served as the starting grid. The method for refinement was to 

decrease the element size in the wake, Figure 29, downstream of the microvane in order to preserve the 

vortex strength as it convected downstream.

Figure 26.—Baseline comparison of experimental and numerical results. 

Figure 27.—Microvane dimensions. 
NASA/CR—2010-216779 21

Figure 28.—Side view of computational domain with microvane (red) and data 

measurement plane (green). 

Figure 29.—Wake refinement downstream of the microvane. 

Three levels of grid density were examined in the grid-convergence study. The first level of 

refinement produced a grid containing 5.2-million cells with an element size of 0.05 in. Refining to the 

second level resulted in a grid with 7.4-million cells and an element size of 0.023 in. The final refinement 

resulted in a 21.8-million-cell grid with an element size of 0.01 in. Normalized velocity contours are 

shown in Figure 30 to Figure 33 for the four grids. The black lines on the images represent the spanwise 

locations at which boundary layer profiles, Figure 34, were extracted. The change in the vortex shape due 

to grid density variation was most noticeable between the coarse, Figure 30 and level 1, Figure 31 grids.

A slight sharpening of the blue upwash peak can be seen from refinement level 1 to refinement level 2, 

Figure 32. Finally, there was negligible difference in the vortex shape between refinement levels 2 and 3, 

Figure 33. From these velocity contours, it is clear that the solution is grid independent at refinement level 

2. More proof of this fact is shown in Figure 34. The legend in this plot references the coarse grid as 

GRS1 and subsequent refinements as GRS2, GRS3, and GRS4. The distance indicator (–3-, 0-, 3-, and 5-

mm) represents how far offset from the duct centerline the profile was taken. For instance, –3 mm refers

to the far left black line in Figure 30 to Figure 33. As with the velocity contours, the biggest shift in the 

profiles occurs between GRS1 and GRS2. There is a slight change from GRS2 to GRS3, but further 

refinement to GRS4 shows very little change in the velocity profiles.

Microvane

Measurement Plane
NASA/CR—2010-216779 22

Figure 30.—Velocity contours depicting vortex for 

coarse grid. 

Figure 31.—Velocity contours depicting vortex for 

level 1 refined grid. 

Figure 32.—Velocity contours depicting vortex for 

level 2 refined grid. 

Figure 33.—Velocity contours depicting vortex for 

level 3 refined grid. 
NASA/CR—2010-216779 23

Figure 34.—Velocity profiles depicting grid convergence. 

Figure 35.—Comparison of measured versus predicted vortex downstream of a microvane.

Validation of predicted against measured velocity profiles downstream of the microvane showed that 

the SA turbulence model with grid spacing in the microvane wake of 0.023 in. most accurately captured 

the microvane-induced vorticity in an adverse pressure gradient in Mach 0.5 flow. In general, the 

simulations over-predicted the strength of the vortex, especially the downwash component. However, the 

simulation captured the qualitative shape of the vortex and overall effects. Figure 35 shows velocity 

contours indicating that the height, width, and structure of the vortex compared well with the experiment. 

The black line indicates the duct centerline location. The difference between the measured and predicted 

spanwise vortex location, downstream of the device could be due to the fact that the predicted, CCWrotating vortex was stronger than the measured vortex, enabling it to move farther outboard from the 

centerline as it propagated downstream. The overprediction of the vortex strength was attributed to the 

fact that the simulations did not accurately capture the corner-flow effects, which serve to dissipate the 

vortex strength. 

Turbulence Model Sensitivity

The sensitivity of the predicted boundary-layer profiles and induced vorticity to turbulence model 

variations was assessed for an isolated microvane installed in the 2–D, profiled-wall tunnel. SA, SA with 

U/Uinf

1.0

0.5
NASA/CR—2010-216779 24

rotation correction (SARC), SST, and RANS versus time-averaged RANS/LES comparisons were made 

with experimental results to determine which model best approximates the flow physics. Figure 36

summarizes these sensitivities through visualization of boundary layer profiles at several spanwise 

locations. Interpretation of the data can be simplified by looking at only the extreme upwash (far left) and 

extreme downwash (far right) profiles for each data set. For example, the far left (7 mm) red curve for the 

SST RANS/LES group can be compared to the far right (–3 mm) red curve for the same group. The band 

enclosed by these two extremes represents the magnitude of the upwash and downwash effects, or 

strength of the vortex. A thicker band would correspond to a stronger vortex, while a thinner band would 

correspond to a weaker vortex.

The experimental results, depicted by the solid green lines labeled “GT”, illustrate that the measured 

vortex band is thinner and thus weaker than the simulations. The time-averaged RANS/LES simulations 

of both SA and SST models appear to predict very similar vortex strengths, and a stronger vortex than all 

other models. The next strongest vortices were predicted by the RANS SST and RANS SARC models. 

Both of these models produce very similar results. The weakest of the simulated vortices was predicted by 

the RANS SA model. This model agrees reasonably well with experiment on the upwash extreme of the 

vortex. On the downwash extreme, however, the RANS SA model predicted a profile, which is noticeably 

fuller. While differences in the predicted vortices exist between models, the differences were small and 

the results were relatively similar between the models. 

Several possibilities exist to explain difference between the measured and predicted vortices.

Assuming the experimental error is negligible and the measured vortex size, strength and location are 

accurate, it is likely that the corner flow effects are serving to dissipate the rotational strength of the 

vortex. Because the simulation fails to capture the corner flow effects, (Figure 26), it produces a stronger 

vortex. Regardless of the cause, the differences exist very near the wall on a very small scale, and it is 

currently unclear how these differences will manifest themselves at the AIP, especially with the use of 

flow control in the s-duct. 

Figure 36.—Boundary layer profile sensitivity to turbulence model variations. 
NASA/CR—2010-216779 25

Figure 37.—BLI diffuser AIP total-pressure-recovery contours. 

Numerical Simulations of a BLI Inlet Offset Diffuser 

As previously stated, the application of the flow-control technologies being developed under this 

program will apply to a future HWB vehicle, such as the BWB. In these types of vehicles, high-speed 

cruise efficiency and low noise signature and fuel burn are facilitated by the use of aft-mounted, 

embedded engines. However, such designs pay a penalty in inlet performance and operability because the 

inlet ingests the large amount of low-energy, boundary-layer flow that builds up along the upper surface 

of the vehicle forebody. Furthermore, because the engines are embedded, the flow must pass through an 

s-duct, which often induces secondary flows that contribute to total pressure losses and unfavorable 

distortion patterns at the engine face, such as that shown in Figure 37. These patterns result in operability 

and fuel consumption penalties. Flow control applied in the diffuser offers the most promising solution to 

mitigate these challenges.

One of the objectives of this study is to provide flow-control technologies that will overcome the 

operability challenges of BLI inlets. Specifically, the flow control should reduce the distortion by 

circumferentially redistributing the low-pressure region at the bottom of the duct to create a more 

favorable distortion pattern at the engine face. However, the flow control must provide failsafe control of 

distortion without significant penalties in TSFC, supportability, or maintainability.

Numerical Simulations of BLI Offset Diffuser

Flow-control technologies for BLI inlets are being developed using an integrated, experimental and 

numerical approach. Initial simulations of the offset duct model in the FMRL facility were conducted to 

provide a preliminary assessment of the effectiveness of various flow-control approaches at improving 

total pressure recovery and distortion at the duct AIP. These assessments were evaluated, along with the 

2–D duct experimental results, to assist in selecting the flow-control technologies most likely to meet the 

duct performance goals. Technologies selected through these assessments will be designed, fabricated and 

tested in the final year of this program. However, initial simulations of the duct test configuration, 

Figure 38, indicated that the test setup did not produce an accurate representation of the flow in a BLI 

inlet duct, Figure 39. Specifically, the AIP distortion was too low because the experimental setup did not 

produce the required boundary-layer thickness on the lower surface, consistent with an inlet installed on a 

HWB vehicle, such as the BWB. This result was anticipated and was the reason for the inclusion of 

provisions for the installation of a boundary-layer fence in the s-duct model. 
NASA/CR—2010-216779 26

Figure 38.—Numerical model of the FMRL S-duct test configuration.

Figure 39.—AIP total-pressure contours resulting from 

unperturbed model inflow conditions.

Numerical methods were employed to aid in establishing the test technique required to represent BLIinlet flow accurately in the S-duct model in the FMRL tunnel. Specifically, incoming boundary-layer 

profiles were imposed as boundary conditions and evaluated to determine which would result in the 

appropriate distortion pattern at the AIP, Figure 37. Iterations on various, simulated approach boundary 

conditions showed that the boundary layer shape, not the thickness, controlled the distortion pattern at the 

AIP. To achieve the necessary AIP recovery pattern, Figure 37, the boundary-layer profile shown in 

Figure 40 was imposed as a boundary condition in a structured grid, upstream of the duct, in the 

computational domain. This block was coupled to an unstructured-grid representation of the s-duct to 

complete the computational mesh. A simulation on the grid with the imposed boundary layer, Figure 40, 

qualitatively revealed that it would provide increased distortion at the AIP, Figure 41. 
NASA/CR—2010-216779 27

Figure 40.—BLI inlet simulation velocity profile boundary condition.

Figure 41.—Centerline Mach and AIP total-pressure contours (interpolated onto a 40-probe 

rake) resulting from an imposed boundary-layer flow condition.

In the absence of an imposed boundary-layer perturbation, the distortion, DPCPave, at the AIP was 

0.015. However, by imposing a modified boundary-layer profile, the AIP distortion, DPCPave, increased 

to 0.058. This level of distortion is consistent with distortion measurements in uncontrolled BLI inlet tests 

(Owens, Allan, and Gorton, 2006) and provides a more realistic baseline for analyzing the effectiveness 

of flow-control devices. As a result, the modified boundary-layer profile was imposed as a boundary 

condition on the subsequent numerical simulations of the test s-duct with flow control. 

Numerical Simulations of Flow Control in a BLI Offset Diffuser

RANS simulations of the s-duct configuration were generated with a number of flow-control concepts 

in order to provide a preliminary assessment of the effectiveness of various technologies at controlling 

distortion and recovery in a BLI duct. All simulations included the imposed boundary layer described in 

the previous section. The geometry employed for this assessment was the numerical model of the test 

configuration, Figure 38. All flow-control devices simulated in the s-duct were located at ~13 in. 

upstream of the AIP Figure 42. The flow-control devices examined numerically were all passive devices 

and included arrays of microramps, as well as arrays of microvanes at various heights and spacing. The 

initial flow-control device evaluated using RANS simulations in the s-duct was an array of microramps. 

y/δ

Structured Block

Unstructured Grid

Axial Velocity Profile Within the Boundary Layer 1.26" Custom Profile

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

u/uinf

y/delta

U/Uinf

Imposed Velocity Profile

AIP Total-Pressure Contours
NASA/CR—2010-216779 28

The array consisted of nine microramps evenly distributed across the lower surface of the duct. The 

microramp design was derived from guidelines developed at Boeing and GRC (Anderson et al. 2004, 

2006). The device guidelines employed were consistent with those applied to design the passive devices 

tested in the 2–D wind tunnel. Actual dimensions were scaled for the duct and corresponding boundary 

layer. Specifically, the microramp half angle was set at 24°, and the height was defined to be 30 percent 

of the baseline boundary-layer height on the centerline of the duct lower surface. However, the remaining 

geometric parameters, namely microramp chord length and width, deviated from the guidelines. At 

present, guidelines do not exist specifically for BLI inlets. Applying the existing guidelines resulted in 

microramps that were too large for the duct. The microramp length and width was instead selected 

somewhat arbitrarily, using only expert opinion. 

Figure 42.—Location at which flow control was simulated in the S-duct. 

Figure 43.—Microramp flow-control 

device dimensions.

Figure 44.—AIP recovery and distortion for baseline and microramp flow control.

Flow control insert 

location

No Flow Control No Flow Control Microramps

• DPCPAVG = 0.058

• Recovery = 0.946

• DPCPAVG = 0.075

• Recovery = 0.933

No Flow Control No Flow Control Microramps

• DPCPAVG = 0.058

• Recovery = 0.946

• DPCPAVG = 0.075

• Recovery = 0.933

0.3 in.

0.75 in. 

24°

0.62 in.
NASA/CR—2010-216779 29

Results of the numerical simulations indicated that the microramps were not effective at controlling 

BLI inlet distortion. Specifically, the simulations showed that the microramps decrease recovery and 

increase distortion, Figure 44. This result was not surprising given that large-scale flow structures are 

required to reduce AIP distortion by redistributing the low-pressure, boundary-layer flow that pools in the 

lower half of the AIP in the baseline duct simulation. Microramps create small-scale structures that are 

useful for energizing a boundary layer and potentially preventing flow separation. However, they do not 

create the large-scale vortices necessary to redistribute the boundary layer of a BLI inlet at the AIP.

Unlike microramp arrays, microvane arrays have been used to create large-scale, vortical structures.

For example, they have been used in diffuser applications to combat the adverse effects of secondary flow 

in highly offset diffusers. Several microvane configurations including straight, tapered, half-height, and a 

sparse array were evaluated numerically to gain a sense of the design factors to which BLI inlet 

performance would be sensitive. The microvane geometries, Figure 45, employed in the numerical 

simulations were derived from optimized BLI inlet microvane geometries (Allan, Owens, and Lin, 2006). 

Tapered and rectangular cross-section microvane geometries were simulated. In addition, half-height 

microvanes, which were identical to the tapered microvanes but with the top half removed, were also 

simulated. Finally, a sparse array, which was identical to the tapered array but with the four center 

microvanes removed was also analyzed.

A comparison of the baseline simulation to that of the straight microvane array, Figure 46, shows that 

the distortion, DPCPave, dropped from 0.058 to 0.014 with the use of the straight microvane array, while 

the recovery experienced a 1.7 percent decrease. The microvanes produce individual vortices that 

coalesce into two, large-scale, counter-rotating vortices as they propagate downstream in the duct. These 

large-scale structures redistribute the single, low-pressure region in the lower half of the duct such that the 

resulting distortion pattern is one that will be more favorable in terms of engine operability. 

Figure 45.—Geometry and dimensions of 

the straight and tapered microvanes.

Figure 46.—Baseline distortion and recovery compared to a straight microvane array.

0.75”

0.36”

Straight Tapered

Baseline Straight 

Vanes

• DPCPAVG = 0.058

• Recovery = 0.946

• DPCPAVG = 0.014

• Recovery = 0.930

Baseline Straight 

Vanes

• DPCPAVG = 0.058

• Recovery = 0.946

• DPCPAVG = 0.014

• Recovery = 0.930
NASA/CR—2010-216779 30

A comparison of simulated total-pressure contours at the AIP of the duct with a tapered microvanearray with those at the AIP of a duct with straight microvane-array flow control indicates that the tapered 

microvanes decrease distortion, DPCPave, from 0.058 to 0.013 and increase recovery by 0.32 percent, 

Figure 47. Strictly from an inlet performance standpoint, the tapered microvanes perform better than 

straight microvanes. However, it is possible that they will fall short on reliability and/or supportability.

Nevertheless, both the tapered and straight microvane arrays appear to provide significant distortion 

improvements with only moderate recovery penalties in BLI inlet ducts. In the absence of a system-level 

assessment of tapered versus straight microvane arrays, tapered microvanes were selected as the 

microvane shape with which to move forward based solely on the predicted inlet performance benefits.

In addition to the microvane cross-section shape, height and spacing parametrics were also examined 

numerically. A comparison between the tapered microvane array, a half-height microvane array, and a 

sparse microvane array shows that all the configurations have comparable recovery. Distortion, however, 

is significantly higher in the case of the half-height microvanes, but only slightly higher with the sparse 

array, Figure 48. This suggests that the flow could be sufficiently controlled by using fewer microvanes, 

or even by using the half-height microvanes in conjunction with synthetic jets. Results from these initial 

microvane parametrics substantiated the need for a more structured, systematic assessment of the 

sensitivity of inlet performance to microvane geometric factors. 

Figure 47.—Straight microvane recovery and distortion compared to a tapered microvane array.

Figure 48.—Comparison of recovery and distortion between three microvane configurations.

Tapered 

Vanes

• DPCPAVG = 0.014

• Recovery = 0.930

• DPCPAVG = 0.013

• Recovery = 0.933

Straight 

Vanes

Tapered 

Vanes

• DPCPAVG = 0.014

• Recovery = 0.930

• DPCPAVG = 0.013

• Recovery = 0.933

Straight 

Vanes

Tapered Vanes

• DPCPAVG = 0.013

• Recovery = 0.933

Half Height Tapered Vanes

• DPCPAVG = 0.028

• Recovery = 0.932

Sparse Array of Tapered Vanes

• DPCPAVG = 0.016

• Recovery = 0.933

Tapered Vanes

• DPCPAVG = 0.013

• Recovery = 0.933

Half Height Tapered Vanes

• DPCPAVG = 0.028

• Recovery = 0.932

Sparse Array of Tapered Vanes

• DPCPAVG = 0.016

• Recovery = 0.933
NASA/CR—2010-216779 31

Figure 49.—Dynamic distortion simulations with SA and SST turbulence models.

Dynamic Distortion Simulations 

In support of the program objective to improve flow-control prediction tools, time-dependent, 

numerical simulations have been made of the baseline s-duct in an effort to determine how to model 

dynamic distortion accurately. Both SA and SST turbulence models have been employed with a hybrid 

RANS/LES model to capture the time-dependent effects in the s-duct. The time step of the simulations 

was 0.09 ms, with one solution being recorded every 10-time steps (0.9 ms). The results shown in 

Figure 49 are AIP recovery contours averaged over 110 saved solutions (~99 ms). The first solution in the 

average is taken after the flow has convected through the duct ~3 times (~110 ms), and the final solution 

is the average ~99 ms after that.

The two solutions exhibit minute differences from one another. When compared with the RANS 

simulation, the recovery decreases almost insignificantly, while the distortion realizes a drop of 

~17 percent. The similarity of recovery and drop in circumferential distortion, DPCPave, between 

steady state and time-dependent simulations has been observed in other BLI S-duct simulations 

conducted previously in Phase 1 of this program. 

Conclusions

The Boeing IFCPT program is developing enabling technologies and validated prediction tools for 

controlling inlet total-pressure distortion that will be applicable across a broad range of future aircraft, 

including subsonic, HWB vehicles. Specifically, fail-safe, hybrid flow-control technologies consisting of 

closely coupled microvanes and synthetic jets are being designed and evaluated as a means to control 

BLI-inlet distortion. In addition, advanced, inlet-distortion prediction methods based on integrated CFD 

and testing, using statistically designed experiments are being developed and validated. 

The flow-control strategy aimed at counteracting AIP distortion resulting from significant inlet-boundarylayer ingestion is to redistribute the concentrated, low total pressure, boundary-layer flow more evenly around 

the perimeter of the AIP, thus creating a distortion pattern to which engines are typically more tolerant. This 

result may be achieved by using flow control to generate two, large-scale, counter-rotating vortices that sweep 

the concentrated boundary layer flow up and around the outer perimeter of the AIP. To accomplish this, an 

array of streamwise vortices must be generated such that they merge into a large-scale vortex farther 

downstream in the duct, which requires consideration of the vortex sources, sizes and spacing. It is this 

prerequisite that dictates the packing density and number of devices. The use of a hybrid system, in place of a 

SA

Recovery: 0.942

Distortion: 0.049

SST

Recovery: 0.943

Distortion: 0.047

Baseline RANS distortion and recovery are 0.058 

and 0.946 respectively
NASA/CR—2010-216779 32

passive system offers the potential of reducing the required number of microvanes, thereby potentially 

reducing total-pressure losses, maintainability, and supportability challenges. 

Experimental results obtained during the current reporting period were based on a 2–D, profiled wall 

test section in which detailed flow measurements were obtained in the vicinity of individual or at most, a 

device pair. During year three of the current program, the results of the isolated-device tests will be used 

to guide the selection of and aid in understanding the physics associated with flow-control actuation in an 

offset, BLI inlet S-duct. During the current reporting period, detailed model design and fabrication of the 

offset duct test hardware was completed. Model installation in the Georgia Institute of Technology FMRL 

and testing of this hardware is planned to commence in early FY10. 

During the current reporting period, the interaction of surface-mounted passive and active flowcontrol devices with a Mach 0.5 cross flow was examined in a small-scale wind tunnel. The evolution of 

streamwise vortices induced by the flow control was investigated in an adverse pressure gradient that 

mimics the pressure gradient within a diffuser compatible with future HWB vehicles. Counter-rotating 

vortex pairs and single-sense vortices were formed and characterized using passive microramps and 

microvanes, respectively. Similar streamwise vortices were also generated using synthetic jet actuators.

The jets had rectangular orifices that were either slanted and/or skewed to produce single-sense vortices 

or streamwise aligned to produce vortex pairs. Finally, hybrid actuation approaches were demonstrated 

where a passive microvane and active synthetic jet were designed and operated in a tandem arrangement 

such that the induced vorticity from the active device enhanced the effects of the passive device, 

improving the overall control effectiveness.

During the current reporting period, integration of a synthetic jet with a pair of microvanes was 

proven to enhance the weakly interactive vortices resulting from passive microvane actuation.

Specifically, hybrid synthetic jet/microvane flow control resulted in vortices that merged into one large, 

coherent structure. Implementation of a row of synthetic jets followed by a row of microvanes could be 

made more efficient by implementing this concept of merging vortices. Furthermore, hybrid 

microvane/jet devices offer the benefit of reducing distortion with a reduced size or number of 

microvanes, thus offering potential improvements in recovery, as well as, system-level requirements such 

as supportability and maintainability.

Experimental data from the flow-control-device characterization studies were also used to validate 

numerical prediction tools. Numerical simulations were carried out using the 3–D, viscous, Navier-Stokes 

CFD code, BCFD. The baseline wind tunnel, in the absence of flow control was simulated, as was the 

wind tunnel with a single microvane. The RANS turbulence models produced similar results for the 

baseline tunnel, but failed to accurately predict the corner flows of the 2–D test section. The inaccurately 

modeled corner flow was assumed to be the result of inadequate grid density in that region. A gridresolution study was conducted on the microvane geometry in the 2–D test section. Four levels of grid 

density were considered. Grid independence was established with the second refinement, which 

determined the necessary element size for future simulations of the microvane. Numerical results based 

on SA, SARC, SST turbulence models, as well as RANS and time-averaged RANS/LES solutions were 

compared with experimental data downstream of a microvane. All the numerical schemes yielded similar 

results and overpredicted the strength of the induced vorticity of a microvane. The SA model produced 

the closest approximation to the measured vortex strength. 

Numerical simulations of a Boundary-Layer-Ingesting (BLI) offset inlet duct consistent with that in 

the BWB vehicle were conducted during the current reporting period. These simulations were used to 

support the development of the test technique for simulating BLI inlet flow in an isolated diffuser. 

Specifically, a boundary-layer profile that could be replicated experimentally with a fence, was applied at 

the duct entrance to generate the thick boundary layer consistent with BLI inlets. 

In addition to the test technique, microvane and microramp, passive, flow-control devices were 

simulated in the offset BLI inlet duct to compare the predicted performance benefits of these devices at 

the aerodynamic interface plane (AIP). The flow-control devices examined numerically were all passive 

devices and included arrays of microramps, as well as arrays of microvanes at various heights and 

spacing. Benefits were quantified in terms of recovery improvement and distortion reduction from the 
NASA/CR—2010-216779 33

baseline, non-actuated flow. Microvane arrays were found to be significantly more effective than 

microramp arrays at improving recovery and distortion in BLI inlet ducts, as they produced the largescale vortical structures necessary to redistribute the ingested, low-energy boundary layer fluid into more 

favorable engine-face pattern. Results from the initial microvane parametrics showed significant inletperformance sensitivity to changes in microvane geometric factors, indicating that significant 

performance gains with reduced supportability and maintainability penalties could be realized through 

optimizing the devices and pairing them with active components. 

Finally, Spalart-Allmaras and SST turbulence models have been employed with a hybrid RANS/LES 

model to simulate the time-dependent effects and attempt to predict dynamic distortion accurately in an 

offset, BLI inlet duct. This work will continue into the final year of this program. As experimental data is 

obtained for the duct test configurations, numerical simulations will be validated against the data. Using this 

data, the prediction tool will be enhanced to predict dynamic distortion more accurately in offset diffusers.

Future Work 

In the final year of Phase 2, detailed experimental testing of active, hybrid flow-control devices and 

numerical prediction tool development will continue. BLI-inlet-duct simulation testing will commence 

and ultimately be used to generate a database of steady-state performance sensitivities to flow-control 

devices and design parameters. Specifically, flow control design guidelines will be generated, and the 

most promising technologies will be identified. The benefits of flow control in BLI inlet ducts will be 

quantified in terms of steady state and dynamic total-pressure recovery and distortion. The dynamic data 

will provide the basis for validation of a new numerical dynamic distortion simulation tool.

In addition to the experimental investigations in an offset BLI inlet duct, numerical simulations of the 

test geometry with and without flow control will continue in the final year of this effort. Unsteady, 

Navier-Stokes codes with a hybrid RANS/LES turbulence model will be employed to predict dynamic 

distortion in the BLI inlet duct. Predicted results will be compared with experimental data. Results of 

these comparisons will be used to improve the distortion-prediction tool and make it available for 

incorporation into the Wind-US NPARC Alliance code.

Lastly, system-level payoffs and penalties will be assessed for flow-control technologies in an offset, 

BLI inlet diffuser. This assessment will include conceptual layouts of diffusers integrated in HWB aircraft 

and tailored with flow-control systems, Figure 50. System-level impacts will be assessed based on 

existing sensitivities in terms of TOGW, performance (TSFC), and risk.

Figure 50.—BLI inlet offset diffuser with flow control.

N+2 HWB

S-Duct

2nd Gen HFC

NASA/CR—2010-216779 35

Appendix.—Nomenclature

AIP aerodynamic interface plane

AFLR Advancing-Front/Local-Reconnection 

BCFD Boeing Computational Fluid Dynamics 

BLI boundary layer ingesting 

BWB blended wing body 

CCW counter-clockwise

CD converging-diverging

CFD Computational Fluid Dynamics 

CRSV counter-rotating streamwise vortices

CW clockwise

DPCPave SAE average circumferential total pressure distortion descriptor

δ boundary layer thickness 

δapex boundary-layer thickness at the wind-tunnel-test-section apex

FMRL Fluid Mechanics Research Laboratory

GaTech Georgia Institute of Technology

GRC NASA Glenn Research Center

h shape factor

h0 baseline shape factor

HFC hybrid flow control 

HLLE Harten-Lax-van Leer-Einfeldt 

HWB Hybrid Wing Body 

IFCPT inlet flow control and prediction technologies 

LES Large Eddy Simulation

M Mach number 

MADCAP Modular Aerodynamic Design Computational Analysis Process 

P0 ambient pressure 

PIV particle image velocimetry

PT total pressure

RANS Reynolds Averaged Navier-Stokes

RSM response surface model

SA Spalart-Allmaras 

SST shear-stress-transport 

SARC Spalart-Allmaras with rotation correction 
NASA/CR—2010-216779 36

TRL Technology Readiness Level

TT total temperature

U streamwise velocity

U* time-averaged streamwise velocity

ΔU streamwise velocity difference relative to the baseline

V cross-stream velocity

V* time-averaged cross-stream velocity

x streamwise direction

y vertical direction

z spanwise direction
NASA/CR—2010-216779 37

References

Allan, B, Owens, L, and Lin, J. (2006), “Optimal Design of Passive Flow Control for a Boundary-LayerIngesting Offset Inlet Using Design-of-Experiments,” AIAA–2006–1049. 

Amitay, M., Pitt, D., and Glezer, A. (2002), “Separation Control in Duct Flows,” Journal of Aircraft, 39, 

616–620. 

Amitay, M., Smith, B., and Glezer, A. (1998), “Aerodynamic flow control using synthetic jet 

technology,”AIAA–98–0208. 

Anabtawi, A.J., Blackwelder, R.F., Lissaman, B.S.P, Liebeck, R.H. (1999), “An Experimental 

Investigation of Boundary Layer Ingestion in a Diffusing S-Duct With and Without Passive Flow 

Control,” AIAA–99–0739. 

Anderson, B.H., Mace, J.L., Mani, M. (2009), “Active “Fail Safe” Micro-Array Flow Control For 

Advanced Embedded Propulsion Systems,” AIAA–2009–741. 

Anderson, B.H., Miller, D.N., Addington, G.A., Agrell, J. (2004), “Optimal Micro-Jet Flow Control for 

Compact Air Vehicle Inlets,” NASA/TM—2004-212937. 

Anderson, B., Tinapple, J., and Surber, L. (2006), “Optimal Control of Shock Wave Turbulent Boundary 

Layer Interactions Using Micro-Array Actuation,”AIAA–2006–3197b. 

Anderson, B., Tinapple, J., and Surber, L. (2006, Jun.), “Optimal Control of Shock Wave Turbulent 

Boundary Layer Interactions Using Micro-Array Actuation,” San Francisco, CA: AIAA–2006–3197. 

Compton, D.A. and Johnston, J.P. (1992), “Streamwise vortex production by pitched and skewed jets in a 

turbulent boundary layer,” AIAA J., 30, 640–647. 

Kim, W., and Menon, S. (1999), “An Unsteady Incompressible Navier-Stokes Solver for Large Eddy 

Simulation of Turbulent Flows,” International Journal for Numerical Methods in Fluids, 31, 

983–1017. 

Lin, J.C. (2002), “Review of research on low-profile vortex generators to control boundary-layer 

separation,” Progress in Aerospace Sciences, 38, 389–420. 

Mani, M. (2004, Jan.-Feb.), “Hybrid Turbulence Models for Unsteady Flow Simulations,” Journal of 

Aircraft , 41 (1).

Mani, M., Cary, A., and Ramakrishnan, S. (2005, Jul.-Aug.), “A General Purpose Euler and NavierStokes Solver for Structured and Unstructured Grids,” AIAA J. of Aircraft, Vol. 42, No.4, pp. 991–

997. 

Mani, M., Winkler, C., Fisher, M., and Mackie, S. (2007), “A post Processing Approach for CFD Error 

Associated with Grid Resolution,” 45th AIAA Aerospace Sciences Meeting, Reno, NV, AIAA–2007–

66809. 

Menter, F. (1993), “Zonal Two Equation k-Turbulence Models for Aerodynamic Flows,” 24th Fluid 

Dynamics Conference, Orlando, FL, AIAA–93–2906. 

Owens, L., Allan, B., and Gorton, S. (2006), “Boundary-Layer-Ingesting Inlet Flow Control,” 

AIAA–2006–839. 

Peake, D.J., Henry, F.S., and Pearcey, H.H. (1999), “Viscous Flow Control with Air-Jet Vortex 

Generators,” AIAA Paper 1999–3175. 

Shin, A., Vanka, S., Mani, M., Dorgan, A., and Michal, T. (2007), “Application of BCFD unstructured 

Grid to Simulation of Microramp Control of Shock/Boundary Layer Interaction,” AIAA CFD 

Conference, Miami, FL.

Smith, B., and Glezer, A. (1998), “The Formation and Evolution of Synthetic Jets,” Physics of Fluids, 

10 (9).
REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188 

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the 

data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this 

burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. 

Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB 

control number. 

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 

1. REPORT DATE (DD-MM-YYYY)

01-10-2010 

2. REPORT TYPE

Final Contractor Report 

3. DATES COVERED (From - To)

4. TITLE AND SUBTITLE

Inlet Flow Control and Prediction Technologies for Embedded Propulsion Systems 

Summary Report for Fiscal Year 2009

5a. CONTRACT NUMBER

NNC07CB76C 

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

McMillan, Michelle, L.; Gissen, Abe; Vukasinovic, Bojan; Lakebrink, Matthew, T.; Glezer, 

Ari; Mani, Mori; Mace, James 

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

WBS 561581.02.08.03.21.66 

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

The Boeing Company 

P.O. Box 516 

MS 064 1231 

St. Louis, MO 63134 

8. PERFORMING ORGANIZATION

 REPORT NUMBER

E-17405 

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/CR-2010-216779 

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified-Unlimited 

Subject Categories: 02, 07, and 34 

Available electronically at http://gltrs.grc.nasa.gov 

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

13. SUPPLEMENTARY NOTES

14. ABSTRACT

Fail-safe inlet flow control may enable high-speed cruise efficiency, low noise signature, and reduced fuel-burn goals for hybrid wing-body 

aircraft. The objectives of this program are to develop flow control and prediction methodologies for boundary-layer ingesting (BLI) inlets 

used in these aircraft. This report covers the second of a three year program. The approach integrates experiments and numerical 

simulations. Both passive and active flow-control devices were tested in a small-scale wind tunnel. Hybrid actuation approaches, combining 

a passive microvane and active synthetic jet, were tested in various geometric arrangements. Detailed flow measurements were taken to 

provide insight into the flow physics. Results of the numerical simulations were correlated against experimental data. The sensitivity of 

results to grid resolution and turbulence models was examined. Aerodynamic benefits from microvanes and microramps were assessed when 

installed in an offset BLI inlet. Benefits were quantified in terms of recovery and distortion changes. Microvanes were more effective than 

microramps at improving recovery and distortion. 

15. SUBJECT TERMS

Aerodynamics; Boundary layer control; Turbulent boundary layer; Jet control; Vortex generators; Computational fluid dynamics; 

Turbulence models 

16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF

 ABSTRACT

UU 

18. NUMBER

 OF 

 PAGES

48 

19a. NAME OF RESPONSIBLE PERSON

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

a. REPORT

U 

b. ABSTRACT

U 

c. THIS 

PAGE

U 

19b. TELEPHONE NUMBER (include area code)

443-757-5802 

Standard Form 298 (Rev. 8-98)

Prescribed by ANSI Std. Z39-18