John Morgenstern and Michael Buonanno

Lockheed Martin, Palmdale, California 

Jixian Yao, Mugam Murugappan, Umesh Paliath, Lawrence Cheung, Ivan Malcevic, 

Kishore Ramakrishnan, Nikolai Pastouchenko and Trevor Wood

GE Global Research, Niskayuna, New York 

Steve Martens, Phil Viars, Trevor Tersmette, Jason Lee, Ron Simmons, and David Plybon 

GE Aviation, Cincinnati, Ohio 

Juan Alonso, Francisco Palacios, Trent Lukaczyk, and Gerald Carrier

Stanford University, Stanford, California

Advanced Concept Studies for Supersonic 

Commercial Transports Entering Service 

in the 2018-2020 Period Phase 2

NASA/CR—2015-218719

July 2015

PMF-01766
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NASA Langley Research Center

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John Morgenstern and Michael Buonanno

Lockheed Martin, Palmdale, California 

Jixian Yao, Mugam Murugappan, Umesh Paliath, Lawrence Cheung, Ivan Malcevic, 

Kishore Ramakrishnan, Nikolai Pastouchenko and Trevor Wood

GE Global Research, Niskayuna, New York 

Steve Martens, Phil Viars, Trevor Tersmette, Jason Lee, Ron Simmons, and David Plybon 

GE Aviation, Cincinnati, Ohio 

Juan Alonso, Francisco Palacios, Trent Lukaczyk, and Gerald Carrier

Stanford University, Stanford, California

Advanced Concept Studies for Supersonic 

Commercial Transports Entering Service 

in the 2018-2020 Period Phase 2

NASA/CR—2015-218719

July 2015

PMF-01766

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared under Contract NNC10CA02C
Acknowledgments

This work was funded by the NASA N+2 Supersonic program administered by Peter Coen at the NASA Langley Research 

Center and Clayton Meyers at the NASA Glenn Research Center under prime contract (Lockheed Martin (LM)) NNC10CA02C. 

LM would like to thank them and many others at NASA including Susan Cliff, Don Durston, Bruce Storms, Maureen Delgado, 

Jonathan Seidel, Teresa Monaco, Lori Ozoroski, Richard Campbell, Eric Walker, Floyd Wilcox and Linda Bangert for their helpful 

contributions, editorial support and encouragement on this program. LM would also like to acknowledge GE Global Research 

Center including specifically Steve Martins, Jixian Yao, and Phil Viars; and Stanford University including Juan Alonso and his 

team, for contributing written material, data, and ideas for this N+2 Phase 2 final report.

Available from

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either expressed or implied, by the National Aeronautics and 

Space Administration.

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NASA STI Program

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This report is available in electronic form at http://www.sti.nasa.gov/ and http://ntrs.nasa.gov/
NASA/CR—2015-218719 iii

Executive Summary

Lockheed Martin Aeronautics Company (LM), working in conjunction with General Electric Global 

Research (GE GR) and Stanford University, executed a 19 month program responsive to the NASA 

sponsored “N+2 Supersonic Validation: Advanced Concept Studies for Supersonic Commercial 

Transports Entering Service in the 2018-2020 Period” contract. The key technical objective of this effort 

was to validate integrated airframe and propulsion technologies and design methodologies necessary to 

realize a supersonic vehicle capable of meeting the N+2 environmental and performance goals. 

The N+2 program is aligned with NASA’s Supersonic Project and is focused on providing systemlevel solutions capable of overcoming the efficiency, environmental, and performance barriers to practical 

supersonic flight. The N+2 environmental and performance goals are outlined in the technical paper, 

AIAA-2014-2138 (Ref. 1) along with the validated N+2 Phase 2 results. Our Phase 2 efforts built upon 

our Phase 1 studies (Ref. 2) and successfully demonstrated the ability to design and test realistic 

configurations capable of shaped sonic booms over the width of the sonic boom carpet.

Developing a shaped boom configuration capable of meeting the N+2 shaped boom targets is a key 

goal for the N+2 program. During the LM Phase 1 effort, LM successfully designed and tested a shaped 

boom trijet configuration (1021) capable of achieving 85 PLdB under track (forward and aft shock) and 

up to 28° off-track at Mach 1.6. In Phase 2 we developed a refined configuration (1044-2) that extended 

the under 85 PLdB sonic boom level over the entire carpet of 52° off-track at a cruise Mach number of 

1.7. Further, the loudness level of the configuration throughout operational conditions calculates to an 

average of 79 PLdB. These calculations rely on propagation employing Burger’s (sBOOM) rounding 

methodology, and there are indications that the configuration average loudness would actually be 

75 PLdB. We also added significant fidelity to the design of the configuration in this phase by performing 

a low speed wind tunnel test at our LTWT facility in Palmdale, by more complete modelling of 

propulsion effects in our sonic boom analysis, and by refining our configuration packaging and 

performance assessments.

Working with General Electric, LM performed an assessment of the impact of inlet and nozzle effects 

on the sonic boom signature of the LM N+2 configurations. Our results indicate that inlet/exhaust streamtube boundary conditions are adequate for conceptual design studies, but realistic propulsion modeling at 

similar stream-tube conditions does have a small but measurable impact on the sonic boom signature.

Previous supersonic transport studies have identified aeroelastic effects as one of the major challenges 

associated with the long, slender vehicles particularly common with shaped boom aircraft (Ref. 3). Under 

the Phase 2 effort, we have developed a detailed structural analysis model to evaluate the impact of 

flexibility and structural considerations on the feasibility of future quiet supersonic transports. We looked 

in particular at dynamic structural modes and flutter as a failure that must be avoided. We found that for 

our N+2 design in particular, adequate flutter margin existed. Our flutter margin is large enough to cover 

uncertainties like large increases in engine weight and the margin is relatively easy to increase with 

additional stiffening mass. The lack of major aeroelastic problems probably derives somewhat from an 

early design bias. While shaped boom aircraft require long length, they are not required to be thin. We 

intentionally developed our structural depths to avoid major flexibility problems. So at the end of Phase 2,

we have validated that aeroelastic problems are not necessarily endemic to shaped boom designs. 

Experimental validation of sonic boom design and analysis techniques was the primary objective of 

the N+2 Supersonic Validations contract; and in this Phase, LM participated in four high speed wind 

tunnel tests. The first so-called Parametric Test in the Ames 9x7 tunnel did an exhaustive look at variation 

effects of the parameters: humidity, total pressure, sample time, spatial averaging distance and number of 

measurement locations, and more. From the results we learned to obtain data faster and more accurately, 

and made test condition tolerances easy to meet (eliminating earlier 60 percent wasted time when 
NASA/CR—2015-218719 iv

TABLE 1.—N+2 ENVIRONMENTAL AND PERFORMANCE GOALS, AND VALIDATED PHASE 2 RESULT

[*Measurements of low N-wave dive booms suggest that Burger’s alone may result in loudness as much as 5 PLdB too high.]

NASA N+2 Supersonic Transport 

(2018-2020) Goals

LM N+2 Phase 2 Predicted 

and Validated Status

Environmental Goals

Sonic Boom 85 PLdB up to 20° off-track

(NASA specified goal)

<85 PLdB over full carpet

79* PLdB average (sBOOM)

Airport Noise 

(cumulative below stage 3) 10 to 20 EPNdB 21.55 (GE - predicted)

Cruise Emissions <10 EINOx 5 EINOx

Performance Goals

Cruise Speed Mach 1.6 to 1.8 shaped boom flight Mach 1.7 (1.8 over water)

Range 4,000 nmi 5,500 nmi

Payload (passengers) 35 to 70 passengers 82 pax

Fuel Efficiency 

(passenger-nmi per lb of fuel) 3.0 3.13 (using GE engines)

condition tolerances could not be held). The next two tests used different tunnels. The Ames 11 ft tunnel 

was used to test lower Mach numbers of 1.2 and 1.4. There were several difficulties using this tunnel for 

the first time for sonic boom including having to shift the measurement Mach numbers to 1.15 and 1.3 to

avoid flow problems. It is believed that the 11 ft could be used successfully to measure sonic boom but 

there are likely to be a number of test condition restrictions. The Glenn 8x6 ft tunnel was used next and 

the tunnel has a number of desirable features for sonic boom measurement. While the Ames 9x7 can only 

test Mach 1.55 to 2.55 and the 11 ft can only test Mach 1.3 and lower, the Glenn 8x6 can test 

continuously from Mach 0.3 to 2.0. Unfortunately test measurement accuracy was compromised by a 

reference pressure drift. Post-test analysis revealed that the drift occurred when Mach number drifted 

slightly. Test measurements indicated that if Mach number drift is eliminated, results from the 8x6 would 

be more accurate, especially at longer distances, than results from the 9x7. The fourth test in the 9x7, 

called LM4, used everything we learned to comprehensively and accurately measure our new 1044-02 

configuration with a full-carpet shaped signature design. Productivity was 8 times greater than our Phase 

1 LM3 test. Measurement accuracy and repeatability was excellent out to 42 in. However, measurements 

at greater distances require the rail in the aft position and become substantially less accurate. Further 

signature processing or measurement improvements are needed for beyond near-field signature validation.

Stanford University 

Stanford University, in collaboration with LM and NASA, developed an adjoint-based capability for 

the inverse shape design of supersonic configurations to match a target equivalent area distribution. 

Stanford derived, implemented, and tested a preliminary version of an adjoint solver that is able to 

produce sensitivities (gradients) of cost functions derived from the aircraft’s equivalent area distribution 

with respect to arbitrary numbers of design variables that affect the shape of the aircraft. Using CAPRI, a 

CAD-neutral software interface, Stanford successfully applied the adjoint-based techniques to the N+2 

trijet configuration. The Stanford Final Report is attached in Appendix B.

General Electric

GE Global Research and Aviation supported Lockheed Martin in this Phase 2 NASA sponsored ‘N+2 

NRA—System Level Experimental Validations for Supersonic Transport Aircraft’ program. Based on 

Lockheed Martin provided updated requirements and targets, GE modified an adaptive cycle engine with 

FLADE tip propulsion system expected to meet or exceed the environmental goals set by NASA, from 
NASA/CR—2015-218719 v

the recent Phase 1 program. This propulsion system takes advantage of cooled cooling air to extend the 

overall pressure ratio of the engine and increase thermal efficiency. A low noise, high performance 

exhaust system takes advantage of the innovative adaptive feature, the FLADE tip on the second fan 

stage, to provide a third air stream to feed the inverted velocity profile and fluid shield on the exhaust for 

lower noise capability. A trapped vortex pilot Mini-Burner is also incorporated in the exhaust to provide 

augmented transonic thrust and potential take-off noise abatement. 

A limited trade study was performed for three fan pressure ratios, 2.45, 2.55 and 2.65. The highest fan 

pressure ratio propulsion system has the best performance and weight impact but based on the jet noise 

assessment a fan pressure ratio of 2.55 is retained as the target. 

The flowpath of the IVP/Shield nozzles tested in Phase 1 was updated to mitigate the overexpansion 

at low power conditions that led to unsteady flow separation. A new exhaust nozzle configuration was 

required to fully mitigate this issue. The new exhaust configuration allows the primary exhaust to operate 

in a purely convergent mode as well as a convergent-divergent mode. This change resulted in higher 

internal nozzle performance across a wider range of the operational map. Large eddy simulations of the 

Phase 1 cutback configuration show very good comparisons to measured acoustic data and Phase 2 results 

show a much improved flow field. 

Analysis was performed of the Lockheed Martin supersonic inlet coupled with a two-stage counterrotating FLADED fan configuration that was designed and tested under the VAATE program. Lockheed 

Martin supplied an inlet/boundary condition with inlet auxiliary doors open, downstream of the inlet 

centerbody and struts. The acoustic analysis again shows the dominant tonal energy is related to the 

wakes from the inlet struts. The GE GR final report is attached in Appendix C. 

NASA/CR—2015-218719 vii

Contents

Executive Summary.....................................................................................................................................iii

Stanford University ...............................................................................................................................iv

General Electric.....................................................................................................................................iv

1.0 Introduction ............................................................................................................................................1

1.1 Subject/Relevancy..........................................................................................................................1

2.0 N+2 Configuration Refinement..............................................................................................................3

2.1 Cruise Mach Trade.........................................................................................................................4

2.2 Sonic Boom Signature Improvement .............................................................................................4

2.3 Configuration Maturation...............................................................................................................9

2.3.1 OML Refinement................................................................................................................9

2.3.2 Landing Gear Integration..................................................................................................13

2.3.3 Vehicle Packaging ............................................................................................................14

2.3.4 Cabin Layout.....................................................................................................................15

2.3.5 Mass Properties.................................................................................................................16

2.3.6 Aerodynamics...................................................................................................................19

2.4 Vehicle Sizing and Mission Performance ....................................................................................20

2.4.1 Vehicle Airport Operations and Configuration.................................................................22

2.5 Propulsion Integration and Sensitivity Analysis..........................................................................24

2.5.1 Inlet Design.......................................................................................................................24

2.5.2 Auxiliary Inlet Design ......................................................................................................26

2.5.3 Propulsion System Sensitivity Analyses...........................................................................26

2.6 LTWT Test...................................................................................................................................30

2.6.1 Model Design....................................................................................................................31

2.6.2 Test Planning ....................................................................................................................34

2.6.3 Test Analysis and Results.................................................................................................37

3.0 N+2 Structural and Aeroelastic Analysis.............................................................................................52

3.1 Introduction..................................................................................................................................52

3.1.1 Purpose..............................................................................................................................52

3.1.2 Objectives.........................................................................................................................53

3.1.3 Scope/Tasks......................................................................................................................53

3.2 Configuration and Structural Arrangement..................................................................................54

3.2.1 Configuration and Systems Layout...................................................................................54

3.2.2 Control Surface Sizing and Definition..............................................................................57

3.2.3 PreCEPT ...........................................................................................................................57

3.3 Structural Design Criteria and Design Loads...............................................................................57

3.3.1 Structural Design Criteria and Design Load Cases...........................................................57

3.3.2 Linear Aerodynamic Maneuver Loads .............................................................................64

3.3.3 CFD-Loads Model and Trim Scheme...............................................................................68

3.3.4 Loads Summary ................................................................................................................71

3.4 Finite Element Modeling..............................................................................................................75

3.4.1 Geometry Processing for FEM Development...................................................................75

3.4.2 Meshing 2D Shells on the Surface Geometry...................................................................77

3.4.3 Meshing 1D Bars and Rod Elements................................................................................80

3.4.4 Additional FEM Details....................................................................................................81

3.4.5 FEM Element and Node Numbering ................................................................................86

3.4.6 FEM Include File Structure ..............................................................................................89

3.4.7 Material Property Definition.............................................................................................91

3.4.8 Nonstructural Mass—0D Elements ..................................................................................93

3.4.9 FEM Morphing to Increase Aft-Deck and Tail Stiffness..................................................96
NASA/CR—2015-218719 viii

3.5 Structural Sizing...........................................................................................................................99

3.5.1 Approach...........................................................................................................................99

3.5.2 Design Criteria................................................................................................................100

3.5.3 Design Variables and Zones ...........................................................................................104

3.5.4 Core and Stiffener Trade Studies....................................................................................114

3.5.5 Final Sizing Results........................................................................................................115

3.5.6 Stress, Strain, and Deflection Plots.................................................................................120

3.5.7 Mode Shapes and Frequencies (With Focus on Final Design).......................................134

3.6 Aeroelastic Analysis...................................................................................................................138

3.6.1 Aeroelastic Model...........................................................................................................138

3.6.2 Comparison of MSC.NASTRAN With FAMAS Results...............................................139

3.6.3 Flutter Results.................................................................................................................141

3.6.4 Aft Deck Stiffening Alternative......................................................................................150

3.7 Structural Deformation Effects on Sonic Boom.........................................................................151

3.8 Conclusions and Recommendations in Sections 5.0 and 6.0 .....................................................156

4.0 N+2 Sonic Boom Testing and Analyses.............................................................................................156

4.1 Ames 9x7-0250 Parametric Test................................................................................................156

4.2 Ames 11-0249 Boeing Test........................................................................................................158

4.2.1 Background and Overview .............................................................................................158

4.2.2 Test Description..............................................................................................................159

4.2.3 Test Conduct...................................................................................................................162

4.2.4 Test Observations and Issues..........................................................................................166

4.2.5 Test Conclusion and Recommendations.........................................................................171

4.3 Glenn 8x6-12-002 Boeing Test..................................................................................................173

4.4 Ames 9x7-0254 LM4 Test .........................................................................................................177

4.5 Test Planning (Productivity Tolerances, Test Models, Processing)...........................................179

4.6 Model Design .............................................................................................................................183

4.7 Test Description .........................................................................................................................188

4.8 Oil and Stereo-RBOS Flow Visualization..................................................................................189

4.9 Test Analysis and Results ..........................................................................................................201

5.0 Summary and Conclusions.................................................................................................................211

5.1 N+2 Configuration Refinement..................................................................................................211

5.1.1 Quiet Shaped Boom........................................................................................................211

5.1.2 Quiet, Clean, Efficient Propulsion..................................................................................212

5.1.3 Efficient, High Performance Vehicle..............................................................................212

5.2 N+2 Structural and Aeroelastic Analysis...................................................................................213

5.3 N+2 Shaped Sonic Boom Testing and Analysis ........................................................................213

6.0 Recommendations..............................................................................................................................214

6.1 N+2 Configuration Refinement..................................................................................................214

6.2 N+2 Structural and Aeroelastic Analysis...................................................................................215

6.3 N+2 Shaped Sonic Boom Testing and Analysis ........................................................................215

.—Acronym List....................................................................................................................217

.—N+2 NRA—System Level Experimental Validations for Supersonic Transport 

Aircraft ...............................................................................................................................................219

.—Development of Design Capabilities Within the NASA/LMCO N+2

Supersonics Effort ..............................................................................................................................325

References.................................................................................................................................................375
NASA/CR—2015-218719 ix

List of Figures

Figure 1.—LM N+2 Organization ................................................................................................................ 2

Figure 2.—N+2 Phase 2 Configuration Evolution (left to right): 1021, 1040, 1043, 1044.......................... 3

Figure 3.—N+2 Phase 2 Baseline Vehicle 3-View....................................................................................... 3

Figure 4.—Mach Effect on Ground Signatures............................................................................................ 5

Figure 5.—Configuration 1021 Full-Carpet Loudness................................................................................. 6

Figure 6.—Configuration 1021 Signature, 5.6 R/(b/2), SOC 4,000 nmi Mission ........................................ 6

Figure 7.—DOE Design Variable Sweep ..................................................................................................... 7

Figure 8.—Phi = 40° Pressure Gradients, Configuration 1040 (top) and 1043 (bottom) ............................. 7

Figure 9.—SOC Full-Carpet Loudness Comparison, 4,000 nmi Mission .................................................... 8

Figure 10.—CFD Result of Representative Inlet and C-D Nozzle............................................................... 8

Figure 11.—Propulsion Effects on Signature, 5.6 R/(b/2)............................................................................ 8

Figure 12.—Configuration 1044-2 Full-Carpet Loudness............................................................................ 9

Figure 13.—Configuration 1021 Issues To-Fix.......................................................................................... 10

Figure 14.—Configuration 1021 Versus 1040............................................................................................ 10

Figure 15.—Configuration 1040 Versus 1043............................................................................................ 10

Figure 16.—Configuration 1044 High-Alpha Pitch-Up Due to Wing Apex Vortex Formation ................ 11

Figure 17.—Configuration 1043 Versus 1044............................................................................................ 12

Figure 18.—Configuration 1044 Versus 1044-2 ........................................................................................ 12

Figure 19.—Configuration 1044 Versus 1044-3b ...................................................................................... 13

Figure 20.—Main Landing Gear Retraction Concept................................................................................. 13

Figure 21.—Engine FOD Considerations: Inlet Suction (top) and NLG Water Spray............................... 14

Figure 22.—Configuration 1044 Ground Operation Clearance.................................................................. 14

Figure 23.—Configuration 1044 Inboards.................................................................................................. 15

Figure 24.—Two-Class, 80 Passenger Cabin Layout................................................................................. 16

Figure 25.—Nominal Passenger Cabin Cross-Section ............................................................................... 16

Figure 26.—Empty Weight (left) and Empty Weight Fraction (right) Comparison................................... 18

Figure 27.—Configuration 1044 In-Flight CG Envelope........................................................................... 18

Figure 28.—Configuration 1044 Drag Polars............................................................................................. 19

Figure 29.—Configuration 1044 L/D Versus CL....................................................................................... 19

Figure 30.—L/D _Max Versus Mach Comparison..................................................................................... 20

Figure 31.—Sizing Mission Profile ............................................................................................................ 21

Figure 32.—N+2 Phase 2 Design Space..................................................................................................... 21

Figure 33.—Mission Performance Comparison: Flight Profile (top) and L/D (bottom)............................ 22

Figure 34.—Global Shaped Boom Supersonic Service Network ............................................................... 24

Figure 35.—N+2 Phase 2 Mach 1.7 Inlet ................................................................................................... 25

Figure 36.—Mach 1.7 Inlet Recovery Caine Curve ................................................................................... 25

Figure 37.—Inlet Peak Recovery Versus Free-Stream Mach..................................................................... 25

Figure 38.—Streamline (left), Mach (center) and Pressure Recovery (right) Maps................................... 27

Figure 39.—Configuration 1044-3b scaled M1.7 Inlet and Non-Proprietary Plug Nozzle ........................ 27

Figure 40.—Plume effects on Equivalent Area (top), 5.6 R/(b/2) Signature (middle) and Ground 

Signature (bottom): Configuration 1044-3b, SOC 4,000 nmi Mission ................................................ 28

Figure 41.—Configuration 1044 Centerline Nacelle Un-Adapted (top) Versus Adapted Grid 

(bottom) CFD Solutions....................................................................................................................... 29

Figure 42.—Grid Adaption Effects on Configuration 1044-2 5.6 R/(b/2) Signature (top) and 

Ground Signature (bottom), SOC 4,000 nmi Mission.......................................................................... 29

Figure 43.—LTWT Wind Tunnel Circuit................................................................................................... 30

Figure 44.—LTWT Test and Model Support Capability............................................................................ 30

Figure 45.—Model Changeable Part Options............................................................................................. 31

Figure 46.—V-Tail Dihedral, Incidence and Rudder Variations................................................................ 32
NASA/CR—2015-218719 x

Figure 47.—Options for Canard, Extended Gear, Nacelle Removal and Centerline Vertical Tail ............ 33

Figure 48.—Leading Edge Plain and Krueger Flaps with 2 Deflections Each........................................... 33

Figure 49.—Trailing Edge Deflection Parts............................................................................................... 34

Figure 50.—1044-2 Model in Test Section at High Alpha......................................................................... 35

Figure 51.—Naming Notation for Flap Deflections................................................................................... 37

Figure 52.—Measurement Extremes, Balance Limits and Reference Quantities....................................... 37

Figure 53.—LTWT332 Output Files of Test Measurements and Photography.......................................... 38

Figure 54.—ColSelect ASCII Data Contains these 118 Key Variables ..................................................... 39

Figure 55.—Weight Tares Taken (28) for Any Significant Configuration Change ................................... 41

Figure 56.—Run Schedule (Illegible but) Included Here for Identification............................................... 42

Figure 57.—Pitch Stability Assessment...................................................................................................... 43

Figure 58.—Smoke Showing Wing Vortex Impingement on V-Tail......................................................... 44

Figure 59.—Roll Stability Assessment....................................................................................................... 44

Figure 60.—Yaw Stability Assessment ...................................................................................................... 45

Figure 61.—Pitch-Up Control Concepts Exploration................................................................................. 46

Figure 62.—Configuration 1044-2 Pitch Break Surface Oil-Flow Visualization....................................... 47

Figure 63.—Aft Deck Flap Control Power Assessment............................................................................. 48

Figure 64.—Ruddervator Control Power Assessment................................................................................ 49

Figure 65.—Ruddervator Size Variation Comparison, Open (baseline) and Closed (50-percent 

oversize) ............................................................................................................................................... 50

Figure 66.—Alternate Planform Study ....................................................................................................... 51

Figure 67.—Low-boom supersonic flight requirements raise natural aeroelasticity concerns................... 52

Figure 68.—N+2 Structural Analysis Tasks and Schedule......................................................................... 53

Figure 69.—Cabin Layout for the 1044-2 Configuration ........................................................................... 54

Figure 70.—Layout of Major Subsystems.................................................................................................. 55

Figure 71.—Concepts for Major Load Paths Developed to Accommodate Cabin and Subsystems 

Layout (Early Concept)........................................................................................................................ 55

Figure 72.—Concept for the Shear Ties Attaching the Forward Wing to the Fuselage. ............................ 56

Figure 73.—Final Structural Layout for the 1044-3 Configuration............................................................ 56

Figure 74.—N+2 Mach, Altitude Flight Envelope ..................................................................................... 59

Figure 75.—Sample V-n Diagram.............................................................................................................. 59

Figure 76.—A uniform pressure is applied to the cabin (left figure) and cargo bay (right figure)............. 61

Figure 77.—Application of Landing and Ground Handling Loads to FEM............................................... 64

Figure 78.—Linear Aerodynamic Model for Loads and Flutter Analysis.................................................. 65

Figure 79.—Spline Verification—Wing Up-Bending ................................................................................ 65

Figure 80.—Spline Verification—Fuselage Down-Bending...................................................................... 66

Figure 81.—Spline Verification—Wing Down-Bending ........................................................................... 66

Figure 82.—Support Point Definition......................................................................................................... 67

Figure 83.—Comparison of Linear Aero Model to LTWT Test Data (Mach 0.14) ................................... 67

Figure 84.—Comparison of Linear Aero Lift Coefficient with Test (Mach 1.7) ....................................... 68

Figure 85.—Wedge Shaped Gaps Needed to Enable 30° of Trailing Edge Flap Actuation....................... 69

Figure 86.—N+2 Configuration 1044-3 Surface Mesh used by Splitflow for CFD-Based 

Aeroelastic Analysis............................................................................................................................. 69

Figure 87.—MDICE uses an Application Programming Interface to Transfer Forces and 

Deflections Between Aerodynamic and Structural Analyses............................................................... 69

Figure 88.—Surface Patch Definitions for Fluid-Structure Interfaces ....................................................... 70

Figure 89.—Force Coefficient Convergence History of Trim Procedure Used to Enhance Grid 

Adaption about the Equilibrium Trim State ......................................................................................... 71

Figure 90.—CFD-based Cp Distribution for Load Case 600, illustrating Vortex Dominated 

Effects on the Upper Surface................................................................................................................ 73
NASA/CR—2015-218719 xi

Figure 91.—Isosurfaces of Vortex Magnitude colored with Helicity to Illustrate the Vortex 

Structure at M=1.41, Nz=2.5 (Load Case 600) .................................................................................... 74

Figure 92.—Vector Plot of Applied Loads Colored by Z-Component of Force for Load Case 600 

Illustrates Nonlinear Vortex Effects Captured by CFD-based Trim Process....................................... 74

Figure 93.—N+2 Raw Geometry Import into Hypermesh ......................................................................... 75

Figure 94.—N+2 Analysis Geometry after PreCEPT Automated Organization and Cleanup ................... 75

Figure 95.—N+2 fully trimmed surface geometry set prior to meshing..................................................... 76

Figure 96.—N+2 Subassembly Map Exploded View................................................................................. 76

Figure 97.—N+2 Model Browser Showing High Level Assembly Structure ............................................ 77

Figure 98.—N+2 Control Surface Hinge Definition in Offset State .......................................................... 77

Figure 99.—N+2 Element Density on Outer Wing Box............................................................................. 78

Figure 100.—N+2 Element Density on Inner Wing and Tail..................................................................... 78

Figure 101.—N+2 Element Density Fuselage ............................................................................................ 78

Figure 102.—N+2 OML Material Direction Definition ............................................................................. 79

Figure 103.—N+2 Substructure Material Direction Definition.................................................................. 80

Figure 104.—N+2 Bar and Rod Element Mesh Definition ........................................................................ 80

Figure 105.—N+2 Landing Gear Models................................................................................................... 82

Figure 106.—N+2 Landing Gear Include File Hierarchy........................................................................... 82

Figure 107.—N+2 Main Landing Gear Attachment................................................................................... 83

Figure 108.—N+2 Landing Gear Doors in Closed Position....................................................................... 83

Figure 109.—N+2 Main Landing Gear Doors Offset to show CBUSH Connections to Vehicle............... 83

Figure 110.—N+2 Typical Control Surface Hinge Structure (Bow-tie)..................................................... 84

Figure 111.—N+2 Control Surface Modeling Details on Trailing Edge Structures................................... 84

Figure 112.—N+2 Control Surface Loads used for Free-body Hinge Moment Checks............................. 84

Figure 113.—N+2 Propulsion System Suspension Interface Model .......................................................... 85

Figure 114.—N+2 Propulsion System Suspension Properties.................................................................... 85

Figure 115.—N+2 Discrete Spring Attachment of Forward Wing to Fuselage.......................................... 85

Figure 116.—Property Set Maturation........................................................................................................ 92

Figure 117.—N+2 Metallic Usage and Overall Composition .................................................................... 93

Figure 118.—N+2 Property Type Counts................................................................................................... 93

Figure 119.—N+2 Distributed Masses....................................................................................................... 94

Figure 120.—N+2 Element Sets for Weight Statement Breakdown .......................................................... 94

Figure 121.—N+2 Smeared Make-up Mass for FEM REV70 ................................................................... 95

Figure 122.—N+2 Fuel Tanks and Fuel System Distributed Masses......................................................... 95

Figure 123.—N+2 Miscellaneous Distributed Masses............................................................................... 96

Figure 124.—N+2 Morphing Shapes Show Magnitude of Thickness Increases........................................ 97

Figure 125.—N+2 Aft Deck Morphing Process......................................................................................... 97

Figure 126.—N+2 Morphing of the Tail Thickness................................................................................... 98

Figure 127.—N+2 Trailing Edge Darts for CFD Loads Generation .......................................................... 98

Figure 128.—Structural Sizing Approach .................................................................................................. 99

Figure 129.—477 Buckling Panels on Inner Wing Skin........................................................................... 100

Figure 130.—367 Buckling Panels on Inner Wing Substructure.............................................................. 101

Figure 131.—252 Buckling Panels on Upper and Lower Outer Skin....................................................... 101

Figure 132.—265 Buckling Panels on Outer Wing Substructure............................................................. 102

Figure 133.—40 Panels on Tail Skins ...................................................................................................... 103

Figure 134.—44 Panels on Tail Substructure ........................................................................................... 103

Figure 135.—346 Buckling Panels on Fuselage OML............................................................................. 104

Figure 136.—Composite Inner Wing Skin Design Zones—109 Zones, 327 Independent Design 

Variables............................................................................................................................................. 105

Figure 137.—Aluminum Inner Wing Skin Design Zones—9 Zones, 9 Independent Design 

Variables............................................................................................................................................. 105
NASA/CR—2015-218719 xii

Figure 138.—Titanium Inner Wing Skin Design Zones—9 Zones, 9 Independent Design 

Variables............................................................................................................................................. 106

Figure 139.—Composite Inner Wing Substructure Design Zones—82 Zones, 82 Independent 

Design Variables ................................................................................................................................ 106

Figure 140.—Aluminum Inner Wing Substructure Design Zones—15 Zones, 15 Independent 

Design Variables ................................................................................................................................ 107

Figure 141.—Titanium Inner Wing Substructure Design Zones—22 Zones, 22 Independent 

Design Variables ................................................................................................................................ 107

Figure 142.—Composite Outer Wing Skin Design Zones—52 Zones, 156 Independent Design 

Variables............................................................................................................................................. 108

Figure 143.—Composite Outer Wing Substructure Design Zones—59 Zones, 59 Independent 

Design Variables ................................................................................................................................ 108

Figure 144.—Aluminum Outer Wing Substructure Design Zones—2 Zones, 2 Independent 

Design Variables ................................................................................................................................ 109

Figure 145.—Titanium Outer Wing Substructure Design Zones—2 Zones, 2 Independent Design 

Variables............................................................................................................................................. 109

Figure 146.—Composite Tail Skin Design Zones—20 Zones, 60 Independent Design Variables.......... 110

Figure 147.—Composite Tail Substructure Design Zones—17 Zones, 17 Independent Design 

Variables............................................................................................................................................. 110

Figure 148.—Titanium Tail Substructure Design Zones—6 Zones, 6 Independent Design 

Variables............................................................................................................................................. 111

Figure 149.—Composite Fuselage OML Design Zones—71 Zones, 213 Independent Design 

Variables............................................................................................................................................. 111

Figure 150.—Aluminum Fuselage OML Design Zones—5 Zones, 5 Independent Design 

Variables............................................................................................................................................. 112

Figure 151.—Aluminum Fuselage Substructure Design Zones—1 Zone, 1 Independent Design 

Variable .............................................................................................................................................. 112

Figure 152.—Composite Fuselage Substructure Design Zones—87 Zones, 87 Independent 

Design Variables ................................................................................................................................ 113

Figure 153.—Titanium Fuselage Substructure Design Zones—3 Zones, 3 Independent Design 

Variables............................................................................................................................................. 113

Figure 154.—Titanium Center Pylon Design Zones—3 Zones, 3 Independent Design Variables........... 114

Figure 155.—Core Thickness Trade Study............................................................................................... 115

Figure 156.—Stiffener Spacing Trade Study............................................................................................ 115

Figure 157.—Tail Deformation for LC 400 (2.5 g Pull-Up, Mach 2.0, DTOW2) ................................... 116

Figure 158.—Inner Wing Composite Skin Thickness (Includes 0.5 in. core).......................................... 117

Figure 159.—Inner Wing Composite Substructure (Includes 0.25 in. core) ............................................ 117

Figure 160.—Inner Wing Metallic Substructure ...................................................................................... 118

Figure 161.—Outer Wing Composite Skin Thickness (includes 0.5 in. core).......................................... 118

Figure 162.—Outer Wing Substructure Thickness (composite includes 0.25 in. core)............................ 119

Figure 163.—Tail Composite Skin Thickness (includes 0.5 in. core)...................................................... 119

Figure 164.—Tail Substructure Thickness (composite includes 0.25 in. core)........................................ 120

Figure 165.—Fuselage OML Thickness (includes 0.5 in. core)............................................................... 120

Figure 166.—Strain XX Load Case LC300—Upper Wing Skin.............................................................. 121

Figure 167.—Strain XX Load Case LC300—Lower Wing Skin ............................................................. 122

Figure 168.—Strain XX Load Case LC300—Upper Inner Wing Skin .................................................... 123

Figure 169.—Strain XX Load Case LC400—Upper Inner Wing Skin .................................................... 124

Figure 170.—Strain XX Load Case LC400—Lower Inner Wing Skin.................................................... 125

Figure 171.—Strain XX Load Case LC400—Upper Tail Skin ................................................................ 126

Figure 172.—Strain XX Load Case LC400—Lower Tail Skin................................................................ 127

Figure 173.—Von Mises Stress—Load Case LC400—Lower Inner Wing.............................................. 128
NASA/CR—2015-218719 xiii

Figure 174.—Von Mises Stress—Load Case LC400—Lower Inner Wing.............................................. 129

Figure 175.—Von Mises Stress—Load Case LC5730—Lower Inner Wing (Landing Gear 

Attachment Area) ............................................................................................................................... 130

Figure 176.—Max Shear Stress—Load Case LC400—Lower Inner Wing Skin ..................................... 131

Figure 177.—Deflection LC400 ............................................................................................................... 132

Figure 178.—Deflection LC300 ............................................................................................................... 133

Figure 179.—Deflection LC600 ............................................................................................................... 133

Figure 180.—N+2 Rigid Body Mode Shapes 1 to 3 for Rev 70 and 71................................................... 135

Figure 181.—N+2 Rigid Body Mode Shapes 4 to 6 for Rev 70 and 71................................................... 136

Figure 182.—N+2 Rigid Body Mode Shapes 7 to 9 for Rev 70 and 71................................................... 137

Figure 183.—N+2 Rigid Body Mode Shapes 10, 11, and 18 for Rev 70 and 71 ..................................... 138

Figure 184.—Modal Convergence Study (Mach 0.9, Symmetric Boundary Conditions, DTOW).......... 139

Figure 185.—Damping Versus Dynamic Pressure from NASTRAN ...................................................... 140

Figure 186.—Damping Versus Velocity (KEAS) from FAMAS............................................................. 141

Figure 187.—FEM017-REV70 Flutter Boundary—Symmetric Boundary Conditions............................ 142

Figure 188.—FEM017-REV70 Flutter Boundary—Antisymmetric Boundary Conditions..................... 143

Figure 189.—Mach 0.95 Antisymmetric Boundary Conditions, Zero Fuel Weight................................. 143

Figure 190.—Likely Spurious Results at Mach 1.1, Antisymmetric Boundary Conditions at 

DTOW................................................................................................................................................ 144

Figure 191.—N+2 Flutter Boundary Engine Mass Trade Study—Symmetric Boundary 

Conditions .......................................................................................................................................... 145

Figure 192.—Damping and Frequency Versus Dynamic Pressure for Symmetric BCs, Mach 0.95, 

DTOW2, 40 percent Increase in Engine Mass................................................................................... 146

Figure 193.—Critical Symmetric Flutter Mode, Aft Deck Bending, Tail Bending and Torsion, qf 

= 1164 psf, f = 5.5 Hz......................................................................................................................... 147

Figure 194.—Engine Mass Trade Study—Antisymmetric Boundary Conditions.................................... 148

Figure 195.—Damping and Frequency Versus Dynamic Pressure for Antisymmetric BCs, Mach 

0.7, DTOW2, 40 Percent Increase in Engine Mass............................................................................ 149

Figure 196.—Critical Antisymmetric Flutter Mode, Tail Bending and Torsion, qf = 1556 psf, f = 

9.3 Hz ................................................................................................................................................. 150

Figure 197.—Aft Deck Stiffening Alternative by Pylon Height Extension Above Wing........................ 150

Figure 198.—Fuel Tank Layout and Tank Burn Sequence at Beginning and End of Cruise................... 152

Figure 199.—Aeroelastic increments in Total Z-Force Normalized by Total Lift for the 

Beginning of Cruise............................................................................................................................ 152

Figure 200.—Aeroelastic Increments in Total Z-Force Normalized by Total Lift for End of Cruise...... 153

Figure 201.—Comparison of the Z Deflections for the Beginning and End of Cruise Trim States......... 153

Figure 202.—Comparison of Planform Surface Pressure Coefficient for Rigid and Flexible 

Simulations at Start of Cruise.............................................................................................................. 154

Figure 203.—Comparison of Empennage Surface Pressure Coefficient for Rigid and Flexible 

Simulations at Start of Cruise............................................................................................................. 154

Figure 204.—Comparison of Ground Signatures for Rigid and Flexible Simulations at Start of 

Cruise.................................................................................................................................................. 155

Figure 205.—Comparison of Ground Loudness Carpets for Rigid and Flexible Simulations at 

Start of Cruise..................................................................................................................................... 156

Figure 206.—Tunnel Ambient DPP Versus Humidity ............................................................................. 157

Figure 207.—Beyond Near-Field Measurements Appear Rounded and Missing Detail.......................... 158

Figure 208.—Aircraft Performance (44 in.) and Boom (16 in.) Models.................................................. 160

Figure 209.—The 14 in. RF1.0 Rail Attached to Floor of 11 ft Transonic Tunnel (Note NonConformal Structural Attachment ~8 in. Aft of LE) .......................................................................... 160

Figure 210.—Drawings of 11 ft Transonic Tunnel Test Section.............................................................. 161
NASA/CR—2015-218719 xiv

Figure 211.—The 11 ft Wind Tunnel Layout—Boeing N+2 Exploratory Sonic Boom Test (side 

view)................................................................................................................................................... 162

Figure 212.—Schlieren Images from 11 ft Transonic Tunnel .................................................................. 163

Figure 213.—Boeing Boom1 Model and RF1.0 Rail in 11 ft Transonic Tunnel ..................................... 164

Figure 214.—Boeing PM1 Sting-Mounted Large Scale Performance Model.......................................... 165

Figure 215.—Schlieren Image of Upstream Shocks Interfering With Boeing PM1 Sting-Mounted 

Large Performance Model.................................................................................................................. 166

Figure 216.—Boeing BM1 Sonic Boom Model (close upper) Over RF1.0 Rail (lower)......................... 167

Figure 217.—Humidity per Run Number Throughout Test ..................................................................... 169

Figure 218.—Schlieren Images Show Ambient Shocks From Upstream Interfering with Boeing 

BM1 Sonic Boom Model ................................................................................................................... 170

Figure 219.—The 11 ft Test Section Slot Baffles as Possible Disturbance Sources and Repairs............ 171

Figure 220.—The 8x6 with Angled Bleed Holes on Walls, Model Hardware on Floor Strut and 

RF1.0 Rail on Ceiling......................................................................................................................... 174

Figure 221.—Glenn 8x6 has Greater P_static Variations Than Ames 9x7, Which Distort Slope 

and Zero of Measurements at Every Rail Orifice............................................................................... 174

Figure 222.—Better Signature Detail Marred by Varying Level Drift..................................................... 175

Figure 223.—Glenn 8x6 Ambient/Reference Drift Too Large (even at same strut height) ..................... 176

Figure 224.—Change in Reference Measurements Correlates With Change in Tunnel Mach ................ 176

Figure 225.—Mach Change With Model Position Not Proportional (red increasing H, blue 

decreasing H)...................................................................................................................................... 176

Figure 226.—Orifice Reference Pressures Change in Direct Proportion to Mach Variation ................... 177

Figure 227.—The Far-Field Correction Method Provides Consistent Results Matching the 

Uncorrected Data's ∞ Distance Asymptote ........................................................................................ 178

Figure 228.—As the 70° Flat-Plate Model is Advanced Through the Flow-Field, Rail 

Measurements Quantify How Reflected Shock Strength Varies With Model Span .......................... 179

Figure 229.—The Model is Moved Back and Up for a Reference Measurement But May Still be 

Measured on the Rail—Orifices Not Ahead of the Model are Omitted from Spatial Averaging ...... 179

Figure 230.—Automatic Reference Measurement Trimming Removes Only the Data With Model 

Intrusion and Keeps the Good Data for Resolving the Trailing Signature......................................... 180

Figure 231.—Omitting a Fraction of the Highest/Lowest Measurements Extremes, Smooths and 

Sharpens the Spatially Averaged Signature........................................................................................ 180

Figure 232.—Reverse Aging, Averaging, then Re-Aging Achieves a Slight Sharpening of the 

Spatially Averaged Signature............................................................................................................. 181

Figure 233.—Doubled Rail Increases Position Options and Translation Length—Improving 

Measurement Accuracy, Plus No Stopping to Move Rail between Near and Far 

Measurements..................................................................................................................................... 182

Figure 234.—LM4 Wind Tunnel Model of 1044-2 Configuration........................................................... 183

Figure 235.—Symmetry Plane Pressure Contours: No Blade (left) and With Blade (right).................... 183

Figure 236.—Blade Effects on Model Signature...................................................................................... 184

Figure 237.—Blade Effects on Centerline Flow-Through Nacelle........................................................... 185

Figure 238.—Centerline Flow-Through Nacelle Mach (left) and Pressure Recovery Map (right).......... 185

Figure 239.—Surface Pressure Contour Comparison............................................................................... 186

Figure 240.—Edge Thickness Effect on Model Signature [M=1.7, AoA=2.1, H=5.4(b/2)].................... 186

Figure 241.—LM4 Model CFD Boundary Layer Comparisons............................................................... 186

Figure 242.—Predicted Boundary Layer Effects on LM4 Signature........................................................ 187

Figure 243.—The 19 Percent Smaller 1044-2 Model Improved Spatial Averaging Positioning ............. 187

Figure 244.—The 70° Flat-Plate Model Fabricated to Improve Measurement and Calibration of 

Reflected Shock Distortions............................................................................................................... 188

Figure 245.—Flat-Plate Approximates 1044-2 Wing Planform............................................................... 188

Figure 246.—Hardware and 1044-2 Model Prepared for Testing ............................................................ 189
NASA/CR—2015-218719 xv

Figure 247.—Test Oil Flow (top) Showed Fully Attached Flow, Like Turbulent CFD (bottom)............ 190

Figure 248.—Close-Up of 1021-01 Oil Streaks Showed Fully Attached Flow, Like Turbulent 

CFD (Some persistent separation streaks formed during tunnel start-up at low Re) ......................... 191

Figure 249.—Dramatic Upper Surface Flow Coloration Showed That Strong Pressure 

Disturbances are Isolated From Underneath the Wing Until Its Trailing Edge, as Intended ............. 192

Figure 250.—Lower Surface Produced Little Flow Disturbance, Supporting Its Intended Mission 

to Quantify Shock Reflection Strength as a Function of Local Span................................................. 193

Figure 251.—Blade Support for 1021-01 (top) and 1044-02 (bottom) Showed Attached Flow 

Until Vortex Separation Region Identified by Dashed Red Line....................................................... 194

Figure 252.—Upper and Lower Surface Oil Flows on 1044-02 Indicated Fully Attached Flow............. 195

Figure 253.—Upper Surface Oil Flow Strongly Showed Rib Pattern Shocks From Blade Trip 

Discs and Bow Shock Waves From Wing Trip Discs........................................................................ 195

Figure 254.—Legacy 69° Delta Wing-Body Model Oil Streaks Indicating Attached Flow and 

Dramatic Oil Evacuation Behind Diamond Airfoil Maximum Thickness Ridge............................... 196

Figure 255.—Mottled Oil at the Root and Outboard Wing and Oil Void Streak at the Nose 

(bottom) Are Not Flow Phenomena, But Rather, Persistent Features From the Initial Oil 

Application (top) ................................................................................................................................ 196

Figure 256.—RBOS of 1021-01 Shaped Boom Model Made Nose and Underwing Nacelle 

Shocks Apparent, Otherwise, Only Background Flow-Field Variations ........................................... 198

Figure 257.—Flat-Plate Model Created Little Disturbance between Nose Shock and Trailing 

Edge.................................................................................................................................................... 198

Figure 258.—RBOS Wake Visualization by Shading on DY (see Figure 260) ....................................... 199

Figure 259.—RBOS Side-View Validated Lack of Disturbance Below Flat-Plate between Nose 

Shock and Trailing Edge and Successful Isolation of Blade Disturbance.......................................... 199

Figure 260.—RBOS of 1044-02 Shaded DX (top) and DY (bottom) ...................................................... 200

Figure 261.—RBOS of 1044-02 Illustrated that Shaped Boom Disturbances are Lower in 

Magnitude than Ambient Disturbances and only Subtly Visible Behind the Wing........................... 201

Figure 262.—The 69° Delta Wing-Body Shocks Weakened and Harder to See at Smaller Size............. 201

Figure 263.—Excellent Test-to-Test Match (validated before proceeding with LM4)............................ 202

Figure 264.—There Does Appear To Be Better and Worse Locations For Measuring in the 

Tunnel—Selective Data Processing Might Improve Results............................................................. 203

Figure 265.—The 70° Flat-Plate Delta Model Produces Little Disturbance Under Itself So 

Reflections Could Be Characterized to Improve Flow-Field Calibration .......................................... 203

Figure 266.—Measurement Fell Between Turbulent and Laminar Solutions.......................................... 204

Figure 267.—Predictions Matched Measurements for Roll Angles from 0° to 60° ................................. 205

Figure 268.—Large Predicted Nacelle Effect is Matched by Measurements at 0° and 40° Roll ............. 206

Figure 269.—Still Matching Well at 13 Semi-Span (b/2) Distance at 0° and 40° Roll............................ 207

Figure 270.—Signature Uncertainty and Detail Degraded With Increased Measurement Distance 

but Aft Rail (17 to 21 semi-spans distance) Position Responsible for Part of Degradation............... 207

Figure 271.—Measurements and Match Showed More Uncertainty Than Required, Further 

Measurement Processing Improvements Needed............................................................................... 208

Figure 272.—Despite a Small Model a "Nonshaped" Boom Design of 3 Coalesced Shocks is Far 

Less Scattered by Tunnel Distortions................................................................................................. 209

Figure 273.—Effect of Alpha Coalesced Into Only a Shock Magnitude Change and Small 

Recompression Detail Difference....................................................................................................... 210

Figure 274.—In Roll, Wing Trailing Edge Shocks Different While Leading Edge Shocks 

Similar—Because Leading Edge Swept Near Mach Cone ................................................................ 210

List of Tables

Table 1.—N+2 Environmental and Performance Goals, and Validated Phase 2 Result ............................. iv
NASA/CR—2015-218719 xvi

Table 2.—Phase 2 Configuration Progression.............................................................................................. 3

Table 3.—Breguet Range Parameter versus Mach ....................................................................................... 4

Table 4.—Configuration 1044 Weight Statement ...................................................................................... 17

Table 5.—Detailed Mission Statement and Maximum Range.................................................................... 21

Table 6.—Detailed Mission Statement, Shaped Boom............................................................................... 22

Table 7.—General Characteristics for Airframe Noise Calculation ........................................................... 23

Table 8.—Configuration and Design Weights Data ................................................................................... 58

Table 9.—N+2 Design Speeds.................................................................................................................... 58

Table 10.—Maneuver Load Cases.............................................................................................................. 60

Table 11.—Cabin Pressure as a function of altitude................................................................................... 60

Table 12.—Landing Load Cases................................................................................................................. 62

Table 13.—Ground Handling Load Cases.................................................................................................. 63

Table 14.—Trim Deflections for Symmetric Maneuver Load Cases......................................................... 72

Table 15.—Trim Deflections for Antisymmetric Component of Maneuver Load Cases........................... 72

Table 16.—Weights and CG for final maneuver load calculations............................................................ 72

Table 17.—N+2 Shell Element Counts by Subassembly............................................................................ 79

Table 18.—N+2 Bar and Rod Element Counts by Subassembly................................................................ 81

Table 19.—N+2 FEM Numbering Master Schema .................................................................................... 86

Table 20.—N+2 Element Type Breakdown and Counts ............................................................................ 89

Table 21.—N+2 NASTRAN FEM Include File Structure.......................................................................... 90

Table 22.—Basic Material Property Values............................................................................................... 91

Table 23.—Design Criteria for Structural Sizing ..................................................................................... 100

Table 24.—Weight Summary of Sized FEMs.......................................................................................... 116

Table 25.—N+2 Vehicle modal frequencies for REV 70 and 71 ............................................................. 134

Table 26.—Comparison of NASTRAN and FAMAS .............................................................................. 139

Table 27.—FEM Weights and CG Locations for Flutter Analysis (FEM017-REV70)............................ 142

Table 28.—Engine Masses for Trade Study ............................................................................................. 145

Table 29.—Minimum Flutter Dynamic Pressure versus Engine Mass (Symmetric Boundary 

Conditions)......................................................................................................................................... 145

Table 30.—Minimum Flutter Dynamic Pressure versus Engine Mass (Antisymmetric Boundary 

Conditions)......................................................................................................................................... 148

Table 31.—Flight condition and weight states for Beginning and End of Cruise States.......................... 152

Table 32.—Trim States Obtained with a CFD-Based Aeroelastic Trim Approach.................................. 152

Table 33.—Boeing Model Reference Dimensions and Size..................................................................... 160

Table 34.—N+2 Environmental and Performance Goals, and Validated Phase 2 Result (Table 1 

Repeat)................................................................................................................................................ 211
NASA/CR—2015-218719 1

Advanced Concept Studies for Supersonic Commercial Transports 

Entering Service in the 2018-2020 Period Phase 2

John Morgenstern and Michael Buonanno

Lockheed Martin

Palmdale, California 93599

Jixian Yao, Mugam Murugappan, Umesh Paliath, Lawrence Cheung, Ivan Malcevic, 

Kishore Ramakrishnan, Nikolai Pastouchenko, and Trevor Wood

GE Global Research

Niskayuna, New York 12309

Steve Martens, Phil Viars, Trevor Tersmette, Jason Lee, Ron Simmons, and David Plybon 

GE Aviation

Cincinnati, Ohio 45215

Juan Alonso, Francisco Palacios, Trent Lukaczyk, and Gerald Carrier

Stanford University

Stanford, California 94305

1.0 Introduction

1.1 Subject/Relevancy

The National Aeronautics and Space Administration’s (NASA) Supersonics Project is aligned with 

the Aeronautics Research Mission Directorate (ARMD) principles of maintaining intellectual stewardship 

of aeronautical core competencies for the nation in the supersonic flight regime and of focusing research 

in areas that are appropriate to NASA’s unique capabilities. The Supersonics Project is a broad-based 

effort designed to develop knowledge, capabilities and technologies that support vehicles that fly in the 

supersonic speed regime. A major focus of this effort is eliminating the efficiency, environmental and 

performance barriers to practical supersonic cruise vehicles. 

The Supersonic project invests a significant portion of its allocated budget in NASA Research 

Announcement (NRA) solicitations—including the N+2 Supersonics Validation program. The objective 

of the NRA investment is to stimulate innovation in integrated transportation concepts and to create an 

environment for collaboration among NASA researchers, commercial organizations, and academia. This 

investment is primarily focused at foundational level research, but has also been used to aid the project in 

developing integrated systems concepts. These concepts can be used to both conduct trade studies to 

determine the requirements for future supersonic aircraft and to assess the effectiveness of the project’s 

work content at overcoming the barriers to supersonic flight. LM’s N+2 Supersonic Validations contract 

fits into NASA’s N+2 Supersonic Program as shown in Figure 1.

The N+2 Supersonic Validations program (supersonic vehicle anticipated to enter service in 2018-

2020) directly aligns with these NRA and Supersonic Project goals and focuses on the validation of 

methodologies to successfully design and develop a shaped boom supersonic air vehicle. The N+2 effort 

is divided into elements that focus on foundational, discipline, multidiscipline and integrated system level 

challenges. To achieve these multi-faceted goals, the N+2 effort requires multi-discipline analysis and 

optimization (MDAO) efforts between airframe and propulsion industries to address system integration. 
NASA/CR—2015-218719 2

Figure 1.—LM N+2 Organization

Integrated vehicle design is performed to simultaneously achieve supersonic cruise efficiency, shaped 

sonic boom signatures, as well as low take-off noise. Other, integrated design concerns include:

• Sonic Boom Reduction

• Cruise Efficiency

• Aero-Propulsive-Servo-Elasticity

• Airport Noise

• Light Weight Structure for Airframe/Propulsion Systems

• High Altitude Emissions

A key element of the Supersonics Project (and N+2 program) is systems level validation testing, 

which leverages the substantial investments made by the project (both internally and through the NRA). 

The system level validation will provide proven capabilities that will be able to address the efficiency, 

environmental and performance challenges of future supersonic aircraft. The LM N+2 program conducts 

shaped boom testing and nozzle testing to achieve these goals.

The N+2 Supersonic program generates knowledge that can benefit the Nation. Therefore, this report 

(and related papers at technical conferences) seeks to share the program’s findings, disseminate test 

results and validate component and system analysis tools that will support the design of an 

environmentally and economically viable product.

Meeting or surpassing these goals stimulates innovation and advances the pursuit of revolutionary 

conceptual designs. System-level multi-disciplinary analysis and optimization (MDAO) and out of the 

box thinking allows for tool and technology validation. This fosters an environment of innovation and 

generates excitement for future supersonic travel.

Overall, the N+2 effort is driven by the need for design and testing validations to meet the efficiency, 

environmental, and performance barriers to practical supersonic flight. Results from these efforts advance 

tools and technologies necessary for integrated supersonic design. 

 

NASA

N+2 Supersonic 

Program

Lockheed Martin N+2 

Supersonic Validations 

Contract

Rolls Royce N+2 

Supersonic Sub-Contract

GE N+2 Supersonic

Sub-Contract

NRA Technology

Contracts

NASA In-House

Development

NASA Test 

Facilities

Stanford N+2 Supersonic

Sub-Contract
NASA/CR—2015-218719 3

2.0 N+2 Configuration Refinement

The LM N+2 low-boom vehicle configuration evolved significantly during the Phase 2 effort. 

Figure 2 provides an overview of OMLs evaluated, while brief description of each configuration is given 

in Table 2.

Low-boom signature improvements and configuration refinements took place concurrently, resulting 

in a highly realistic design with a full-carpet, 4-PLdB reduction over Phase 1 boom loudness, Figure 3. 

Aerodynamic and mass properties database were developed for the baseline design and used in sizing and 

mission performance analyses. A sized, 335,000 lb-class vehicle with a 5,500 nmi range was selected as 

the going-forward design.

Figure 2.—N+2 Phase 2 Configuration Evolution (left to right): 1021, 1040, 1043, 1044

TABLE 2.—PHASE 2 CONFIGURATION PROGRESSION

Configuration Description Note

1021 Phase 1 baseline LM3 OML

1040 Configuration 1021 DOE refinement

1043 Aft-deck

1044 Propulsion effects/Phase 2 baseline

1044-2 Stanford optimization LM4 and LTWT OML

1044-3b Post-1044 mod rollup

1044-X Sized going-forward design

Figure 3.—N+2 Phase 2 Baseline Vehicle 3-View

 

 

83’10”

30’6”

244’
NASA/CR—2015-218719 4

2.1 Cruise Mach Trade

A zero-order cruise Mach trade was performed at the start of Phase 2 effort to evaluate the practicality 

of M1.6-plus cruise. Breguet range parameter, defined as (M / SFC * L/D), was used to identify the cruise 

Mach that delivers best range performance, while predicted boom signatures were examined to 

characterize Mach-driven effects.

Range parameter calculation inputs extracted from Configuration 1021 aerodynamic database and 

GE62_FLF4 engine deck are tabulated in Table 3. The analysis assumed constant L/D via climbing 

cruise, as well as altitude-independent specific fuel consumption. As shown, range parameter continued to 

increase until around M1.7 before dropping off.

Analysis of Configuration 1021-based boom signatures showed an expected steepening of front shock 

slope due to increased shock strength at higher Mach, Figure 4. Upon closer examination it was found 

that the front shock slope also steepens with increasing off-track angle even though the magnitude of 

pressure rise is lower. This is attributed to the coalescing of shock, i.e., aging, over the longer distance 

traveled before the shock reaches ground. Note that as the roll angle increases, the swath of area affected 

by the boom also increases. As such, off-tack signature shaping could be as critical, if not more so, than 

under-track signature shaping, and need to be addressed early on as oppose to trying to recover the 

signature after the fact.

Based on the above findings, as well as environmental (jet emission) and health (high-altitude 

radiation exposure) considerations, M1.7 was selected as the design cruise Mach for the Phase 2 effort, 

with M1.8 for over-water, boom-unconstrained operation pending engine cycle update.

2.2 Sonic Boom Signature Improvement

Phase 1 final under-track signature at the SOC of the 4,000-nmi low-boom mission was 85 PLdB and 

less than 87 PLdB across the full carpet, Figure 5. The predicted boom loudness corresponds to a 0.26 psf 

N-wave based on 1 ms/psf rise time, slightly higher than the target 0.22 psf N-wave. As shown in Figure 

6, the aft shock is a well-shaped ramp consists of multiple small spikes out to 20° off-track. At 30° offtrack, the aft shock split into two distinct sharp spikes due to shock coalescence, resulting in increased 

loudness. This trend continued through approximately 40° off-track, at which point the trend reversed. 

The front ramp remains well-shaped across the full carpet.

TABLE 3.—BREGUET RANGE

PARAMETER VERSUS MACH

Mach Range parameter

1.4 15.9

1.6 16.9

1.7 17.2

1.8 17.0
NASA/CR—2015-218719 5

Figure 4.—Mach Effect on Ground Signatures

 

-1.0

-0.5

0.0

0.5

1.0

0 50 100 150 200 250

Over Pressure (dp/p)

Time (ms)

PHI = 0.0

M1.6/56k ft

M1.7/58k ft

M1.8/59.9k ft

-1.0

-0.5

0.0

0.5

1.0

0 50 100 150 200 250

Over Pressure (dp/p)

Time (ms)

PHI = 20.0

M1.6/56k ft

M1.7/58k ft

M1.8/59.9k ft

-1.0

-0.5

0.0

0.5

1.0

0 50 100 150 200 250

Over Pressure (dp/p)

Time (ms)

PHI = 40.0

M1.6/56k ft

M1.7/58k ft

M1.8/59.9k ft
NASA/CR—2015-218719 6

Figure 5.—Configuration 1021 Full-Carpet Loudness

Figure 6.—Configuration 1021 Signature, 5.6 R/(b/2), SOC 4,000 nmi Mission

Using Configuration 1021 as a starting point, a 23-design variable DOE was performed to screen 

fuselage, wing and tail geometric parameters that have largest impact on boom signature, Figure 7.

Response surface equations were generated for eight selected design variables and entered into a generic 

optimization routine to attempt to simultaneously improve configuration realism and boom signature. The 

resulting configuration, designated Configuration 1040, has better aerodynamic performance and 

packaging efficiency but no significant improvement in signature. It was concluded that the incremental 

adjustments to the POD design were not sufficient to eliminate the off-track lift gap between the wing and 

centerline v-tails, and that alternate approach is needed.

To address the off-track lift gap issue, the aft-deck concept explored as part of Phase 1 configuration 

trade studies was revisited. The original concept was to extend the inboard wing TE aft to increase vehicle 

cross-sectional area at off-track angles. The concept was taken one step further in Phase 2 by splitting up 

the centerline v-tails and moving it outboard. The outboard wing pylons were enlarged and extended aft 

to pick up the newly-relocated canted vertical tails. Comparison of near-field pressure gradient at 40° offtrack angle shows the LE shock coming off the semi-span v-tails weakened the strong expansion fan 

coming off the wing TE, Figure 8. Configuration 1043 full-carpet signature shows that the aft-deck/v-tails 

relocation successfully revert the increasing trend at high off-track angles, Figure 9. Separately, front 

shock signature is further reduced by lengthening of the nose spike to shallow out the front shock ramp. 

Overall full-carpet loudness is now less than 85 PLdB.

 

75

80

85

90

-60 -40 -20 0 20 40 60 PLdB

Roll Angle (degree)

0.22 psf N-wave

0.26 psf N-wave

SOC, 4k nm

SOC, 2k nm

EOC

dp/p -0.5

0.0

0.5

1.0

50 100 150 200 250 300 350

Time (ms)

 

-20°

-60°

-40°

0deg

10deg

20deg

30deg
NASA/CR—2015-218719 7

Figure 7.—DOE Design Variable Sweep

Figure 8.—Phi = 40° Pressure Gradients, Configuration 1040 (top) and 1043 (bottom)

 

 

 
NASA/CR—2015-218719 8

Figure 9.—SOC Full-Carpet Loudness Comparison, 4,000 nmi Mission

Figure 10.—CFD Result of Representative Inlet and C-D Nozzle

Figure 11.—Propulsion Effects on Signature, 5.6 R/(b/2)

Up to this point, flow-through nacelles were used in the signature analysis. To improve boom 

signature fidelity, notional M1.7 inlet centerbody and convergent-divergent nozzle were incorporated 

under Configuration 1044, Figure 10. As illustrated in Figure 11, inlet spillage and exhaust plume altered 

the local flow field characteristics, resulting in an approximately 2-PLdB increase in boom loudness. The 

signature hit was recovered through adjustment of under-wing reflex geometry and equivalent area offset.

 

 

70

75

80

85

90

-60 -40 -20 0 20 40 60 PLdB

Roll Angle (degree)

1021 

1043

 

 

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

0 100 200 300 400

Time (ms)

DP/P

Flow-thru

Prop Effect
NASA/CR—2015-218719 9

Figure 12.—Configuration 1044-2 Full-Carpet Loudness

The full-carpet signature of the final configuration that came out of the signature improvement 

effort is shown in Figure 12. Configuration 1044’s average loudness at the start of cruise (SOC) of the 

4,000 nmi low-boom mission is 82 PLdB, 80 PLdB at mid-cruise and 78 PLdB at EOC. The results equal 

to approximately a 4-PLdB reduction over the Phase 1 final signature.

2.3 Configuration Maturation

Vehicle configuration realism was significantly enhanced under the Phase 2 effort. Design 

deficiencies identified at the end of Phase 1 were addressed while additional refinements were 

incorporated. Detailed structure layout was created in support of FEM analysis, and low-speed wind 

tunnel test conducted to assess stability and control characteristics.

2.3.1 OML Refinement

Several design aspects of Configuration 1021 were deemed nonideal and required further refinement, 

Figure 13. The fuselage cross-section was shaped primarily to meet equivalent area target, resulting in a 

varying cross-section that encroached upon cabin space and increased manufacturing complexities. In 

addition, available fuselage volume was insufficient for subsystems packaging. The droop nose severely 

reduced forward cabin height and resulted in breaks in cabin attitude. Unrefined landing gear layout 

resulted in excessive gear length that constrained retraction options, as well as undesirable on-ground 

cabin inclination. Aerodynamically, the dihedral high-wing resulted in excess roll in sideslip.

Incremental changes were introduced under Configuration 1040 to address Phase 1 design 

deficiencies, Figure 14. Fuselage volume was increased to minimize cabin space encroachment and to 

provide additional packaging volume. Excessive nose droop was reduced to improve cabin layout. Upper 

surface wing-body blend was added to increase fuel volume and to improve structural load path. The 

outboard engine pylons were extended above upper wing surface to increase structural stiffness while 

doubling as anti-shock bodies to improve off-track area distribution. Finally, wing dihedral was reverted 

to improve roll stability in sideslip.

Additional refinements were incorporated along with the outboard relocation of v-tails under 

Configuration 1043, Figure 15. To support the v-tails at their new semi-span location, outboard engine 

pylons were further enlarged and extended aft, while the inboard portion of the wing was extended aft to 

bridge the gap between the pylon and fuselage for additional structural support. Nose droop angle was 

further reduced, which required lengthening of nose to compensate off-track front shock steepening due 

to aging. The centerline nacelle, no longer supporting the v-tails, was shortened to eliminate excess length 

and reduce drag.
NASA/CR—2015-218719 10

Figure 13.—Configuration 1021 Issues To-Fix

Figure 14.—Configuration 1021 Versus 1040

Figure 15.—Configuration 1040 Versus 1043

 

Insufficient fuselage volume

High-wing roll instability

Undesirable on-ground cabin inclination

Non-ideal landing gear layout

 

Wing anhedral

Reduced nose droop

Increased fuselage volume

Upper surface wing root blend

Enlarged pylon

Config 1021

Config 1040

 

Semi-span v-tails

Reduced nose droop/lengthened nose spike

Shortened centerline nacelle Aft-deck

Enlarged pylon

Config 1040

Config 1043
NASA/CR—2015-218719 11

Internal review by LM Tech Fellows from design and structure disciplines identified high-alpha 

pitch-up as a potential complication that warrants further investigation. Low-speed CFD analysis using 

clean configuration confirmed worsening of pitch-break due to wing-root vortices impinging on the semispan v-tails, Figure 16. Given the limited usefulness of clean configuration aerodynamics at high alpha 

and impracticality of running hundreds of CFD cases to explore stability and control concepts, LM 

proposed and received NASA authorization to plan, build, and test LTWT wind tunnel model to assess 

low-speed aerodynamic characteristics and control surface effectiveness (see Section 2.6 LTWT Test).

Configuration 1044 marked the end of boom signature improvement as well as vehicle OML freeze in 

support of discipline analyses and database generation, Figure 17. Lower surface wing reflex, designed to 

attenuate shock coming off of outboard nacelles, was modified to match the altered flow pattern from the 

newly introduced inlet centerbody. Aft-deck trailing edge was un-swept to better shield nozzle exhaust 

from impacting the aft shock signature. Finally, wingtip trailing edge was cropped to reduce tip drag.

Figure 16.—Configuration 1044 High-Alpha Pitch-Up Due to Wing Apex Vortex Formation

 

 

α=18 o

α=14 o

α=10 o

α=6 o

SL, M0.3

-5

0

5

10

15

20

0.20 0.10 0.00 -0.10Alpha (deg)

Cm

1044

1021
NASA/CR—2015-218719 12

Figure 17.—Configuration 1043 Versus 1044

Figure 18.—Configuration 1044 Versus 1044-2

Two additional rounds of modifications were made post-Configuration 1044 to capture new 

information as they became available. No further update to the 1044-based database were made either 

because the magnitude of changes was sufficiently small and unlikely to affect the results, or the purpose 

of modifications was to simply document lessons-learned for future work. The first post-Configuration 

1044 update, designated Configuration1044-2, implemented one of Stanford-recommended OML 

refinements based on adjoint-based optimization results. CFD analysis predicted the aft-deck upper 

surface modification reduce drag by approximately one-percent with negligible effect to the boom 

signature, Figure 18. Additional discussion on Stanford adjoint-based optimization can be found in 

Appendix C. The modified OML was used in the design of both LTWT and LM4 wind tunnel models.

Configuration 1044-3b served as a repository of design changes based on discipline analysis results, 

Figure 19. Captured OML modifications include: aft-deck-fuselage fillet to remove load path 

discontinuity; enlarged outboard engine pylons to increase structure stiffness; scaled inlet to match engine 

mass flow requirement; simplified, nonexport control GE plug nozzle and nacelle closeout; straightened 

aft-deck TE to improve body flap integration; and raised v-tails to reduce interaction with wing root 

vortices at high-alpha. Configuration 1044-3b OML was used to perform propulsion system sensitivity 

trades (see Section 2.5.3).

 

Inlet spike/C-D nozzle

Clipped wingtip

Unswept aft deck TE

Modified wing reflex

Config 1043

Config 1044

 

Config 1044

Config 1044-2

Tau

Cp

3 4

-0.010

-0.005

0.000

0.005

0.010

1044

1044-2
NASA/CR—2015-218719 13

Figure 19.—Configuration 1044 Versus 1044-3b

Figure 20.—Main Landing Gear Retraction Concept

2.3.2 Landing Gear Integration

Configuration 1044 posted some inherent landing gear integration issues. The high-wing arrangement 

led to an elevated CG and favored a wide wheel track to produce an acceptable turnover angle. However, 

the location of the outboard engines constrained the span-wise location of the wing-mounted main gear. 

Main gear ground contact point, and hence the length of the main strut, is defined by tip-back and tailstrike angles. The extended length prevented the main gear from retracting directly inboard into the 

fuselage, and instead required a forward and inboard retracting scheme with a highly-inclined trunnion 

axis, Figure 20. The nose gear is located as far forward as static load distribution permits to minimize 

forward fuselage cantilever, as well as maximize the base of the turnover angle. Static nose gear load 

varies from 7.5-percent at TOGW to 9.6-percent at ZFW. The 25.5x8.0-14 nose and H44.5x16.5-21 main 

tires were selected assuming a seven-percent safety factor and zero-growth due to the stringent vehicle 

weight limit imposed by boom signature requirements. 

The location of the nose and main gear relative to the TE-mounted outboard engines is a potential 

FOD (foreign object damage) concern. As illustrated in Figure 21, debris and water spray kicked up by 

the nose wheels have a direct path to the engine inlet. Although the same cannot be said for the main 

wheels, objects dislodged from the main wheelwell either on the ground or in flight could be injested by 

the engine depending on the strength of inlet suction and object’s trajectory. Additional analysis is needed 

to characterize the flow field around the inlet to determine the probability of such occurrence.

 

Config 1044

Config 1044-3b

Enlarged outbd pylon

Raised v-tails 

Representative plug nozzle

Straightened aft-deck TE

Lower surface wing-fuselage fillet

Scaled inlet

Shortened, representative nacelle closeout

 
NASA/CR—2015-218719 14

Figure 21.—Engine FOD Considerations: Inlet Suction (top) and NLG Water Spray

Figure 22.—Configuration 1044 Ground Operation Clearance

Configuration 1044 ground clearances are shown in Figure 22. The fuselage is nearly level with a 

minimum ground clearance of 4 ft, 7 in., while the bottom of the outboard nacelles is over 8 ft off the 

ground. Tail-strike angle is 14° with ample clearance for control surface deflection. Turnover angle, 

ranging from 58° at ZFW to 60° at TOGW, is comparable to typical high-wing transports but considered 

borderline for ground-based aircraft. Minimum turning radius is 52 ft, 8 in. assuming a maximum nose 

wheel steering angle of ± 60°, allowing the aircraft to make a 180° turn on a 150-ft wide runway.

2.3.3 Vehicle Packaging

Configuration 1044 inboard profiles are shown in Figure 23. The simplistic bones, developed based 

on common practices and prior supersonic aircraft design experiences, were used to define major load 

paths and usable volume. The layout also served as starting reference for the detailed structure layout in 

support of FEM analysis (see Section 3.4). 26-in. fuselage frame spacing was selected assuming 

composite sandwich skin with improved buckling strength over conventional aluminum structure. 

Longerons, keels and fuselage tube together provided longitudinal bending stiffness. Wing bending loads 

were carried inboard via spars orientated parallel to wing TE and into the wing box located aft of the main 

wheel well, while shear ties were used to transfer loads into fuselage frames in the absence of wing 

 

R12’ bubble R15’ bubble R18’ bubble

10° vertical, 30° horizontal fan

 

14’

91’3” 14°

60°

TOGW CG shown

26°

20°

4’7” 8’3”
NASA/CR—2015-218719 15

carry-throughs in the main cabin. The slender, boom shaping nose forward of cockpit is treated as a lightweight aerodynamic fairing.

The location of the full-depth main wheel well effectively established the aft-most limit of the main 

cabin, resulting in a forward CG bias with the passenger aboard. The main wheel well also prevented the 

cargo hold, which is required to be pressurized to accommodate live cargo, being located directly aft of 

the cabin. As such, two separate pressure vessels are required, resulting to design inefficiencies and 

weight penalties. The fuselage under-floor space is of insufficient height to carry cargo and is used 

primarily for ducting, plumbing and wire routing.

Wing, aft-deck and center wing-box fuel tanks were laid out to counteract the forward payload CG 

and to minimize the need of in-flight fuel pumping and/or ballast to maintain desired vehicle attitude. 

Total fuel capacity is 176,000 lb assuming an 80-percent packaging factor. Aircraft subsystems, as 

functionality permits, were clustered toward the rear of the vehicle aft of the cargo hold to take advantage 

of the available volume, as well as shifting aircraft empty weight CG further aft.

2.3.4 Cabin Layout

Phase 2 baseline cabin layout assumed 80 passengers in two-class seating arrangement, Figure 24. 

Note that the fuselage still has a slight bulge around the mid-section due to equivalent area matching. The 

goal is to completely eliminate the cross-section variation to simplify cabin layout and to reduce 

manufacturing complexity. Two Type B exit doors are located in the front and two Type C service doors 

in the rear of cabin. Overhead emergency exits are envisioned for water-landing evacuation, where the 

cabin is likely to be submerged due to the high-wing arrangement. The 10- by18-in. windows are spaced 

26 in. apart, providing each seat-row with at least one window to reduce claustrophobia. 

Figure 23.—Configuration 1044 Inboards

 
NASA/CR—2015-218719 16

Figure 24.—Two-Class, 80 Passenger Cabin Layout

Figure 25.—Nominal Passenger Cabin Cross-Section

The First-class section featured eight 50-in. pitch seats in two-abreast arrangement and is serviced by 

the forward galley and lavatory located adjacent to the forward exits. The Economy-class section featured 

72 32-in.-plus pitch seats in four-abreast arrangement and is serviced by a pair of lavatories and the aft 

galley. Nominal cabin cross-sections are shown in Figure 25. Seat-width varied from 22 in. in the Firstclass to 17 in. in Economy-class. Aisle height is 82 in. throughout the cabin, with a slight reduction 

forward of the forward exits due to nose droop. Minimum aisle width is 24 in. in the First-class and 20 in. 

in the Economy-class. Overhead bins are sized to accommodate standard carry-on luggage, while 

additional closet space are located forward of the First-class section.

2.3.5 Mass Properties

Parametric weight estimation developed under Phase 1 effort was reviewed for accuracy before being 

adopted for Phase 2 work: weight double-bookings were eliminated; vehicle model was updated to reflect 

the change in configuration; assumptions and known component weights were updated. Configuration 

1044 geometry was then entered into the spreadsheet to generate the detailed weight buildup as shown in 

Table 4. Note that a 6-percent empty weight margin was added for conservatism, and that the gross 

weight was limited to 320,000 lb via fuel off-load to match the boom signature-specified vehicle weight. 

 

First

(x8)

Economy+

(x20)

Economy

(x28)

Type B Exit Door (34”x72”) 26” Window Spacing (10”x18”) Type C Service Door (30”x52”)

Economy

(x24)

Galley Lavatory Closet 90-percentile male shown

A B B A 82”

First Economy/Economy+

IML shown

 

Pitch (in) Width (in) A (in) B (in)

First Class 50 22 27.5 24+

Economy+ 36 18.5 43 20

Economy 32 17 40 20
NASA/CR—2015-218719 17

The effect of the forward payload CG is evident given the 56-in./0.06MAC CG shift between OEW 

and ZFW. Using fuel to counteract the payload CG shift proved effect, as the TOGW CG was returned to 

FS2,045/0.51MAC.

TABLE 4.—CONFIGURATION 1044 WEIGHT STATEMENT

AN Group Weight

(lb)

X CG (FS)

Gear Up

Wing 31,503 2,070.9

Aft Deck 6,934 2,567.9

V-Tail 4,013 2,713.9

Body 15,086 1,554.2

Nose Landing Gear 1,371 931.2

Main Landing Gear 11,096 2,004.2

Nacelle 1 4,260 2,339.1

Nacelle 2 2,363 2,570.9

Inlet 1 1,742 2,285.9

Inlet 2 2,581 2,307.1

Structure 80,949 2,061.5

Engine & Nozzle 1 14,533 2,407.1

Engine & Nozzle 2 7,266 2,533.3

Start & Controls 309 1,957.2

Fuel System 2,506 2,054.5

Propulsion 24,614 2,402.8

Flight Controls 3,525 2,331.4

Aux Power 1,008 2,478.1

Instruments 277 1,353.6

Hydraulics 2,354 2,178.9

Electrical 3,337 2,356.6

Avionics 986 774.8

Furnishings 9,097 1,482.3

ECS 2,688 1,622.4

Load & Handling 54 1,747.2

Systems 23,326 1,834.4

Margin 7,733 2,085.6

EW 136,622 2,085.6

Std Operating Items 2,930 1,109.6

Unusable Fuel and Oil 2,056 2,090.8

OperatingEquipment 4,986 1,514.2

OEW 141,608 2,065.4

Passengers 13,600 1,428.1

Baggage 2,400 1,983.9

Payload 16,000 1,511.5

ZFW 157,608 2,009.2

Fuel 162,560 2,079.9

TOGW 320,168 2,045.1
NASA/CR—2015-218719 18

Calculated empty weight and empty weight fractions were compared against developmental and 

production supersonic cruise aircraft, Figure 26. As illustrated, the predicted empty weight show high 

correlation with historical trend, while empty weight fraction fell into the low end of the range primary 

due to the application of advanced light-weight composite materials.

First-pass fuel-burn sequence was used to create the in-flight CG envelope shown in Figure 27. The 

aircraft is shown to be neutral to stable in-flight throughout the entire mission. Although the stated goal is 

to achieve supersonic cruise attitude without aerodynamic trim, the vehicle as currently configured could 

not meet the objective with fuel management alone. However, potential aeroelastic consideration-driven 

structure thickening could increase aft-deck fuel volume and shift the CG envelope to the right.

Figure 26.—Empty Weight (left) and Empty Weight Fraction (right) Comparison

Figure 27.—Configuration 1044 In-Flight CG Envelope

 

Concorde

Mirage IV

Tu-22

SR-71A

Tu-144

1044 1021

T-4

XB-70A

10,000

100,000

10,000 100,000 1,000,000

WE (lb)

MTOW (lb)

Concorde

Mirage IV

Tu-22

SR-71A

Tu-144

1044

1021

T-4

XB-70A

0.30

0.35

0.40

0.45

0.50

10,000 100,000 1,000,000

WE Fraction

MTOW (lb)

 
NASA/CR—2015-218719 19

2.3.6 Aerodynamics

Navier-Stokes CFD-based aerodynamic database was developed with Configuration 1044 OML and 

used in performance and sizing analysis. Forces and moments were calculated with realizable k-e 

turbulent model at specific Mach/altitude combinations. Drag polar and L/D versus CL plots at 40,000 ft 

are shown in Figure 28, Figure 29, respectively. Configuration 1021 and 1044 maximum L/D versus

Mach comparison is shown in Figure 30. Overall, Configuration 1044 subsonic performance is slightly 

worse due to added aft-deck wetted area, while supersonic performance is similar. Further drag reduction 

is possible using Stanford adjoint-based optimization.

Figure 28.—Configuration 1044 Drag Polars

Figure 29.—Configuration 1044 L/D Versus CL

 

0.0

0.1

0.2

0.3

0.4

0.5

0.00 0.02 0.04 0.06 0.08

CL

CD

M0.3

M0.9

M1.1

M1.7

M1.8

 

0

2

4

6

8

10

12

0 0.1 0.2 0.3 0.4 0.5

L/D

CL

M0.3

M0.9

M1.1

M1.7

M1.8
NASA/CR—2015-218719 20

Figure 30.—L/D _Max Versus Mach Comparison

2.4 Vehicle Sizing and Mission Performance

Vehicle sizing and performance analysis were conducted using ASSET mission code with fixedgeometry/weight option, i.e., the vehicle was not optimized and the range is a fallout of available fuel. 

The default maximum range mission profile is shown in Figure 31. The mission started with a 5-min

ground warm-up/takeoff, followed by a 250 KEAS climb to 10,000 ft, where the vehicle performed a 

level acceleration to M0.9 before climbing again at 360 KEAS. Upon reaching 36,000 ft, the vehicle 

performed a level transonic acceleration to M1.28 before initiating the final climb to best cruise altitude at 

400 KEAS. Cruise climb with a ceiling of 60,000 ft was employed to achieve best L/D and range. At the 

end of the cruise, the vehicle decelerate from cruise Mach to M1.4 at altitude before initiating a 

250 KEAS descent to 10,000 ft, where it loitered for 5 min at best endurance before landing with an

8-percent reserve. Note that no distance and time credits were given to both loiter and landing.

For the low-boom mission, in which case the TOGW was limited to 283,000 lb via fuel offload, the 

transonic acceleration altitude was raised from 36,000 to 40,000 ft to reduce boom loudness while 

everything else remained unchanged.

Design space sweep with TOGW and ESF variations was performed to identify the optimum 

combination that delivers maximum range. Baseline GE62_FLF4 engine thrust and TSFC were scaled 

using ESF. At any given TOGW, payload weight remains fixed while engine weight, and hence OEW, is 

a function of ESF. The difference in OEW then became the fuel weight delta that is applied to the 

baseline fuel weight.

For the design space sweep, TOGWs of 300,000, 320,000 and 340,000 lb and ESFs of 1.0, 1.1, 1.2, 

and 1.3 were selected. Results were plotted against fuel volume and balanced field length constraints and 

shown in Figure 32. For the un-sized Configuration 1044, an additional 150 nmi range can be gained 

either by fixing the TOGW at 320,000 lb while up-sizing the engine by 15 percent, or by increasing gross 

weight to 330,000 lb, i.e., maximum fuel capacity (red limit on plot), while holding the ESF to 1.0. 

Further improvement in range can be achieved by increasing both TOGW and ESF at the same time. The 

shape of the plot indicated maximum range occurs somewhere between ESF of 1.1 and 1.2 across TOGW 

range considered. Using maximum fuel weight as constraint, a design point corresponds to a TOGW of 

335,000 lb and ESF of 1.15 were selected and designated as Configuration 1044-X. Estimated balanced 

field length is in the low 10,000s, matching subsonic vehicles like 747-400 and near Concorde’s mid9,000s

Figure 33 compares Configuration 1044-X as-flown maximum-range and low-boom mission profiles 

and performance, while detailed mission statements are given in Table 5 and Table 6, respectively. While 

the data show some subtle differences such as initial cruise altitude and climb cruise profile, the vehicle 

 

8

10

12

14

0.0 0.5 1.0 1.5 2.0

L/D_max

Mach

1044

1021
NASA/CR—2015-218719 21

was able to achieve an average cruise L/D of 8.7 in both missions. For the maximum-range case, the 

vehicle is capable of reaching a destination 5,500 nmi away in just over 6 hr. As of the low-boom case, 

airport-to-airport distance is 4,150 nmi with a 4.8-hr trip time.

Figure 31.—Sizing Mission Profile

Figure 32.—N+2 Phase 2 Design Space

TABLE 5.—DETAILED MISSION STATEMENT AND MAXIMUM RANGE

Segment Description Initial alt

(ft)

Initial Mach Initial 

weight

(lb)

Seg fuel

(lb)

Seg distance

(nmi)

Mission 

time

(min)

Average

L/D

Average

SFC

1 WUTO 0 0.00 335,000 1,245 0 5.0 ----- 0.71

2 Climb 0 0.38 333,755 3,391 10 7.2 7.61 0.89

3 Accel 10,000 0.45 330,364 992 4 7.9 9.68 0.94

4 Climb 10,000 0.66 329,372 7,719 68 16.0 10.20 1.03

5 Accel 36,000 0.90 321,653 4,992 65 22.4 9.35 1.06

6 Climb 36,000 1.28 316,661 6,773 97 29.2 8.73 1.14

7 Cruise 48,000 1.70 309,888 133,216 5,088 342.3 8.73 0.94

8 Decel 60,000 1.70 176,672 93 22 343.8 9.21 2.95

9 Descent 60,000 1.42 176,579 1,972 146 363.9 10.47 2.32

10 Loiter 10,000 0.50 174,608 1,341 0 368.9 10.49 0.97

11 Reserve --------- ---- ---------- 14,0808 ------ ------ ------ -----

Departure

1. Warm-up/Takeoff

2. Climb to 10,000 ft

3. Accel to Climb

4. Climb to 36,000 ft

5. Transonic Accel

6. Climb to BCA

Arrival

7. M1.7 Climb Cruise

8. Decel to M1.4

9. Descent to 10,000 ft

10. Loiter for 5 min

11. Land w/ 8% Reserve

1, 2

3 4

5 6

7

8

9

10

11

 

ESF

Going-fwd Design Point

(335k lb TOGW, 1.15 ESF)

1044

BFL
NASA/CR—2015-218719 22

TABLE 6.—DETAILED MISSION STATEMENT, SHAPED BOOM

Segment Description Initial alt

(ft)

Initial Mach Initial 

weight

(lb)

Seg fuel

(lb)

Seg distance

(nmi)

Mission 

time

(min)

Average

L/D

Average

SFC

1 WUTO 0 0.00 283,000 1,245 0 5.0 ----- 0.71

2 Climb 0 0.38 281,755 2,377 7 6.5 8.84 0.89

3 Accel 10,000 0.45 279,377 742 3 7.1 10.24 0.94

4 Climb 10,000 0.66 278,636 7,549 77 16.2 9.91 1.04

5 Accel 40,000 0.90 271,087 5,593 85 24.4 9.25 1.09

6 Climb 40,000 1.41 265,494 3,806 59 28.4 8.22 1.16

7 Cruise 51,000 1.70 261,688 89,386 3,753 259.3 8.74 0.95

8 Decel 60,000 1.70 172,210 92 22 260.7 9.71 2.95

9 Descent 60,000 1.42 172,210 1,976 146 280.9 10.43 2.32

10 Loiter 10,000 0.50 170,233 1,329 0 285.9 10.40 0.98

11 Reserve --------- ---- ---------- 9,920 ------ ------ ------ -----

Figure 33.—Mission Performance Comparison: Flight Profile (top) and L/D (bottom)

2.4.1 Vehicle Airport Operations and Configuration

The 1044-X vehicle takes advantage of unique supersonic configuration characteristics during takeoff 

and landing operations. An N+2 vehicle and propulsion system is sized and designed to the certification 

noise conditions. Generally sideline noise is dominant. Our propulsion solution balances the sideline 

noise and thrust requirement with the cruise thrust requirement to develop a solution that satisfies both as 

efficiently and with a light of a propulsion system as possible. It has been LM’s objective to reduce the 

 
NASA/CR—2015-218719 23

stringency of the takeoff thrust and throttle setting through integrated exploitation of propulsion, airframe, 

acoustics, certification rules and safety considerations. The operational procedures result in relatively high 

speeds with greater excess power, for lower throttle settings and thereby lower noise (particularly for 

critical noise conditions). Further as a noise sized vehicle, N+2 designs must incorporate comprehensive 

noise reduction technologies throughout: turbomachinery, combustors, inlet (acoustic liners, choking), 

nozzle, airframe (gap seals), propulsion-airframe interaction (acoustic liners), flaps (unslotted), landing 

gear (shrouds) and airframe excrescences (streamlined). Table 7 tabulates characteristics for airframe 

noise calculations.

TABLE 7.—GENERAL CHARACTERISTICS FOR AIRFRAME NOISE CALCULATION

Trailing Edge Noise Flap/Elevon Side Edge

Maximum takeoff weight, lb 322,000 Number of flap side edges 6

Maximum landing weight, lb 220,000 (For each flap side edge)

Wing area, ft2 3,600 Flap chord, ft 11.67 8.33 8.33 6.33 6.33 4.42

Wing span, ft 83.8 Flap maximum thickness at side edge, ft 1.125 0.7 0.7 0.5 0.5 0.32

Horizontal tail area, ft2 545 (total) Flap sweep angle, deg 39 39 39 39 39 39

Horizontal tail span, ft 14.7 (1 side) Flap span, ft 10.75 10.75 6.6 6.6 6.6 6.6

Vertical tail area, ft2 N/A Flap deflection angle, deg ‐ Approach 25 25 20 20 0 0

Vertical tail span, ft N/A Flap deflection angle, deg ‐ Sideline 10 10 10 10 0 0

Flap deflection angle, deg ‐ Cutback 10 10 10 10 0 0

Flap lift coefficient ‐ Approach 1.5 1.5 1.25 1.25 0.25 0.25

Main Landing Gear Flap lift coefficient ‐ Sideline 0.75 0.75 0.75 0.75 0.25 0.25

Number of main gear 2 Flap lift coefficient ‐ Cutback 0.75 0.75 0.75 0.75 0.25 0.25

Number of tires per main gear strut 4 Total lift coefficient ‐ Approach 0.767

Main gear alignment angle, deg 0 Total lift coefficient ‐ Sideline 0.548

Main gear strut length, ft 11 Total lift coefficient ‐ Cutback 0.515

Main gear strut diameter, ft 1.33

Main gear tire diameter, ft 3.7

Main gear tread width, ft 1.25 Slat

Local flow velocity into gear (ft/sec) 265 Number of slats per wing 4

Nose Landing Gear (For each slat)

Number of nose gear 1 Slat chord length, ft 2 2 2 2

Number of tires perstrut of nose gear 2 Slat span length, ft 21 16.4 26.7 24

Nose gear strut length, ft 4.4 Slat sweep angle, deg 75 72 67 67

Nose gear strut diameter, ft 0.6 Slat gap (inches) ‐ Approach 0.25 0.25 0.25 0.25

Nose gear tire diameter, ft 1.1 Slat gap (inches) ‐ Sideline 0.25 0.25 0.25 0.25

Nose gear tread width, ft 0.67 Slat gap (inches) ‐ Cutback 0.25 0.25 0.25 0.25

Local flow velocity into gear (ft/sec) 280 Slat deployment angle, deg ‐ Approach 35 35 35 35

Slat deployment angle, deg ‐ Sideline 35 35 35 35

Slat deployment angle, deg ‐ Cutback 35 35 35 35

Slat lift coefficient ‐ Approach

Slat lift coefficient ‐ Sideline

Slat lift coefficient ‐ Cutback

Total lift coefficient ‐ Approach 0.767

Total lift coefficient ‐ Sideline 0.548

Total lift coefficient ‐ Cutback 0.515

* The landing gear are intended to have strut/linkage shrouding that reduce their wake about 60 percent and anti-FOD (xFOD) spats over the wheels that also reduce 

drag and noise

Flap to flap edges should have flexible seals during same deflection and LE simple flap deflects as one with flush seals between every segment.
NASA/CR—2015-218719 24

Figure 34.—Global Shaped Boom Supersonic Service Network

Figure 34 shows the potential global supersonic service network based on the predicted Configuration 

1044-X performance. Although the envisioned vehicle does not have the range to provide nonstop service 

between some extremely distance city-pair, i.e., Los Angeles to Dubai, a one-stop flight with a stop-over 

at London can still significantly reduce actual flight time, 8.8 versus 16.5 hr.

2.5 Propulsion Integration and Sensitivity Analysis

Propulsion system was continuously updated during Phase 2 effort. The flow-through nacelles were 

first updated with notional inlet centerbody and convergent-divergent nozzle, and later with LMdeveloped M1.7 inlet and GE-furnished nozzle and closeout lines. Off-design and grid-size sensitivities 

were examined to guide future effort.

2.5.1 Inlet Design

A clean-sheet, M1.7 inlet was developed to improve propulsion system realism as well as to assess 

low-speed fan noise characteristics. The inlet is of bleed-less, external-compression type developed based 

on 2D MOC analysis, with geometrical parameters (capture area, strut axial location, etc.) referenced to 

GE62_FLF fan diameter, Figure 35. The nonmoving axisymmetric center-body is supported by three 

radial struts positioned at 0°, 130° and 230° to reduce flow resonance. Note that actual geometry of the 

GE fan spool has yet to be defined, resulting in the mismatch at the fan face interface that will need to be 

revised in the future. 

Figure 36 shows the inlet recovery Caine curve. The peak performance corresponds to fan face Mach 

of 0.525 and corrected mass flow rate of 831 lb/sec, which is approximately four-percent less than the 

860 lb/sec required by the 1.0-scale GE62_FLF4 engine. As such, the as-drawn inlet capture area need to 

be increased in the next design iteration to match performance requirements.

Figure 37 shows the peak recovery versus free-stream Mach plot with the inset showing engine fan 

face pressure recovery map. As illustrated, boundary layer buildup and flow separation over the centerbody resulted in a significant region of low pressure around the hub. Future design iterations will attempt 

to reduce flow distortion through center-body/strut shaping and boundary layer control techniques such as 

microvanes and boundary layer blowing.

 
NASA/CR—2015-218719 25

Figure 35.—N+2 Phase 2 Mach 1.7 Inlet

Figure 36.—Mach 1.7 Inlet Recovery Caine Curve

Figure 37.—Inlet Peak Recovery Versus Free-Stream Mach

 

65.0”

71.5”

111.6”

130°

100°

 

M2= 0.505 M2= 0.515 M2= 0.525

 

0.525 0.515

0.505

0.95

0.96

0.97

0.98

0.99

1.00

0.94 0.95 0.96 0.97 0.98 0.99 1.00

Pt2/Pt0

MFR

0.90

0.92

0.94

0.96

0.98

1.00

0.0 0.5 1.0 1.5 2.0

Pt2/Pt0

M0

 
NASA/CR—2015-218719 26

LM is pleased to note that the M1.7 inlet was independently evaluated by NASA and received 

favorable reviews. The first-pass inlet with its strikingly short diffuser duct achieved the same critical 

performance level as the Gulfstream Dual Stream Inlet (0.965 at 0.972 vs. 0.965 at 0.969, respectively) 

yet with the simplicity of the Gulfstream Single Stream Inlet. Stability margin at the design cruise Mach 

was found to be greater or equal to 45-percent, well above the HSCT goal of 10-percent. The inlet is also 

tolerant to changes in free-stream Mach, capable of operating between M1.6 and M1.8.

2.5.2 Auxiliary Inlet Design

Translating cowl ahead of center-body strut LE was selected to provide auxiliary airflow during lowspeed operations. Note that actuation mechanism and support structure has yet to be defined. Figure 38

shows the M0.1 and M0.3 flow characteristics and performance of the inlet with the cowl extended. 

Insufficient lip radius at the cowl and nacelle lips resulted in nonideal flow separation at very low speed 

but improves with increasing Mach. Translating shroud to increase lip radius is being investigated to 

improve pressure recovery. Overall fan face pressure recovery at M0.01and M0.3 is 0.872 and 0.989, 

respectively.

2.5.3 Propulsion System Sensitivity Analyses

Exhaust plume and grid adaptation sensitivity analyses were performed to assess the effects of NPR 

and grid density variations. The analyses were based on Configuration 1044-3b OML with the most upto-date propulsion geometry: 1.04-scale M1.7 inlet; GE FluidShield nacelle closeout, and simplified, 

nonproprietary GE plug nozzle, Figure 39.

The impact of plume size on signature was evaluated by varying NPR. Figure 40 shows the effect of 

reduced NPR signature. The loss of lift associated with reduced plume size allowed the aft shocks to 

coalesce and increased the boom loudness.

Boom signature sensitivity due to grid density was evaluated to determine the suitability and costeffectiveness of grid adaption. As illustrated by Figure 41 and Figure 42 grid adaption provides better 

resolution of flow features around and inside the nacelle. Comparison of predicted signature shows a 

small (approximately 1 PLdB) but measureable impact primarily due to reduced inlet spillage with the 

adapted grids. However, the improved fidelity came with a price, namely, an approximately eight-fold 

increase in computational cost as the number of cells ballooned from four million un-adapted to 

19 million adapted. As such, un-adapted grids are preferred at the conceptual design stage where rapid 

configuration turn-around is required, while adapted grids are more suitable for high-fidelity signature 

predictions prior to design-freeze. Furthermore if the correct amount of spillage is known, the inlet 

boundary condition of the un-adapted solution could be adjusted to match, producing even more nearly 

the same result as the adapted solution.
NASA/CR—2015-218719 27

Figure 38.—Streamline (left), Mach (center) and Pressure Recovery (right) Maps

Figure 39.—Configuration 1044-3b scaled M1.7 Inlet and Non-Proprietary Plug Nozzle

 

SL Mach =0.3, Engine face Mach = 0.53

SL Mach =0.01, Engine face Mach = 0.53 PRavg = 0.872

PRavg = 0.989

 

 
NASA/CR—2015-218719 28

Figure 40.—Plume effects on Equivalent Area (top), 5.6 R/(b/2) 

Signature (middle) and Ground Signature (bottom): 

Configuration 1044-3b, SOC 4,000 nmi Mission

 

 

 

0 50 100 150 200 -2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

Time (ms)

Over pressure (psf)

-0.2 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 -0.015

-0.010

-0.005

0.000

0.005

0.010

0.015

dp/p

Tau

-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0

50

100

150

200

250

300

350

Tau

Equivalent area (ft^2)

Design NPR (9.48)

Reduced NPR (4.50)
NASA/CR—2015-218719 29

Figure 41.—Configuration 1044 Centerline Nacelle Un-Adapted (top)

Versus Adapted Grid (bottom) CFD Solutions

Figure 42.—Grid Adaption Effects on Configuration 1044-2 5.6 R/(b/2)

Signature (top) and Ground Signature (bottom), SOC 4,000 nmi Mission

 

 

Un-adapted (4M cells)

Adapted (19M cells)

 

0 50 100 150 200 -2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

time - ms

Overpressure - psf

-0.020

-0.015

-0.010

-0.005

0.005

0.010

0.015

0.020

0.025

0.030

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

DP/P

Tau

Adapted

Unadapted
NASA/CR—2015-218719 30

2.6 LTWT Test 

The N+2 program conducted a low-speed wind tunnel test to look at stability and control 

characteristics of the 1234 Configuration. The test was conducted at the Lockheed Martin Experimental 

Fluid Dynamics Center using the Low Turbulence Wind Tunnel (LTWT).

The LTWT no. 332 test was conducted primarily between September 20 to 27, 2012, with a handful 

of runs on October 2 to 3, 2012, with 569 runs obtained in 8 days of occupancy time. Not all days were 

full days, so test time was about 60 hr. The test was attended by Mike Buonanno, John Morgenstern, Bob 

Langberg, and Shawn Whitcomb. The test was also observed by Don Durston of NASA Ames.

The LTWT in Figure 43, provides a low-cost, high-value testing option and is commonly used to 

quickly obtain nonlinear aerodynamic effects, such as vortex interaction between wing/forebody areas and 

vertical tails. This capability is particularly valuable in the conceptual phase of a vehicle design. The tunnel 

provides metric data using six component balances and can be used for surface flow and smoke flow 

visualization.

The LTWT features a 2x3 ft test section, and operates up to Mach 0.15 and dynamic pressures of up 

to 32 psf (115 mph), Figure 44. Most tests are run at 25 to 30 psf. The tunnel is a closed circuit and has a 

large settling chamber with flow straightening screens. Models can be sting or blade supported, with 

measurements made using an internal Mk61 Task 0.75-in. six-component balance. In addition, floorsupported half-span models can be testing using a six-component floor balance. The N+2 model was 

designed for sting and blade supports and will be described below. A simulated ground plane can be 

installed in the tunnel to measure ground effects as well.

Figure 43.—LTWT Wind Tunnel Circuit

Figure 44.—LTWT Test and Model Support Capability
NASA/CR—2015-218719 31

Figure 45.—Model Changeable Part Options

2.6.1 Model Design

The model is the Lockheed Martin N+2, Configuration no.1044, which features the aft deck, three 

engines, and canted vertical tails, Figure 45. It is the same loft as the model testing in the LM4 test at the 

NASA Ames 9x7 Supersonic Wind Tunnel, (97-0254) in October 2012. The model is fabricated out of 

stereolithography resin (SLA) and uses steel pins and tape to join removable parts for proper alignment 

and strength. In addition, bent metal parts made of 0.025 in. brass sheet stock can be used to fabricate 

strakes and flaps as desired. The model contains a machined aluminum sleeve designed for the balance 
NASA/CR—2015-218719 32

interface. This aluminum sleeve is bonded inside the models and uses two locating pins for repeatable 

balance attachment.

The LTWT model is sting supported but also has provisions for blade mounting for Ground Effects 

measurements. Control surfaces were available on the RH and LH sides of the model, but were at times 

tested on the LH side only to obtain six-component incremental forces and moments.

The V-tail dihedral and incidence have parts for angle variations, and two different size rudders were 

tested, Figure 46. A removable centerline vertical tail was built and tested, though it is not part of the 

baseline configuration.

The three flow-through engine nacelle parts are removable, but were always present on the model in 

the configurations tested.

Landing gear and doors are modeled with extended (compressed/loaded condition) and retracted gear

configurations, Figure 47. A deflectable canard was fabricated, but not tested.

Leading edge flaps of two types were tested, Figure 48, with plain flaps at +10° and +30°, and 

Krueger flaps at +30° and +45°. Leading edge flaps were broken into three spanwise segments which 

could be deflected in various combinations.

Figure 46.—V-Tail Dihedral, Incidence and Rudder Variations
NASA/CR—2015-218719 33

Figure 47.—Options for Canard, Extended Gear, Nacelle Removal and Centerline Vertical Tail

Figure 48.—Leading Edge Plain and Krueger Flaps with 2 Deflections Each
NASA/CR—2015-218719 34

Figure 49.—Trailing Edge Deflection Parts

Trailing edge flaps (Ailerons and Elevators) were available in three segments on each of the wings 

and also in three segments on the aft deck, Figure 49. Deflections of ±10°, 20°, 30° were available on the 

wing TE parts (except only 10° inboard), and deflections of ±20°, 30° on the aft deck.

Bent metal parts were fabricated and taped in various locations and configurations, size, and 

orientations. Outboard wing Vortex Flaps (upper and/or lower surface devices for roll and directional 

control) were tested; these are a pop-up device like a spoiler, which may avoid aeroelastic issues. Wing 

Root / Fuselage junction deployable control surface options to explore vortex impingement on tails and 

pitch break. Forebody chine or strake options, symmetric and asymmetric were used to explore impact on 

lift and pitching moment characteristics.

2.6.2 Test Planning

The test focused on stability and control characteristics of the no. 1044 configuration, primarily at low 

speeds appropriate for take-off and landing evaluation. A large number of control surfaces were measured 

and evaluated, including alternate parts which are not in the baseline configuration. The incremental 

effect of landing gear and doors were evaluated and the aerodynamic changes in ground effect (IGE) were 

measured.

Goals of the test and the control effectors are as follows: Low-speed stability and control 

characteristics and control power increments were evaluated using yawed polars ranging from alpha –8°

to +16° which were conducted at 0°, ±5°, ±10° beta, Figure 50.
NASA/CR—2015-218719 35

Figure 50.—1044-2 Model in Test Section at High Alpha

Flow visualization studies of vortex flow structures were conducted in the smoke tunnel and 

documented with still images and video recording. Surface flow (oil flow) visualizations were conducted 

with a kerosene/talc mixture to show surface flow patterns and areas of attached flow, separation, and 

flow reversals.

Ground effects were evaluated with the aircraft model supported by an upper surface blade and a 

simulated ground plane was installed in the tunnel. Landing gear were set in a deployed state to simulate 

take-off and landing configurations.

The vast majority of the 561 test runs were made to measure basic stability and control effects 

(494 runs). Necessary balance angle measurements and model weight tares were conducted as needed 

(29 runs), Surface flow visualization was performed in 18 runs, and 20 runs were devoted to ground 

effects measurements. Of the 561 runs, 8 runs (nos. 195 to 202 were bad—incorrectly conducted) and are 

not of value. Smoke flow visualization runs were conducted on the 2nd day of testing and do not have run 

numbers assigned.
NASA/CR—2015-218719 36

In developing the test matrix, a number of discrete index/variables were assigned to define the overall 

configurations. A six-digit integer is used to represent the support, nacelles, and tails as listed. The major 

categories of configurations are listed with their configuration numbers below:

Configuration Nos. Tested

• 1st digit: (1) is Sting, (2) is Blade

• 2nd and 3rd digits: (11) = Nacelles ON, (always)

• 4th and 5th digits: LH & RH Tails, (1) On (0) Off (2,3) Dihedral (5,7) Incidence

• 6th digit: Center Vert Tail, (1) = 100 percent size, (2) 150 percent size

Baseline Configuration

• 111110 == (111) Sting, (110) Tails ON, Center Tail OFF (baseline)

Tail On/Off Variations

• 111111 == (111) Sting, (111) Tails ON, Center Tail ON

• 111001 == (111) Sting, (001) Tails OFF, Center Tail ON

• 111002 == (111) Sting, (002) Tails OFF, Lg Ctr Tail ON

• 111000 == (111) Sting, (000) Tails OFF, Center Tail OFF

Canted Tail Variations, RH & LH

• 111220 == (111) Sting, (220) Tail Dihedral +5, Center Tail OFF

• 111330 == (111) Sting, (330) Tail Dihedral -5, Center Tail OFF

• 111550 == (111) Sting, (550) Tail Incidence -2, Center Tail OFF

• 111770 == (111) Sting, (770) Tail Incidence +2, Center Tail OFF

Blade Support for Ground Effects

• 211110 == (211) Blade, (110) Tails ON, Center Tail OFF

In addition control surface deflections are represented in the test matrix, with the following sign 

convention and variable names, Figure 51. “LEF” and “DE” refer to leading edge flap and trailing edge 

flap parts respectively. The location, inboard/mid/outboard is indicated with the letter “I” / “M” / “O” and 

the RH/LH side of the control is indicated with the last letter in a configuration name. Rudders and 

Canards are similarly defined with “DR” and “DC”, followed by location and side.

The sign convention is positive leading edge down for LE flaps, positive trailing edge down for aft 

end controls and canards. Rudders are positive trailing edge to the left.

Finally, miscellaneous bent metal parts are assigned numbers and descriptions and are documented in 

the run matrix as they were run. The text in the run matrix documents the size of the part and its location 

and orientation on the model. Configuration photos were taken prior to each run to document the 

configuration and state of the model. A summary of conditions and measurement extremes, balance limits 

and reference quantities are in Figure 52.
NASA/CR—2015-218719 37

Figure 51.—Naming Notation for Flap Deflections

Figure 52.—Measurement Extremes, Balance Limits and Reference Quantities

2.6.3 Test Analysis and Results

Overall, the test showed the vehicle to be controllable in all axes with the control surfaces as defined.

Handling qualities should be excellent, especially at the low angles-of-attack used throughout all flight—

except for takeoff and landing approach, where handling should still be acceptable with room for 

improvement with further refinement of strake design and pitch-up reduction. In fact, pitch-up handling is 
NASA/CR—2015-218719 38

good using the best performing flap configuration, but it would be desirable to have a simpler means of 

pitch-up suppression than a full leading edge flap.

LTWT #322 testing encompassed:

Total of 569 runs

• 28 Wt Tares and Sting Deflection Check (SAG)

• 18 Surface Flow Visualization

• N/A Smoke Flow Vis (no run #’s), est 2 to 3 hr total time in smoke tunnel

• 18 Blade Supported IGE runs

• 494 Stability and Control Runs (excluding 8 “bad” runs, which had an incorrect config)

The test acquired:

• Low Speed Stability and Control Power

○ Angle of Attack = -6 to +15°; Sideslip = ± 10°

○ Wing LE (IB,MB,&OB), Plain (0, +10, +30) and Kreuger (0, +30, +45)

○ Left Wing TE (IB,MB,&OB): 0, ±10, ±20, ±30

○ Left Deck TE: 0, ±10, ±20, ±30

○ Left Tail deflections: 0, ±10, ±20, ±30

• Flow Visualization of Vortex Flow Structures

○ Surface Oil Flow

○ Smoke Tunnel Mount

• Allow for alternate blade mount for future IGE or clean tail configuration

Test repeatability may be as good as:

CL = ± 0.002 CrS = better than ±0.0001

CD = ±0.0001 CnS = better than ±0.0001

Cm = ±0.0002 CYS = ±0.0002

Figure 53 and Figure 54 describe data files available from the test. During the test, weight tares were 

taken for repeatability and any time there was a configuration change that might change the model 

weight. Figure 55 describes the new configuration for each 28 weight tares that were taken during the test.

Figure 56 shows a shrunken version of the Run Matrix for confirmation that one has the correct file.

Figure 53.—LTWT332 Output Files of Test Measurements and Photography
NASA/CR—2015-218719 39

Figure 54.—ColSelect ASCII Data Contains these 118 Key Variables
NASA/CR—2015-218719 40

Figure 54.—Concluded
NASA/CR—2015-218719 41

Figure 55.—Weight Tares Taken (28) for Any Significant Configuration Change

Figure 57 shows longitudinal stability trends for the baseline configuration. Lift, pitching moment 

and percent stability are shown versus Alpha. Of note on these plots is the pitch break which occurs at 

about Alpha = 6° to 8°. This is a wing/planform tail-interaction effect. This is an interaction between 

wing/forebody vortex structure interacting with the tail which is dependent on alpha. This can be seen by 

comparing the tail-on / tail-off configurations. The vortex structure can be seen in Figure 58 in smoke 

flow visualization showing the interaction with the canted vertical tails.

Figure 59 shows rolling moment and directional stability trends with alpha. The negative CrBeta 

values at positive alpha are stable slopes. Figure 60 shows yawing moment and directional stability 

characteristics. The positive CnBeta values for Alphas up to 8° to 12° show good directional stability. At 

higher alphas, the tails have reduced effectiveness at angles of attach beyond the pitch break.

The effect of leading edge flaps on longitudinal characteristics is presented in Figure 61. Changes in 

lift are negligible, but favorable reduction in the pitch break are shown with the plain flaps deflected at 

30° (leading edge down). The benefit is best when the gaps between flap segment is left open creating 

leading edge snags, but some benefit is gained even with flaps taped over. A long, low-aspect ratio 

Forebody Strake (FB#7) is shown in the photograph and on the data plots. Some benefit is obtained from 

the strake, but not as much as from the flap deflection. The flap deflection with open snags delays the 

pitch break by about 2°, and the change in stability is dramatically mitigated. Figure 62 shows surface 

flow visualization of the leading edge deflected 30° with the gaps open and taped over. The vortex 

created at the open snag can be clearly seen in two streaks on each wing originating at the gap in the 

leading edge flap.
NASA/CR—2015-218719 42

Figure 56.—Run Schedule (Illegible but) Included Here for Identification
NASA/CR—2015-218719 43

Figure 57.—Pitch Stability Assessment
NASA/CR—2015-218719 44

Figure 58.—Smoke Showing Wing Vortex Impingement on V-Tail

Figure 59.—Roll Stability Assessment
NASA/CR—2015-218719 45

Figure 60.—Yaw Stability Assessment

The pitch control power of the left, outboard, aft deck flap is presented in Figure 63. Due to the long 

Delta Xfs moment arm to the CG, the control is effective in pitching moment. With its relative proximity 

to the centerline, the rolling moment is rather small. 

Rudder control power on the canted vertical tails is shown for longitudinal and lateral moments in 

Figure 64. Due to the canted angle of the vertical tails, the rudder produces pitch, yaw and roll when 

deflected as expected. The degradation in roll effectiveness is due to the plotting of the stability axis 

coefficient with alpha. Had body axis incremental rolling moment been plotted, the surface would be 

constant with alpha as can be seen in the plot of side force, CY versus alpha. Figure 65 shows the effect 

of sizing the rudder by moving the hingeline to have 100 and 150 percent of the baseline rudder. Both 

configurations have the same physical tail area. As expected, the larger tail has greater control power. The 

proper rudder size requirements would be determined by a control power requirements analysis, but the 

nonlinear sensitivity due to tail size is documented in the wind tunnel data.

Finally, a planform variation is presented in Figure 66 where the canted vertical tails have been 

replaced with a web between the aft deck and the wing trailing edges. These parts are fabricated out of 

thin brass stock (0.025 in. thick) and taped in place with aluminum tape. This is a good example of the 

configuration explorations which can be accomplished quickly in the LTWT facility. The new planform 

shows excellent behavior in longitudinal stability and mitigates the pitch break found in the original 

baseline configuration by half.
NASA/CR—2015-218719 46

Figure 61.—Pitch-Up Control Concepts Exploration
NASA/CR—2015-218719 47

Figure 62.—Configuration 1044-2 Pitch Break Surface Oil-Flow Visualization
NASA/CR—2015-218719 48

The pitch control power of the left, outboard, aft deck flap is presented in Figure 63. Due to the long 

Delta Xfs moment arm to the CG, the control is effective in pitching moment. With its relative proximity 

to the centerline, the rolling moment is rather small. 

Rudder control power on the canted vertical tails is shown for longitudinal and lateral moments in 

Figure 64. Due to the canted angle of the vertical tails, the rudder produces pitch, yaw and roll when 

deflected as expected. The degradation in roll effectiveness is due to the plotting of the stability axis 

coefficient with alpha. Had body axis incremental rolling moment been plotted, the surface would be 

constant with alpha as can be seen in the plot of side force, CY versus alpha. Figure 65 shows the effect 

of sizing the rudder by moving the hingeline to have 100 and 150 percent of the baseline rudder. Both 

configurations have the same physical tail area. As expected, the larger tail has greater control power. The 

proper rudder size requirements would be determined by a control power requirements analysis, but the 

nonlinear sensitivity due to tail size is documented in the wind tunnel data.

Finally, a planform variation is presented in Figure 66 where the canted vertical tails have been 

replaced with a web between the aft deck and the wing trailing edges. These parts are fabricated out of 

thin brass stock (0.025 in. thick) and taped in place with aluminum tape. This is a good example of the 

configuration explorations which can be accomplished quickly in the LTWT facility. The new planform 

shows excellent behavior in longitudinal stability and mitigates the pitch break found in the original 

baseline configuration by half.

Figure 63.—Aft Deck Flap Control Power Assessment
NASA/CR—2015-218719 49

Figure 64.—Ruddervator Control Power Assessment
NASA/CR—2015-218719 50

Figure 65.—Ruddervator Size Variation Comparison, Open (baseline) and Closed (50-percent oversize)
NASA/CR—2015-218719 51

Figure 66.—Alternate Planform Study
NASA/CR—2015-218719 52

3.0 N+2 Structural and Aeroelastic Analysis

3.1 Introduction

3.1.1 Purpose

Low-boom supersonic flight for a commercial transport places demanding requirements on aircraft 

configuration design. Sustained supersonic flight results in thin, plate-like lifting surfaces and long 

slender bodies with high fineness ratios. Shaped boom requirements also produce high fineness ratio 

vehicles with relatively large empennage sections on a thin aft deck as evidenced by the N+2 1044-3 

configuration (see Figure 67).

These aircraft design features have raised obvious concerns about the aeroelastic characteristics of 

low-boom supersonic transports, both in terms of open-loop flutter speed and also interaction with the 

vehicle control laws due to the presence of low-frequency flexible modes that may interact with vehicle 

rigid body dynamics (i.e., aeroservoelasticity (Ref. 4)). These were also areas of concern for the High 

Speed Research program of the 1990’s, which led to the fabrication and test of an aeroelastically-scaled 

semi-span wind tunnel model in the NASA Langley Transonic Dynamics Tunnel (Ref. 5).

As a result of these concerns and in the interest of addressing technical barriers to safe, efficient and 

economical supersonic flight, NASA, under the High Speed Program of the Fundamental Aeronautics 

Program, has funded Lockheed Martin to develop a full vehicle dynamic finite element model (FEM) of 

the N+2 1044-3 configuration. The purpose of this FEM is to be used as an analysis testbed by which 

aeroelastic and aeroservoelastic issues for low-boom supersonic configurations can be analyzed and 

addressed, including high fidelity computational analyses. This report discusses our approach and 

assumptions towards building the FEM and also initial linear open-loop flutter analysis.

Figure 67.—Low-boom supersonic flight requirements raise natural aeroelasticity concerns
NASA/CR—2015-218719 53

3.1.2 Objectives

The objectives of the structural analysis task are to develop a full-vehicle FEM suitable for aeroelastic 

analysis, provide configuration recommendations based on structural analysis, update the FEM with these 

recommendations, and perform linear open loop flutter analysis for an initial assessment of flutter. An 

essential deliverable of the project is the aeroelastic FEM, which will then be used by NASA for highfidelity study of aeroelastic, aeroservoelastic, and aeropropulsiveservoelastic challenges and development 

of associated tools.

3.1.3 Scope/Tasks

A FEM that is suitable for aeroelastic analysis requires that it have both representative stiffness and 

mass, including nonstructural mass, of the full vehicle. Towards this end, a preliminary structural layout 

of the vehicle, considering major loads paths and accounting for major cut-outs such as landing gear bays, 

cabin, cargo holds, major systems and control surfaces was developed. We then built a FEM of the 

structural design with materials appropriate for the temperature range and Mach number at which the 

vehicle will be flying. We assumed use of advanced composite materials, with some metallics at major 

load introduction points such as at landing gear and tail to body attachments. The FEM was then sized to 

a set of design load cases that were derived based on our structural design criteria assumptions and 

consisted of both maneuver and ground load cases. These criteria were based on FAR Part 25 and our 

experience from the Quiet Small Supersonic Transport (QSST) program. Aerodynamic loads were 

initially based on linear aerodynamics, but select cases were also based on CFD (Euler) aerodynamics.

The loads also included inertial loads, which were derived through a distribution of nonstructural mass on 

the FEM, such that the total FEM weight and CG were consistent with the Level 1 mass properties 

statement of the vehicle. Nonstructural mass included fuel, system masses, passengers and cargo, engine 

masses, and other nonmodeled structural weight. Having developed a FEM sized to representative loads 

and to strength, buckling, and composite best-practices criteria, we then performed open-loop flutter 

analysis to determine critical flutter speed. In the course of these studies, we determined that the tail/aftdeck region deformed more than desired under load, and thus we thickened both the tail and the aft deck 

of the vehicle to produce a naturally stiffer structure. Each of these analyses will be explained in greater 

detail in the body of this report. Figure 68 shows our original program schedule with delineation of major 

technical tasks.

Figure 68.—N+2 Structural Analysis Tasks and Schedule
NASA/CR—2015-218719 54

3.2 Configuration and Structural Arrangement

As a basis for the FEM development of the N+2 1044-3 configuration, we developed a representative 

structural layout, generating analysis-ready geometry using LM Aero’s PreCEPT tool which facilitates 

the development of a full configuration FEM (Ref. 6). This section describes the approach used to 

develop a structural layout that accommodates major requirements such as cabin configuration and 

subsystem layout of the vehicle.

3.2.1 Configuration and Systems Layout

The general arrangement of the cabin and major internal subsystems is shown in Figure 69 and 

Figure 70. This configuration represents some significant structural challenges due to the lack of forward 

wing carry-through, a large cutout for main landing gear, and reduced structural depth towards the rear of 

the vehicle. Because of the unique vehicle configuration, an aft center-of-gravity (CG) is necessary, 

resulting in major subsystems being located toward the rear of the vehicle. This further limits the effective 

carry-through depth.

Figure 70 illustrates the approach taken to develop load paths that accommodate the cabin and 

subsystems layout. Longitudinal bending stiffness is provided primarily by large fore-aft keels within the 

wings. Major bulkheads around the main landing gear bay as well as wing carry-through aft of the main 

landing gear bay handle wing carry-through loads. The frame spacing throughout the fuselage was 26 in., 

and was largely determined by the spacing of the seating throughout the passenger cabin. This spacing 

drove much of the topology in the inner wing as well. The structural layout shown in Figure 71 represents 

an early approach, where the spars in the outer wing were distributed in a manner that aimed to direct load 

towards the wing carry through region. As the structural layout matured, it became difficult to resolve 

spacing issues near the intersection of these spars with the wing leading edge. As a result, a more uniform 

spacing approach was adopted.

Figure 69.—Cabin Layout for the 1044-2 Configuration

• 80-passenger, three-class layout

• Under-floor space insufficient for cargo stowage 

(<24” height)

Galley Lavatory Closet

Cargo hold
NASA/CR—2015-218719 55

Figure 70.—Layout of Major Subsystems

Figure 71.—Concepts for Major Load Paths Developed to Accommodate Cabin and Subsystems 

Layout (Early Concept)

Due to the lack of a forward wing carry-through, shear ties were used to avoid transmission of local 

bending loads into the fuselage OML, as described in Figure 72. Additional work is needed to mature this 

concept. However, in the absence of more detailed design and analysis work, we decided that this was a 

feasible solution to integrating the forward wing with the fuselage. The concept relies on the transmission 

of shear loads only through a pin/link-type arrangement. The goal of this approach is to avoid the 
NASA/CR—2015-218719 56

transmission of bending loads, and thus compression, into the fuselage, which may lack sufficient 

structural depth to accommodate these types of loads.

Figure 73 contains several views of the final structural arrangement; with an inset image of an early 

approach for comparison of the spar configuration on the outer wing (see also Figure 71 for this early 

concept). The final spar spacing on the outer wing is roughly 20 in., which is similar to spacing used on 

the Quiet Supersonic Transport (QSST). Control surface substructure was added to facilitate trimmed 

aeroelastic loads, and details like fuselage stringers, door framing, and pressure bulkheads were added to 

increase the applicability of the FEM towards characterizing the major structural aspects of the fuselage.

Figure 72.—Concept for the Shear Ties Attaching the 

Forward Wing to the Fuselage.

Figure 73.—Final Structural Layout for the 1044-3 Configuration
NASA/CR—2015-218719 57

3.2.2 Control Surface Sizing and Definition

The control surface scheme used for this study is illustrated in Figure 70. The control surface suite 

consists of 4 leading edge flaps, 3 trailing edge flaps (flaperon and two ailerons), a ruddervator, and a 

body flap. A low turbulence wind tunnel test was conducted in September of 2012 to assess the 

effectiveness of the initial control surface layout and provide guidance for sizing. A variety of control 

surface configurations and planform modifications were tested. Overall, the vehicle exhibited good 

characteristics with the exception of an expected pitch up at low speed, due to the interaction of wing 

vortex with the canted vertical tails. Krueger flaps were initially considered for the leading edge devices.

However, it was found that plain flaps were sufficient. The trailing edge flaps were shown to be sufficient 

in size, although the body flap size was reduced as a result of the test.

3.2.3 PreCEPT

LM Aero’s PreCEPT tool was used to develop the geometry representing the structural layout.

PreCEPT is a tool that implements a Smart Product Model, and using automation, creates components in 

CATIAV5 that contain geometry and metadata that are consumed during downstream analysis model 

development. PreCEPT is also linked with Altair’s Hypermesh, which was used to develop the FEM.

During the generation of the FEM, additional automation routines are used to perform cleanup, meshing, 

and material and property assignments, reducing the time required for model development. PreCEPT also 

supports a unique scripting language, which enables the automation of repetitive component creation. A 

majority of the structural layout for the N+2 configuration was scripted, with variables that would allow 

variable spacing throughout portions of the vehicle. These procedures were used to conduct alternative 

arrangements such as the early outer wing spar arrangement, to arrive at the final structural arrangement.

Reference 6 has further detail about the PreCEPT tool.

3.3 Structural Design Criteria and Design Loads

We developed preliminary structural design criteria for the purposes of defining a set of design load 

cases to which the structural FEM would be sized. The main goal behind this task was to develop a set of 

preliminary design load cases that would be major drivers of the primary airframe structure. This exercise 

was not intended to be an exhaustive loads development process as would be done in a full-scale 

development program, but to provide select major load cases and thus produce a FEM that, overall, has 

representative stiffness for flutter analysis.

3.3.1 Structural Design Criteria and Design Load Cases

The N+2 gross weight of nearly 350,000 lb makes the vehicle applicable to FAR Part 25, and thus we 

pulled heavily from this document to define our structural design criteria. In addition, we also leveraged 

the design criteria and design load work that had been done in LM’s Quiet Small Supersonic Transport 

(QSST) program (Document No. 422DS201, Ref. 7). This document facilitated identification of flight 

maneuver, landing and ground handling load cases.

Basic vehicle parameters and weights, important for loads calculations, are shown in Table 8.

Maneuver and gust load cases were developed at 3 vehicle weights: Zero Fuel Weight (ZFW), which 

would be indicative of near landing, Design Take-off Weight (DTOW), indicative of near takeoff, and 

Design Take-off Weight 2 (DTOW2), corresponding to the beginning of the cruise segment. In addition, 

the Design Landing Weight is defined as the zero fuel weight plus 35 percent of available fuel. It is at this 

weight that sink speeds of 10 ft/s for landing load calculations are assumed. At the heavier DTOW, a sink 

speed of 6 ft/s is assumed for landing load calculations. The main and nose gear ground contact locations, 

relative to the vehicle center-of-gravity (CG) are needed in order to determine balanced loads at each gear 

for the ground and landing load cases.
NASA/CR—2015-218719 58

TABLE 8.—CONFIGURATION AND DESIGN WEIGHTS DATA

Parameter Value

Wing Reference Area .............................................................................................3600 ft2

Wing Span .......................................................................................................... 83 ft 10 in

Vehicle Length .......................................................................................................... 244 ft

Main Gear Ground Contact Point ........... FS = 2,112.26 in., BL = ±84 in., WL = 89.61 in.

Nose Gear Ground Contact Point .......... FS = 1,002.64 in., BL = 0.0 in., WL = 126.85 in.

Zero Fuel Weight............................................................................................... 157,608 lb

Design Landing Weight..................................................................................... 220,308 lb

Design Take-off Weight (DTOW)..................................................................... 332,732 lb

DTOW2 (Begin Cruise)..................................................................................... 301,210 lb

Total Fuel........................................................................................................... 175,124 lb

TABLE 9.—N+2 DESIGN SPEEDS

Design Speed Value

Cruise Speed (VC)................................................................................ 400 KEAS

Dive Speed (VD) .................................................................................. 450 KEAS

Maneuvering Speed (VA)..................................................................... 318 KEAS

Speed for Max Gust Intensity (VB)............................... 275 KEAS (FAR 25.335)

Landing Speed ..................................................................................... 169 KEAS

Cruise Mach Number (MC)................................................................. Mach = 1.8

Dive Mach Number (MD) ................................................................... Mach = 2.0

Table 9 shows the design speeds that were the basis of our preliminary design loads. While these 

speeds were defined with the guidance of FAR Part 25, this is not the complete list of structural design 

speeds. There are other speeds identified in FAR Part 25, such as flap speed and gear extended speed, but 

these were felt to not be as significant design drivers of primary airframe structure, such as wing box, 

fuselage, and tail structure.

As a companion to Table 9 is the Mach, Altitude flight envelope in Figure 74, which defines the 

vehicle maximum Mach number as a function of altitude. This envelope is also important from a flutter 

perspective as the vehicle is required to be flutter-free 15 percent beyond dive speed. While the N+2 

vehicle cruises at Mach 1.8, provision is made in emergency situations to dive at Mach 2.0. The dark 

circles on the figure indicate where there are design load cases used for structural sizing.

Maximum and minimum design load factors are defined for this vehicle to be +2.5 g and -1 g, 

respectively, consistent with commercial transports. Several V-n diagrams which plot maximum and 

minimum maneuver load factor against vehicle speed (in knots equivalent or KEAS) were created at 

different gross weights and flight altitudes. In addition, gust load factors which are defined by standard 

discrete gust equations in FAR Part 25 are also included on the V-n diagram. From the V-n diagram 

analysis, one gust load case was identified for the design loads and is associated with a ZFW condition at 

20,000 ft altitude. The discrete gust load factor for this case is 2.7 g and is identified in Figure 75 by the 

dark circle.
NASA/CR—2015-218719 59

Figure 74.—N+2 Mach, Altitude Flight Envelope

Figure 75.—Sample V-n Diagram

Table 10 is a summary of the maneuver load cases used for preliminary sizing of the structure that 

were developed based on the previous design criteria. They include a mix of pull-up and push-over 

maneuvers at various design speeds, altitudes, and gross weights. In addition, they include some 

asymmetric maneuvers, along with a gust case and a cruise condition. The landing condition is developed 

and the air loads associated with it are applied along with the gear loads to develop balanced landing load 

conditions.

Each of the maneuver load cases has an associated cabin pressure, as well, which is a function of 

altitude. The cabin pressure is maintained to be no less than that corresponding to an altitude of 6,000 ft.

Thus above 6,000 ft there is a pressure difference between the cabin and the atmosphere which is defined 

in Table 11.

Structural Design 

Envelope
NASA/CR—2015-218719 60

TABLE 10.—MANEUVER LOAD CASES

Case 

no.

Description Mach Speed 

(KEAS)

Nz Weight Altitude 

(ft)

100 Pull up 0.66 438.00 2.50 DTOW Sea Level

200 Push over 0.66 438.00 -1.00 DTOW Sea Level

300 Pull up 0.48 318.0 2.50 DTOW Sea Level

400 Pull up 2.00 450.00 2.50 DTOW2 49,770

500 Push Over 2.00 450.00 -1.00 DTOW2 49,770

600 Pull up 1.41 318.00 2.50 DTOW 49,770

700 Pull up 0.66 438.00 2.50 ZFW Sea Level

800 Push over 0.66 438.00 -1.00 ZFW Sea Level

900 Pull up 2.00 450.00 2.50 ZFW 49,770

1000 Push over 2.00 450.00 -1.00 ZFW 49,770

1100 Steady roll

(Roll rate = 30°/sec) 0.48 318.00 0.00 DTOW Sea Level

1200 Abrupt roll 

(Roll accel = 30°/sec2) 0.48 318.00 0.00 DTOW Sea Level

1300 Steady roll 

(Roll rate = 30°/sec) 0.48 318.00 1.67 DTOW Sea Level

1400 Abrupt roll 

(Roll accel = 30°/sec2) 0.48 318.00 1.67 DTOW Sea Level

1500 Landing 1g 0.26 169.00 1.00 DTOW Sea Level

1600 Cruise 1g 1.80 357.00 1.00 DTOW 55,000

1700 Gust Loads 0.89 400.00 2.70 ZFW 20,000

TABLE 11.—CABIN PRESSURE AS A FUNCTION OF ALTITUDE

Altitude 

(ft)

Cabin Pressure 

(psi)

Ambient Pressure 

(psi)

Cabin ∆P 

(psi)

0 14.7 14.7 0

1000 14.18 14.18 0

2000 13.67 13.67 0

3000 13.18 13.18 0

4000 12.7 12.7 0

5000 12.23 12.23 0

6000 11.78 11.78 0

7000 11.78 11.34 0.44

8000 11.78 10.92 0.86

9000 11.78 10.51 1.27

10,000 11.78 10.11 1.67

20,000 11.78 6.76 5.02

30,000 11.78 4.37 7.41

40,000 11.78 2.72 9.06

50,000 11.78 1.68 10.1

60,000 11.78 1.04 10.74

64,000 11.78 1.04 10.74
NASA/CR—2015-218719 61

Figure 76.—A uniform pressure is applied to the cabin (left figure) and cargo bay (right figure)

The pressure difference was applied to the FEM, as shown in Figure 76, both to the main cabin and 

the cargo hold. The magnitude of the pressure depended on the altitude of the maneuver and was specified 

by the right hand column of Table 11.

Table 12 shows the landing load cases and associated gear reactions that were considered for N+2. A 

1g air load was also applied to the vehicle for all landing cases. The vehicle gross weights that are 

reported are those corresponding to an earlier FEM, which was a little heavier than the mass properties 

weight target of 332,732 lb reported in Table 8. For the landing loads, we extensively leveraged previous 

work that had been done in the QSST program. Main and nose gear vertical reaction forces for the Level 

Landing (LL) case were scaled up based on the weight from the QSST reaction forces according to the 

following equation:

FFFFFFFFNN+2 = FFFFFFFFQQQQQQQQ

WWWWWWWWhttNN+2

WWWWWWWWhttQQQQQQQQ

(1)

For Spin-Up (SU) and Spring-Back (SB) load cases, the main and nose gear vertical reaction forces were 

scaled from Level Landing loads as follows: 

FFFF(SSSSSSSSSSSS)NN+2 = 0.8 ∗ FFFFFFFFNN+2 (2)

Fore and aft drag forces were computed from the vertical forces by:

FFFFNN+2 = ±0.8 ∗ FFFFNN+2 (3)

The ground handling load cases are defined in Table 13. In this case, the reaction forces were 

computed according to FAR Part 25, Sections 25.471-25.507. No air loads were applied to the vehicle for 

these cases.

The color changed due to 

reversal of element normal
NASA/CR—2015-218719 62

TABLE 12.—LANDING LOAD CASES

Design Takeoff Weight Design Landing Weight

Weight (lb) 334139 220308

Xcg (in.) 2020 2069

Zcg (in.) 256 243

Sink Rate (ft/s) 6 10

Right MG Left MG Nose Gear Right MG Left MG Nose Gear

Static Condition FZ 153139 153139 27861 105885 105885 8538

FY 0 0 0 0 0 0

FX 0 0 0 0 0 0

Level Landing FZ 120290 120290 21341 264368 264368 32535

FY 0 0 0 0 0 0

FX 30073 30073 5335 66092 66092 8134

Air Load FZ 334139 220307

Spin-up Case FZ 96232 96232 17073 211495 211495 26028

FY 0 0 0 0 0 0

FX 76986 76986 13658 169196 169196 20822

Air Load FZ 334139 220307

Spring-back Case FZ 96232 96232 17073 211495 211495 26028

FY 0 0 0 0 0 0

FX -76986 -76986 -13658 -169196 -169196 -20822

Air Load FZ 334139 220307

Lateral Drift Landing FZ 90218 90218 21341 198276 198276 32535

FY 22554 22554 5335 49569 49569 8134

FX 36087 36087 8536 79311 79311 13014

Air Load FZ 334139 220307

One Gear Landing FZ 120290 0 0 264368 0 0

FY 0 0 0 0 0 0

FX 30073 0 0 66092 0 0

Air Load FZ 334139 220307

Side Load Landing FZ 60145 60145 0 132184 132184 0

FY 48116 36087 0 105747 79311 0

FX 0 0 0 0 0 0

Air Load FZ 334139 220307

Side Load Landing – L to R FZ 60145 60145 0 105747 105747 0

FY -48116 -36087 0 -84598 -63448 0

FX 0 0 0 0 0 0

Air Load FZ 334139 220307
NASA/CR—2015-218719 63

TABLE 13.—GROUND HANDLING LOAD CASES

Design Takeoff Weight Design Landing Weight

Weight (lb) 334139 220308

Xcg (in.) 2020 2069

Zcg (in.) 256 243

Sink Rate (ft/s) 6 10

Right MG Left MG Nose Gear Right MG Left MG Nose Gear

Static Condition FZ 153139 153139 27861 105885 105885 8538

FY 0 0 0 0 0 0

FX 0 0 0 0 0 0

3-point Braked Roll FZ 153139 153139 67875 127061 127061 39432

FY 0 0 0 0 0 0

FX 122511 122511 0 101649 101649 0

2-point Braked Roll FZ 153139 153139 0 127061 127061 0

FY 0 0 0 0 0 0

FX 122511 122511 0 101649 101649 0

Dynamic Roll Braking FZ 153139 153139 92275

FY 0 0 0

FX 122511 122511 0

Turning Condition FZ 153139 153139 27861 105885 105885 8538

FY 76569 76569 13931 52942 52942 4269

FX 0 0 6965 0 0 2134

Nwhl yaw and steering (1) FZ 153139 153139 27861 105885 105885 8538

FY 0 0 22289 0 0 6830

FX 0 0 0 0 0 0

Nwhl yaw and steering (2) FZ 153139 153139 47868 105885 105885 27815

FY 0 0 -10118 0 0 -6671

FX 122511 0 0 84708 0 0

Reversed Braking FZ 153139 153139 0 105885 105885 0

FY 0 0 0 0 0 0

FX -84226 -84226 0 -58237 -58237 0

2-G Taxi FZ 306278 306278 83584 211769 211769 25614

FY 0 0 0 0 0 0

FX 0 0 0 0 0 0

The inertia relief capability in MSC.NASTRAN was used to compute inertia forces that balance the 

externally applied landing and ground handling forces for a free-free vehicle boundary condition (i.e., free 

to translate vertically, fore-aft, and pitch). As shown in Figure 77, ground reactions were applied at the 

ground contact points for the ground control cases and landing cases where side force was present. For the 

remainder of the landing cases, the vertical and drag forces were applied at the axle.
NASA/CR—2015-218719 64

Figure 77.—Application of Landing and Ground Handling Loads to FEM

3.3.2 Linear Aerodynamic Maneuver Loads

External flight loads were computed via static aeroelastic analysis in MSC.NASTRAN (SOL144).

We utilized the linear aerodynamic doublet-lattice method available in the solution and for initial loads 

calculations assumed rigid aerodynamics, eventually transitioning to flexible aerodynamics as the 

structural design matured. We also developed loads for a select number of cases using CFD (Euler) 

predictions as described in the next section.

Figure 78 shows the linear aerodynamic model used both for maneuver loads and flutter analyses.

The lifting surface waterline is at z=285.83 in, which corresponds to the most inboard leading edge flap 

hinge point. As shown in the figure, there are 9 control surfaces, 4 leading edge flaps (LEF), 2 ailerons, 

1 trailing edge flap (TEF), 1 body flap and 1 rudder. For this semispan aerodynamic model there are 

1900 individual aerodynamic boxes. Guidelines for developing the model included: avoiding abrupt 

element size changes between panels, maintaining streamwise continuity between panels (which required 

splitting the control surfaces into several pieces), and aiming for boxes with aspect ratios of 1 to 2 as 

much as possible without letting the number of elements become excessive.

Aerodynamic forces were transferred to the structural model via infinite plate splines. Conversely, 

displacements from the structural model were then transferred to the aerodynamic model by the same 

splines (which can be derived through principles of virtual work). As a check of the spline quality, 

flexible modal displacements from the FEM were mapped to the aerodynamic model, and as seen from 

Figure 79 through Figure 81, the splines adequately transfer displacements from the FEM to the linear 

aerodynamic model.
NASA/CR—2015-218719 65

Figure 78.—Linear Aerodynamic Model for Loads and Flutter Analysis

Figure 79.—Spline Verification—Wing Up-Bending

4 Leading Edge Flaps 2 Ailerons 

Rudder

Body 

Flap

Trailing 

Edge 

Flap
NASA/CR—2015-218719 66

Figure 80.—Spline Verification—Fuselage Down-Bending

Figure 81.—Spline Verification—Wing Down-Bending

For both the maneuver load calculations and the landing/ground handling loads, the structural model 

was supported at a grid point on the symmetry plane. For symmetric trim cases, the support point is fixed 

in all degrees of freedom except in vertical translation and pitch. For antisymmetric cases, only rigid body 

roll is free at the support point. The support point is shown in Figure 82 and was developed to not add 

stiffness to the FEM.

For the symmetric maneuvers of Table 10, the trim variables were angle of attack (AOA) and body 

flap. For cases where the body flap deflection exceeded 30°, trailing edge control surfaces on the main 

wing were scheduled to deflect with the body flap in a 1:1 ratio. The two wing ailerons were used to trim 

the vehicle in roll and were linked together to deflect equally.
NASA/CR—2015-218719 67

Figure 82.—Support Point Definition

Figure 83.—Comparison of Linear Aero Model to LTWT Test Data (Mach 0.14)

Figure 83 shows a comparison of the linear aerodynamic model with Low Turbulence Wind Tunnel 

(LTWT) test results, for both lift and pitching moment. The Mach number is 0.14, and the LTWT data is 

a sweep in AOA from –8° to +12°. Pitching moments were computed about the wind tunnel data moment 

reference center (X=2035.21 in., Y=0 in., Z=261.5 in.). The objective of this study was to compare linear 

aerodynamic results to LTWT test results and assess the impact of under-wing nacelle on lift and pitching 

moment. Rigid aerodynamics was assumed in this analysis as the wind tunnel model was rigid. For the 

linear aerodynamic model, geometric incidence of the fuselage and wing twist were modeled and are part 

of the linear data in the plots. In general, the lift curve of the linear aerodynamic model matches well that 

of the LTWT data, particularly at modest AOA (–4° to +4°), and there is little impact to removing the 

under-wing cruciform nacelle. As expected the pitching moment trends of the linear model do not match 

wind tunnel test data well, not predicting at all the unstable pitch break at about 4° AOA. However, the 

slope of the pitch curve is matched reasonably well near 0° AOA.

Figure 84 is a comparison of the lift curve as predicted by the linear aerodynamic model and 

compared to supersonic (Mach 1.7) wind tunnel test data. The linear aerodynamic model matches the 

measured lift curve quite well, especially when fuselage incidence and wing twist are removed from the 

model. Based on this data and the previous figure, we decided to include the incidence and twist for 

subsonic maneuver load cases and remove it for supersonic load cases.

Analysis

Analysis

Support 

Point

Support point attaches to 3 points on 

the fuselage in a way that does not 

add stiffness to the model
NASA/CR—2015-218719 68

Figure 84.—Comparison of Linear Aero Lift Coefficient with Test (Mach 1.7)

3.3.3 CFD-Loads Model and Trim Scheme

CFD-based, trimmed flight loads were developed for select maneuver load cases of Table 10. To 

accomplish this, a CFD loads model with sufficient detail to allow the actuation of control surfaces was 

developed. The baseline control surface scheme consisted of 4 leading edge flaps, 3 trailing edge flaps, a 

body flap, and a ruddervator. Due to spanwise camber on the wing, the wedge-shaped gaps between the 

trailing edge flaps were generated in order to allow deflection of the control surfaces without collision up 

to 30°. Figure 85 shows the shape of the gaps, as well as the shape of the trailing edge flaps when 

deflected.

The trimmed CFD-based (Euler) maneuver loads were generated using LM Aero’s computational 

aeroelasticity toolset, consisting of in-house aerodynamic and structural solvers linked together with the 

MultiDisciplinary Computing Environment (MDICE) (Refs. 8 and 9). MDICE is a commercial product 

maintained by CFD Research Corporation in Huntsville, AL. The aerodynamic portion of the CFD-based 

aeroelastic analysis was performed by LM Aero’s in-house solver Splitflow. Splitflow is a Cartesian, 

Euler/Navier-Stokes Solver. It requires a triangulated surface grid as input, and generates its own 

volumetric grid. Figure 86 contains images of the surface mesh used by Splitflow. MDICE is used to 

coordinate the transfer of information between the aerodynamic and structural codes. MDICE uses an 

application programming interface (API) layer to link with analyses, transferring information between 

these processes in memory, thereby avoiding file-based transfer of data. Figure 87 contains an image of 

this process. MDICE also manages the execution of the simulation via a scripting language, thus enabling 

tailoring of the aeroelastic simulation to meet various goals and trim schemes.

The structural portion of the aeroelastic analysis is handled by LMMS, which is a linear structural 

analysis solver developed in-house. LMMS uses the normal modes computed by NASTRAN in 

combination with the nodes and element connectivity of the FEM to compute deflections due to loads 

transmitted from the aerodynamic analysis through MDICE. MDICE then maps the resulting deflections 

back to the aerodynamic grid in preparation for the next iteration of loads analysis. This process is 

repeated until convergence is achieved. LMMS also has utilities for computing deformation due to control 

surface deflections, which are superimposed on the elastic deflections before sending to MDICE.
NASA/CR—2015-218719 69

Figure 85.—Wedge Shaped Gaps Needed to Enable 30° of Trailing Edge Flap Actuation

Figure 86.—N+2 Configuration 1044-3 Surface Mesh used by Splitflow for CFD-Based Aeroelastic Analysis

Figure 87.—MDICE uses an Application Programming Interface to Transfer Forces and Deflections 

Between Aerodynamic and Structural Analyses.

A09-26741-008

MDICE

Fluid Side of FluidStructure interface

Solid Side of FluidStructure Interface

Aerodynamics

Code

Structures 

Code

Pointers to

X, Y, Z, &

Pressures

Integrate pressures,

interpolate forces

Interpolate

deflections

Pressures 

on Fluid Faces

Forces on

Structural Nodes

Deflections

at Structural Nodes

Deflections

at Fluid Nodes

Pointers to

X, Y, Z, Forces, &

Deflections

Application Programming Interface (API) Layer
NASA/CR—2015-218719 70

For most aeroelastic simulations, the surface meshes of both the fluid and structural models are 

segregated into individual patches which afford control over how forces and deflections are mapped 

between the disciplines. Corresponding pairs of aerodynamic and structural patches are referred to as 

fluid-structure interfaces. Control surfaces are usually separated in a manner distinct from the wing box, 

to allow the specification of control surface deflections without impacting the surrounding structure, as 

well as to capture aeroelastic phenomenon where there is a discontinuity between surfaces such as control 

surface blow-back. For the N+2 configuration, the surface mesh was discretized into 16 different patches, 

with an additional patch representing the symmetry plane to blend fuselage deformations into this portion 

of the mesh. Control surface deflections are generated by computing the needed motion on the structural 

model, and then super-imposing these deflections on the elastic deflections before they are passed to the 

fluids grid via MDICE. Figure 88 illustrates the discretization of the structural model surface mesh into 

these patches.

To develop trimmed flight loads, the overall vehicle force and moment coefficients are monitored 

during the aeroelastic simulation. An initial sensitivity analysis is done to develop coarse stability 

derivatives, then a two degree of freedom trim problem (usually involving angle of attack and a trim 

surface deflection angle) is solved to compute a new trim state. This trim state is incrementally 

approached using a relaxation factor, which applies a fraction of the difference between the current and 

computed trim state as an update to the current trim state to avoid numerical instabilities.

The aerodynamic solver, Splitflow, has the ability to adapt the volumetric grid based on changes to 

the surface geometry or to requested gradients in the flowfield. In order to focus the majority of the mesh 

refinement near the final equilibrium state, a multi-step process is followed during the trim solution. This 

phased approach begins with a coarse grid, which is used to develop stability derivatives based on trim 

variable perturbations. Next, a trim is performed without allowing adaptation of the volumetric mesh.

This allows the geometry to move near the equilibrium state. Finally, grid adaption based on flowfield 

gradients is enabled to increase the fidelity of the flow solution and allow the geometry to reach the final 

trim state. Figure 89 shows the convergence history of the Z-Force Coefficient in a typical trim simulation 

with this approach.

Figure 88.—Surface Patch Definitions for Fluid-Structure Interfaces
NASA/CR—2015-218719 71

Figure 89.—Force Coefficient Convergence History of Trim Procedure Used to 

Enhance Grid Adaption about the Equilibrium Trim State

During the structural model development effort, portions of the upper aft deck between the centerline 

nacelle and tail boom were thickened to increase structural depth. Additionally, the vertical tail was 

thickened, and a fillet on the lower aft deck surface was smoothed out. Because this operation was 

performed directly on the FEM through a morphing procedure, updated CAD geometry was not available, 

and would have required a remesh of the aerodynamic model. An alternate approach was developed 

where the motion of the nodes due to the morphing process was converted to a mode shape. This mode 

shape was loaded by LMMS, treated as a structural deflection, and mapped to the aerodynamic model 

through the fluid structure interfaces defined for these portions of the vehicle. This allowed for a 

relatively quick update of the aerodynamic model without the need to go back to the CAD model.

3.3.4 Loads Summary 

Table 14 and Table 15 show the final trimmed values for the maneuver load cases of Table 10. These 

flight loads were based on the FEM013 structural model and incorporated a thickening of the tail and aft 

deck to reduce deformation under load. The weights and CG locations assumed for the final maneuver 

load calculations are given in Table 16 and differ slightly from the weights of Table 8, because they were 

based on an earlier FEM.

The highlighted far-right columns of Table 14 are those load cases that were developed with CFDbased loads and their associated trim deflections. The CFD-results for load cases 300, 400 and 600 were 

selected for the final design loads because these were load cases that were major structural design drivers 

and because they also varied significantly from linear predictions due to their relatively high AOA, where 
NASA/CR—2015-218719 72

separation and vortex lift dominated more. Also, for the linear load cases that required more than 30° of 

body flap deflection to trim, the other wing control surfaces that were needed are shown in the column 

labeled “Linked to BF”. 

TABLE 14.—TRIM DEFLECTIONS FOR SYMMETRIC MANEUVER LOAD CASES 

[Cases that were used in final design loads are highlighted in gray.]

Case 

no.

Description Mach Speed 

(KEAS)

Nz Weight Altitude 

(ft)

Flexible Linear Linked to BF Rigid CFD AOA 

(deg) Body Flap 

(deg)

AOA 

(deg)

Body Flap 

(deg)

TEF, AIL1, 

AIL2 

(deg)

100 Pull up 0.66 438 2.5 DTOW Sea Level -0.75 6.88 -3.58 0.00 6.15

200 Push over 0.66 438 -1 DTOW Sea Level 3.64 -3.82 2.30 0.00 -3.34

300 Pull up 0.48 318 2.5 DTOW Sea Level -3.06 13.63 -0.99 0.00 10.73

400* Pull up 2 450 2.5 DTOW2 49,770 -21.38 7.91 TEF -8.88 -8.88 6.61

500 Push Over 2 450 -1 DTOW2 49,770 16.08 -3.02 17.03 17.03 -4.10

600* Pull up 1.41 318 2.5 DTOW 49,770 -34.04 16.35 TEF,AIL1&2 -5.56 -5.56 12.45

700 Pull up 0.66 438 2.5 ZFW Sea Level -3.44 2.84 -2.98 0.00 2.92

800 Push over 0.66 438 -1 ZFW Sea Level 4.72 -2.20 1.60 0.00 -2.01

900* Pull up 2 450 2.5 ZFW 49,770 -24.88 4.60 TEF -9.78 -9.78 3.60

1000 Push over 2 450 -1 ZFW 49,770 18.65 -1.68 15.75 15.75 -2.83

1100 Steady roll 0.48 318 0 DTOW Sea Level 2.22 -0.77

1200 Abrupt roll 0.48 318 0 DTOW Sea Level 2.22 -0.77

1300 Steady roll 0.48 318 1.67 DTOW Sea Level -1.31 8.85

1400 Abrupt roll 0.48 318 1.67 DTOW Sea Level -1.31 8.85

1500 Landing 1g 0.3 204 1 DTOW Sea Level -3.28 12.92 24.17 0.00 14.28

1600* Cruise 1g 1.8 357 1 DTOW 55,000 -20.39 5.66 TEF -10.09 -10.09 4.53

1700 Gust Loads 0.89 400 2.7 ZFW 20,000 -5.62 3.70 -4.97 0.00 3.79

TABLE 15.—TRIM DEFLECTIONS FOR ANTISYMMETRIC

COMPONENT OF MANEUVER LOAD CASES

Case 

no.

Description Mach Speed 

(KEAS)

Nz Weight Altitude 

(ft)

AIL1 AIL 2

1100 Steady roll 0.48 318 0 DTOW Sea Level 10.22 10.22

1200 Abrupt roll 0.48 318 0 DTOW Sea Level 11.84 11.84

1300 Steady roll 0.48 318 1.67 DTOW Sea Level 10.22 10.22

1400 Abrupt Roll 0.48 318 1.67 DTOW Sea Level 11.84 11.84

TABLE 16.—WEIGHTS AND CG FOR FINAL

MANEUVER LOAD CALCULATIONS

Configuration Weight 

(lb)

X-CG 

(in.)

Y-CG 

(in.)

Z-CG 

(in.)

DTOW 333,167 2032.5 0.0 258.4

DTOW2 301,645 2061.6 0.0 256.11

ZFW 158,042 2006.4 0.0 244.48
NASA/CR—2015-218719 73

3.3.4.1 CFD-Based Trimmed Load Results

Rigid CFD (Euler) trim solutions were generated for symmetric critical flight conditions as identified 

in Table 14. Good correlation between linear aerodynamic and CFD aerodynamics was obtained for 

subsonic and supersonic conditions at low angles of attack. Larger differences in the predicted trim state 

were noted at higher angles of attack. Based on these results, CFD-based results for load cases 300, 400, 

and 600 replaced the linear results for structural optimization. Vortex dominated flow features can be seen 

in the CFD-based results. Figure 90 shows the Cp distribution on the vehicle for load case 600, clearly 

illustrating the vortex scrub pattern on the upper surface of the wing and vertical tail. Figure 91 shows 

isosurfaces of vorticity magnitude colored by helicity, which gives more insight to the structure of the 

vortices for this condition. These effects can be seen in the applied loads once the pressures have been 

mapped to forces on the structural model. Figure 92 contains an image of the applied loads for load case 

600, in which the vortex scrub over the upper wing surface is clearly seen as a region of higher load.

Figure 90.—CFD-based Cp Distribution for Load Case 600, illustrating Vortex Dominated 

Effects on the Upper Surface
NASA/CR—2015-218719 74

Figure 91.—Isosurfaces of Vortex Magnitude colored with Helicity to Illustrate the Vortex 

Structure at M=1.41, Nz=2.5 (Load Case 600)

Figure 92.—Vector Plot of Applied Loads Colored by Z-Component of 

Force for Load Case 600 Illustrates Nonlinear Vortex Effects Captured by 

CFD-based Trim Process
NASA/CR—2015-218719 75

3.4 Finite Element Modeling

3.4.1 Geometry Processing for FEM Development

Development of the N+2 vehicle FEM was assisted by PreCEPT. This tool operates in conjunction 

with CATIA V5 to produce a clean idealized geometry dataset which has metadata associated to each 

surface. The metadata enables automated organization and simplification of the geometry dataset after 

import into a dedicated FEM pre- and post-processor. Altair Hypermesh was used as the FEM processor 

for this effort due to its open architecture, integration with our PreCEPT tool, and overall usability.

Importing the CATIA V5 part file into a session of Hypermesh that is linked to PreCEPT allows 

automated identification, naming and organization of the surface data set. The following figures show the 

raw import geometry (11,631 surfaces, lines and points) and the organized surface set (approximately 

1700 surfaces). The automated cleanup is possible because PreCEPT can discover and operate on the 

metadata associated to each surface. 

The surfaces in Figure 94 that represent the substructures (ribs, spars, frames) and lifting surfaces 

(wing, fuselage OML) are only trimmed by their own boundary. A single spar or rib surface is not 

trimmed by the internal intersection of other substructure members or the top and bottom lifting surface. 

The internal surface intersections were executed and the resulting shared surfaces topologically stitched 

together to form the final analysis surface geometry. The final geometry set consisted of 6261 distinct 

surfaces belonging to 793 components (see Figure 95). 

Figure 93.—N+2 Raw Geometry Import into Hypermesh

Figure 94.—N+2 Analysis Geometry after PreCEPT Automated Organization and Cleanup
NASA/CR—2015-218719 76

Figure 95.—N+2 fully trimmed surface geometry set prior to meshing

Figure 96.—N+2 Subassembly Map Exploded View

Minor manual geometry correction was performed as well as additional detailed component creation 

and organization that is reflected in the final Hypermesh database. In addition to the assembly/subassembly 

structures created automatically by PreCEPT, additional assembly structures were defined to aid discussion 

and exchange of data among the N+2 analysis team. Figure 96 shows pictorially the high level 

subassemblies created for this purpose.

These high level assemblies were further subdivided into parent assemblies for 2D entities (surfaces 

and shells), and 1D entities (substructure caps and stiffeners). A third general category was defined as a 

collection of additional details. Figure 97 shows the Hypermesh model browser used to navigate these 

groupings.

Notional manufacturing breaks were defined and the components were offset to break the continuous 

surface topology. Figure 98 shows the control surfaces, nacelles, tail and fuselage in this offset position. 

After meshing was complete, the subassemblies were offset back to the original location but retained the 

desired topology breaks. The ability to identify and operate on both large components and small details of 

the model was invaluable during FEM development.
NASA/CR—2015-218719 77

Figure 97.—N+2 Model Browser Showing High Level Assembly Structure

Figure 98.—N+2 Control Surface Hinge Definition in Offset State

3.4.2 Meshing 2D Shells on the Surface Geometry

Meshing the surface geometry was partly automated with PreCEPT and the Hypermesh automesh 

functionality. Extensive use of the element edge density interactive tools allowed for tuning of the automeshed surfaces to achieve the desired element density. Specifically, a minimum of 3 elements through 

the depth of the wing primary structures was generated to capture shear stress distributions. The lifting 

surfaces were adjusted to generate approximately 3x4 element densities on wing skin panels bounded by 

substructure. This was not a hard requirement but only a guideline. This resulted in an average OML 

element size of 6 to 9 in. The shell elements are located on the OML surfaces. The following figures show 

the mesh densities used in various portions of the model.
NASA/CR—2015-218719 78

Figure 99.—N+2 Element Density on Outer Wing Box

Figure 100.—N+2 Element Density on Inner Wing and Tail

Figure 101.—N+2 Element Density Fuselage

The 2D shell elements were output in NASTRAN format using CQUAD4 and CTRIA3 linear 

elements. Table 17 lists the 2D element counts for the various major subassemblies.

Material directions were generated for all 2D shell elements on the OML and substructure. Figure 102

shows the material directions on the lifting surfaces and fuselage. The OML material directions follow 

these guidelines.

• Inner wing - Positive span wise direction

• Outer wing - Parallel to trailing edge spar

• Tail - Parallel to trailing edge spar

• LE Control Surfaces - Parallel to trailing edge spar

• TE Control Surfaces - Parallel to leading edge spar

• Fuselage OML - Positive X axis

• Nacelle OML - Positive X axis

• Gear Doors - Positive X axis
NASA/CR—2015-218719 79

TABLE 17.—N+2 SHELL ELEMENT COUNTS BY SUBASSEMBLY

Subassembly 2D Elements

Pylon Center 157

Nacelle Center 611

Nacelle Right 990

LEF 1153

TEF 1607

Rudder 932

Body Flap 326

Vertical Tail 2287

Wing Outer 7943

Wing Boom 3333

Fuselage (includes LG door) 7394

Wing Inner (includes LG door) 7614

Control Surface Stiffeners 192

FEM 2D Shell Elements 34539

Figure 102.—N+2 OML Material Direction Definition

The substructure material directions are shown in Figure 103 and were determined using these 

guidelines.

• Ribs and Spars - Along the long direction of the substructure

• Generally in the plane 

of the parent lifting surface

• LE Control Surface - Positive Z axis (exception to above rule)

• Fuselage Floor - Positive X axis

• Under Floor Frame - Positive Z axis

• Pressure Domes - Rotation axis projected onto element surface
NASA/CR—2015-218719 80

Figure 103.—N+2 Substructure Material Direction Definition

Figure 104.—N+2 Bar and Rod Element Mesh Definition

3.4.3 Meshing 1D Bars and Rod Elements

Substructure (ribs, spars, frames) in the wing, tail and fuselage are connected to the OML surfaces 

through some fastening scheme. This can be discrete fasteners, rivets, bonding or a combination. In the 

N+2 FEM development, that level of detail has not been determined so the substructure/lifting surface 

interfaces were modeled with continuous meshes. To account for the additional load carrying capability 

of substructure caps, a bar or rod element is typically constructed along the intersection node line and 

assigned a property that is appropriate for the construction concept being modeled.

After the shell meshing was completed, PreCEPT generated 1D elements along the top and bottom 

intersections of each substructure component (rib, spars, frames) with the associated lifting surfaces 

(upper wing skin, lower wing skin, fuselage OML). The 1D elements were collected into upper or lower 

cap components and organized into the appropriate subassembly structure in Hypermesh. This greatly 

accelerated adding this detail to the FEM. These 1D elements were configured as NASTRAN ROD 

elements. 

Additional 1D elements were created in areas of the model that were not based on PreCEPTgenerated components (primarily fuselage). The ring frame stiffeners around the fuselage were modeled 

with BAR elements that were 4 by 0.25 in. sections without end offsets. In those cases, tools native to the 

Hypermesh pre-processor were used to complete the element mesh construction. Figure 104 and Table 18

summarize the 1D element mesh.
NASA/CR—2015-218719 81

TABLE 18.—N+2 BAR AND ROD ELEMENT

COUNTS BY SUBASSEMBLY

Subassembly 1D Elements

Pylon Center 0

Nacelle Center 508

Nacelle Right 780

LEF 817

TEF 968

Rudder 554

Body Flap 200

Vertical Tail 758

Wing Outer 2436

Wing Boom 1059

Fuselage 3295

Wing Inner 2319

Miscellaneous Details 1128

FEM 1D Bar and Rod Elements 14822

3.4.4 Additional FEM Details

The details discussed in the following sections are landing gear, gear doors, control surface hinges 

and engine suspension modeling. These details were added through manual processes using available 

tools.

3.4.4.1 Landing Gear FEM

A landing gear model that provided load transfer from the ground handling cases into the primary 

substructure was added for both a gear up and gear down configuration. The landing gear was modeled 

with CBAR, CROD, RBAR and MPC elements. Figure 105 shows the basic configuration of the main 

landing gear retraction and extension path superimposed on the FEM and the details of the FE abstraction. 

The retracted FEM for the main gear folds in a similar fashion.

Modeling both configurations of the landing gear was required to account for both retracted and 

extended load cases being used in the optimization solution. When specific static cases were being run or 

modal analyses being executed, one of the configurations was commented out in the bulk data. To 

facilitate modification for these situations, the fixed portion of the landing gear model as well as the 

attachment to the airframe was placed in the ‘incLandingGearModel.bdf’ file. This file in turn includes 

one or both of the ‘incLandingGearExtended.bdf’ or ‘incLandingGearRetracted.bdf’ files. The 

‘incLandingGearMasses.bdf’ file contains the concentrated masses associated with the fixed parts of the 

landing gear and is included at all times. This structure is illustrated in the Hypermesh include file 

browser in Figure 106.

The attachment of the main landing gear to the airframe primary substructure is shown in Figure 107. 

The attachment is modeled with rigid RBE2 to distribute the landing gear loads into the airframe. The 

landing gear section properties were estimated but not sized. Therefore, to prevent introduction of low 

frequency modes due to the un-sized landing gear, the steel material property was scaled up to provide a 

pseudo rigid structure that possessed reasonable mass and inertia properties and did not adversely affect 

the matrix solution.
NASA/CR—2015-218719 82

Figure 105.—N+2 Landing Gear Models

Figure 106.—N+2 Landing Gear Include File Hierarchy
NASA/CR—2015-218719 83

Figure 107.—N+2 Main Landing Gear Attachment

Figure 108.—N+2 Landing Gear Doors in Closed Position

Figure 109.—N+2 Main Landing Gear Doors Offset to show CBUSH Connections to Vehicle

3.4.4.2 Landing Gear Doors

The landing gear doors were modeled as load introduction structures attached to the airframe with 

discrete springs to provide load transfer normal to the door due to aerodynamic pressure, but limited 

stiffness elsewhere. The gear doors were not sized in the optimization but the appropriate mass was rolled 

up into the appropriate subassembly (fuselage or wing). The gear doors were modeled in the door closed 

configuration. No open door configuration was explicitly modeled. Figure 108 and Figure 109 show the 

various details of the doors and door attachment.
NASA/CR—2015-218719 84

The main landing gear doors were attached to the fuselage and lower wing substructure with hinge 

connections along the top and bottom edges, the sides were connected with radial springs. The seam 

between the doors was also modeled with springs but free in the seam direction. The doors are composite 

honeycomb skins with an aluminum stiffener frame.

3.4.4.3 Control Surface Hinges

The control surface hinge structures were modeled with a bow-tie structure (Figure 110) to provide a 

modeled representation of the hinge fitting mass and inertia and to provide coincident points for elastic 

connections on the defined hinge line. 

PreCEPT-generated control surfaces enable automated generation of the mesh structures, local 

coordinate systems, and elastic connections for all control surfaces. The trailing edge control surfaces are 

shown in Figure 111. The local coordinate systems are defined with the Y-axis along the hinge line and a 

positive rotation deflects the control surface trailing edge downward. The hinges were locked in rotation 

(1.0×1010 in-lb/rad) and the bearing stiffness was set to a large value (1.0×109 lb/in). 

Extensive checks were performed on the control surface load transfer to verify proper hinge 

operation. An individual load case (LC400) was selected from the linear aerodynamic model hinge 

moment results and was compared to the applied force free-body of each control surface (Figure 112). 

The free-body forces were resolved about a point on each hinge line using the control surface local 

coordinate system and summing the results for each hinge. The computed free-body hinge moments 

matched the panel aero hinge moment data.

Figure 110.—N+2 Typical Control 

Surface Hinge Structure (Bow-tie)

Figure 111.—N+2 Control Surface Modeling Details on Trailing Edge 

Structures

Figure 112.—N+2 Control Surface Loads used for Free-body Hinge Moment Checks
NASA/CR—2015-218719 85

3.4.4.4 Engine Suspension System

The engine mass and nacelle structures are modeled as a single fore and aft coincident point 

connection to the center pylon and under wing pylon structures. The points are interconnected with 

CBUSH elastic elements. The stiffness properties of these springs represent the suspension system for 

each engine/nacelle subsystem. Figure 113 shows the definition of the suspension system model. 

The engine mass was modeled as a concentrated mass along the center line of the nacelle and 

connected to the interior of the nacelle with a single RBE3 spider element to distribute the mass. The 

mass, inertia and stiffness properties for the right engine system are listed in Figure 114.

These values were selected to produce a first roll mode of approximately 3.5 Hz and a first pitch 

model of 6 to 10 Hz. These values were targeted after a literature search of other modern day aircraft with 

pylon-engine-nacelle arrangements.

Figure 113.—N+2 Propulsion System Suspension Interface Model

Figure 114.—N+2 Propulsion System Suspension Properties

Figure 115.—N+2 Discrete Spring Attachment of Forward Wing to Fuselage
NASA/CR—2015-218719 86

3.4.4.5 Wing—Fuselage Attachment

The wing forward of the main landing gear bay is only attached to the fuselage at select locations 

along the structural stiffeners on the fuselage and the forward root rib of the inner wing. This attachment 

is modeled with NASTRAN CBUSH elements as shown in Figure 115. The stiffness properties are 

essentially rigid in the translational degrees of freedom creating a pinned condition. Further definition of 

this attachment arrangement is needed to refine the model in this area. A trade study of attachment 

concepts and the effect on fuselage / wing interaction would be beneficial.

3.4.5 FEM Element and Node Numbering

The various subassemblies were individually renumbered according to a defined schema that was 

maintained in a spreadsheet. After the initial renumbering, element sets were exported in several formats 

to support use of the model in other software tools. The numbering schema is keyed by subcomponent 

names defined in the Hypermesh database. Table 19 defines the numbering used in this effort.

TABLE 19.—N+2 FEM NUMBERING MASTER SCHEMA

Assembly Subcomponent Element

Start

Size End

Miscellaneous at model level 1 999 999

1000 1000 1999

2000 3000 4999

5000 5000 9999

Connections 10000 10000 19999

cnxfuselage2innerwing 10000 1000 10999

cnxpylon2nacellecenter 11000 500 11499

cnxboom2nacelleright 11500 500 11999

cnxboom2verticaltail 12000 500 12499

cnxlef2innerwing 12500 500 12999

cnxlef2outerwing 13000 500 13499

cnxtef2outerwing 13500 500 13999

cnxrudder2verticaltail 14000 500 14499

cnxbodyflap2innerwing 14500 500 14999

cnxnlg2fuselage 15000 100 15099

cnxmlg2innerwing 15100 200 15299

cnxnlgdoor2fuselage 15300 100 15399

cnxmlgdoor2fuselage 15400 100 15499

cnxmlgdoor2innerwing 15500 100 15599

cnxmlgdoor2mlgdoor 15600 100 15699

OPEN 20000 10000 29999

30000 20000 49999

AERO PANELS 100000 100000 199999

Inner Wing 200000 100000 299999

innerwingUpperskin 200000 4000 203999

innerwingLowerskin 204000 4000 207999

innerwingInterior2D 210000 5000 214999

innerwingInterior1D 215000 5000 219999

innerwingOther 220000 5000 224999

innerwingMainGearDoor 225000 1000 225999

bodyflapUpperskin 230000 200 230199

bodyflapLowerskin 230200 200 230399

bodyflapInterior2D 230400 200 230599

bodyflapInterior1D 230600 300 230899

bodyflapOther 232000 0 231999

Boom 300000 100000 399999
NASA/CR—2015-218719 87

TABLE 19.—N+2 FEM NUMBERING MASTER SCHEMA

Assembly Subcomponent Element

Start

Size End

boomUpperskin 300000 1500 301499

boomLowerskin 301500 1500 302999

boomInterior2D 303000 3000 305999

boomInterior1D 306000 2000 307999

Pylon 308000 2000 309999

pylonOML 308000 500 308499

pylonInterior2D 308500 500 308999

pylonInterior1D 309000 500 309499

Outer Wing 400000 100000 499999

outerwingUpperskin 400000 3000 402999

outerwingLowerskin 403000 3000 405999

outerwingInterior2D 410000 5000 414999

outerwingInterior1D 415000 3000 417999

outerwingOther 420000 0 419999

Wing Flaps 430000 20000 449999

lefUpperskin 430000 400 430399

lefLowerskin 430400 400 430799

lefInterior2D 431000 500 431499

lefInterior1D 431500 1000 432499

lefOther 433000 0 432999

tefUpperskin 440000 600 440599

tefLowerskin 440600 600 441199

tefInterior2D 441400 600 441999

tefInterior1D 442000 1000 442999

tefOther 443000 0 442999

Vertical Tail 500000 100000 599999

vtailUpperskin 500000 3000 502999

vtailLowerskin 503000 3000 505999

vtailInterior2D 510000 5000 514999

vtailInterior1D 515000 3000 517999

vtailOther 520000 0 519999

rudderUpperskin 540000 400 540399

rudderLowerskin 540500 400 540899

rudderInterior2D 541000 400 541399

rudderInterior1D 541400 600 541999

rudderOther 542000 0 541999

Fuselage 600000 100000 699999

fuselageNoseFairing 600000 1000 600999

fuselageOML 601000 9000 609999

fuselageInterior2D 610000 5000 614999

fuselageInterior1D 615000 5000 619999

fuselageOther 620000 5000 624999

fuselageNoseGearDoor 625000 1000 625999

fuselageMainGearDoor 626000 1000 626999

fuselageNoseGearStructure 630000 1000 629999

fuselageMainGearStructure 631000 1000 630999

AERO 700000 100000 799999

Engine Nacelle 800000 100000 899999

nacelleCenterUpperskin 800000 200 800199

nacelleCenterLowerskin 800200 200 800399

nacelleCenterInterior2D 800400 300 800699

nacelleCenterInterior1D 802000 1000 802999
NASA/CR—2015-218719 88

TABLE 19.—N+2 FEM NUMBERING MASTER SCHEMA

Assembly Subcomponent Element

Start

Size End

-1

nacelleRightUpperskin 803000 400 803399

nacelleRightLowerskin 803400 400 803799

nacelleRightInterior2D 803800 600 804399

nacelleRightInterior1D 806000 2000 807999

Concentrated Masses 900000 100000 999999

incNonStructuralMass.bdf 900000 3000 902999

incWingSmearedMass.bdf 903000 5000 907999

incAftDeckSmearedMass.bdf 908000 1000 908999

incVerticalTailSmearedMass.bdf 909000 1000 909999

incFuselageSmearedMass.bdf 910000 4000 913999

incEngineStartAndControlsMasses.bdf 914000 1000 914999

incWeightMargin.bdf 915000 15000 929999

incFuelTank1Mass100pct.bdf 930000 1000 930999

incFuelTank2Mass100pct.bdf 931000 1000 931999

incFuelTank3Mass100pct.bdf 932000 1000 932999

incFuelTank4Mass100pct.bdf 933000 1000 933999

incFuelTank5Mass100pct.bdf 934000 1000 934999

incFuelTank6Mass100pct.bdf 935000 1000 935999

incFuelTank7Mass100pct.bdf 936000 1000 936999

incFuelTank8Mass100pct.bdf 937000 1000 937999

incFuelTank9Mass100pct.bdf 938000 1000 938999

incFuelTank10Mass100pct.bdf 939000 1000 939999

incFuelSystemMass.bdf 940000 4000 943999

incUnusableFuelAndOilMass.bdf 944000 4000 947999

The node numbering is not as consistent because adjacent subcomponents share many nodes. 

Therefore, the numbering generally follows the same schema but will have inconsistencies due to the 

sharing of nodes between many subcomponents.

3.4.5.1 FEM Element/Node Counts

Table 20 lists the total counts for each NASTRAN element type. The data is consistent with the 

Hypermesh database used to export the basic N+2 FEM and the solution decks used for static and modal 

analyses. Specialty analyses such as linear panel aerodynamics or CFD cosimulation may incorporate 

additional elements to account for coupling/interaction between the multidisciplinary analyses.

The total number of nodes in the basic FEM is 28557. Two nodes are used as origin locations for 

coordinate systems and are not used by any structural elements. Again, for specialty analyses, additional 

nodes may be included as appropriate.
NASA/CR—2015-218719 89

TABLE 20.—N+2 ELEMENT TYPE BREAKDOWN AND COUNTS

Element 

Category

Category 

Total

Element 

NASTRAN

Element 

Count

MASS 30122

CONM2: 30122

WELD 4

RBAR: 4

RIGID 35

RBE2: 35

RBE3 33

RBE3: 33

SPRING 213

CBUSH: 213

ROD 11932

CROD: 11932

BAR2 2890

CBAR: 2890

TRIA3 1412

CTRIA3: 1412

QUAD4 33127

CQUAD4: 33127

TRIA6 0

QUAD8 0

BAR3 0

GAP 0

PLOT 0

JOINTS 0

INTERFACE PANELS 0

TETRA4 0

PENTA6 0

HEX8 0

TETRA10 0

PENTA15 0

HEX20 0

Total Elements 79768 79768

3.4.6 FEM Include File Structure

The model was further organized into an include file structure. This structure is retained in the 

delivery package for the FEM and is documented in the delivery package README.TXT file. Relevant 

portions of the file are reproduced in Table 21.
NASA/CR—2015-218719 90

TABLE 21.—N+2 NASTRAN FEM INCLUDE FILE STRUCTURE

[[[[ FE MODEL INCLUDE FILES ]]]]

incGridBasic.bdf

incElementBasic.bdf

incTailRootRibExtraAttach.bdf

incLandingGearModel.bdf >>> Stationary components of LG model <<<

incLandingGearExtended.bdf >>> Included in LG model gear down config. [Commented out] <<<

incLandingGearRetracted.bdf >>> Included in LG model gear up config. <<<

incLandingGearDoors.bdf >>> Doors Closed <<<

incGenericProperties.bdf

incProjectMaterials.bdf

incProjectPcompProperties.bdf >>> SIZED COMPOSITE PROPERTY DATASET <<<

incProjectMetalProperties.bdf >>> SIZED METALLIC PROPERTY DATASET <<<

incProjectProperties.bdf

incProjectPbushProperties.bdf

incBoundaryConditions.bdf >>> Symmetric boundary conditions <<<

incBoundaryConditions_AntiSYM.bdf

incEngineStartAndControlsMasses.bdf

incLandingGearMasses.bdf

incFuelSystemMass.bdf

incFuelTank10Mass100pct.bdf

incFuelTank1Mass100pct.bdf

incFuelTank2Mass100pct.bdf

incFuelTank3Mass100pct.bdf

incFuelTank4Mass100pct.bdf

incFuelTank5Mass100pct.bdf

incFuelTank6Mass100pct.bdf

incFuelTank7Mass100pct.bdf

incFuelTank8Mass100pct.bdf

incFuelTank9Mass100pct.bdf

incUnusableFuelAndOilMass.bdf

incNonStructuralMass.bdf >>> Non-structural mass parent file <<<

incBodyFlapActuatorMasses.bdf

incRudderActuatorMasses.bdf

incOutboardEngineMass.bdf

incCenterEngineMass.bdf

incPassengerMass.bdf

incBaggageMass.bdf

incStdOperatingItems.bdf

incAuxPower.bdf

incInstruments.bdf

incAvionics.bdf

incFunishings.bdf

incLEFActuatorMasses.bdf

incTEFActuatorMasses.bdf

incWeightMargin.bdf

incAftDeckSmearedMass.bdf>>> Make-up mass files <<<

incFuselageSmearedMass.bdf

incVerticalTailSmearedMass.bdf

incWingSmearedMass.bdf
NASA/CR—2015-218719 91

3.4.7 Material Property Definition

The physical properties and material properties of the N+2 vehicle evolved as the development effort 

matured. A brief discussion of this process is provided, followed by a summary of the final configuration.

3.4.7.1 Material Systems Used

The material systems and associated ID’s used in the N+2 final FEM are summarized in Table 22.

3.4.7.2 Material Property Maturation Process

Automated methods were used early in the N+2 effort to generate metallic properties that sped FEM 

development. The properties were assigned to the PreCEPT components (ribs, spars, lifting surfaces, 

control surfaces, etc...) and named accordingly. Initial check-out solutions were performed with this 

property set.

The generic properties were replaced with a definition of an all-composite airframe that used 

sandwich panel construction for all surfaces. Additional properties were defined as the model matured to 

account for model details not present in the original PreCEPT definition. Thorough check-out solutions 

were performed with this property set.

A cloned copy of the composite property from the parent component was generated and assigned to 

each geometric surface and the associated elements. This greatly expanded the number of unique 

properties in the model but formed a basis set of properties for defining optimization design zones and 

buckling panels. The string ‘.Surf.xxxx’ is appended to the master property for each component where 

‘xxxx’ is replaced by the surface ID from the Hypermesh database. The automated generation and 

consistent naming of properties was invaluable in the FEM development process and enabled application 

of other semi-automated tools for manipulating the designed property zones used when sizing the vehicle.

The design zone definition process merged many of these unique properties into constant property zones 

that were used for optimizing the structure. After reviewing the results of the optimization, the design 

zones were further subdivided in areas of active sizing. Lastly, metallic properties (titanium and 

aluminum) were introduced in critical load carrying areas. This maturation process is illustrated in 

Figure 116.

TABLE 22.—BASIC MATERIAL

PROPERTY VALUES

Isotropic ID

Generic Aluminum 1

Aluminum (typical) 100

Titanium (typical) 103

Stiff Aluminum 303

Stiff Steel 304

Cap Material 22000

Orthotropic ID

Core 10056

Graphite fabric 20000

Graphite tape 20075
NASA/CR—2015-218719 92

Figure 116.—Property Set Maturation

The introduction of metallic materials into the sizing process produced a FEM that was representative 

of material utilization on modern aircraft. Figure 117 summarizes the areas where metallics were used and 

shows a comparison to current commercial aircraft. The commercial aircraft compositions were 

determined from general literature sources and are approximate values. The N+2 composition percentages 

were: composites 55 percent, aluminum 26 percent, titanium 16 percent, and steel 3 percent. 

The final property set consisted of 3165 properties of which 685 are currently used. The vast majority 

of those properties are the cloned PCOMPs for each OML and substructure surface. Most of these were 

subsequently merged in the design zone creation process. The property type breakdown and counts are 

shown in Figure 118.

The composite structures are sandwich construction consisting of carbon fiber facesheets over a 

honeycomb core. Various parts of the vehicle were assigned different core thicknesses based on trade 

studies discussed in Section 3.5.4. The structural optimization solution determined the total thickness of 

each facesheet ply direction (see Section 3.5.5 for final sizing results). The core thickness for the 

composites of major areas is below:

• OML Skins - 0.50 in.

• Substructure Webs - 0.25 in.

• Fuselage Interior - 0.25 in. (aft cabin floor is 0.75)

• Pressure Domes - 0.50 in.

• Access Doors - 0.75 in.

• Gear Doors - 0.75 in.
NASA/CR—2015-218719 93

Figure 117.—N+2 Metallic Usage and Overall Composition

Figure 118.—N+2 Property Type Counts

This construction was modeled using NASTRAN PCOMP bulk data entries with ‘SMCORE’ 

specified in the LAM field. This allows modeling the total thicknesses of each ply direction without 

introducing an actual stacking sequence. The smeared approach is highly desirable when running 

optimization solutions since it reduces the potential design variables to 3 or 4 per property zone. 

3.4.8 Nonstructural Mass—0D Elements

The modeled primary structural components of the N+2 FEM comprise 18 percent of the total gross 

take-off weight. The remainder is comprised of nonstructural masses for fuel, systems, payload, 

nonmodeled structure and growth margin. The weight statement provided by the mass properties group 
NASA/CR—2015-218719 94

defines estimates of these weights and also roll-ups to major subassembly target weights for each 

configuration.

These nonstructural masses are incorporated in the FEM by creating concentrated mass elements on 

the nodes that are located along intersections between the skin OML and the substructure. Extensive 

organization of these mass elements into 32 separate bulk data include files enables rapid updates and 

configuration of the vehicle model for various mass states (Figure 119).

This technique distributes the system masses over the proper area of the model without introducing 

spurious panel modes on the lifting surface OML. Discrete system masses were modeled at their center of 

gravity and connected to the FEM through constraint elements (RBE2 and RBE3).

The FEM was divided into major element groups that closely mapped to the weight statement 

subassemblies for primary structure. These groups are shown in Figure 120.

Figure 119.—N+2 Distributed Masses

Figure 120.—N+2 Element Sets for Weight Statement Breakdown
NASA/CR—2015-218719 95

These element sets were weighed and compared to the weight statement estimate. The difference was 

divided by the number of intersection nodes in each set and scaled to bring the total of the element group 

up to the weight statement estimate. When a new set of thicknesses were output from the gage 

optimization, these make-up or ‘smeared’ masses were re-scaled to recover the proper subassembly 

weight. This technique enables reuse of trimmed load sets for a given weight state of the air vehicle. 

Figure 121 shows the smeared masses for REV70 of the optimized airframe.

Figure 122 illustrates the distribution of the actual fuel mass as well as the distributed fuel system 

masses (pipes, pumps, etc...). 

The remainder of the distributed masses is shown in Figure 123. Lastly, the growth margin mass was 

distributed over the entire vehicle. No nonstructural mass terms on structural elements were used.

Figure 121.—N+2 Smeared Make-up Mass for FEM REV70

Figure 122.—N+2 Fuel Tanks and Fuel System Distributed Masses
NASA/CR—2015-218719 96

Figure 123.—N+2 Miscellaneous Distributed Masses

3.4.9 FEM Morphing to Increase Aft-Deck and Tail Stiffness

Morphing as applied to a FEM allows the mesh of the model to be deformed according to rules and/or 

equations that the user defines. This type of model modification changes the geometric properties of the 

model but does not disturb the node or element IDs, the element property assignments or existing load 

definitions. It is a desirable solution to geometric design changes when a significant number of man-hours 

have been invested in development of a FEM that has been distributed to other engineering disciplines 

that depend on a defined model numbering scheme. Significant rework can be involved if a published 

FEM must be renumbered or reconstructed. 

In the course of sizing the N+2 vehicle, concerns arose over the tail and aft deck deflections under 

load. These concerns initiated an effort to modify the FEM to provide better load transfer from the tail 

to the centerline of the aft fuselage. Several options were considered and 3 modifications selected

(Figure 124).

1. Thicken the aft deck up to 50 percent of the local depth to increase stiffness of the aft deck 

substructure and the tail attachment

2. Remove a sharp angle between the aft lower skin and the aft fuselage (referred to as a crease in the 

OML) to increase the minimum depth of the aft deck substructure

3. Increase the tail T/C by 26 percent from 2.5 to 3.16 percent to reduce bending of the tail structure

The morphing domains (related mesh entities) and the morphing handles (movable points on a 

domain) were constructed in Hypermesh. The data applied to the morph handles for the aft deck was 

generated by defining the desired profiles at 4 locations in the span wise direction and then performing a 

Matlab 3D surface fit to the profiles (Figure 125). The function returned by the surface fit was used to 

calculate the handle deflections. The morph shape was imported into Hypermesh and applied to the 

model.
NASA/CR—2015-218719 97

Figure 124.—N+2 Morphing Shapes Show Magnitude of Thickness Increases

Figure 125.—N+2 Aft Deck Morphing Process

Tail Thickness

Aft Deck Depth

Eliminate Crease
NASA/CR—2015-218719 98

The tail morphing was done entirely in Hypermesh with the addition of constraints to prevent the 

leading and trailing edges from moving. The rudder hinge line was also constrained prior to the 

symmetric thickening of the tail (Figure 126).

Lastly, removing the crease in the OML was also done entirely in the preprocessor. However, all the 

morphing shapes (the perturbations applied) were exported and saved to provide an updating capability 

for prior versions of the N+2 FEM.

One additional morphing operation was performed but not incorporated into the structural FEM. The 

structural FEM used in the CFD loads mapping process requires that the control surfaces not collide when 

deflected for trimming. Therefore, the trailing edge ailerons and flaps were morphed to provide clearance 

when deformed during trimming (Figure 127). The morphing shape was developed along with the 

structural FEM for accurate mapping across the fluid structure interfaces used during the CFD aeroelastic 

analysis.

Figure 126.—N+2 Morphing of the Tail Thickness

Figure 127.—N+2 Trailing Edge Darts for CFD Loads Generation
NASA/CR—2015-218719 99

3.5 Structural Sizing

The structural gauges of the FEM were sized to the design load cases of Section 3.3 in order to arrive 

at an airframe with representative stiffness for a credible flutter analysis. We went through several 

iterations of structural sizing, starting with a coarse all-composite design model and then refining the 

model to have more design zones so as to avoid large zones being sized by small stress concentrations. In 

the end, we conducted a final sizing of the FEM with metallic and composite materials. It is the results of 

this final sizing that are summarized in this section. 

3.5.1 Approach

Our process for sizing the structure is shown in Figure 128. The structural sizing is ultimately done in 

MSC.NASTRAN SOL200, which utilizes gradient based optimization to determine optimal gauges that 

minimize weight while satisfying strength, buckling, and manufacturability criteria. Our in-house tool, 

AS3, serves as a front-end to the SOL200 optimization by translating design variable definition, 

stress/strain constraint definition, and buckling panel definition from a simple format to the NASTRAN 

bulkdata format. The buckling constraints are implemented through an external function call by SOL200 

to our in-house panel buckling routine, TM1A, which assumes simply supported boundary conditions.

AS3 creates the bulkdata for the structural design variables and constraints, which is then combined with 

the rest of a ready-to-run SOL200 model that contains the basic FEM, design loads, design objective 

function (in our case weight), and case control statements.

Figure 128.—Structural Sizing Approach

AS3 Input Deck

SOL200 Deck

Design Load Cases (STATICS)

Structural FEM

Design Variables

Design Constraints

Objective Function

(Minimize Weight)

MSC.NASTRAN SOL200

(MDNAST10r1) AS3/Sizer
NASA/CR—2015-218719 100

3.5.2 Design Criteria

Our design criteria for sizing the structure under the given set of design loads consisted of 

stress/strain criteria, minimum gauge criteria, ply percentage criteria for composites, and buckling criteria.

These criteria were evaluated at ultimate load which was defined as 1.5 times the design limit load cases 

of Section 3.3. Table 23 summarizes the criteria for both the composite parts and metallic parts. The 

strength criteria were knocked down from their room temperature allowable to account for an elevated 

temperature of 210 °F which corresponds to an adiabatic wall temperature at a cruise condition of 

Mach 2.0, as specified in our Mach, Altitude envelope (see Figure 74). The ply percentage constraints, 

both minimum and maximum values, were based on our composite best-practices design guidelines. Ply 

percentage is defined as the ratio between the thickness of one material orientation (e.g., 0° plies) to the 

thickness of the overall laminate, not including the core thickness.

The buckling criteria are evaluated by MSC.NASTRAN SOL200 through an external function call to 

TM1A. Area averaged running loads from the FEM analysis (NXX,NYY,NXY), along with current laminate 

definition, core thickness, and panel dimensions are provided by NASTRAN to TM1A, which then 

returns the buckling eigenvalue, which is constrained to be greater than 1.0 at ultimate load. Figure 129 to

Figure 135 show the buckling panels that are defined as part of the structural sizing model. In total there 

are 1791 buckling panels. The total number of stress/strain criteria per load case is 199,524.

TABLE 23.—DESIGN CRITERIA FOR STRUCTURAL SIZING

Criteria Graphite/BMI with Ti Core Aluminum Titanium

Strength criteria Fiber strain based on damage tolerance 

allowable at elevated temp

von Mises ≤ XX ksi

Max Shear Stress ≤ XX ksi

von Mises ≤ XXX ksi

Max Shear Stress ≤ XX ksi

Minimum gauge Skins—8 plies per facesheet

Substructure—4 plies per facesheet 0.06 in. 0.06 in.

Ply percentage Yes No No

Buckling Yes—Core thickness remains constant Yes—number of stiffeners 

remains constant

Yes—number of stiffeners 

remains constant

Figure 129.—477 Buckling Panels on Inner Wing Skin 

Close-up of Buckling Panels on Boom
NASA/CR—2015-218719 101

Figure 130.—367 Buckling Panels on Inner Wing Substructure 

Figure 131.—252 Buckling Panels on Upper and Lower Outer Skin
NASA/CR—2015-218719 102

Figure 132.—265 Buckling Panels on Outer Wing Substructure
NASA/CR—2015-218719 103

Figure 133.—40 Panels on Tail Skins

Figure 134.—44 Panels on Tail Substructure
NASA/CR—2015-218719 104

Figure 135.—346 Buckling Panels on Fuselage OML

3.5.3 Design Variables and Zones

The design variables for structural sizing are the thickness of the shell elements that comprise the 

primary structure of the vehicle. This includes the main outer wing box, tail wing box, inner wing and 

boom, and fuselage. We did not size the control surfaces, nor did we size the vehicle nose. As indicated in 

Section 3.4 the FEM makes significant use of composite materials with metallics used for major load 

introduction and bulkheads. We divided the designed shell elements of the airframe into constant property 

zones, where each element in the zone is assigned to the same property (either a PCOMP or PSHELL).

The strategy behind breaking up the model into design zones was to have enough resolution such that the 

thickness of large panels was not being driven by a small stress or strain concentration, while at the same 

time keeping the number of independent design variables (and thus run-times) to a reasonable level. In the 

end, the sizing model contained 1078 independent design variables.

The elements associated with skins were comprised of a Graphite composite tape system with 4 

material directions, 0°, 45°, –45°, and 90° directions. The thickness of each material direction was 

allowed to be a design variable, except that the +45° and –45° thickness were constrained to be equal in 

order to maintain a balanced laminate per composite design best practices. Thus there are 3 independent 

design variables for a composite skin design zone. The composite substructure (rib, spars, bulkheads, and 

longerons) were comprised of a Graphite composite fabric system, and we enforced these elements to be 

quasi-isotropic laminates (equal numbers of 0, 45, –45, and 90), since ribs and spars tend to be shear 

dominant. Thus there is 1 independent design variable for a composite substructure property zone. For the 

metallic property zones, there is naturally only 1 design variable per zone, the thickness of the shell 

element, since metals are isotropic.

Figure 136 to Figure 154 show the design zones that were considered as part of the structural sizing, 

with the number of independent design variables per region also provided. Each uniquely colored region 

is an independent design zone.

Fuselage panels are curved to 

reflect curvature of fuselage OML
NASA/CR—2015-218719 105

Figure 136.—Composite Inner Wing Skin Design Zones—109 Zones, 327 Independent Design Variables

Figure 137.—Aluminum Inner Wing Skin Design Zones—9 Zones, 9 Independent Design Variables
NASA/CR—2015-218719 106

Figure 138.—Titanium Inner Wing Skin Design Zones—9 Zones, 9 Independent Design Variables

Figure 139.—Composite Inner Wing Substructure Design Zones—82 Zones, 82 Independent Design Variables
NASA/CR—2015-218719 107

Figure 140.—Aluminum Inner Wing Substructure Design Zones—15 Zones, 15 Independent Design Variables

Figure 141.—Titanium Inner Wing Substructure Design Zones—22 Zones, 22 Independent Design Variables
NASA/CR—2015-218719 108

Figure 142.—Composite Outer Wing Skin Design Zones—52 Zones, 156 Independent Design Variables

Figure 143.—Composite Outer Wing Substructure Design Zones—59 Zones, 59 Independent Design Variables
NASA/CR—2015-218719 109

Figure 144.—Aluminum Outer Wing Substructure Design Zones—2 Zones, 2 Independent Design Variables

Figure 145.—Titanium Outer Wing Substructure Design Zones—2 Zones, 2 Independent Design Variables
NASA/CR—2015-218719 110

Figure 146.—Composite Tail Skin Design Zones—20 Zones, 60 Independent Design Variables

Figure 147.—Composite Tail Substructure Design Zones—17 Zones, 17 Independent Design Variables
NASA/CR—2015-218719 111

Figure 148.—Titanium Tail Substructure Design Zones—6 Zones, 6 Independent Design Variables

Figure 149.—Composite Fuselage OML Design Zones—71 Zones, 213 Independent Design Variables
NASA/CR—2015-218719 112

Figure 150.—Aluminum Fuselage OML Design Zones—5 Zones, 5 Independent Design Variables

Figure 151.—Aluminum Fuselage Substructure Design Zones—1 Zone, 1 Independent Design Variable
NASA/CR—2015-218719 113

Figure 152.—Composite Fuselage Substructure Design Zones—87 Zones, 87 Independent Design Variables

Figure 153.—Titanium Fuselage Substructure Design Zones—3 Zones, 3 Independent Design Variables
NASA/CR—2015-218719 114

Figure 154.—Titanium Center Pylon Design Zones—3 Zones, 3 Independent Design Variables

3.5.4 Core and Stiffener Trade Studies

For the original FEM sizing studies with the all-composite vehicle, we had been assuming 3⁄4 in. thick 

honeycomb core for all of the designed elements. Core thickness chiefly affects buckling margins, and we 

learned that the buckling criteria drove very little of the structural sizing. This suggested that our original 

assumptions for core thickness were too conservative and thus possibly carrying unnecessary amounts of 

core weight. Hence, we initiated a sizing trade study to look at variation in the core thickness and to 

identify the minimum weight core thickness on a global level. As shown in Figure 155, we conducted 

sizing with various combinations of skin and substructure core thickness (ranging in increments of 0.75, 

0.5, and 0.25 in.). From the results of these studies, we concluded that on an overall level, 0.5 in. of core 

for the skins and 0.25 in. of core for the substructure was the minimum weight solution. Further 

optimization of the core thickness could be conducted for each panel, but this sort of detail optimization 

of each panel was beyond the scope of this study and felt to have little impact on the overall aeroelastic 

characteristics of the vehicle.

The metallic buckling panels were stiffened with discrete stringers. Like the core thickness, the 

stringer spacing principally influences the buckling margins. As a result, we conducted a trade study in 

the stringer spacing applied uniformly over every metallic panel. Figure 156 shows the results of this 

trade study, plotting the total weight of the sized elements plus the weight of the stiffeners (which are not 

modeled directly in the FEM but only on the buckling panel definition). Based on the results of this study, 

we selected 9 in. stringer spacing for all metallic buckling panels. Like the core thickness, further 

optimization could be performed on each individual panel, but that was beyond the scope of this activity.
NASA/CR—2015-218719 115

Figure 155.—Core Thickness Trade Study

Figure 156.—Stiffener Spacing Trade Study

3.5.5 Final Sizing Results

The FEM was sized to the criteria of the previous section. This FEM was designated FEM017-

REV70. Upon analysis of this structural design to the design loads, we discovered that tail deformation 

for one of the elevated g pull-ups (LC400) was still excessive, nearly 47 in. for a tail span of roughly 

250 in. (see Figure 157). In discussions with subject matter experts in the field of nonlinear mechanics, 

beyond 10 percent displacement of the span of the surface, geometric nonlinearity begins to become more 

significant. Consequently, we also ran a sizing where we added a constraint on tail deformation for load 

case 400 to be less than 25 in. (roughly 10 percent of the tail span). This FEM was designated FEM017-

REV71. A comparison of the mass properties of each FEM is found in Table 24.

As seen in Table 24, the inclusion of the displacement constraint on the tail adds 1731 lb to the 

airframe weight, with most of it being added to the aft deck and the rest being added to the tail. This is 

also reflected in the plots of sized airframe thickness, shown in Figure 158 to Figure 165.

12000

12500

13000

13500

14000

14500

15000

15500

skin 0.75 sub 0.75 skin 0.75 sub 0.5 skin 0.5 sub 0.5 skin 0.5 sub 0.25 skin 0.25 sub 0.25

Weight of Sized Elements [lb]

Total Sized Element Weight versus Core Thickness

Total Sized Element Weight

14000.00

14100.00

14200.00

14300.00

14400.00

14500.00

14600.00

2 3 4 5 6 7 8 9 10

Sized Weight, including stiffeners [lb]

Stiffener spacing [in]

 
NASA/CR—2015-218719 116

Figure 157.—Tail Deformation for LC 400 (2.5 g Pull-Up, Mach 2.0, DTOW2)

TABLE 24.—WEIGHT SUMMARY OF SIZED FEMS

Full Span Mass Property 

Statement

FEM 017

Metal Rev70 SS

Adjusted Mass

FEM 017

Metal Rev71 SS

Adjusted Mass

Wing 31504.0 24818.3 24755.2

Aft Deck 6934.0 4252.0 5371.6

Tail 4014.0 3703.3 4504.3

Fuselage 15086.0 11891.5 11764.9

Subtotal 57538.0 44665.2 46395.9

Nacelle+Inlet (structure) 10946.0 12664.4 12664.4

NLG (structure + mass) 1377.0 1374.6 1374.6

MLG (structure + mass) 11096.0 11095.6 11095.6

Non‐Fuel Sys Mass 76651.0 68007.0 68007.0

FEM Makeup Mass 0.0 19801.3 18070.6

Zero Fuel Weight 157608.0 157608.1 157608.1

Fuel Mass (updated) 175124.0 175124.0 175124.0

GTOW 332732 332732 332732

CG‐X Gear Up) 2045.00 2021.75 2022.89

CG‐Y

CG‐Z 257.10 258.39 258.51

~70” displacement

(47” limit)

REV70 Structural Design

Ultimate Load
NASA/CR—2015-218719 117

Figure 158.—Inner Wing Composite Skin Thickness (Includes 0.5 in. core)

Figure 159.—Inner Wing Composite Substructure (Includes 0.25 in. core)

FEM017 – REV70 FEM017 – REV71

4171 lb 4578 lb

Includes 0.5” core

Upper Skin

Lower Skin

Upper Skin

Lower Skin

[in] [in]

FEM017 – REV70 FEM017 – REV71

1156 lb 1173 lb

Includes 0.25” core

[in] [in]
NASA/CR—2015-218719 118

Figure 160.—Inner Wing Metallic Substructure

Figure 161.—Outer Wing Composite Skin Thickness (includes 0.5 in. core)

FEM017 – REV70 FEM017 – REV71

736 lb 760 lb

[in]

[in]

FEM017 – REV70 FEM017 – REV71

2226 lb 2243 lb

Includes 0.5” core

Upper Skin

Lower Skin

Upper Skin

Lower Skin

[in] [in]
NASA/CR—2015-218719 119

Figure 162.—Outer Wing Substructure Thickness (composite includes 0.25 in. core)

Figure 163.—Tail Composite Skin Thickness (includes 0.5 in. core)

FEM017 – REV70 FEM017 – REV71

593 lb 589 lb

40 lb 29 lb

Includes 0.25” core Includes 0.25” core

Composite Composite

Metallic Metallic

[in] [in]

[in] [in]

FEM017 – REV70 FEM017 – REV71

657 lb 1091lb

Includes 0.5” core

Upper Skin

Lower Skin

Upper Skin

Lower Skin

[in] [in]
NASA/CR—2015-218719 120

Figure 164.—Tail Substructure Thickness (composite includes 0.25 in. core)

Figure 165.—Fuselage OML Thickness (includes 0.5 in. core)

3.5.6 Stress, Strain, and Deflection Plots

A select set of stress, strain and deformation plots is presented in this section. As with most FE 

analyses, the volume of data produced is very large and should be reviewed in a dedicated postprocessing tool. However, these plots are representative of the sizing results. The use of a tail tip

deflection constraint in the optimization is the difference between REV70 (without) and REV71 (with).

3.5.6.1 Strain Plots

Figure 166 to Figure 172, the strain plots, are average strain at the middle surface for each element set 

shown and are at ultimate load (includes 1.5 safety factor). 

FEM017 – REV70 FEM017 – REV71

78 lb 85 lb

121 lb 81 lb

Includes 0.25” core Includes 0.25” core

[in] [in]

[in] [in]

Composite Composite

Metallic Metallic

FEM017 – REV70 FEM017 – REV71

2328 lb 2302 lb

Includes 0.5” core Includes 0.5” core Composite Composite

[in] [in]

[in] [in]
NASA/CR—2015-218719 121

Figure 166.—Strain XX Load Case LC300—Upper Wing Skin

Rev 70

Rev 71

εXX

εXX
NASA/CR—2015-218719 122

Figure 167.—Strain XX Load Case LC300—Lower Wing Skin

Rev 70

Rev 71

εXX

εXX
NASA/CR—2015-218719 123

Figure 168.—Strain XX Load Case LC300—Upper Inner Wing Skin

Rev 70

Rev 71

εXX

εXX
NASA/CR—2015-218719 124

Figure 169.—Strain XX Load Case LC400—Upper Inner Wing Skin

Rev 70

Rev 71

εXX

εXX
NASA/CR—2015-218719 125

Figure 170.—Strain XX Load Case LC400—Lower Inner Wing Skin

Rev 70

Rev 71

εXX

εXX
NASA/CR—2015-218719 126

Figure 171.—Strain XX Load Case LC400—Upper Tail Skin

Rev 70

Rev 71

εXX

εXX
NASA/CR—2015-218719 127

Figure 172.—Strain XX Load Case LC400—Lower Tail Skin

3.5.6.2 Stress Plots

Figure 173 to Figure 176, the maximum von Mises stress plots, focus on the metallic elements of the 

vertical tail and outboard engine mounting locations on the lower inner wing. The tail loads and engine 

nacelle loads must pass through these structures and they are the critical responses for load case 400. A 

Rev 70

Rev 71

εXX

εXX
NASA/CR—2015-218719 128

single plot of von Mises stress near the main landing gear attachment area on the lower inner wing is also 

shown. The plots shown, like the strain plots, are also under ultimate load (includes 1.5 safety factor).

Figure 173.—Von Mises Stress—Load Case LC400—Lower Inner Wing

Rev 71

Rev 70
NASA/CR—2015-218719 129

Figure 174.—Von Mises Stress—Load Case LC400—Lower Inner Wing

Rev 71

Rev 70
NASA/CR—2015-218719 130

Figure 175.—Von Mises Stress—Load Case LC5730—Lower Inner Wing (Landing Gear Attachment Area)

Rev 70

Rev 71
NASA/CR—2015-218719 131

Figure 176.—Max Shear Stress—Load Case LC400—Lower Inner Wing Skin

3.5.6.3 Deflection Plots

The deflection plots (Figure 177 to Figure 179) clearly show the effect of the tail tip deflection 

constraint on the optimized vehicle. Figure 177 compares the displacement of REV70 and REV71 FEMs 

under load case 400 and shows that the REV71 FEM meets the tail displacement constraint of 37.5 in.

under ultimate load (25 in. under limit).

Rev 70

Rev 71
NASA/CR—2015-218719 132

Figure 177.—Deflection LC400

Rev 71

Rev 70
NASA/CR—2015-218719 133

Figure 178.—Deflection LC300

Figure 179.—Deflection LC600

Rev 70

Rev 70
NASA/CR—2015-218719 134

3.5.7 Mode Shapes and Frequencies (With Focus on Final Design)

A modal solution was performed on both the Rev70 and Rev71 FEMs. The results of these analyses 

are presented in Table 25. The rigid body frequencies and mode shapes are characteristic of a free-free 

symmetric model that has symmetry boundary conditions enforced at the vehicle centerline.

The larger differences noted in modes 6, 7, 13, 16, and 20 are due to the effects of the engine nacelle 

suspension system coupling differently with different structural vehicle modes. The structural modes are 

directly affected by the gage optimization performed on each model. However, the suspension modes are 

relatively unaffected. Therefore, the engine inertia will couple or uncouple from adjacent structural modes 

as in an auxiliary mass system. Modes 12 to 17 show varying amounts of this behavior and involve much 

of the aft structure. Selected mode shapes are presented in Figure 180 to Figure 183 with a brief 

description of each.

TABLE 25.—N+2 VEHICLE MODAL FREQUENCIES FOR REV 70 AND 71

Mode 

no.

Rev 70,

Frequency

(Hz)

Rev 71,

Frequency

(Hz)

Diff. 

70‐>71

1 0.00 0.00 0.0%

2 0.00 0.00 0.0%

3 0.00 0.00 0.0%

4 2.15 2.23 3.7%

5 2.42 2.44 1.0%

6 2.93 3.38 15.2%

7 3.50 3.67 4.8%

8 4.37 4.41 1.1%

9 5.33 5.60 5.0%

10 5.92 5.94 0.3%

11 7.07 7.34 3.8%

12 7.96 8.07 1.3%

13 8.24 9.36 13.5%

14 9.69 10.03 3.5%

15 10.54 10.85 2.9%

16 10.93 11.88 8.7%

17 12.26 12.28 0.1%

18 12.89 13.23 2.7%

19 13.66 14.14 3.5%

20 14.19 15.72 10.8%

21 15.34 16.04 4.6%
NASA/CR—2015-218719 135

Figure 180.—N+2 Rigid Body Mode Shapes 1 to 3 for Rev 70 and 71

Mode 1

Rigid body mode

Mode 2 

Rigid body mode

Mode 3 

Rigid body mode

Rev 70

0.0 Hz

Rev 71

0.0 Hz

Rev 70

0.0 Hz

Rev 71

0.0 Hz

Rev 70

0.0 Hz

Rev 71

0.0 Hz
NASA/CR—2015-218719 136

Figure 181.—N+2 Rigid Body Mode Shapes 4 to 6 for Rev 70 and 71

Mode 4 

1st fuselage bending

Mode 5 

1st wing bending

Mode 6 

Tail bending coupled 

with in-phase 

outboard engine roll

Rev 70

2.15 Hz

Rev 71

2.23 Hz

Rev 70

2.42 Hz

Rev 71

2.44 Hz

Rev 70

2.93 Hz

Rev 71

3.38 Hz
NASA/CR—2015-218719 137

Figure 182.—N+2 Rigid Body Mode Shapes 7 to 9 for Rev 70 and 71

Mode 7

Tail bending coupled 

with out-of-phase 

outboard engine roll

Mode 8

2nd fuselage and 2nd

wing bending

Mode 9

Aft deck bending 

coupled with tail 

bending and weak 

wing torsion

Rev 70

3.5 Hz

Rev 71

3.67 Hz

Rev 70

4.37 Hz

Rev 71

4.41 Hz

Rev 70

5.33 Hz

Rev 71

5.60 Hz
NASA/CR—2015-218719 138

Figure 183.—N+2 Rigid Body Mode Shapes 10, 11, and 18 for Rev 70 and 71

3.6 Aeroelastic Analysis

Open-loop flutter analysis was performed on the final structural designs, FEM017-REV70 (sized 

without the constraint on tail displacement) and FEM017-REV71 (sized with the constraint on tail 

displacement). We utilized MSC.NASTRAN SOL145 for our flutter solutions which use the doublet 

lattice method for subsonic Mach numbers and ZONA51 for supersonic Mach numbers. The same linear 

aerodynamic model and spline model that was used for loads calculations in Section 3.3 was also used for 

flutter analysis.

3.6.1 Aeroelastic Model 

Section 3.3 discusses the check on the quality of the spline models that provided transfer of forces 

and displacement between the linear aerodynamic model and the FEM. We also conducted a modal 

convergence study, plotting the critical flutter dynamic pressure as a function of the number of structural 

modes retained for the analysis. This trade is shown in Figure 184, where we looked at 22, 27, and 32 

retained modes, including 3 rigid body modes for symmetric boundary conditions. Based on this study, 

we retained 27 modes for subsequent flutter analyses balancing accuracy of the flutter solutions with 

efficiency of the runs, since we had plans to run many trades.

Mode 10

Fwd fuselage 

bending with 

higher order wing 

bending

Mode 11

Outboard engine 

pitch with weak 

tail torsion

Mode 18

Center engine 

pitch

Rev 70

5.92 Hz

Rev 71

5.94 Hz

Rev 70

7.07 Hz

Rev 71

7.34 Hz

Rev 70

12.89 Hz

Rev 71

13.23 Hz
NASA/CR—2015-218719 139

Figure 184.—Modal Convergence Study (Mach 0.9, Symmetric Boundary Conditions, DTOW)

TABLE 26.—COMPARISON OF NASTRAN AND FAMAS

Flutter

Crossing

FAMAS Flutter Speed 

(KEAS)

FAMAS Flutter 

Dynamic Pressure 

(psf)

NASTRAN Flutter 

Dynamic Pressure 

(psf)

Percent Difference 

(FAMAS/NASTRAN)

1 562 1070 1071 -0.1%

2 704 1679 1653 1.6%

3 916 2842 2875 -1.1%

3.6.2 Comparison of MSC.NASTRAN With FAMAS Results

While we utilized MSC.NASTRAN for our flutter surveys, we also ran a flutter solution using our inhouse PK flutter solver, FAMAS, as a check against the NASTRAN results (Table 26). FAMAS utilized 

the aerodynamic influence coefficient (AIC) matrices from NASTRAN as well as the structural mode 

shapes and frequencies, but the matched point PK aeroelastic root solver was performed by FAMAS.

Figure 185 shows the damping versus dynamic pressure from NASTRAN, while Figure 186 shows 

damping versus KEAS from FAMAS. Not including the mildly unstable rigid body root, which is 

attributed to the vehicle being statically unstable, predicted by NASTRAN, both methods agree on the 

speed of 3 flutter crossings when converted to dynamic pressure. The comparison presented is for Mach 

0.95, symmetric boundary conditions at DTOW. The analysis was done on an earlier all-composite FEM, 

hence the difference in the flutter dynamic pressure from subsequent, more recent, flutter analyses 

presented in the next section.
NASA/CR—2015-218719 140

Figure 185.—Damping Versus Dynamic Pressure from NASTRAN
NASA/CR—2015-218719 141

Figure 186.—Damping Versus Velocity (KEAS) from FAMAS

3.6.3 Flutter Results

Flutter analysis was initially performed on the FEM017-REV71 structural design, the design that 

included a displacement constraint on horizontal tail deflection for LC 400. However, for both symmetric 

and antisymmetric boundary conditions and for 3 gross weights: DTOW, DTOW2 and ZFW, there were 

no unstable flutter roots below 1600 psf and in most cases, especially for antisymmetric boundary 

conditions, there were none below 3500 psf (minimum required flutter dynamic pressure is roughly 

900 psf for most Mach numbers). This stood in stark contrast to previous flutter analyses that we had 

performed on the all-composite FEM, where we saw critical flutter dynamic pressures around 1000 psf.

As a result we speculated that inclusion of the displacement constraint considerably stiffened the tail and 

aft deck thereby increasing the flutter speed dramatically which was characterized largely by tail and aft 

deck bending. Consequently, we also ran flutter surveys on the FEM017-REV70 design which did not 

include the tail displacement constraints, and the results of these surveys are presented below, since the 

flutter speeds were much lower and closer to the flutter boundary. Table 27 shows the FEM weight and 

CG locations of the gross weight configurations of FEM017-REV70 that were considered as part of the 

flutter surveys.

Figure 187 shows the minimum flutter dynamic pressure as a function of Mach number and vehicle 

gross weight for symmetric boundary conditions. In addition, the minimum required flutter dynamic 
NASA/CR—2015-218719 142

pressure (including the 15 percent margin on flutter speed) is represented by the solid red line. Flutter 

dynamic pressure below this line would indicate that the vehicle flutter requirement is not met, and so, 

based on this linear flutter analysis, we conclude that flutter speed is not critical. The dashed lines indicate 

that a flutter root was not found within the dynamic pressure range of the analysis (up to about roughly 

4000 psf). However, in this transonic region, a more appropriate nonlinear transonic flutter solver is 

needed. For the DTOW and DTOW2 flutter solutions, the flutter modes in the 3000 to 4000 psf range 

have a flutter frequency of about 10 to 12 Hz. The flutter modes that go unstable at lower dynamic 

pressure near 1500 to 2000 psf have flutter frequencies of about 5 to 6 Hz. The flutter frequency for the 

ZFW cases ranges from 6 to 7 Hz.

Figure 188 shows the minimum flutter dynamic pressure as a function of Mach number and vehicle 

gross weight for antisymmetric boundary conditions. In general, the flutter speeds are higher than in the 

symmetric cases, though further investigation is needed with a suitable transonic aerodynamic theory.

This is particularly evident when looking at the frequency and damping versus dynamic pressure at Mach 

0.95 and 1.1, as shown in Figure 189 and Figure 190. At Mach 0.95, there are several zero frequency 

roots that go unstable, where one as shown in Figure 189, looks like a divergence mode consist of lateral 

nose bending. However, it is somewhat questionable how realistic this mode is for a semi-span model and 

at Mach 0.95 with linear aerodynamic theory. The aeroelastic roots at Mach 1.1 are very sporadic with 

many unstable roots at low dynamic pressures, indicating that the results may be spurious. Both of these 

plots emphasize the need that future studies should look at flutter in the transonic regime with an 

appropriate transonic code.

TABLE 27.—FEM WEIGHTS AND CG LOCATIONS

FOR FLUTTER ANALYSIS (FEM017-REV70)

Configuration Semi-span FEM Weight

(lb)

X-CG

(in.)

DTOW 166,366 2021.8

DTOW2-AFT 150,605 2049.8

ZFW 78,804 1983.4

Figure 187.—FEM017-REV70 Flutter Boundary—Symmetric Boundary Conditions

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0.6 0.8 1 1.2 1.4 1.6 1.8 2

Flutter Dynamic Pressure (psf)

Mach Number

 

 

No Structural Damping Added

Mission Dynamic Pressure - 15% Margin (psf) Mission Dynamic Pressure(psf)

DTOW - FEM017_REV70 DTOW2 - AFT - FEM017_REV70

ZFW - FEM017_REV70
NASA/CR—2015-218719 143

Figure 188.—FEM017-REV70 Flutter Boundary—Antisymmetric Boundary Conditions

Figure 189.—Mach 0.95 Antisymmetric Boundary Conditions, Zero Fuel Weight

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0.6 0.8 1 1.2 1.4 1.6 1.8 2

Flutter Dynamic Pressure (psf)

Mach Number

 

 

No Structural Damping Added

Mission Dynamic Pressure - 15% Margin (psf) Mission Dynamic Pressure(psf)

DTOW - FEM017_REV70 DTOW2 -AFT- FEM017_REV70

ZFW- FEM017_REV70
NASA/CR—2015-218719 144

Figure 190.—Likely Spurious Results at Mach 1.1, Antisymmetric Boundary Conditions at DTOW
NASA/CR—2015-218719 145

Since there is considerable uncertainty associated with the weight of the propulsion system and 

because the flutter mechanism involves aft-deck and tail bending near where the engines are mounted, we 

conducted a sensitivity study of flutter speed to increases in engine weight. We increased the engine 

weight from 20 percent of its baseline value to 40 percent above its baseline value, in an effort to bound 

the maximum amount of expected increase in engine weight.

Table 28 shows the weight per engine for each trade study. In addition, the engine moment of inertia 

was also increased in proportion to its weight by assuming a fixed radius of gyration for the baseline engine.

Figure 191 shows the flutter survey for symmetric boundary conditions for the 3 different engine 

weights. For the DTOW and DTOW2 gross weight conditions increasing the engine mass decreases the 

flutter dynamic pressure. For the ZFW condition increasing engine mass increases the flutter dynamic 

pressure, though the ZFW condition is not the critical weight for most Mach numbers. Table 29

summarizes the minimum flutter dynamic pressure for each of the engine weight studies. In general, a 

40 percent increase in engine weight drops the critical flutter dynamic pressure by 12 percent, but still 

meeting the required flutter margin, though as mentioned earlier further analysis is needed with a 

transonic aeroelastic code.

Figure 191.—N+2 Flutter Boundary Engine Mass Trade Study—Symmetric Boundary Conditions

TABLE 28.—ENGINE MASSES FOR TRADE STUDY

Percent Increase Weight per Engine 

(lb)

0% (Baseline) 7,226

20% 8,671

40% 10,116

TABLE 29.—MINIMUM FLUTTER DYNAMIC PRESSURE VERSUS

ENGINE MASS (SYMMETRIC BOUNDARY CONDITIONS)

Percent Increase Minimum Flutter Dynamic Pressure 

(psf)

Weight 

(lb)

Mach

0% (Baseline) 1327 ZFW 0.8

20% 1277 DTOW2 0.95

40% 1164 DTOW2 0.95

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0.6 0.8 1 1.2 1.4 1.6 1.8 2

Flutter Dynamic Pressure (psf)

Mach Number

 

 

No Structural Damping Added

Mission Dynamic Pressure - 15% Margin (psf) Mission Dynamic Pressure(psf)

DTOW - FEM017_REV70 DTOW - 20 % Increase Engine

DTOW - 40 % Increase Engine DTOW2 - AFT - FEM017_REV70

DTOW2 - 20 % Increase Engine DTOW2 - 40 % Increase Engine

ZFW - FEM017_REV70 ZFW - 20 % Increase Engine

ZFW - 40 % Increase Engine

No Flutter

No Flutter
NASA/CR—2015-218719 146

Figure 192.—Damping and Frequency Versus Dynamic Pressure for Symmetric BCs, Mach 0.95, 

DTOW2, 40 percent Increase in Engine Mass
NASA/CR—2015-218719 147

Figure 192 and Figure 193 show the V-g, V-f plots and the flutter mode, respectively, of the critical 

flutter solution at Mach 0.95 with an increase of 40 percent engine mass and symmetric boundary 

conditions. The flutter crossing is highlighted by the dark green line and is a shallow-crossing hump mode 

that combines aft deck bending, tail bending and tail torsion. The mode that goes unstable even earlier is a 

rigid body mode (zero frequency) and is associated with the vehicle being statically unstable in this 

weight configuration (DTOW2 along the aft CG burn schedule).

Figure 194 shows the flutter survey for antisymmetric boundary conditions for the 3 different engine 

weights. In this case, the reduction in flutter dynamic pressure with an increase in engine mass is more 

modest, decreasing only 5 percent for a 40 percent increase in engine mass (see Table 30).

Figure 195 and Figure 196 show the V-g, V-f plots and the flutter mode, respectively, of the critical 

antisymmetric flutter solution at Mach 0.7 with an increase of 40 percent engine mass. The flutter 

crossing is highlighted by the dark turquoise line and is a steeper crossing than the symmetric flutter 

mode and combines tail bending and tail torsion.

Figure 193.—Critical Symmetric Flutter Mode, Aft Deck Bending, Tail Bending and Torsion, qf = 1164 psf, f = 5.5 Hz
NASA/CR—2015-218719 148

Figure 194.—Engine Mass Trade Study—Antisymmetric Boundary Conditions

TABLE 30.—MINIMUM FLUTTER DYNAMIC PRESSURE VERSUS

ENGINE MASS (ANTISYMMETRIC BOUNDARY CONDITIONS)

Percent Increase Minimum Flutter Dynamic Pressure 

(psf)

Weight 

(lb)

Mach

0% (Baseline) 1646 DTOW2 0.7

20% 1590 DTOW2 0.7

40% 1556 DTOW2 0.7

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0.6 0.8 1 1.2 1.4 1.6 1.8 2

Flutter Dynamic Pressure (psf)

Mach Number

 

 

No Structural Damping Added

Mission Dynamic Pressure - 15% Margin (psf) Mission Dynamic Pressure(psf)

DTOW - FEM017_REV70 DTOW - 20 % Increase Engine

DTOW - 40 % Increase Engine DTOW2 -AFT- FEM017_REV70

DTOW2 -AFT - 20 % Increase Engine DTOW2 - AFT - 40 % Increase Engine

ZFW- FEM017_REV70 ZFW - 20 % Increase Engine

ZFW - 40 % Increase Engine
NASA/CR—2015-218719 149

Figure 195.—Damping and Frequency Versus Dynamic Pressure for Antisymmetric BCs, Mach 0.7, 

DTOW2, 40 Percent Increase in Engine Mass
NASA/CR—2015-218719 150

Figure 196.—Critical Antisymmetric Flutter Mode, Tail Bending and Torsion, 

qf = 1556 psf, f = 9.3 Hz

Figure 197.—Aft Deck Stiffening Alternative by 

Pylon Height Extension Above Wing

3.6.4 Aft Deck Stiffening Alternative

Boom shaping and wave drag are primarily sensitive to large changes in area or lift. There are wing 

rib/longerons that support the wing nacelles and carry back into the outer edges of the aft deck. They can 

be extended in height above the wing with little cross-sectional area increase. We would like to examine 

adding (like an above wing “T” stiffener) a pylon height extension, like Figure 197, as much as 24 in. in 

additional height: starting in the wing box, peaking in height around the start of the aft deck (wing TE) 

and carrying back to the TE of the aft deck and supporting the V-tails more rigidly in pitch and torsion. 
NASA/CR—2015-218719 151

3.7 Structural Deformation Effects on Sonic Boom

The impact of operational structural deflections on sonic boom was investigated. Structural 

deformations affecting boom shaping could come from:

1. Aeroelastic trim variations from beginning to end of cruise and during climb/descent

2. Control surface and structural deflections countering turbulence

3. Maneuver control surface and structural deflections

With regard to the third source listed above, nonemergency maneuvers are executed slowly, due to 

limited excess power available, so they seem unlikely to cause changes (but may limit maneuver rates).

The second source, countering turbulence, involves aeroelastics, design, flight control system response 

and pilot response; while the first source involves only aeroelastics and vehicle design, so it should be 

most expedient to quantify the first source initially.

Our CFD-based aeroelastic tools were used to determine the aeroelastic trim state at the beginning 

and end of cruise. Those aeroelastic structural deflections are then analyzed with the high fidelity gridding 

scheme and CFD solver used for boom assessment. The aeroelastic trims require lift of the deformed 

geometry to equal weight and pitching moment to be zero about the specified center of gravity. The 

aeroelastic trim process includes structural deflections, as well as deflections from the control surfaces 

designated as pitch trim devices. Angle-of-attack is also included as a trim variable. Table 31 contains the 

flight conditions for the beginning and end of cruise states, as well as the vehicle weight and Fuselage 

Station (FS) CG location. The finite element model used to support this analysis was based on 

Configuration 1044-3, including thickness modifications made to the aft deck. The structural design used 

was FEM17 Rev 70, which was a design that included the results of metallic trades, and was structurally 

optimized without a vertical tail deflection constraint because the flutter analysis of Section 3.6.3

indicated that the additional weight of the deflection constraint was unnecessary.

To accurately model the mass distribution of the beginning and end of cruise states, spent fuel tanks 

masses were removed from the model and partial fuel tank masses were created based on an aft-CG fuel 

tank burn sequence. Figure 198 illustrates the tank layout, as well as the fuel tank burn sequence used.

These fuel tank weights account for the additional 10.6 percent fuel volume available in tank 10 due to 

thickening of the aft deck.

With the fuel tanks modeled, the beginning of cruise CG location was at FS 2081.1 in, and the end of 

cruise state was located at FS 2025.3 in. These CG locations were used as the FS moment reference 

location in the trim analyses.

Table 32 contains the trim states obtained with the CFD-based Aeroelastic Approach. Rigid trim 

states using a CFD-based approach were also computed and are included for comparison. For the 

beginning of cruise state, the body flap was used as the only pitch trim control device. An increase in 

10.44° in body flap deflection was needed to account for aeroelastic effects. For the end of cruise state, 

large body flap deflections were encountered due to the more forward CG, therefore the trailing edge flap 

and ailerons were used as well. For this state, only a modest increase of approximately 0.7° was needed to 

account for aeroelastic effects. To better understand the underlying factors producing these differences 

trim state, a comparison of aeroelastic increments in integrated loads for different portions of the vehicle 

was constructed. Normalizing these values relative to the total lift, and plotting these values over the 

vehicle illustrates the sources of largest change. Figure 199 contains a plot of the aeroelastic increments in 

the Z force for various parts of the vehicle, normalized by total lift for the beginning of cruise state. As 

can be seen, the vertical tail is unloading due to aeroelastic effects (negative percentage of total lift), and 

to compensate for this impact on the pitching moment, the body flap is increasing its deflection to 

generate more lift (positive percentage of total lift) to balance the vehicle. A comparison of the integrated 

force in the Z direction showed that the lift on the vertical tail was in the positive Z-direction during the 

rigid analysis, and that this was reduced to 52 percent of this value during the aeroelastic analysis. The 

wing also slightly unloads during this trim state as well.
NASA/CR—2015-218719 152

TABLE 31.—FLIGHT CONDITION AND WEIGHT STATES

FOR BEGINNING AND END OF CRUISE STATES

State Mach Weight 

(lb)

Altitude 

(ft)

Target CG FS 

(in.)

Cruise Start 1.7 261,688 48,400 2083

Cruise End 1.7 172,302 57,100 2047

Figure 198.—Fuel Tank Layout and Tank Burn Sequence at Beginning and End of Cruise

TABLE 32.—TRIM STATES OBTAINED WITH A CFD-BASED AEROELASTIC TRIM APPROACH

Cruise Start Rigid Cruise Start Aeroelastic Cruise End Rigid Cruise End Aeroelastic

CX 0.00497 0.00531 0.00545 0.00495

CZ 0.13659 0.13661 0.13632 0.13633

CMY 0.00000 0.00000 0.00000 0.00000

ALPHA (deg) 2.25 2.27 2.67 2.57

LEF (deg) 0.00 0.00 0.00 0.00

TEF (deg) 0.00 0.00 –5.67 –4.98

BFLAP (deg) –3.83 6.61 –5.67 –4.98

Figure 199.—Aeroelastic increments in Total Z-Force Normalized by Total Lift for the Beginning of Cruise
NASA/CR—2015-218719 153

A similar plot for the end of cruise state is shown in Figure 200. For this trim state, the aeroelastic, 

integrated load on the vertical tail in the Z-direction was 70 percent of the rigid value, therefore a smaller 

aeroelastic increment compared to the beginning of cruise state. The more-forward CG also results in a 

smaller change in control surface deflection needed to balance the aeroelastic increment. The wing also 

shows a modest increase in lift, rather than the decrease shown at the beginning of cruise state. This is 

most likely due to the use of trailing edge control surfaces, which impose a positive or “wash-in” twist on 

the wing due to their downward load on the trailing edge of the wing. This trend can be seen in the 

comparison of Z-deflections for both trim states shown in Figure 201.

To assess the impact of these deflections on the boom signature of the vehicle, the deflections 

obtained for the trim states were mapped to the CFD mesh used for boom prediction. This was performed 

using the tools used during the CFD-based aeroelastic analysis. The aerodynamic models used in the 

aeroelastic trims were modified to allow control surface actuation during the simulation. These 

modifications were not present in the models used for boom assessment, therefore, only structural 

deflections (i.e., no control surface deflections) were mapped during this process.

Figure 200.—Aeroelastic Increments in Total Z-Force Normalized by Total Lift for End of Cruise

Figure 201.—Comparison of the Z Deflections for the Beginning and End of Cruise Trim States
NASA/CR—2015-218719 154

Both rigid and deformed grids were analyzed at the Start of Cruise condition with a Mach number of 1.7 

and an Angle of Attack of 2.1° in CFD++ using the Euler equations. Issues were encountered using grids 

that included the propulsion system details so flow-through propulsion systems were used instead to capture 

the incremental impact of structural deformation and control surface deflection to trim on sonic boom. A 

comparison of top surface pressures shown in Figure 202 shows that the loading on the wing changes only 

slightly, but that more significant changes occur around the centerline nacelle and empennage. 

The region of increased pressure near the mid-point of the centerline nacelle is a result of the 

structural thickening applied to the FEM to increase the rigidity of the aft deck. A comparison of the 

loading on the bottom surface of the empennage shown in Figure 203 clearly shows the impact of the 

body flap deflection and the reduced tail loading due to aeroelastic washout.

Figure 202.—Comparison of Planform Surface Pressure Coefficient for Rigid and Flexible Simulations at Start of Cruise

Figure 203.—Comparison of Empennage Surface Pressure Coefficient

for Rigid and Flexible Simulations at Start of Cruise

Rigid

Rigid

Flexible, Trimmed

Rigid Flexible, Trimmed
NASA/CR—2015-218719 155

Figure 204.—Comparison of Ground Signatures for Rigid and Flexible Simulations at Start of Cruise

Off body pressures were extracted at a distance of 345 ft from both the rigid and flexible solutions 

and propagated to the ground using the Burgers-equation solver sBOOM. The comparison of the ground 

signatures shown in Figure 204 shows that flexibility effects only have a small impact on the front ramp 

portion of the signature, but there are significant changes to the aft part of the signal due to the changes in 

tail loading. A comparison of the carpet loudness of the two configurations, plotted in Figure 205, shows 

that the change in loudness varies between 0.5 and 4.0 PL dB across the carpet.

0 deg offtrack

20 deg offtrack

40 deg offtrack
NASA/CR—2015-218719 156

Figure 205.—Comparison of Ground Loudness Carpets for Rigid and Flexible Simulations at Start of Cruise

When considering these results it should be noted that the reason for significantly degraded sonic 

boom performance when flexibility effects are included is that the 1044 configuration was not designed to 

accommodate these effects. For future shaped boom design efforts, aeroelastic simulation should be used 

during the preliminary design phase to modify the aircraft jig shape so that the loading at the cruise design 

point matches the design loading and the vehicle produces the desired sonic boom characteristics. The 

design and analysis tools used to perform this work could also be used to modify the design to eliminate 

these adverse effects.

3.8 Conclusions and Recommendations in Sections 5.0 and 6.0

Conclusions and recommendations are compiled at the end of this report in Section 5.0 Conclusions 

and Section 6.0 Recommendations, respectively.

4.0 N+2 Sonic Boom Testing and Analyses

4.1 Ames 9x7-0250 Parametric Test

The “Parametric” test of April 2012 contributed to the productivity and accuracy improvements 

achieved on the final 97-0254 or LM4 test (Section 4.4). Test 97-0250 investigated tunnel flow control 

(particularly humidity and PT), spatial averaging measurement procedures and beyond near-field 

measurement distances. It sought to better define limits for: humidity, PT, sampling time, sampling 

translation distance, reference position and other parameters—for best productivity with still acceptable 

accuracy.

Just before the start of N+2 early in 2010, NASA ran two sonic boom tests to study the effect of 

humidity on sonic boom measurements. In the Ames 9x7 tunnel, lower humidity resulted in sharper sonic 

boom measurements and variations in humidity resulted in measurement changes (later understood to be 

due to ambient flow field pressure changes or pressure position shifts). However, humidity of 175, 200,

and 225 ppm produced good measurements as long as that humidity value did not vary during a 

measurement set. So for the LM1-2 tests in November through December 2010, humidity was planned to 

be nominally 200 ppm ±25; however, depending on outside weather and other conditions 225 ppm could 

84

85

86

87

88

89

90

91

-20 -15 -10 -5 0 5 10 15 20

Loudness (PL dB)

Distance off track (nm)
NASA/CR—2015-218719 157

be time consuming to reach or even be unreachable. In testing over the next year and a half, humidity up 

to 350 ppm was found to provide acceptable results as long as it did not vary during a measurement set.

Invariant humidity was originally defined to be the best the tunnel could achieve: ±3 ppm. While the 

tunnel could hold this tolerance for several sets, it would often start drifting. When this happened during a 

measurement set, data acquisition was stopped until the tunnel was readjusted to a steady humidity—

which often took large amounts of time (using manual flow adjustments). In fact, time spent initially 

evacuating the tunnel and refilling it with dry air to get dry enough to start, plus time spent readjusting the 

tunnel to a steady humidity was consuming as much as 60 percent of run time nonproductively. In LM3, 

running PT (total pressure) at 2300 psf, instead of 1450 psf, made initially drying the tunnel faster and 

made holding humidity easier (presumably because the tunnel was closer to atmospheric pressure 

reducing leakage and requiring less dry air flow to maintain steady humidity). To reduce nonproductive 

time, the 97-0250 Parametric test further investigated humidity and PT limitations using actual model 

signatures for assessments (Ref. 2).

The Parametric test investigated humidity and PT thoroughly. Running humidity at 200, 300, 400 and 

500 ppm showed that dryer air resulted in sharper signatures, fewer spurious measurement spikes and 

more flow-field consistency between measurement and reference runs. In addition, plotting the reference 

runs at different humidity levels in Figure 206 shows that the tunnel ambient flow does change with 

humidity changes. There is some level shift but also a shift of the pressure pattern forward on the rail that 

increases in displacement going from orifices 50 to 300. So humidity affects the ambient flow in the 9x7.

However, other runs where ambient pressure was repeatedly measured for 15 min while the humidity 

slowly drifted as much as 100 ppm, did not show a conclusive link between changing humidity and the 

measured ambient pressure. So for humidity it was decided that testing would be done at whatever dry 

tunnel level was easy to achieve and hold that day and the variation would be kept as steady as possible.

Humidity requirements were set at less than 350 ppm and no more than ±15 ppm during a measurement 

set, which was always achieved without having to stop data acquisition.

For this tunnel, PT has both coarse and fine control loops that are different for increasing or 

decreasing pressure in the settling chamber. After investigating rail pressure with the different control 

loops set to auto, manual and off, no effect was seen on the rail pressures due to PT variations. A 

requirement of ±3 psf tolerance was specified because in practice ±1.5 psf was achieved by the tunnel 

controls. The following LM4 test was able to use these larger tolerance values to avoid stopping data 

acquisition while still obtaining accurate measurements.

Figure 206.—Tunnel Ambient DPP Versus Humidity

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0 50 100 150 200 250 300 350 400 450

DPPru

Orifice#

Rail Ambient DPP vs. Humidity Humidavg 201

285

403

502
NASA/CR—2015-218719 158

Figure 207.—Beyond Near-Field Measurements Appear Rounded and Missing Detail

This Parametric test also performed refined spatially averaged measurement sets for the 1021-01 

Configuration validated in LM3. LM3 post-test analysis indicated that the 4 in. translation used for most 

LM3 measurements was insufficient for best accuracy (12+ in. is needed in the 9x7) but 30 sec time 

samples could be cut to 2 sec (excepting the reference measurement). Measurement set accuracy was 

improved and acquisition time was decreased from 50 min in LM3 to 20 min in this Parametric test.

Sonic boom propagation methodology requires starting signatures to be outside near-field interference 

distortion. Typically this requires the CFD or wind tunnel data to be 15 to 25 semispans [R/(b/2)] from 

the vehicle. The LM3 test measured the 1021-01 model at 5 and 8 semispans. The tunnel is capable of 

measuring the model up to 17 semispans; but to do so, the rail needed to be moved to the aft window 

blank and the adapter between the strut and roll mechanism needed to be rotated 180°. The rail reposition 

was not done in LM3 so it was first investigated in this Parametric test with spatial averaging. Both the 

Boeing BOOM1 model and the LM 1021-01 model were measured at the farthest distance possible with 

just a rail move, Figure 207. The smaller size of the Boeing model (about 16 in.) allowed a slightly 

greater measurement distance, more translation distance (21 in.) and more measurement points (39) than 

the LM 1021-01 model (12 in., 20 points). Unfortunately, there was not a published result for what the 

BOOM1 model’s sonic boom should look like going from 9 to 18 semi-spans. There is general 

rounding/smearing of detail throughout the signature. The nose bluntness spike is almost gone. And there 

are some shape changes in the signature, like a higher end of ramp magnitude relative to the ramp ahead 

of it, that have not scaled in amplitude with square root of distance. CFD analyses of the 1021-01 model 

indicate the signature shape stays the same and mostly scales in amplitude with distance going from 8 to 

14 semi-spans, and the measurements match these analyses. There seems to be less detail rounding but the 

nose bluntness spike is also almost gone. These results suggested that extra consideration should be given 

to improving far measurement accuracy in the LM4 test. 

4.2 Ames 11-0249 Boeing Test

4.2.1 Background and Overview

Test 11-249 was conducted at the NASA Ames Unitary 11 ft Transonic Wind Tunnel June 4 to 8, 

2012, with single-shift operations. The test was conducted by the Boeing Company and was attended by 

NASA Analysts and a Lockheed Martin observer. The test was funded by, and in support of, the 

Fundamental Aerodynamics Supersonics Project based at NASA Langley. This test was an extension of 

testing performed in the last few years by NASA, Lockheed and Boeing to measure sonic boom 

signatures in the wind tunnel environment, with most previous testing being conducted at the Ames 

Unitary 9x7 ft Supersonic Wind Tunnel, with test conditions in the Mach 1.60 to 1.80 range. The 11 ft

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

DP/P -4 -2 0 2 4 6 8 10 12 14 16

X-Beta*R (in model scale)

Boeing BOOM1 Model, Mach 1.8

T250:R1247_1273-1273

T250:R2006_2044-2045

H = 30”

H = 60”

[ H/(b/2) = 17.6] -0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

-4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

DP/P

X-Beta*R (in model scale)

LM Low Boom Model 1021-01

T231:Runs829_854-876

T250:Runs1931_1950-1951

H = 31.8”

H = 57.3”

[ H/(b/2) = 14.3]

 

 
NASA/CR—2015-218719 159

Transonic Tunnel was selected to explore the feasibility of measuring sonic boom signatures at Mach 1.20 

and 1.40. Existing aircraft and reference models and existing pressure measurement equipment was used.

In addition to sonic boom measurements, measurements of transonic drag rise were also obtained.

Test attendees were the following:

• Boeing: Eric Adamson, Alicia Bidwell, Stephen Shaw, Todd Magee, and Pete Wilcox

• Lockheed Martin: Bob Langberg

• NASA Langley: Eric Walker and Alaa Elmiligui

• NASA Ames: Don Durston, Susan Cliff, and Scott Thomas

The test was largely successful with the desired sonic boom signatures being measured. However, the 

desired test conditions of Mach 1.20 and 1.40 were not attainable. At the tunnel’s top speed of Mach 1.40, 

excessive model lateral dynamic motion was observed with transient loads approaching the internal 

balance’s side force gage limits. As a result measurements desired at 1.40 Mach were taken at 1.30 Mach.

At the Mach 1.20 condition, a half-dozen ambient shocks originating on the floor upstream in the test 

section propagated aft, reflected off the ceiling and adversely affected the flow quality at the location of 

the models. Testing was conducted initially at Mach 1.20 and later at Mach 1.15, a speed where this flow 

quality issues did not directly affect the model.

4.2.2 Test Description

The test took place over the course of one calendar week and operated during the day shift, nominally 

6:30 to 3:30, but with overtime available at the end of the shift which was used to make up for tunnel 

downtime periods.

Pretest preparations had taken place in the model buildup and prep area and continued with 

installation in the 11 ft test section prior to the week of testing. Installation activities were complete by 

Tuesday afternoon of the test week and testing was completed a few days later, by mid-shift on Friday.

Test conditions were primarily Mach 1.15 to 1.40 at a total pressure, PT = 2300 psf, with one brief 

investigation at PT = 1450 psf. In addition, transonic drag rise was investigated between 0.80 to 

1.10 Mach.

Three models were used during testing. The two aircraft models are shown in Figure 208:

• “AS2”, an axisymmetric body-of-revolution (“Axi” or “BOR”) model which was a SEEB body 

with a 0.35 psf flat-top pressure distribution, used as a reference model.

• “BM1” or “Boom 1” was the Boeing aircraft “N+2 Quiet Experimental Validation Concept” 

model at 0.65 percent scale. It was supported by a swept upper blade support and featured flow 

through nacelles on the upper surface of the wing. It was the primary model for sonic boom 

measurement.

• “PM1” or the Performance Model of the Boeing aircraft concept is a 1.79 percent scale stingsupported model which is approximately three times larger than the Boom 1 model and has 

corresponding greater detail in the loft reproduction. The model aft end is distorted due to the 

sting mounting, and the model is too long to obtain a clean sonic boom signature with the 

instrumentation used in this test.
NASA/CR—2015-218719 160

Figure 208.—Aircraft Performance (44 in.) and Boom (16 in.) Models

TABLE 33.—BOEING MODEL REFERENCE DIMENSIONS AND SIZE

Boom 

Model 

Performance

Model 

Model Scale, SCALE 0.006496713 0.01786596

Wing Reference Area (ft2), SREF 0.1064 0.8048

Span (in.), B 6.7111 18.4555

Model Length (in.), LREF 15.7480 43.3071

Figure 209.—The 14 in. RF1.0 Rail Attached to Floor 

of 11 ft Transonic Tunnel (Note Non-Conformal 

Structural Attachment ~8 in. Aft of LE)

Instrumentation was primarily the 14 in. RF 1.0 pressure rail which is a thin blade, featuring 420 

pressure measurement locations along a 66 in. portion of its length. The rail is floor-mounted in the 11 ft

tunnel, with pressure ports 14 in. from the tunnel floor. This rail was designed and fabricated for use in 

the 9x7 transonic tunnel and as a result, the installation in the 11 ft tunnel was a bit different with a 

nonflush attachment foot and a cantilevered forward edge which normally would have been constrained in 

the 9x7 installation. Loads were monitored on the cantilevered leading edge portion of the rail and were 

not a problem in testing. The leading edge of the 14 in. rail as installed on the floor of the test section is 

shown in Figure 209.

Loads were monitored with the 1.0 in. diameter Task Mk14C six-component balance, located in the 

balance housing aft of each of the three models. The same balance was used throughout the entire test.

A model fouling circuit was used and model base pressures were measured and corrected.
NASA/CR—2015-218719 161

The model support sting stack was essentially the same as used previously in the 9x7 testing activities 

and consisted of the Large Model Roll Mechanism, SR-47 Primary Adapter, SR-158 Adapter, Linear 

Actuator, and the SR-205 Sting.

The test was conducted to measure both X-axis sweeps and Z-axis sweeps.

Humidity was desired to be 300 ±25 ppm, and within ±3 ppm of the reference run. In practice, these 

requirements were changed at the start of testing, and as the test progressed, humidity varied open-loop 

with the OCPE (Open Circuit Plenum Evacuation) system. Humidity values were substantially drier than 

the initial plan and will be discussed in greater detail later.

Schlieren images were obtained via live video feed and were viewable in the control room as testing 

was conducted.

The test section of the 11 ft Transonic Tunnel is presented in Figure 210.

Figure 210.—Drawings of 11 ft Transonic Tunnel Test Section
NASA/CR—2015-218719 162

4.2.3 Test Conduct

4.2.3.1 Pretest Installation Activities

Prior to Monday June 4, 2012, tunnel installation activities were conducted with the usual installation 

procedures followed. All model support hardware was installed and checked out. The hardware stack was 

installed in a similar configuration to past test efforts, with the linear actuator, SR-205 sting adaptor and 

other parts. Check loads were conducted on the balance and were verified to be adequate, and motion and 

limits verified.

Rail pressure and PSI modules were checked and all leaking ports were fixed. The rail was installed 

with strain gages to monitor loads during the test. The pressure modules were located under the floor, 

where the rail was installed. The PSI modules were placed on top of the OCPE (Open Circuit Plenum 

Evacuation) system’s vent exit pipe. The PSI modules were monitored during testing via video feed and 

were subject to a dynamic environment in the tunnel plenum. In past testing at the 9x7 tunnel, the PSI 

modules were located outside the tunnel at ambient conditions and were not subject to the tunnel 

temperature, pressure, and vibration environment. The installation at the 11 ft transonic tunnel appeared 

to work adequately during the test, despite these conditions.

One note, the pressure line length was left quite long, the same as the previous Parametric 9x7 test in 

April 2012 (97-250), and the LM3 test from October 2011 (97-231). These pressure line lengths should 

be subject to lag similar to that observed at the 9x7 tunnel. Later testing in October 2012 at the 9x7 for 

LM4 (97-254) used shorter line lengths, which reduced pressure lag.

Midway through the 11-0249 test, signatures obtained on the AS2 model were reviewed with Eric 

Walker’s MATLAB plots to compare discrete 2-sec time slices (0 to 2, 2 to 4, 4 to 6, 6 to 8, and 8 to 

10 sec) to evaluate the necessary settle time in the pneumatic tubes. Trends were neither orderly nor 

monotonic, as had been previously observed in the 9x7 data. The 6 sec settle time before 10 sec of data 

acquisition time was used in the 11 ft testing similar to the April 2012 Parametric Test (97-250).

Figure 211.—The 11 ft Wind Tunnel Layout—Boeing N+2 Exploratory Sonic

Boom Test (side view).
NASA/CR—2015-218719 163

4.2.3.1.1 Monday, June 4, 2012

Installation activities continued during test week, with the AS2 model installed. Model XYZ 

measurements were taken and motion and safety checks completed. Although initial runs were expected 

Monday afternoon, these were not completed until Tuesday afternoon. Occupancy charges began on 

Monday, June 4, 2012.

4.2.3.1.2 Tuesday, June 5, 2012

By mid-day on Tuesday, motion checks were completed with AS2 from the control room, and the 

tunnel crew finished prerun inspections, safety briefings and other pretest activities. However multiple 

tunnel issues prevented running until about 2 pm that day, close to the end of shift.

One issue was a pressure transducer / data acquisition problem related to cavity pressure 

measurements. Another issue was a problem commanding the IGV (inlet guide vanes) in the main drive 

rotor / stator required for startup. After solving these issues, an initial shakedown run was accomplished 

with some initial humidity purge cycles. One reference run and an x-survey were accomplished with the 

AS2 model at Mach 1.20 / 2300 psf / Hrel = 60 in. Initial plots looked reasonable. As is the case with all 

axisymmetric models, tunnel angularity measurements upright/inverted are not obtained.

It was observed that as the tunnel approached Mach 1.40, the higher of the two desired test Mach 

numbers, the AS2 model experienced strong lateral motion, which hit the dynamic balance limits for the 

side-force gages in BLAMS (balance loads monitoring system). This continued to be a problem for all 

models at high speeds during this test.

The AS2 model is shown in Schlieren images in Figure 212. Note the model shock structure is 

traversed near the nose by a half-dozen tunnel ambient oblique shocks which will be discussed later.

4.2.3.1.3 Wednesday, June 6, 2012

Wednesday morning, testing proceeded as planned with the AS2 model and was completed with 190 

runs acquired by that afternoon. Due to the large lateral model motion, and problems with the model 

exceeding BLAMS limits, Mach number was reduced from the desired Mach 1.40, down to 1.30 which 

still had lateral motion and vibration, but at a more tolerable level. Various X and Z sweeps were acquired 

at Mach 1.20 and 1.30 with the AS2 model, and Schlieren images were obtained.

Figure 212.—Schlieren Images from 11 ft Transonic Tunnel
NASA/CR—2015-218719 164

At the Mach 1.20 condition, there was a strong reflected shock from the start of the test section 

upstream, where the baffled test section slots begin. Based on Schlieren images and the tunnel geometry, 

it appeared that these ambient shocks originated on the floor, propagated downstream, and reflected off 

the ceiling, which placed them in a position to run directly across the model, however not necessarily 

along the tunnel centerline. There appeared to be somewhere between 6 to 8 weak shocks that varied and 

oscillated slightly in the tunnel flow, and visually appeared to be a loose braid of disturbances. It is 

believed that these originate at various YBL locations across the tunnel, so the impact on the model

signature is unknown. Some similar disturbances were observed originating on the ceiling, but were not 

as large. If similar shocks originate on the side walls, they would not be visible in the Schlieren images.

At the Mach 1.30 speed, the shock angle sweeps back enough that these ambient shocks pass behind the 

model, and should not adversely affect the data measurements at the higher speed. Because of the 

problem at 1.20 Mach, data collection was done at Mach 1.15 later in the test instead.

After the AS2 model runs were completed, the model was de-installed, and the blade-mounted 

Boeing Aircraft, “Boom 1”, model installation began. Although the model was ready to run late on 

Wednesday’s day shift, a facility problem prevented running that day. The problem was a tripped motor 

electrical breaker which failed to close properly. After fixing this issue, one shakedown run was obtained 

with the Boom 1 model on Wednesday, primarily to observe model dynamics and see if they were similar 

to the Axisymmetric AS2 model. The Boom 1 model was run up to 1.40 Mach, and displayed similar 

severe lateral dynamic motion, even when tunnel total pressure was reduced (from PT = 2300, down to 

1450 psf). All previous AS2 runs had been performed at PT = 2300 psf and that total pressure was 

planned for all models during the 11-0249 test. Because the lower total pressure did not provide any 

relief, it was not pursued. The Boom 1 model was also run as Mach 1.20 and 1.30 and demonstrated that 

these conditions could be obtained during the data runs on Thursday.

It should be noted that the Boeing Boom 1 model does not use any trip dots or other boundary layer 

energizing devices. The Boom 1 model is shown in Figure 213.

Figure 213.—Boeing Boom1 Model and RF1.0 Rail in 11 ft Transonic Tunnel
NASA/CR—2015-218719 165

As a mitigation strategy for the dynamic motion, stabilizing fins were attached to the balance housing 

Wednesday night to attempt to provide some aerodynamic damping behind the aircraft model and blade 

support. As had been expected, testing Thursday morning showed no improvements with the addition of the 

fins.

By Wednesday afternoon, the test was approximately one day behind schedule, primarily due to 

tunnel downtime due to facility issues. Some of the cost impact was offset by downtime charges, but the 

test had fallen behind pace. By running some overtime after the normal end of shift, some of this 

productivity was made up, but progress was still a bit slower than desired. Additional funds were 

requested from Peter Coen at the Supersonic Program Office by Don Durston as the current funding level 

would have been exhausted by Thursday afternoon. The additional funds allowed testing to be completed 

on all three models by midday on Friday.

4.2.3.1.4 Thursday, June 7, 2012

On Thursday, testing was conducted with the Boeing Boom 1 model from about 08:30 to 11:30.

Flow angle checks were conducted at various Mach numbers with the Boeing Boom1 model. To 

accomplish each model pitch angle change, the model must be translated from a measurement location at 

Hrel = 38 in. to the home location at Hrel = 60 in. and then the pitch angle is changed and the model was 

translated back down to 38 in. The desired angles ranged from Alpha = –1.0° to +5.0° in 0.50°

increments. As a result of the awkward process, these surveys took a long time to complete. A similar 

problem exists at the 9x7 tunnel and is an unfortunate consequence of the hybrid model control consisting 

of the primary tunnel model control and the grafted on model roll mechanism and the x-ram linear 

translation actuator. These latter two items are not integrated into the standard tunnel model positioning 

and control software and are operated separately and independently.

During Thursday’s testing, both X and Z translation sweeps were conducted at 0° roll, as well as 

sweeps at 20°, 30°, and 40° roll, all at Mach 1.25. Running at the higher Mach 1.40 speed was not 

possible due to poor model/sting dynamics, similar to that observed with the AS2 model earlier in the test, 

even with the "stabilizing" fins on the balance adapter. It was believed that the lateral vibrations were an 

excitation of the entire sting support system, so the small balance fins did not mitigate the motion much.

With the Boom 1 model, runs at Mach 1.25 were avoided because of the ambient tunnel shocks 

potentially interfering with the model signature. It was thought that Mach 1.25 was a good work around to 

avoid the tunnel flow quality issue.

By midshift on Thursday, efforts were underway to begin changing to the larger-scale, sting-supported, 

Boeing Performance Model (PM1), with plans to begin testing this model on Friday. Installation of the PM1 

model was completed Thursday at the end of the day shift. The existing sting/balance/Xram assembly was 

used without any changes. This is the same sting assembly stack as was used for the other two models (AS2 

and Boom1). Official installation photos of the Boeing PM1 Performance model were taken Thursday 

afternoon by a NASA photographer. The PM1 model is shown in Figure 214.

Figure 214.—Boeing PM1 Sting-Mounted Large Scale Performance Model
NASA/CR—2015-218719 166

4.2.3.1.5 Friday, June 4, 2012

On the last day of testing, the larger Boeing PM1 performance model was run. The model is stingsupported, as opposed to the blade support of the smaller Boom 1 model. The PM1 performance model 

was to be used for transonic drag measurements from Mach 0.80 to 1.20, as well as to measure signatures.

It was noted that signatures would be compromised by reflections of the nose signature reflecting from 

the wall and interfering with the aft-end of the model signatures for this very large model, which is three 

times the size of the Boom1 signature measurement model (15.75 in. vs. 43.31 in.).

The large PM1 model experienced the same lateral oscillations as were observed with the AS2 

axisymmetric model and with the Boom 1 blade-supported model. Although the PM1 model motion was 

somewhat smaller in angular magnitude, likely due to the increased weight, the larger model’s physical 

size produced higher loads. As a result the PM1 model had similar problems with side force balance 

limits on the 1.00 in. balance as were observed with the Boom 1 model. Friday’s test runs obtained one Zsurvey and drag polars to get transonic drag trends. Testing was completed at 12:30.

There was a Boeing-NASA discussion regarding options to replace the Xram or other parts of the 

stack with tapered spacers, but this was not attempted at the end of the test. The consensus opinion was 

that this change would not correct the issue. A shorter stack was discussed to change the sting/model 

natural frequency, but there are limited options for potential locations to mount the 14 in. RF 1.0 rail on 

the floor of the 11 ft tunnel. This rail location dictates the model location needed, and thus the sting 

buildup stack cannot be easily shortened. With the sting assembly as installed for the 11-0249 test, there 

were three models which were all limited to 1.3 Mach.

In addition, testing at 1.2 Mach was hampered by the shocks which originate at the floor at the start of 

the test section upstream. These shocks reflect off the ceiling and interfere with the model location when 

the tunnel is run at 1.20 Mach. Testing was conducted at 1.25 and 1.31 as a result of these two limitations.

This shock can be seen in Figure 215 crossing the nose of the PM1 model.

4.2.4 Test Observations and Issues

4.2.4.1 Model Dynamics

Model lateral dynamics were a problem at high speeds for each and every model during the 11 ft test.

Three models were run, all with distinct and different characteristics, and all three displayed undesirable 

model lateral (left-to-right) dynamic motion. The lateral motion was worse at higher Mach numbers, and 

testing was typically limited to 1.31 Mach, rather than the 1.40 Mach desired.

Figure 215.—Schlieren Image of Upstream Shocks Interfering With

Boeing PM1 Sting-Mounted Large Performance Model
NASA/CR—2015-218719 167

The AS2 axisymmetric reference model (a nonlifting body) had this tendency, as did the Boom 1 

blade-supported aircraft model. The much larger Boeing performance model is approximately three times 

the size of the Boom 1 model and it sting supported. Because all three models experienced this problem, it 

is believed to be rooted in the long sting support stack and the relative stiffness and inertia of the 

components. Some dynamic tap-tests were conducted air-off to observe the natural frequency of the 

assembly. No free play was observed in the sting assembly when inspected by hand.

Mitigation attempts included the addition of stabilizing fins to the balance housing to provide 

aerodynamic damping and to improve stability, however these were unable to overcome the sting 

dynamics. These balance housing fins can be seen in Figure 216.

The addition of lateral damper cables was considered, but it was believed that with the increased 

blockage due to the 0.50 to 1.00 in. cables, the tunnel maximum speed would be reduced to about 1.30 

Mach, which could already be run without excessive model dynamics.

Running at reduced dynamic pressure of PT = 1450 psf was attempted but without any substantial 

reduction in observed sting dynamics.

Figure 216.—Boeing BM1 Sonic Boom Model

(close upper) Over RF1.0 Rail (lower)
NASA/CR—2015-218719 168

One work around which helped tunnel productivity was to increase BLAMS (balance loads 

monitoring system) side-force alarm limits to a higher level before setting off an audible warning and 

automatically stopping the run and returning the model to a “safe” or “home” position. Model dynamics 

are mostly observed in the S1 and S2 side force gages, and balance roll loads were well below balance 

limits. The alarm limits were initially set to 61 percent of balance capacity, and this was increased first to 

64 percent and then to 74 percent of balance capacity.

When these hard limits were exceeded, the model was “safed” and was moved to a home position, 

alpha 0, Hrel 60 in. This doubled the amount of time it took to conduct a sweep survey, due to the fact 

that after every few points, data acquisition would be stopped, and the model would reset. In one 20 to 

30 min time period, 12 safe/resets were observed while attempting to measure 15 to 20 Z-positions, with 

approximately 50 percent wasted time.

In addition, upon review of the early AS2 signatures, it was apparent that when the model survey was 

abruptly interrupted by the BLAMS system, the data acquisition was not completed with the full timeaveraging desired (6 sec settle time, followed by 10 sec data averaging). The data which was cut short 

looked suspicious, although the fully-averaged X-surveys looked reasonable. Care must be exercised 

when using partially-averaged/interrupted data points, and signatures might be improved by removing 

suspicious stray points.

Increasing BLAMS limits allowed the number of resets to be dramatically reduced to a somewhat 

tolerable level. At the increased BLAMS settings, the limits are still exceeded on occasion, but at a more 

acceptable frequency.

Late in the test, there was a discussion regarding options to replace the Xram or other parts of the 

stack with tapered spacers, but this was not attempted. The hope was that this change might change the 

sting/model natural frequency or reduce any mechanical freeplay in the existing components. However, 

the 14 in. pressure rail location dictates the model location needed, and thus the sting buildup stack cannot 

be easily shortened.

4.2.4.2 Humidity

During the test, we learned that humidity control at the 11 ft tunnel is an “all or nothing” situation.

We could not control humidity as was possible in the 9x7 tunnel. The tunnel can have either full injection 

of very dry air, or none at all. There is no true throttle-ability as was used in the 9x7 by modifying the 

mass flow of the HPA (high pressure auxiliary air). The initial plans at the start of the 11 ft test proposed 

running at/about 300 ppm with a 10 ppm tolerance from the reference run. Some discussion followed 

suggesting 250 ppm. The reality is that the tunnel frequently runs at humidity levels between 100 to 

200 ppm, with the actual values trending based on whether the dry air is added or not.

At the start of the test, there was a lively discussion about the desired humidity levels and tolerances 

to run in the 11 ft tunnel. Originally, Don Durston and Eric Walker reviewed plots and requested that the 

tunnel should set a target humidity of 300 ppm ±25 with a tolerance of ±10 between reference and data 

runs. It was noted that this tolerance was more than 3x’s what had been used in the recent 9x7 tests. It was 

hoped that it would be easier to stay within humidity levels with the relaxed tolerance. Alaa Elmiligui 

recalled that a prior discussion had concluded that a level of 250 ppm or lower was desired. Susan Cliff 

recommended humidity as low as practical, rather than attempt to control humidity at a level such as 

300 ppm. Initial data runs on Tuesday afternoon showed humidity immediately dropping to the mid200 ppm range, and somewhat stabilizing at this low level.
NASA/CR—2015-218719 169

Figure 217.—Humidity per Run Number Throughout Test

On Wednesday, the first full day of running, the humidity control approach was better understood. An 

initial starting value could be set or established, but there is no way to control this during a run by 

throttling dry air injection similar to the approach developed in the 9x7 tunnel. At the 11 ft, the only 

option was to dump a continuous flow of ~30 lb/sec of very dry air into the tunnel. After running in this 

OCPE (Open Circuit Plenum Evacuation) mode for a few hours, the humidity continued to be reduced, 

resulting in very low humidity values. For example on Wednesday morning at the start of test runs, 

(08:30) the initial humidity was set to 250 ppm, and by midday (11:30), the tunnel was down to 100 ppm.

There was not any method to actively control this behavior and data was taken as planned, to obtain 

reference runs, obtain and X or Z sweep, and then another reference run. Often there could be a humidity 

change of up to 10 to 15 ppm during a sweep. It was believed that the impact on the data would be 

acceptable because the humidity was at such low overall levels. This tunnel characteristic was similar 

throughout all testing in the 11 ft on each day that week, from Tuesday through Friday.

The humidity levels throughout the week of testing are presented in Figure 217.

4.2.4.3 Data Reduction Enhancements

The SDS (online data plotting system) had a new capability to plot spatially averaged signatures as 

data is acquired. Customer attendees were given a tutorial on the software Monday afternoon. This was a 

good real-time plotting tool and supplemented the analysis conducted by Eric Walker using MATLAB 

and by Susan Cliff using her methods. The SDS-based plotting was useful for viewing a signature for a 

quick check, but did not have the complexity, and analysis flexibility that is used with the more-capable 

MATLAB scripts.

Bob Langberg worked with Eric Walker, NASA Langley to add an additional layer of data reduction 

to his MATLAB scripts. This extra step removed tunnel residual effects which had not been completely 

removed by the initial reference run subtraction, but still appeared to be tunnel-fixed artifacts. The cleaner 

data was then model-aligned and spatially averaged to obtain a cleaner signature.

4.2.4.4 Schlieren Images and Test Section Flow Quality

Testing at 1.2 Mach was hampered by a number of shocks that originated at the floor at the start of 

the test section. They reflected off the ceiling and were located at the model location when running at 

1.20 Mach. These shocks can be seen clearly in Figure 218 and interfere with the model shock signature.
NASA/CR—2015-218719 170

Schlieren images were acquired at 10,000 frames per second and played live at a downsampled rate.

The Schlieren images showed the expected shocks off the models and support structure as well as the 

ambient tunnel conditions similar to that observed in the 9x7 using their RBOS (retro-reflective 

background-oriented Schlieren) techniques.

The pressure rail leading edge shocks were visible as anticipated, with the shock from the rail/wall 

junction being the strongest, though a weaker shock from the rail leading edge corner was also visible.

Also visible was the shock from the rail leading edge corner, after being reflected off the wall/boundary 

layer on the tunnel floor. These three shocks showed up and were positioned as had been expected in 

tunnel layout diagrams.

Curiously, there was a weak and periodic secondary flow phenomenon visible in the Schlieren 

images. When 200 frames were shown at 10 frames per second, some weak shocks (swept at angles less 

than the main flow Mach angle) were also visible. They seemed to originate on the floor and ceiling of the 

tunnel and appeared to translate upstream at an estimated rate of about 0.20 Mach. Speculation was that 

these may be occurring in the secondary plenum flow, outside the test section due to circulation and air 

extraction. This phenomenon is curious, and repeatable, but it was not believed to adversely affect the 

acquisition of the pressure signatures inside the test section itself. Images and video were taken for 

reference.

Schlieren images were viewed live and the flow phenomenon displayed were interesting. First, when the 

model was moving side-side, the shocks coming off the model were certainly affected, and this was easily 

viewed in the Schlieren images. This likely affected measured signatures and was expected to have 

increased rounding at the peaks. Secondly, the tunnel ambient flow displayed a half-dozen shocks, 

originating upstream on floor baffle covers. The shocks reflected off ceiling, and returned to cross the tunnel 

near the model. It was hoped that impact on measured model signatures was minimal, as the tunnel YBL 

might not be the same as the model which was located at the tunnel centerline. However, the actual impact 

on the measured pressure data was unknown. Still images and video of the Schlieren images were recorded. 

Figure 218.—Schlieren Images Show Ambient Shocks From

Upstream Interfering with Boeing BM1 Sonic Boom Model
NASA/CR—2015-218719 171

Figure 219.—The 11 ft Test Section Slot Baffles as Possible Disturbance Sources and Repairs

The suspect slot baffle inserts are shown in Figure 219 during testing, and during an attempt to repair 

them and make them flush with the test section floor. Only floor-located baffles were adjusted, as they 

were the suspect source of the oblique shocks which interfered with model measurements at Mach 1.20.

By reviewing Schlieren images following the repairs, the problem had not been resolved. It was unknown 

what the exact source was for the oblique shocks and testing was conducted at higher and lower speeds to 

locate this flow feature ahead of, or behind the model.

4.2.5 Test Conclusion and Recommendations

The post-test debrief was held Thursday afternoon with all customers and tunnel operations leads 

present. Overall everyone on both sides of the table was happy with the execution of the exploratory test 

in the 11 ft transonic tunnel. It was recognized that the tunnel experienced a number of downtime issues, 

but that the tunnel crew addressed each as it developed and worked through each to resume testing.

A summary of the identified issues and the discussion topics is presented below.

• The 14 in. RF 1.0 pressure rail was said to have “no leaking ports”, whereas in the past, some 

leaks (typically 5 to 10 ports) could not be fixed and the blame was pointed at the internal hard 

lines or ferrules, which are a hardware issue that cannot be fixed with the flexible pressure lines 

or with the PSI module interface. A question was raised if we should confirm that all ports were 

indeed “good”, or if bad data (leaking ports) had been simply eliminated from the data stream, 

rather than being flagged with “99’s” as had been standard practice in earlier tests.
NASA/CR—2015-218719 172

• Baffle slot inserts. Repair/adjustment was completed Thursday afternoon. Of the 14 baffle slots, 

about half (6) were protruding. Two had parts projecting from the wall by up to 1/8th in. Most 

were on the order of 1/16th of an inch from the flush wall. All may have been casting shocks back 

towards the model. Schlieren images from Friday’s runs showed that, unfortunately, this repair 

effort did not correct the observed ambient tunnel flow shocks. This should be investigated 

further and mitigated if possible.

• Lateral sting dynamics problems remained unresolved throughout the test, and were observed 

with three very different models. Although a higher capacity balance would make it possible to 

run with the model dynamic motion, without tripping the BLAMS system, the motion would 

likely have the effect of rounding the measured signatures. It would be best to understand / fix 

root cause of the structural dynamics and mitigate this effect, rather than just work around it.

• Cavity pressure transducers were wired incorrectly at the start of the test. Downtime was credited, 

but troubleshooting did not quickly reveal the root cause, instead the data system “1413” card and 

enclosure were swapped out, thinking this was the glitch. The wiring problem was discovered 

later. The tunnel should be commended for being forthcoming about the issue and the resolution.

This was an example of the great working relationship between the tunnel operators and the 

NASA analysts and Customer representatives which has developed over the course of testing 

activities.

• It was noted that with OCPE active, the tunnel dried out a lot, and humidity varied open loop. The 

air was very dry, but also highly variable and uncontrolled. Humidity during the 11-249 test was 

normally less than 200 ppm, and was commonly in the range of 130 to 140 ppm. In some of the 

more extreme cases, humidity was as low as 65 ppm.

The consensus was that this was a very successfully exploratory / shake down test, with issues 

identified rapidly in this short-duration test, rather than if they had been discovered for the first time in the 

middle of a more substantial testing effort. Lessons learned from this test should be considered when 

planning future sonic boom measurement test activities.

Testing was conducted at 1.15 and 1.31 as a result of the flow quality and sting dynamic limitations, 

rather than the desired 1.20 and 1.40 Mach numbers in the pretest planning.

Boeing stated that despite the challenges and issues encountered in running this exploratory test, that 

the objectives of this exploratory test were met by “investigating and learning about the particular issues 

and challenges of how to conduct sonic boom measurement testing in the NASA Ames 11-ft Transonic 

Wind Tunnel”. Data obtained can be used to characterize the spatial and temporal uncertainty in the 

signatures measured in this tunnel.

It is Lockheed’s opinion that the NASA Ames 11-ft Transonic Wind Tunnel can be used to measure 

sonic boom signatures, but only at specific test conditions, and that care must be exercised in test 

planning to select desired data runs which can be measured successfully. In particular the following issues 

were test concerns that should be mitigated if possible before another test:

• The tunnel maximum Mach number of 1.40 may not be achievable due to adverse model / sting 

dynamics. Efforts should be conducted to understand and possibly mitigate this issue.

• The possible contamination of the measured signatures by easily observed upstream tunnelambient shocks are a concern when testing at/near Mach 1.20. Empty-tunnel data runs with the 

Schlieren system can be used to diagnose and perhaps understand and mitigate this issue. It is 

possible that the shocks originate in the flow interaction taking place at the initiation of the tunnel 

wall boundary layer removal baffle slots, and that with these slots open and present this adverse 

flow behavior may not be easily corrected.
NASA/CR—2015-218719 173

• It is noted that at some lower Mach number, the model shock reflection from the floor will limit 

testing with the 14 in. RF 1.0 pressure measurement rail.

4.3 Glenn 8x6-12-002 Boeing Test

The NASA Glenn 8x6 tunnel showed promise for covering a greater Mach range (0.3 to 2.0), 

including continuous coverage of the sonic boom range 1.0 to 2.0, and being able to make sharper sonic 

boom measurements than the (previous best) 9x7 tunnel. However, drift in the tunnel ambient pressures 

whenever models were moved, introduced offset errors large enough to spoil measurements. Indications 

are that keeping the Mach number from drifting when models moved, would result in superior 

measurements.

The NASA Glenn 8x6 tunnel has several unique features that make it attractive to use for sonic boom 

measurement. First among them is its tri-sonic ability to run all Mach numbers from 0.3 to 2.0.

Supersonic tunnels, like the Ames 9x7, cannot run Mach numbers below 1.55, and the Ames 11 ft 

transonic tunnel had difficulties taking sonic boom measurements in the Mach 1.0 to 1.4 range, as 

detailed in the previous Sections 4.2, 5.2.4, Figure 215 and Figure 218. The Glenn 8x6 also has straight, 

symmetrical flexible walls for its nozzle and a porous transonic test section implemented through angled 

cylindrical bleed holes of 1 in. diameter (Figure 220). The porosity did not seem to help measurements at 

Mach 1.6 and there were indications that if the rail had been positioned in the influence of porosity, 

measurements probably would have been worse. Future testing is likely to try the upstream nonporous 

test section and perhaps the porous section for Mach’s close to 1.0. These facility options provide some 

advantages; however, there was also previous experience with problems measuring sonic boom in the 

8x6.

The DARPA QSP (Quiet Supersonic Platform) program funded the F-5SSSBD (Shaped Sonic Boom 

Demonstrator) modification and did their sonic boom testing in the 8x6 in 2002. Using the best setup of 

the time, moving their model past fixed probes (10 in. from the wall), they measured varying, erroneous 

results. They even made a second entry with somewhat improved results (from changes in probe tunnel 

location), but the validation accuracy achieved was much worse than our current requirement. Further, 

Figure 221 shows that the 8x6 has a larger Pstatic variation through its test section (as measured by the 

RF1.0 rail), which is the cause of flat-top pressure distortions from the tunnel flow-field discussed in the 

earlier N+2 Phase 1 report (Ref. 10) (section 5.1.1 LM1 and 5.1.3 LM3 descriptions). Consequently, there 

was concern before the test that this might not be a good facility for the highly precise shaped boom 

measurements that our advanced designs now require.

During the test, spatially averaged signature calculations were not matching previous measurements, 

as they had between the LM3 97-0231 and the Parametric 97-0250 tests. After much analysis, a parameter 

was found with a repeating, proportional relationship to the problem: tunnel Mach number was drifting 

with model motion (but not proportional to motion) and rail reference pressure changed proportional to 

the Mach change. Changing the tunnel strut height caused particularly large rail pressures drift of ±0.015 

Δp/p. Measurements and references were tried at a fixed strut height using only linear actuator X motion.

This reduced rail pressures drift an order-of-magnitude to ±0.0015 Δp/p, as shown in Figure 222 (and 

expanded in Figure 223). Signature detail from 49 in. distance looked even better than the Ames Test 250 

data (scaled from 30 in., blue line), but the level drifts (red arrows) were large enough to cause 

unacceptable errors, greater than 5 PLdB.
NASA/CR—2015-218719 174

Figure 220.—The 8x6 with Angled Bleed Holes on Walls, Model

Hardware on Floor Strut and RF1.0 Rail on Ceiling

Figure 221.—Glenn 8x6 has Greater P_static Variations Than Ames 9x7,

Which Distort Slope and Zero of Measurements at Every Rail Orifice 8x6 Test Section

 

 

 

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

XOrfR001XOrfR018XOrfR035XOrfR052XOrfR069XOrfR086XOrfR103XOrfR120XOrfR137XOrfR154XOrfR171XOrfR188XOrfR205XOrfR222XOrfR239XOrfR256XOrfR273XOrfR290XOrfR307XOrfR324XOrfR341XOrfR358XOrfR375XOrfR392XOrfR409

Ames 9x7, M1.8

Glenn 8x6, M1.8

 

 
NASA/CR—2015-218719 175

Figure 222.—Better Signature Detail Marred by Varying Level Drift

Looking into more details of this comparison revealed and quantifies some relationships that seem to 

explain the level drift. From orifices on the measurement rail ahead of the model, the tunnel pressure

minus reference is plotted in Figure 223 for these same signature measurement sets. The upper left plot of 

Figure 223 has 26 lines of pressure through each orifice for the 26 time points that measurements were 

taken in the 9x7. The upper left plot is the same data from the 8x6 (with fewer orifice repeats toward the 

right side of the plot). The lower plots show a line with the average value of the above data. Excluding 

drift, the Glenn 8x6 data has significantly less variation. But averaging the Ames data results in pressure 

virtually identical to the reference, making the model average measurement minus reference very 

accurate. Plotting the Mach for the same Glenn 8x6 runs reveals that the reference measurements (Xram 

at 0 in.) both before and after the model measurement translation (Xram from 16 to 24 in.) are at the same 

Mach, 0.001 greater than the model measurements, Figure 224 (plotted from dataset column 36 with 

header “Mach”), and the reference measurements match, unlike measurements when the Mach is 0.001 

lower. Figure 225 shows the change in tunnel Mach with change in model position (in this case height 

change). Unfortunately, pressure changes and tunnel Mach changes are not proportional to model position 

change and also seems to have a time-dependent lag behavior. However, Figure 226 shows that pressure 

changes are directly proportional to changes in the measured tunnel Mach—each orifices’ pressure 

changes in a straight line with changes in tunnel Mach. The proportionality indicates that drifting changes 

in reference pressure in the Glenn 8x6 would be avoided if the Mach could be held to an average variation 

less than ±0.0002 during a measurement and reference set. (More specifically, Mach could vary more 

than ±0.0002 as long as the averaged Mach was within ±0.0002 of the reference measurement Mach; 

however, less variation should result in sharper spatially averaged measurements.) Ideally, Mach variation 

should be eliminated even when the strut height is changed, so the best measurement locations and 

procedures can be used without restrictions.

Best 8x6 Signature: G61refG62 (h = 49”) dPPrc

0.0 10.0 20.0

Better signature detail at 49”,

but level errors from reference 

drift (arrows) are too large

T250run1274-1299ref1300

T8x6-12-002run818-843ref844

 

 
NASA/CR—2015-218719 176

Figure 223.—Glenn 8x6 Ambient/Reference Drift Too Large (even at same strut height)

Figure 224.—Change in Reference Measurements

Correlates With Change in Tunnel Mach

Figure 225.—Mach Change With Model Position

Not Proportional (red increasing H, blue decreasing H)

Ames 9x7 Glenn 8x6

 

Best 8x6 reference DP/P drifted > ±0.0010, with corresponding 

signature error. 9x7 had more variability but averaged < ±0.0001 DP/P - Reference

-0.002

-0.0015

-0.001

-0.0005

0

0.0005

0.001

0.0015

0.002

176 178 180 182 184 186

-40 -35 -30 -25 -20 -15

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2 x 10-3 Grand Mean Boom Signature --SBPr1274_1299-1300

dPPrc

XpRam

Pressure Average from Reference (More 

Variable but Without Drift) During 

Measurements (T250_1274_1299-1300)

-50 -45 -40 -35 -30 -25 -20 -15 -10

Grand Mean Boom Signature --SBPr1274_1299-1300

XpRam

Median SBPr1274_1299-1300

-0.002

-0.0015

-0.001

-0.0005

0

0.0005

0.001

0.0015

0.002

DPPRC

176 178 180 182 184 186

X_Orifice, inches

Pressure Drift from Reference During 

Measurements (G61ref62)

Pressure Average Drift from Reference 

During Measurements (G61ref62)

< ±0.0001 > ±0.0010 DP/P - Reference

Pressure Variation from Reference

1.7768

1.777

1.7772

1.7774

1.7776

1.7778

1.778

1.7782

1.7784

1.7786

817 818 820 822 824 826 828 830 832 834 836 838 840 842

Mach

Run#

Mach versus Measurement Run

G60-G62, aka Runs817,818-843,844

 

 

Reference Measurements at Xram = 0”

Measurements at Xram 16” to 24”

1.776

1.778

1.78

1.782

1.784

1.786

1.788

1.79

1.792

35 40 45 50 55 60 65 70

Mach

Height, inches

Set56, H=68-38 ref68

Set48, H=38-48 ref68

 

 

 
NASA/CR—2015-218719 177

Figure 226.—Orifice Reference Pressures

Change in Direct Proportion to Mach Variation

Understanding the extreme sensitivity of shaped sonic boom measurements to tunnel Mach changes 

(and potentially other parameters like humidity, temperature, etc.) can be derived from understanding the 

measurement process and supersonic test section flow. The model pressures we are measuring are smaller 

than the static pressure variations found through all supersonic test sections. We measure model sonic 

boom from the difference between model propagated pressure (Mach cones) on the rail and reference 

ambient by moving the model aft and/or up until its propagated pressure (Mach cones) is behind the 

model pressure when measured. The reference ambient flow-field must be maintained exactly as it was 

when the model was being measured. One challenge with precisely holding the flow-field condition is 

that any slight parameter change that changes the Mach will change Mach angle, and thereby, position of 

the shocks propagating from the nozzle through the test section. All supersonic tunnels have test sections 

crisscrossed with shocks that form at the tunnel nozzle. The nozzle diverges after its throat to accelerate 

the flow supersonically, and then, bends the flow back straight with relatively substantial turning angles, 

often in double digit degrees. Such compression shocks have zero dissipation in the short distance of a 

wind tunnel. These “shock diamonds” and their static pressure variations (note: little total pressure change 

since Mach and static pressure titer totter isentropically) tend to be 10 to 20 times larger than subsonic test 

section variations (unique supersonic consideration for testing of laminar flow, sonic boom, model 

surface pressure, etc.). During sonic boom measurement, any parameter that slightly changes Mach angle 

will shift the entire test section flow-field forward or aft. And just an orifice or two displacement of the 

flow-field at the rail will unacceptably change the reference pressure because of the static pressure 

oscillations (Figure 221).

4.4 Ames 9x7-0254 LM4 Test

The NASA Supersonics Program’s overall objective was to promote and validate the capabilities 

needed for supersonic transportation. Shaped sonic boom over land was identified as a “game changing” 

enabler and was selected for primary development by this N+2 Supersonic Validations contract. Our 

Phase 1 results validated shaped boom near the quietness expected for over land flight with a very high 

performance design, showing that shaped boom and high performance are not mutually exclusive. However, 

that design did not achieve full carpet shaped boom. Its shaped boom shaping broke down for ray roll angles 

of 30° to 50°, which were louder than 0° to 20°. So for Phase 2, LM had 3 objectives in mind:

1. Validation of a refined, full-carpet shaped boom configuration

2. Accurate shaped boom measurements beyond near-field interference (48 to 80 in.)

3. Improved productivity shaped boom test procedures P/P 

- Ambient -0.015

-0.01

-0.005

0

0.005

0.01

1.781 1.782 1.783 1.784 1.785 1.786 1.787 1.788

DPPRC

MACH

Reference DPP Change vs Mach 

(G56ref55)

DPPRC001

DPPRC002

DPPRC003

DPPRC004

DPPRC005

DPPRC006

DPPRC007

DPPRC008

DPPRC009

DPPRC010

 

 
NASA/CR—2015-218719 178

These 3 objectives were chosen to demonstrate that total mission shaped boom flight is achievable. The 

previous LM3 test results validated our CFD predictions in the near-field. To determine how close the 

measurements matched prediction (expressed in ground sonic boom loudness) compared with the less than 

2 PLdB difference requirement, the measured data was substituted for the predicted data where available; 

then, the mixed measured and predicted data was run through our LMBOOM cylinder/multipole far-field 

correction method to get a far-field propagated ground signature. We’d like to further boost our 

methodology validation confidence by matching a second model.

Figure 227 shows an application of the far-field correction to CFD with results extracted at various 

distances from the vehicle. The far-field corrected solutions are all nearly identical, except a slight 

difference for the shortest extraction distance of 2.8 semi-spans—which still has a corrected aft signature 

between the 11 semi-span and ∞ results. At close distances, the multipoles can have difficulty fitting the 

strong shock discontinuities; so flow-field accuracy suffers, the correction accuracy degrades and the 

error is multiplied by a larger correction magnitude. But Figure 227 shows the 2.8 semi-span corrected 

extraction is closer to far-field than the uncorrected extraction from 11 semi-spans. The far-field 

correction method uses an 11-term Fourier-fit versus roll angle for each X-station’s half-circle of CFD 

data. The method “misfit” error quickly diminishes at greater distances with weaker pressures to fit and a 

smaller correction magnitude. While a repeatable far-field result is obtained from different extraction 

distances and the far-field result appears to be a reasonable far distance asymptote, it has never been test 

validated. And the method’s far-field correction almost always results in stronger pressures and greater 

loudness than uncorrected results. So accurate measurements at distances beyond near-field interference 

were desired to validate the pessimism of our far-field correction method.

To validate full-mission shaped boom, there are more roll angles and conditions to measure, so 

productivity improvements make all validations more affordable. Many improvements were successfully 

identified and implemented in the LM4 test, reducing measurement set time to 5 to 10 min and increasing 

the number signature sets measured to 5 times greater than LM3 (in slightly less time).

Figure 227.—The Far-Field Correction Method Provides Consistent

Results Matching the Uncorrected Data's ∞ Distance Asymptote

-1

-0.5

0

0.5

1

1.5

0 50 100 150 200 250

2.8 H/(b/2)

5.6 H/(b/2)

8.3 H/(b/2)

11 H/(b/2)

11 H/(b/2) with

multipole correction

Time(ms)

DP/P (psf)

-1

-0.5

0

0.5

1

1.5

0 50 100 150 200 250

Time(ms)

DP/P (psf)

No Correction Applied Far-Field Corrected

2.8 H/(b/2)

5.6 H/(b/2)

8.3 H/(b/2)

11 H/(b/2)

No Far-Field Correction

 

 
NASA/CR—2015-218719 179

4.5 Test Planning (Productivity Tolerances, Test Models, Processing)

Test planning focused on achieving the three objectives, briefly put: 1044-02 validation, accurate far 

measurements and improved productivity. Near validation measurements were planned similarly to Test 

250 to repeat its success with the 1021-01 model. Most new planning focused on ways to improve far 

measurements and on diagnostics to understand why measurements degrade as measurement distance 

increases. The smaller 1044-02 model allowed Z sweeps to measure heights from 70 to 42 in. (rail at aft 

window) and 42 to 20 in. (rail at forward window) for a continuous investigation of measurement height 

variation. X sweeps were run at heights of 31.8, 42, 54, 61, and 70 in. And to improve far measurement 

accuracy, the old SEEB-ALR and new 70° Flat-Plate calibration models were run at 54 and 70 in. X 

sweeps and 70 to 42 in. Z sweeps (both Mach 1.6 and 1.7 with repeats). As illustrated in Figure 228, the 

70° Flat-Plate Delta model produces little disturbance below it. Shock reflections from the Flat-Plate are 

expected to get stronger as the model is translated forward and the shock reflects off an increasing span.

The relationship with local span may improve calibration matching to models with wings and tails.

Further improvements to far measurement accuracy were expected from three new data processing 

refinements that were implemented on this test’s measurements termed: automatic reference measurement 

trimming, elimination of highest/lowest measurements and reverse aging in spatial averaging. Simply put, 

automatic reference trimming omits any orifices on the rail that still are impacted by model pressures 

when the model is in its back and up position for reference measurements. Figure 229 shows that during a 

reference measurement the model pressures may still impact the aft portion of the rail, so rail orifices 

corrupted by the model during a reference measurement are omitted from spatial averaging. Figure 230

shows the improvement from omitting/trimming the aft rail orifices corrupted by the model.

Figure 228.—As the 70° Flat-Plate Model is Advanced Through the Flow-Field,

Rail Measurements Quantify How Reflected Shock Strength Varies With Model Span

Figure 229.—The Model is Moved Back and Up for a Reference Measurement But May Still be

Measured on the Rail—Orifices Not Ahead of the Model are Omitted from Spatial Averaging
NASA/CR—2015-218719 180

Elimination of the outlying highest/lowest measurement extremes before averaging tends to omit the 

same orifices where the tunnel flow-field distortions are largest. Eliminating the worst 10 to 15 percent of 

extremes, Figure 231, has been as effective as taking 10 to 15 percent more measurements, so a 

significant efficiency boost.

Even over the relatively short measurement distances in the wind tunnel, signatures age—meaning 

that the higher pressures propagate faster while lower pressures propagate slower. When pressures are 

distorted in amplitude by the flow-field, it affects their aging and leads to a correspondingly distorted 

position, grey lines in the left plot of Figure 232. Reversing the aging realigns the pressures with the 

locations where they were produced, grey lines in the right plot. The realigned pressures are averaged and 

re-aged to produce the final spatially averaged signature. This implementation of reverse aging in spatial 

averaging is still being refined and may produce even better improvements in future versions. Reverse 

aging is also particularly important to include when interpolating on CL or alpha, and this routine is being 

expanded to include CL interpolation. These measurement, calibration and processing improvements 

were intended to address the objective of improving far measurement accuracy.

Figure 230.—Automatic Reference Measurement Trimming Removes Only the Data With

Model Intrusion and Keeps the Good Data for Resolving the Trailing Signature

Figure 231.—Omitting a Fraction of the Highest/Lowest Measurements

Extremes, Smooths and Sharpens the Spatially Averaged Signature

 

 

Corrupted

Reference model positions 

relative to measurements

Trimmed

 

 

fmeomit=0.121
NASA/CR—2015-218719 181

Figure 232.—Reverse Aging, Averaging, then Re-Aging Achieves a

Slight Sharpening of the Spatially Averaged Signature

The last objective of improved productivity was addressed through rigorous reconsideration of 

measurement procedures and tolerances. A thorough investigation was made of tunnel data time stamps to 

determine where nonproductive time was being consumed. As mentioned previously, much time was lost 

when humidity or other test conditions could not be maintained within tolerance. Study of Test 250’s 

sensitivities to condition variations, using statistical data analyses by NASA and the high accuracy of 

spatial averaging, allowed substantial increases in tolerances to be implemented that resulted in no time 

being lost off-condition. Further, the tolerance and process by which the tunnel crew set a model’s CL, 

alpha, roll angle, etc. could be very time consuming, or not, depending on a string of many factors.

Lengthy communications with the tunnel crew to compare their facility capabilities and limitations with 

our measurement needs, eventually revealed why certain condition specifications were time-consuming.

Ways were found in all cases to make changes, which still satisfied our data needs, but were far less timeconsuming for the tunnel crew to execute and made more run time productive. While the process analyses 

consumed a lot of planning time, it was clear that we were going to achieve substantial productivity 

improvements and a lot of the communications occurred during the 231, 250, and 249 tests. LM4 97-0254 

post-test analyses documented the dramatic improvements in productivity achieved since LM3: 

LM3 LM4

Model Changes 5 10 

Measurement Runs 671 5377 (8 times more)

Signature Sets 25 137

Shifts Consumed (8-hr) 16 15 

In summary, 8 times more measurement runs were taken. Because more measurements were taken 

per set to try to improve far measurement accuracy, only 51⁄2 times more signature set spatial averages 

were compiled from those 8 times greater measurement runs. And twice as many model configurations 

were tested, including a rail location shift from the aft to the forward window. Measurement of a 

signature set that took 50 min on LM3 was down to 10 min on LM4.

One productivity improvement was not considered early enough to obtain the required funding, but 

would have eliminated 3 tunnel stops for model changes and eliminated the 4 hr required to move the rail 

from the aft window to the forward window. The RF1.0 rail was originally designed to span both windows 

continuously; but to reduce the initial cost of trying the concept, only half of the measurement portion was 

built with removable leading and trailing edge pieces that would allow the other half to be built and inserted 

later. In the meantime, near and far measurements are accommodated by moving the half rail from one 

window to the other. Since spatial averaging is the standard measurement technique, its accuracy is 

improved by longer lengths of measurements taken in tunnel positions with better flow. As shown in 

 

 
NASA/CR—2015-218719 182

Figure 233, doubling the rail eliminates a large band of obstructed measurement locations where model 

measurement translation would cross over the aft/forward rail positions. There is also a 19 in. zone where X 

sweeps cannot be run between heights of 35 to 54 in., unless a stack extender (for example the MSA) is used 

to locate the model farther forward with the forward rail. With a 36 in. extension, the 1044-02 model could 

be measured at a height of 68 in. on the forward rail but this setup’s minimum height would then be 44 in.

With the rail doubled, longer translations in better flow can be used for more accurate averaged 

measurements and all heights can be measured without shutting down the tunnel and moving the rail.

Figure 233.—Doubled Rail Increases Position Options and Translation Length—Improving

Measurement Accuracy, Plus No Stopping to Move Rail between Near and Far Measurements

South Wall

North Wall

Strut South Travel Limit Strut North Travel Limit Tunnel Strut Leading Edge

Rail and Orifices

Window Blank Window Blank

SR-57 Primary

Roll Mech Linear Actuator Ram LM Bal Adapter

LB 1044 Model

Ceiling Hatch

1044 Blade

-10

0

10

20

30

40

50

60

70

80

90

100

110

0 -10 -20 -30 -40 -50 -60 -70 -80 -90 -100 -110 -120 -130 -140 -150 -160 -170 -180

Distance from North Wall (in.)

Tunnel Station (in. from strut CoR)

9x7 Wind Tunnel Layout — LM4 Sonic Boom Test 97-0254 (overhead view)

Zsweep

Xsweep

Using 

MSA

New Nose 

shape

 

 

South Wall

North Wall

Strut South Travel Limit Strut North Travel Limit Tunnel Strut Leading Edge

Rail and Orifices

Window Blank Window Blank

SR-57 Primary

Roll Mech Linear Actuator Ram LM Bal Adapter

LB 1044 Model

Ceiling Hatch

1044 Blade

-10

0

10

20

30

40

50

60

70

80

90

100

110

0 -10 -20 -30 -40 -50 -60 -70 -80 -90 -100 -110 -120 -130 -140 -150 -160 -170 -180

Distance from North Wall (in.)

Tunnel Station (in. from strut CoR)

9x7 Wind Tunnel Layout — LM4 Sonic Boom Test 97-0254 (overhead view)

Zsweep

Using 

MSA

Xsweep

Improvements from Double RF1.0 Rail

RF1.0 Rail Measurement Limitations

Height 

54”-70”

&

24”-42”

(& Rail 

Move)

Height 

15”-70”
NASA/CR—2015-218719 183

4.6 Model Design

The 0.0065-scale LM4 wind tunnel model offered more location flexibility with wind tunnel support 

hardware and allowed testing at lower Mach compared to the larger, 0.008-scale LM3 model. The 19-in. 

long model featured removable flow-through nacelles that allowed exploration of alternate configurations, 

e.g., centerline nacelle-only and outboard nacelles-only, Figure 234. The highly-swept, LM3-based blade 

support attaches with no model geometric distortion, and its flow field distortion is minimized by being 

primarily on the model upper surface and swept greater than the Mach cone angle, illustrated by CFD 

analysis in Figure 235. The blade thickness was sized to a minimum at the vehicle, which necessitated a 

slightly increasing thickness going away from the model to counter the increasing moment arm from the 

running loads. Figure 235 provides a vivid illustration of the model support blade’s effect of the flowfield above the vehicle and very small effect below the vehicle. Unlike the LM3 model, no provision was 

made for a second, alternative model sting support.

Figure 236 compares predicted boom signatures of the supported model with that of a free-floating 

model in the tunnel. The data show that the blade mount has a minimal impact on the signature out to 40° 

off-track, with a gradually increasing influence beyond that point due to the compression and expansion 

generated by the blade spilling over the wing.

Figure 234.—LM4 Wind Tunnel Model of 1044-2 Configuration

Figure 235.—Symmetry Plane Pressure Contours: No Blade (left) and With Blade (right)

 

26.5”

19.0”

6.5”

 
NASA/CR—2015-218719 184

Figure 236.—Blade Effects on Model Signature

 
NASA/CR—2015-218719 185

Viscous simulations at wind tunnel Reynolds numbers show centerline inlet ingesting the blade wake, 

strengthening the shocks inside the duct, Figure 237. However, pressure recovery map indicated the flow 

will not choke, Figure 238. Note that the analysis was done using sharp nacelle LE and TE.

The 0.010-in. edge thickness and 0.030-in corner fillets were incorporated to facilitate model 

fabrication. Wing, tail and aft-deck edge thickening were accomplished via a simple upper surface offset

without changes to the lower surface contour. As of the flow-through nacelles, the thickening involved 

both OML and IML modifications. To prevent the thickened LE and boundary layer buildup from 

chocking the flow, the IML was given a small outward expansion after first being offset toward the 

center. For the centerline nacelle, which has minimal boom signature contribution due to 

empennage/fuselage shielding, the aft 1/3 of the OML was re-lofted to match the increased exit area with 

thickness increment. The outboard nacelles, which have significant influence on the signature, required a 

different approach. Instead of outward expansion of OML that adds compression and intensifies the 

reflected shock, the OML was held fixed while the exit plane was moved forward until the distance 

between the IML and OML matches the desired edge thickness. Figure 239 shows the surface pressure 

distribution of the sharp and thickened models. A stronger yet more diffused lip shock is evident with the 

thickened model. In the case of the centerline nacelle, the compression from the flare-out weakened the 

expansion fan at the nacelle closeout. As of the outboard nacelles, pylon shock attenuation was reduced 

due to weaker expansion coming off the truncated nacelle, resulting in stronger reflected shock. Figure 

240 shows the manufacturing-driven modifications have limited effect to the predicted boom signature 

and can be easily removed with increments.

Figure 237.—Blade Effects on Centerline Flow-Through Nacelle

Figure 238.—Centerline Flow-Through Nacelle Mach (left) and Pressure Recovery Map (right)

 

 

 
NASA/CR—2015-218719 186

Figure 239.—Surface Pressure Contour Comparison

Figure 240.—Edge Thickness Effect on Model Signature [M=1.7, AoA=2.1, H=5.4(b/2)]

Figure 241.—LM4 Model CFD Boundary Layer Comparisons

Due to the relatively low Reynolds number of the test, significant viscous effects were expected. As 

such, wind tunnel model-scale boom predictions were made using both laminar and turbulent boundary 

layers. Surface pressure gradient and streamline traces for the laminar and turbulent runs are shown in 

Figure 241. For the laminar case, the nacelle cowl and blade support causes shock-induced separation 

while the flow remains attached for the turbulent condition. Figure 242 compares the predicted boom 

 

Sharp LE/TE

Round LE/Thick TE

Sharp LE/TE

Round LE/Thick TE

 

 

Tau

Cp -0.015

-0.010

-0.005

0.000

0.005

0.010

3 4 5

Sharp LE/TE

Round LE/Thick TE

 

Laminar Turbulent

Inviscid Inviscid
NASA/CR—2015-218719 187

signatures of different types of boundary layer. LM3 test experience show that it is desirable to have trip 

discs on the upper wing surface and blade to energize the boundary layer to inhibit flow separation.

Model fabrication subcontractor was Tri Models, Inc, who also built the tight-tolerance axibody and 

LM3 models. The model and blade were machined out of 15-5PH and PH13-8 high strength stainless 

steel, respectively. Both were designed to meet all tunnel stress requirements and have safety factors well 

above the 4.0 desired by the facility.

The 1044-02 model scale size of 0.0065 was a reduction from the 0.008 of the 1021-01 model, 

Figure 243. The almost 4 in. reduction in model size to 18.68 in. helped better position the model in the 

tunnel for spatially averaged measurements. The prior 1021-01 model’s signature, allowing 1 in. ahead 

and 3 in. behind for its recompression, was over 26 in. making it longer than our linear actuator travel.

We were able to get more measurements, over greater distance, with better positioning in the tunnel with 

the new, smaller model that appears to have resulted in better accuracy. 

Figure 242.—Predicted Boundary Layer Effects on LM4 Signature

Figure 243.—The 19 Percent Smaller 1044-2 Model Improved Spatial Averaging Positioning

 

 

 
NASA/CR—2015-218719 188

Figure 244.—The 70° Flat-Plate Model Fabricated to 

Improve Measurement and Calibration of Reflected 

Shock Distortions

Figure 245.—Flat-Plate Approximates 1044-2 Wing Planform

Five models were tested during LM4. In addition to the 1044-02, the LM3 1021-01 model was retested 

first to verify that results were matching previous measurements, indicating that the measurement hardware 

was functioning properly. For far measurements only (with the measurement rail on the aft window) the 

SEEB-ALR and a new 70° delta planform flat-plate calibration models (Figure 244) were tested next. They 

provided flow-field characteristics and calibration information useful for improving the processing of the 

other models’ signatures. The delta’s varying span with length was planned to characterize how a model’s 

local span affects its reflection of shocks striking its lower surface. The model was inexpensive and 

produced little pressure change underneath it (Figure 244) so reflections are easier to measure. It also reuses 

the 1044-02 model’s blade support and approximates the 1044-02 planform for improved calibration 

matching (Figure 245). The final model is a legacy NASA 69° Delta Wing-Body symmetrical model first 

built and tested in 1972 and reported in NASA TN D-7160, 1973 (Ref. 11). It was built with several other 

models at the time to investigate wing planform effects on sonic boom.

4.7 Test Description

The LM4 97-0254 test took place October 9 to 19, 2012 at the NASA Ames Research Center’s 9x7 ft

Unitary Plan supersonic wind tunnel. Two 8 hr shifts were employed each day with the test consuming 15 

shifts total. This was exclusively a sonic boom test with sonic boom models and measurements 

comprising the whole test. A six-component force balance was utilized and flow angularity runs were 

made to record the CL and alpha of the boom measurements, but no force measurement runs were 

intended from these small models. Oil surface flow visualization and RBOS flow-field shock 

photography were performed on most every series at the same time as sonic boom measurements. The test 

was very successful, achieving continued refinements in measurement accuracy and a large improvement 

in productivity allowing an early completion with all desired validation data and many more diagnostic 

measurements than originally conceived.

 

 

 

 
NASA/CR—2015-218719 189

Figure 246.—Hardware and 1044-2 Model Prepared for Testing

The 1021-01 model previously measured in Parametric Test 97-0250 and LM3 Test 97-0231 was 

measured first to check that all hardware results were matching previous tests. Results repeated previous 

test measurements with extremely good precision, so only one comparison is shown in the results section.

Also, the measurement results of this model are being used for an AIAA sonic boom prediction workshop 

http://lbpw.larc.nasa.gov, so some continued refinement of the measurements will be performed until the 

workshop takes place in January 2014.

The SEEB-ALR and 70° Flat-Plate Delta were measured next with X sweeps at 54 and 70 in., Z 

sweeps from 70 to 42, at Machs 1.6 and 1.7, and with repeats of all measurement sets. Results matched 

previous tests and characterized flow-field distortions for far measurement distances. These models were 

not used or needed for near measurement distances.

The 1044-02 model was only run at its shaped boom design Mach number of 1.7, as previously 

described. The 9x7’s minimum Mach of 1.55 is only a 4° change in Mach angle, so the boom difference 

is minimal and the flow quality in the tunnel tends to be worse at lower Machs. Figure 246 shows the 

model being prepared for testing in the tunnel using the same hardware stack used throughout all tests 

(also seen in Figure 233).

After finishing testing nine configurations of four LM sonic boom models (two distances and two 

nacelle-off options), there was time available to re-test the NASA legacy 69° Delta Wing-Body model.

This model, like the 1021-01, is also being used for the AIAA Workshop in January 2014. Since it is a 

smaller model, near (forward rail) measurement distances of 21.2, 24.3, and 31.8 in. corresponded to 

beyond near-field semi-span, H/(b/2), ratios of 15, 18, and 23.

4.8 Oil and Stereo-RBOS Flow Visualization

Measuring sonic boom has many differences from typical supersonic testing. The first N+2 test, LM1, 

revealed that the measurement accuracy needed for N+2 shaped boom validations did not exist at the 

beginning of the program. Prior to N+2, several sonic boom tests yielded completely erroneous results 

and the cause was not determined at the time. Under N+2, we have discovered sources of measurement 

error from probe design, from tunnel flow-field distortions and tunnel reference drift. Flow diagnostic 

information was the key to making such discoveries, so we arranged for oil flow visualization and 

accommodated one of the first tests with NASA’s Stereo RBOS capability. Both provided useful 

visualizations of different flow aspects.

Oil flow visualization proved very useful for validating model surface flow, was easy and quick to 

apply and required no run time—running concurrent with sonic boom measurements without interference.

NASA provided a powerful LED ultraviolet light source that really improved the contrast of photographs 

 

 
NASA/CR—2015-218719 190

in the following figures. Oil surface flow visualization was particularly useful for showing that CFD was 

predicting the correct model flow. Surface oil flow patterns were examined for signs of unpredicted flow 

separations or other differences with CFD predictions, but care must be taken in interpreting results.

CFD analysis produced different upper wing and blade flow when the boundary layer was switched 

from turbulent to laminar. Previous testing using sublimation (NASA’s HSR program ~1993) to detect 

boundary layer transition at these flow conditions indicated that several inches of laminar flow can be 

expected. Prior to testing, trip discs were placed on the wing upper surface, on the blade and ahead of the 

top-center nacelle. Based on the previous HSR testing, the trip discs (0.0075 in. tall) would not guarantee 

transition, but hopefully, at least energize the boundary layer enough to prevent separation. (In previous 

testing, 0.012 to 0.018 in. disc height brought transition to 1 in. behind the trips at a minimum, but the trips 

were up into supersonic flow at these heights, generating bow shock waves in the surface flow 

visualization.) N+2 design perturbation studies indicated the trip discs themselves on lower surfaces would 

affect sonic boom but not on upper surfaces. Further, laminar flow on the lower surface stayed mostly 

attached and did a better job of replicating the full scale thin boundary layer, so disc were not used there.

Figure 247 and Figure 248 show a comparison between CFD analyses, laminar and turbulent, and wind 

tunnel oil flow. Trip discs helped visualize streamlines, but care must be taken in interpreting results 

because tunnel start-up artifacts remained in the oil patterns. The dark streaks, starting on the wing inboard 

of the sweep change and running to the trailing edge just outboard of the lower wind nacelles, form during 

the very low pressure (PT=700 psf, Re = 1.2M/ft) start-up of the tunnel. Testing was originally planned and 

run at Re = 2.55M/ft, also with attached flow, but the tunnel humidity was easier to reach and hold at 

4.5M/ft, so extra attachment margin. But the separated flow on start-up proved the uncertainty indicated by 

CFD. The Figure 248 close-up reveals some cross-flow sweeping streaks around the start of the dark streaks 

just mentioned. Prominent in both pictures are a rib-like pattern on the inboard wing around the blade 

support that are formed by the trip discs on the blade and are surprisingly visible from the small discs.

Though other trip sizes were available (or none), this trip disc arrangement was kept on this 1021-01 model 

and repeated on the 1044-02 without variation since the desired flow-fields were achieved.

Figure 247.—Test Oil Flow (top) Showed Fully Attached Flow, Like Turbulent CFD (bottom)
NASA/CR—2015-218719 191

Figure 248.—Close-Up of 1021-01 Oil Streaks Showed Fully Attached Flow, Like Turbulent CFD

(Some persistent separation streaks formed during tunnel start-up at low Re)

After achieving expected measurement results with the 1021-01 model in the aft rail position, the 

flow calibration models were run for far measurement distances. The tunnel flow-field is different at the 

two different rail positions and the aft position had not had extensive flow diagnostic measurements taken 

since (the more limited measurements of) LM1. Oil flow was not deemed worth the tunnel pollution on 

the high-fineness axisymmetric SEEB-ALR model, but the extreme low-cost 70° Flat-Plate Delta’s re-use 

of the 1044-02 blade support with external, blunt-faced mounting did have some failure modes if the flow 

did not match CFD predictions. Oil flow of the Flat-Plate (Figure 249 and Figure 250) confirmed that it 

did isolate upper surface disturbances from the region of calibration measurements underneath the wing.

There is also a possibly confusing feature of the oil flow that deserves commentary, the upper surface 

flow of Figure 249 has dark (oil evacuated) streaks going back streamwise from the blade support bow 

shock at mid-span that might seem like a separation. We believe there is not a separation (and no 

separation at the bow shock impingement either, excepting a very small vortex separation and 

reattachment within the boundary layer) just a point where the boundary layer flow allows the oil to flow 

streamwise (perhaps a change in the character of the boundary layer vortex that makes the bow shock’s 

dark streak).

 

 
NASA/CR—2015-218719 192

Figure 249.—Dramatic Upper Surface Flow Coloration Showed That Strong Pressure Disturbances

are Isolated From Underneath the Wing Until Its Trailing Edge, as Intended 

 
NASA/CR—2015-218719 193

Figure 250.—Lower Surface Produced Little Flow Disturbance, Supporting Its Intended

Mission to Quantify Shock Reflection Strength as a Function of Local Span

The 1044-02 blade support was a re-scaled duplicate of the 1021-01 design based on its success and 

the oil flow images validated its flow, Figure 251. The support blade is expected to and has a separated 

flow vortex along its trailing edge. With a sweep greater than the Mach cone angle, the trailing edge is 

inclined to separate; furthermore, when the shock from the center nacelle intersects the blade, separation 

is almost guaranteed. CFD analysis has shown that enough separated flow ingested into the upper center 

nacelle can unstart the nacelle’s internal flow, creating significant spillage and a consequential flow 

disturbance. The unswept base of the blade trailing edge keeps flow attached there and keeps the trailing 

edge vortex from forming until above the nacelle, especially where the nacelle shock intersects.

Figure 252 shows the 1044-02 upper and lower surface oil flows. The streaks indicate fully attached flow 

everywhere as predicted. The trip discs on the upper surface made high contrast flow streaking. Around 

the blade support there was some smearing out of the streaking left over from start-up. The lower surface 

had some oil evacuated dark lines where there were strong nacelle shock impingements, but predictions 

and streamlines indicated the flow stayed attached straight through these regions. There was also some 

angled flow indicated on the lower surface visible behind the plaster filled dots that was due to the effect 

of gravity on the oil before the tunnel flow became strong when the model was rotated 180° from the 

orientation in which this picture was taken (referring to streaks trailing back and up from the plaster dots 

in this image orientation). Curiously the oil flow of Figure 252 did not indicate any rib pattern induced by 

the trip discs on the blade, but the oil flow images in Figure 253 show the rib pattern and show shocks 

from the wing trip discs themselves. The oil and fluorescent powder were the same, but the proportion 

could be different, the application thickness could be different, the tunnel time spent at low Re could be 

different.

 

 
NASA/CR—2015-218719 194

Figure 251.—Blade Support for 1021-01 (top) and 1044-02 (bottom) Showed Attached Flow

Until Vortex Separation Region Identified by Dashed Red Line 

 
NASA/CR—2015-218719 195

Figure 252.—Upper and Lower Surface Oil Flows on 1044-02 Indicated Fully Attached Flow

Figure 253.—Upper Surface Oil Flow Strongly Showed Rib Pattern Shocks From

Blade Trip Discs and Bow Shock Waves From Wing Trip Discs

The NASA 69° Delta Wing-Body model was run last. It was made top/bottom symmetrical with a 

5 percent thick wedge airfoil for the wing, so the maximum thickness at mid-chord has a sudden surface 

slope change, creating a strong expansion that is highlighted by the oil it evacuates behind the mid-chord 

ridge as seen in Figure 254. Attached flow is maintained over the model. Care must be taken when 

interpreting some oil flow features. In Figure 255, some missing and mottled regions of oil flow (below) 

are left over from the initial oil application (above)—not surface flow features.

 

 

 
NASA/CR—2015-218719 196

Figure 254.—Legacy 69° Delta Wing-Body Model Oil Streaks Indicating Attached Flow and Dramatic Oil

Evacuation Behind Diamond Airfoil Maximum Thickness Ridge

Figure 255.—Mottled Oil at the Root and Outboard Wing and Oil Void Streak at the Nose (bottom) Are Not Flow 

Phenomena, But Rather, Persistent Features From the Initial Oil Application (top)

 

 

 

 
NASA/CR—2015-218719 197

The RBOS (Retro-reflective Background Oriented Schlieren) flow visualization technology takes 

Schlieren-like pictures using pattern recognition technology with some important differences in flow-field 

sensitivity. A retro-reflective material (strongly reflects light back in the direction from which it came) is 

speckled with a random pattern of ink blotting (seen in the background of several figures but especially 

the top image of Figure 255 behind the model) is adhered to the far wall inside the wind tunnel. Pictures 

taken of the material through the flow-field will have the pattern displaced in position by pressure 

variations in the test section. However, a pressure change in the middle of the tunnel will displace the wall 

pattern’s position more; while conversely, a pressure change nearer to the wall pattern or camera cause 

less and less pattern displacement until at the limit of the wall or camera there is no displacement effect at 

all. (Bending of the light at very short distance from the reflective material results in little pattern 

displacement at the camera; and near the camera, light rays coming in from the range of angles from the 

camera’s field of view are compressed onto a very small sensor, making small angle changes near the 

camera also result in little pattern displacement.) Being able to focus on pressure changes in the middle of 

the tunnel in the vicinity of the model is preferred to Schlieren’s that give more equal weighting. Another 

important consideration when viewing any Schlieren-like images, particularly when comparing the 

model’s disturbances to the background disturbances, is the tunnel’s orientation in the photography. Most 

supersonic tunnels have nozzle contraction/expansion concentrated or completely in one plane more than 

the other, horizontal or vertical. In this Ames 9x7 tunnel, the side walls are flat and all nozzle 

contraction/expansion is in the floor and ceiling, making much larger flow disturbance shock diamonds 

traveling between the floor and ceiling. In this tunnel, models have always been tested rolled 90° so the 

larger floor/ceiling distortions affect yaw angularity instead of pitch angularity. Sonic boom testing is also 

done at 90° of roll with the measurement rail mounted on the wall. The larger floor/ceiling shock 

diamonds are in the measured ambient pressure variation; however, the steadier pitch angularity 

variations have less interaction with the model and result in improved sonic boom measurements with this 

tunnel orientation. However, the tunnel’s window positions did not allow us to photograph the flow-field, 

perpendicular to the model-rail, from ceiling to floor. Instead we are RBOS imaging the model 

disturbances going toward the floor and ceiling (not the wall with the measurement rail) as they propagate 

in the tunnel’s far larger ambient floor/ceiling shock diamonds. Again these floor/ceiling disturbances are 

in the tunnel’s ambient reference pressures, but the orientation makes a big difference when making 

model to tunnel comparison’s or tunnel to tunnel comparisons. Ideally, we would take RBOS images in 

both wall-to-wall and ceiling-to-floor planes. In this tunnel, the wall-to-wall orientation compares the 

magnitude of the smaller tunnel disturbances that interact with the model to the magnitude of model 

disturbances. The ceiling-to-floor orientation compares the magnitude of the larger tunnel disturbances 

(that change the tunnel yaw angularity running across the model from side-to-side in its measurement 

orientation) of the tunnel’s ambient flow-field to the magnitude of model disturbances.

In these following RBOS images appreciate that we are photographing wall-to-wall along the plane of 

the strongest ambient flow disturbances created in the contraction/expansion in the ceiling/floor. Most 

tunnel’s measure upright, contract/expand their walls and Schlieren photograph wall-to-wall; so the large 

magnitude of ambient disturbances is not seen or appreciated. To visualize the actual magnitude of their 

ambient disturbances compared to the magnitude of model disturbances, they would also need to take 

Schlieren/RBOS images from ceiling-to-floor. It is these worst-case images that are shown in the

following RBOS images.

This first RBOS image, Figure 256, is the larger 1021-01 model in side-view shown with the larger 

ceiling/floor ambient shock diamonds of the tunnel. The image scale grey-shade is in proportion to the 

pixel displacement scale shown. The field-of-view clips the back end of the model because of limits to the 

model positioning (with alpha/height being from the tunnel yaw control in this plane), a limited size and 

location for the retro-reflective material in the tunnel and limited window position for the camera. Still, 

the only model shocks of much visibility are the nose shock and some shocks from the underwing 

nacelles. Some tunnel ambient disturbances are larger than the shaped boom model’s nose shock. Model 

shocks are often only positively identifiable as model shocks because they do not continue upstream. By 

showing this image sharpened and +40 percent contrast, some subtle model variations to the flow-field 
NASA/CR—2015-218719 198

become apparent downstream that did not exist upstream. The following pictures are also enhanced with 

increased sharpness and contrast.

The 70° Flat-Plate Delta model was made to quantify ambient tunnel disturbance reflection strength 

off its bottom as a function of local span. As such, it was desired for the model to produce little 

disturbance itself below its wing. Figure 257, Figure 258, and Figure 259 confirmed the model produces 

little disturbance below its wing (or even off to the side) until the strong expansion at its trailing edge.

Figure 256.—RBOS of 1021-01 Shaped Boom Model Made Nose and Underwing Nacelle Shocks Apparent, 

Otherwise, Only Background Flow-Field Variations

Figure 257.—Flat-Plate Model Created Little Disturbance between Nose Shock and Trailing Edge

 

 

 
NASA/CR—2015-218719 199

Figure 258.—RBOS Wake Visualization by Shading on DY (see Figure 260)

Figure 259.—RBOS Side-View Validated Lack of Disturbance Below Flat-Plate between Nose Shock and Trailing 

Edge and Successful Isolation of Blade Disturbance

 

 

 
NASA/CR—2015-218719 200

The 1044-02 is shown above twice in Figure 260, the top one was shaded based on pattern 

displacement distance in X, while the bottom one was shaded based on pattern displacement in Y.

Processing the image using only X hides the vehicle wake. Some shades shift between the two images, 

but results are similar. (We are suggesting an image shaded based on total [sqrt(DX2

+DY2)] pattern 

displacement.)

The 1044-02 configuration was designed for very low sonic boom, so in Figure 261 its disturbance 

was small compared with the ambient tunnel variations (again, RBOS orientation showing tunnel 

maximum shock strengths, generally 90° off typical tunnel Schlieren orientation). As could be expected 

from this figure, measuring weak signatures in a supersonic wind tunnel was difficult. This is why spatial 

averaging measurement processing was necessary to achieve the required measurement accuracy.

Figure 262 showed that the 69° Delta Wing-Body model produced less disturbance because of its 

smaller (6.9 in. long) size, somewhat offset by its reduced fineness relative to the shaped boom models.

Here at angle of attack, a nose and wing disturbance can be seen under the model along with a wing flowfield change above the model. Even over our 20 to 30 in. measurement distances to the rail, the wing 

disturbance coalesces into a single shock. 

Figure 260.—RBOS of 1044-02 Shaded DX (top) and DY (bottom) 
NASA/CR—2015-218719 201

Figure 261.—RBOS of 1044-02 Illustrated that Shaped Boom Disturbances are Lower in

Magnitude than Ambient Disturbances and only Subtly Visible Behind the Wing

Figure 262.—The 69° Delta Wing-Body Shocks Weakened

and Harder to See at Smaller Size

4.9 Test Analysis and Results

There are generally two challenges to CFD for sonic boom: getting the correct vehicle solution and 

propagating that solution with sufficient resolution to beyond near-field interference (necessary for 

starting other propagation methodologies). To understand which portion of the solution is responsible for 

a mismatch, it is desirable to have measurement distances both close, near-field, and far, beyond nearfield. Near-field differences are caused by vehicle disturbances that are laterally away from the vehicle 

centerline—so typically the wing (and anything it contains) is the primary source of near-field differences 

that go away with enough distance. Though not guaranteed, typically propagating measurements from 15 

to 25 semi-spans [R/(b/2)] away is sufficient. Much previous technical work has expressed this distance 

as a ratio of vehicle length [R/L], but since length has nothing to do with the phenomena and semi-span 

does, we always express measurement or CFD extraction distance as a ratio of semi-span. Part of the 

January 2014 AIAA Sonic Boom Prediction Workshop’s objective is to further investigate and refine the 

distance needed to be beyond the near-field.

 

 

 

 
NASA/CR—2015-218719 202

LM4 measured models with the rail mounted on both the aft and forward window blanks to be in 

position to measure far and near distances, respectively. Measurements started with the 1021-01 model 

for both rail positions because its measurement results could be compared to other tests to validate that 

test equipment was working correctly. Figure 263 shows that LM4 results are very closely matching 

previous LM3 measurements, with the LM4 results being a bit sharper and lower in CL. Excellent 1021-

01 test-to-test repeatability provided confidence for proceeding with further testing.

For far measurements, the SEEB-ALR and 70° Flat-Plate Delta models were measured for flow 

diagnostics at both Mach 1.6 (for 1021-01) and Mach 1.7 (for 1044-02). The gray lines in Figure 264

show individual measurements of the SEEB-ALR flat-top signature. The scatter in flat-top amplitude 

quantifies variations in local distortion. Some very extreme peaks are seen at X’s of -3 to 3 and 7 to 19, 

but not between 3 and 7. These spikes have a spacing between them that matches the movement spacing 

between measurements, so the spikes are likely to be repeating from the same 2 spots in the flow-field as 

the model is moved through them. Dropping data selectively around these strongest flow-field distortions 

can help reduce rounding in spatial averaging. Methodology is being modified to investigate such 

improvements.

Rail measurements (Figure 265) of the 70° Flat-Plate Delta model confirmed that the underside of the 

model produced little disturbance of the flow on its own, making it suitable for its intended purpose of 

measuring reflections of flow-field disturbances. In fact, the grey lines above from individual 

measurements were an example of reflections because the model underside did not produce much 

disturbance on its own, so these variations were mostly reflections. Reflections do not produce the 

extreme peaking that can come from flow-field distortions.

Figure 263 .—Excellent Test-to-Test Match (validated before proceeding with LM4) 

-0.02

-0.015

-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

-5 0 5 10 15 20 25 DP/P

X-βr (inches)

Average over Distance - Blade (TSig 22/23)

Runs 829 - 854 (Ref 876)

CL = 0.142 / CL=0.151

CFD CL = 0.142

Test CL = 0.142

CFD CL =0.151

Test CL = 0.151
NASA/CR—2015-218719 203

Figure 264.—There Does Appear To Be Better and Worse Locations For 

Measuring in the Tunnel—Selective Data Processing Might Improve Results

Figure 265.—The 70° Flat-Plate Delta Model Produces Little Disturbance Under 

Itself So Reflections Could Be Characterized to Improve Flow-Field 

Calibration

 

 
NASA/CR—2015-218719 204

Figure 266.—Measurement Fell Between Turbulent and Laminar Solutions

The 1044-02 model was measured in the tunnel at distances from 6 to 21 semi-spans for near-field 

and beyond near-field CFD validation. Design of the full-scale 1044-02 configuration included inlets with 

spillage and nozzles with exhaust. To greatly simply testing and reduce test cost, flow-through nacelles 

were used for the model. These simulate typically about 85 percent of the inlet disturbance but produce 

more nacelle trailing edge expansion than our nozzle exhaust operating at maximum efficiency. The flowthrough nacelles do produce a majority of the same flow disturbance according to the small signature 

difference predicted by CFD. And the model’s scale creates a much thicker boundary layer with 

substantial portions of laminar flow (found in other tests). So the model will always have some signature 

differences with the full-scale vehicle. We made the model match the vehicle in as many ways as 

possible, validating as much as possible; and then, use CFD to predict the remaining small differences.

For both the 1021-01 and 1044-02 configurations, the difference between models and full-scale were 

quite small, but still a significant difference to the shaped boom signatures. Further, it was found in 

general that while full-scale analysis can be done mostly with Euler CFD, matching models requires NSCFD modeling of the boundary layer. Figure 266 shows a spatially averaged measurement in dark blue 

for the 1044-02 model at Mach 1.7, near the desired cruise CL of 0.121 and measured 10 semi-spans from 

the model where good measurement accuracy was evident from the narrow uncertainty band also shown.

The magenta prediction used a fully turbulent boundary layer assumption, and mostly matches very well.

However also performing CFD with a fully laminar boundary layer assumption and plotting it in dashed 

red, created some small signature differences. Significantly, where the turbulent and laminar were 

different, the measured data tends to fall in between the differences. In reality, the model had mixed flow 

with substantial portions laminar and turbulent, so the measurement falling in between was exactly what 

should have been expected. Generally, much of the signature difference between laminar and turbulent is 

due to the different boundary layer displacement thickness, so differences can be estimated approximately 

without needing a full analysis. The following figures only plotted turbulent CFD.

CFD Prediction CL=0.121 (Pre-Test)

Spatially Averaged Measurement

(light blue ±2σ uncertainty band)

CFD Laminar

 
NASA/CR—2015-218719 205

Figure 267.—Predictions Matched Measurements for Roll Angles from 0° to 60°

Figure 6.6e Predictions matched 

measurements for roll angles from 0o to 60o 

with high precision at 10 semi-span distance
NASA/CR—2015-218719 206

The undertrack (roll 0°) match is in the upper left corner of Figure 267, along with the other roll 

angles every 10° to 60° and all achieved excellent matches of sufficient accuracy to match ground sonic 

boom predictions (applied far-field correction to measurement and prediction based on prediction).

Since nacelle on measurements matched predictions, nacelle off measurements matching is not as 

significant. The signature tends to be very sensitive to nacelles, especially when mounted under the 

vehicle, and there are more sources of possible difference between the predicted and wind tunnel nacelle 

flows. Whenever there is a mismatch between prediction and measurement the nacelles are most often 

involved; so in such a case, the next match to check is a nacelles off comparison. In the past, the analysis 

and measurement of the rest of the geometry has matched, allowing focus on the problematic nacelle 

region thereafter. Our nacelle off CFD analyses matched measurements as shown in Figure 268 at two 

roll angles.

Measurements were taken at several distances between 7 and 21 semi-spans to understand the 

worsening of measurement accuracy with distance. The Figure 269 measurements at 13 semi-spans are at 

the greatest measurement distance possible with the rail in the forward window blank. Figure 270 showed 

that signature uncertainty increased as measurement distance was increased. More rounding and smearing 

of signature features also occurred as measurement distance increased. These degradations were expected 

based on previously measured increased effect of flow-field distortions at increased measurement distance 

and previously observed measurement accuracy loss with distance. But in addition, Figure 270 indicated 

that part of the degradation in the 9x7 at greater distance comes from reduced measurement accuracy with 

the rail on the aft window blank.

Figure 271 showed the match between predictions and measurements for roll angles of 0°, 20°, 40°, 

60° at 21 semi-spans distance. The uncertainties are large and there is a substantial mismatch in the peak 

of the signature at 20° in roll. Matching the CFD CL condition better would help some, but other 

improvements are needed too. Flow calibration model measurements should be looked into further along 

with further signature processing improvements.

Figure 268.—Large Predicted Nacelle Effect is Matched by Measurements at 0° and 40° Roll

Wing 

Nacelles 

Off

CFD Prediction CL=0.121 (Pre-Test)

Spatially Averaged Measurement

(light blue uncertainty band)

CFD (Nacelles On)

 
NASA/CR—2015-218719 207

Figure 269.—Still Matching Well at 13 Semi-Span (b/2) Distance at 0° and 40° Roll

Figure 270.—Signature Uncertainty and Detail Degraded With Increased Measurement Distance but Aft Rail (17 to

21 semi-spans distance) Position Responsible for Part of Degradation

 

13 Semi-Span Distance

 

 

Increasing Uncertainty from 13 to 21 Semi-Spans

(same 21 H/(b/2) CFD prediction shown for comparison)

13 H/(b/2) 17 H/(b/2)

19 H/(b/2) 21 H/(b/2)
NASA/CR—2015-218719 208

Figure 271.—Measurements and Match Showed More Uncertainty Than Required,

Further Measurement Processing Improvements Needed

With some extra time available aided by the large productivity improvements achieved, 

measurements and flow visualizations were taken of the NASA 69° Delta Wing-Body model. Its 

signature magnitude and length were reduced relative to the others because of the smaller model size, 

only 6.9 in. long and 1.36 in. semi-span. But since it is not a shaped boom model, its signature has 

relatively large shock strengths. The model’s signature coalesces into 3 shocks, predominantly from the 

nose, wing compression (usually coalesced from the first 2/3 - 3/4 of the wing) and wing trailing edge.

These three coalesced shocks will average the tunnel distortions located across the ~1/3 of the vehicle 

length that coalesced into that shock, so tunnel distortions affect a 3 shock signature much less than a 

shaped boom signature. As seen in Figure 272, individual measurements had far less scatter to begin with 

even before they were averaged. In the original 1973 paper on this model (and other models investigating 

the effect of wing planform variations) it was tested using a single orifice probe 8 in. off the wall and 

measurements were made at three CLs. Figure 273 showed the effect of a CL or alpha change on the 

model’s signature. It mostly increases the magnitude and impulse of the wing compression shock with 

little change to the signature elsewhere.

Figure 274 shows the signatures that resulted from measuring the model at 0°, 30°, 60°, and 90° of 

roll. The nose shock did not change at different roll angles because the nose is axisymmetric; but 

interestingly, the wing compression shock also changed very little at the different roll angles. The wing’s 

leading edge sweep is near the Mach cone angle, so its volume growth is not very different at different 

roll angles. In contrast, the close to unswept wing trailing edge results in a substantially changing aft 

 

 

21 Semi-Span Distance (Beyond Near-Field)
NASA/CR—2015-218719 209

shock that splits into two. As the measurements rolled from 0° to 90°, the wing trailing edge first 

terminates at nearly the same X station, but as roll angle increases the wing halves on either side of the 

body shift in position relative to each other in the signature, breaking into two shocks that get farther and 

farther apart. These kinds of differences are important to notice when trying to design a full-carpet shaped 

boom vehicle.

Figure 272.—Despite a Small Model a "Nonshaped" Boom Design of 3

Coalesced Shocks is Far Less Scattered by Tunnel Distortions

NASA 69o Delta Wing-Body

(23 Semi-Spans)

 

 

 

Thin Grey: individual 

measurements

Yellow: averaged

measurement

NASA 69o Delta Wing-Body

(Roll 0o, 30o, 60o and 90o)

 

 

 
NASA/CR—2015-218719 210

Figure 273.—Effect of Alpha Coalesced Into Only a Shock Magnitude Change

and Small Recompression Detail Difference

Figure 274.—In Roll, Wing Trailing Edge Shocks Different While Leading

Edge Shocks Similar—Because Leading Edge Swept Near Mach Cone

NASA 69o Delta Wing-Body

(Alpha 0o and 3.6o)

 

 

 
NASA/CR—2015-218719 211

5.0 Summary and Conclusions

5.1 N+2 Configuration Refinement

LM validated that it’s refined N+2 Phase 2 1044-X configuration was an environmentally compliant 

and high efficiency conceptual design. We concluded that such a design could be developed into a viable 

product; and as such, this 1044-X configuration continues to play a key role in future research as an 

example of an N+2 product and its characteristics. Those characteristics meet N+2’s environmental goals 

with shaped sonic boom, competitively low airport noise and low cruise emissions.

5.1.1 Quiet Shaped Boom

A key boom shaping improvement, full carpet <85 PLdB, was achieved in Phase 2. Off-track and 

under-track shaping requirements were simultaneously met. These predicted levels are based on our 

validated LMBOOM-sBOOM Burger’s propagation methodology, which matched NASA and the rest of 

industry within 1 PLdB (Ref. 12). Plus, there are a number of other factors that impact the typical 

loudness that would be heard from N+2 operations. Simulating actual operations within an airline citypair database, a lot of flying is done for shorter trips and by the end of every trip—flight is at reduced fuel 

weight, which reduces loudness. So the average loudness of the vehicle is biased toward the end-of-cruise 

(EOC) weight and would be 79 PLdB for this configuration, based on Burger’s rounding methodology.

In additional to rounding, in actual flights turbulence and winds scatter sonic boom propagation and 

cause the actual loudness at any point on the ground to vary several PLdB. Helpfully, turbulence tends to 

decrease loudness much more often, with median shifts of –3 to –6 PLdB having been measured (from Nwaves) in light to heavy turbulence, respectively (Ref. 13). In a comparison between Burger’s and 

30 weak N-wave dive booms, the flight measurement trend was found to be 5 PLdB quieter than the 

Burger’s prediction. So our best indication from this small dive boom sample is that the N+2 1044-X

would average 74 PLdB in practice. Because this increment is so significant, more measurement—

prediction comparisons with Burger’s should be made to quantify this increment as accurately as possible.

(To determine the best flight test correction to Burger’s methodology, and incorporate its effect into

vehicle design.)

TABLE 34.—N+2 ENVIRONMENTAL AND PERFORMANCE GOALS, AND

VALIDATED PHASE 2 RESULT (TABLE 1 REPEAT)

[Measurements of low N-wave dive booms suggest that Burger’s alone may result in loudness as much as 5 PLdB too high.]

NASA N+2 Supersonic Transport 

(2018-2020) Goals

LM N+2 Phase 2 Predicted and 

Validated Status

Environmental Goals

Sonic Boom 85 PLdB up to 20° off-track

(NASA specified goal)

<85 PLdB over full carpet

79* PLdB average (sBOOM)

Airport Noise 

(cumulative below stage 3)

10 to 20 EPNdB 21.55 (GE - predicted)

Cruise Emissions <10 EINOx 5 EINOx

Performance Goals

Cruise Speed Mach 1.6 to 1.8 shaped boom flight Mach 1.7 (1.8 over water)

Range 4,000 nmi 5,500 nmi

Payload (passengers) 35 to 70 passengers 82 pax

Fuel Efficiency 

(passenger-nmi per lb of fuel)

3.0 3.13 (using GE engines)
NASA/CR—2015-218719 212

5.1.2 Quiet, Clean, Efficient Propulsion

In addition to shaping boom, the N+2 Supersonic Validations program also performed large scale 

acoustic nozzle testing of both Rolls-Royce and GE designs in Phase 1 (Ref. 14). In Phase 2 the GE 

design was redesigned to overcome some shortcomings found in the first test and the new design was 

tested. Details of acoustic nozzle redesign and testing are covered in Appendix B. Acoustic nozzle results 

are particularly important to this vehicle because it is a primary propulsion sizing condition. Typical 

transport engines are sized for takeoff field length and cruise thrust. A new supersonic transport’s engines 

are sized for take-off noise and cruise thrust, and take-off noise is very dominant when trying to be as 

quiet as modern subsonic aircraft. Achieving competitive airport noise with a minimal sacrifice in

efficiency has meant finding improvement contributions throughout the vehicle system, including 

operational procedure changes, rule modifications and other changes that don’t put all the burden of 

meeting noise on technology improvements. This whole system solution has required close collaboration 

with engine suppliers as well as multidisciplinary internal expertise. And our improvements meeting the 

airport noise challenge are probably the most significant advances toward viability for supersonic 

transportation. In the late 1990s a supersonic propulsion system was predicted at Stage III – 7 dB,

required a take-off thrust-to-weight ratio (T/W) of 0.32 and the propulsion system T/W was 3.2. Our N+2 

system is predicted at Stage III – 22 dB, required a take-off T/W of 0.31 and the propulsion system T/W 

was 4.0—so in summary, the N+2 solution is 15 dB lower in noise and 22 percent lower in weight than 

that projected late 1990s solution. Our projected N+2 airport noise reduction is consistent with the latest 

subsonic vehicle levels.

And similar efficiency improvements were achieved by the propulsion system. Again, a combined

system solution to propulsion and noise made these remarkable advances possible. A high-recovery, 

bleed-less inlet allowed the large airflow of a higher BPR (4+) engine to operate efficiently supersonically

(contrary to strong past belief). The higher BPR achieves better cruise SFC and is particularly helpful at 

providing more airport thrust with reduced noise (as quantified above). Overall, this propulsion solution 

with its many cycle technologies and variability, detailed in Appendix C, combine to achieve a 29 percent

SFC improvement over Concorde.

Contrary to most of the N+2 effort, the cruise emissions goal was met by the engine suppliers 

independently. Combustor technology was being developed in other programs. Under N+2 we were only 

to show that we could make a successful system solution that integrated that technology. That meant 

mostly that the engine supplier reduced the combustor temperatures to be compatible with the expected 

limits of the low NOx designs matching our technology availability date. Accommodating the 

temperature limits and slightly longer low NOx combuster only incurred a 1.5 percent increase in fuel 

flow because some of the temperature reduction impact was offset by needing less cooling air, thereby 

less consequential losses.

5.1.3 Efficient, High Performance Vehicle

For the terminology of this NASA supersonic research, N+1 indicates the first product enabled by 

these new environmentally compatible and high efficiency technologies. Prospective manufacturers have 

proposed products from medium cabin business jet size up to small regional jet size for private and public 

travel. N+2 intends to describe a product that will follow N+1. Typically the next product is made larger 

for greater efficiency and range and serves growth in existing markets. “One more engine” may determine 

how much bigger the product can be. There is likely to be more emphasis on pubic, scheduled service 

where cost sensitivity is greater, so low recurring cost and high utilization will have increased emphasis.

On the N+2 Supersonic Validations program, we found that high efficiency could also be achieved along 

with the environmental goals. LM’s investment in sonic boom shaping methodology was always 

developed to achieve quiet shaped boom with minimal impact on efficiency. LM also teamed with 

Stanford University on N+2 to adapt some new optimization techniques to the design task. The N+2 
NASA/CR—2015-218719 213

design also exceeded its 3.0 passenger-nmi/lb fuel goal, which was a 70 percent improvement over 

Concorde’s 1.8. This 1044-X design was made to comprehensively and with good fidelity meet the 

requirements of an N+2 vehicle. As such, a relatively small amount of effort has gone into its refinement 

for optimizing its performance. Further efficiency improvements should be readily obtained with 

additional refinement efforts.

5.2 N+2 Structural and Aeroelastic Analysis

A structural model of the N+2 1044-3 configuration has been developed and sized to preliminary 

design load cases, consisting of maneuver loads, landing and ground handling loads. Nonstructural mass, 

representing various system weights, passengers, cargo, fuel, etc., has been included in the model in order 

that the FEM have representative mass distribution for a credible flutter analysis. After initial sizing of an 

all-composite FEM to the design load cases, we learned that the tail was deforming significantly under 

load. Thus we provided a configuration recommendation to thicken the tail and aft deck by about 

26 percent. The FEM was also updated to include metallic properties at major load introduction points, 

such as at gear attachment, wing root spar, tail-to-boom attachment and select areas of the aft deck and 

boom. The analysis and sizing of this hybrid metallic/composite FEM is presented in this report.

Even after increasing the tail and aft deck thickness we found that the tail was still deforming 

excessively under a 2.5 g, pull up design load case. Hence, we applied a displacement constraint on the 

tail to be no more than 10 percent of the tail span and re-sized the model. This FEM was designated 

FEM017-REV71. This constraint added 1731 lb to the airframe weight or roughly 2 percent of vehicle 

structural weight. In turn, a flutter analysis of this model showed that the flutter speed increased 

considerably with a minimum flutter dynamic pressure of 1600 psf (the minimum required flutter margin 

is roughly 900 psf). Consequently, we performed a flutter survey on the FEM sized without the tail 

displacement constraint, designated as FEM017-REV70. The flutter speeds for this FEM were lower but 

still met the required flutter margin. The primary flutter mechanism involved aft deck bending, tail 

bending and tail torsion.

Given the uncertainty associated with the propulsion system weight, we conducted a sensitivity study 

of flutter speed to increased engine mass. For a 40 percent increase in engine mass, the flutter dynamic 

pressure dropped by 12 percent, yet still met the minimum required flutter margin.

The initial open-loop flutter analysis of the N+2 1044-3 configuration has not revealed any showstopping aeroelastic issues, especially in consideration that with a 2 percent increase in airframe weight 

we were able to increase minimum flutter dynamic pressure by 24 percent (FEM017-REV70 vs. 

FEM017-REV71). However, we acknowledge the weaknesses of the linear aerodynamic theory used in 

this analysis for the transonic regime.

5.3 N+2 Shaped Sonic Boom Testing and Analysis

The N+2 program’s comprehensive wind tunnel validation of shaped sonic booms resolved longstanding problems, increased productivity and accuracy by more than an order-of-magnitude and 

validated efficient shaped boom designs 30 PLdB quieter than Concorde. For the comprehensive shaped 

boom measurement program planned for N+2, new measurement hardware and diagnostic reference 

models were built. In many past shaped boom wind tunnel tests, sometimes results were reasonable and 

sometimes results were unreasonable (Ref. 15). With the new hardware and tenacious analysis of data 

patterns, it was discovered that small, innate flow variations (found in all supersonic tunnels) are enough 

to distort weak, shaped signatures. With understanding of the fundamental cause of the measurement 

problem, a measurement procedure and data processing, termed “spatial averaging,” was developed that 

only slightly increased measurement times while virtually eliminating distortions. By the final LM4 test, 
NASA/CR—2015-218719 214

the new RF1.0 rail measured complete signatures with an order-of-magnitude better accuracy in as little 

as 5 min, instead of the 50+ min required prior to N+2. 

Most shaped boom testing was done at the NASA Ames 9x7 ft Supersonic Tunnel. A parametric test 

was run to optimize test condition variation tolerance, measurement oversample timing, total pressure 

control schemes and other measurement conditions. What turned out to be most important was that 

conditions that could affect the test section flow field, like humidity, needed not to change during a 

measurement set (all spatially averaged measurements and the reference measurement). Measurement 

procedures for spatial averaging: timing, distance and number of positions were optimized. Measurements 

and productivity were excellent in the 9x7 in the near-field, but the measurements lost detail and became 

rounded at distances beyond the near field, R > 12 to 15 (b/2) semi-spans (or approximately R > 3 to 4 L 

to use the old comparison), where validation was also desired. Additionally, the 9x7 tunnel cannot test 

below Mach 1.55. So tests were run in the Ames 11 ft tunnel and the Glenn 8x6 ft tunnel. The Ames 

tunnel had some flow disruptions at particular Mach conditions that required changing testing from 

Mach 1.2 and 1.4 to Mach 1.15 and 1.3. While the 11 ft could be used with such limitations, 

improvements would be desirable. The Glenn tunnel has the unique capability of testing continuously 

from Mach 1.0 to 2.0 and has symmetrical flexible walls for its nozzle. During testing though, pressure 

measurements were drifting with model movement but not proportionally or consistently. In analyses 

after testing, it was found that the drift was directly proportional to very slight Mach changes, so it is 

believed that if the Mach changes are eliminated the pressure drift will also be eliminated. Without the 

drift, 8x6 measurements taken at R = 12 (b/2) semi-spans look to have better detail and less rounding than 

9x7 measurements at that distance, which seems to be due to the 8x6 having about 1/3 the reference 

pressure variation with time as the 9x7.

6.0 Recommendations

6.1 N+2 Configuration Refinement

• Following the success of this N+2 Supersonic Validations program, a Shaped/Low Boom Flight

Demonstration (LBFD) program is being pursued. To support traceability of an LBFD program or 

system assessments of other continuing supersonic research, a very small effort is recommended 

to keep this N+2 design up to date with on-going technology refinements.

• Direct on-going indoor shaped boom annoyance research to specifically support and provide 

guidance for signature shaping to reduce indoor annoyance.

• By the end of March 2015, complete a best assessment of the loudness difference expected 

between measurements and predictions based on Burger’s propagation methodology (to have 

6 months until September 2015 to update LBFD design and traceability results to accommodate 

the increment).

• Continue cooperative airframe-engine system studies to further reduce system weight and noise 

without increasing propulsion noise stringency by optimizing the many unique aspects of this 

propulsion system: sideline noise sized, inlet choked/absorbed approach/take-off noise, 

supersonic exclusive engine rating integrated into noise abatement procedures, integration for 

sonic boom, packaging for external drag, cruise critical thrust and temperature and emissions, 

dual-Mach cruise inlet and afterburner noise/climb integration.

• Determine whether validated low noise nozzle and complex cycle performance can buy their way 

onto a next-gen supersonic vehicle when a turbofan with simple nozzle is adequate.

• Along with propulsion system studies, restart/continue a modest level of highly-leveraged 

research on weight reduction, laminar drag reduction and design optimization methodology to 

facilitate increasing vehicle efficiency and avoid enactment of “gas guzzler” restrictions.
NASA/CR—2015-218719 215

6.2 N+2 Structural and Aeroelastic Analysis

As a result, we recommend that additional flutter analysis be performed in the transonic regime with a 

more suitable aerodynamic method (e.g., Euler, Transonic Small Disturbance, Navier-Stokes). Also, 

considering that the primary flutter mechanism involves aft-deck bending and because the vehicle is 

statically unstable in some configurations, we recommend that aeroservoelastic (ASE) analysis be 

performed with a pitch controller in the loop. It would be of interest to understand how a pitch controller, 

if the body flap is used for this purpose, interacts with the critical flutter mode since it involves aft deck 

bending.

All composite—thicken tail and aft deck 26 percent, switched to metallic properties at major load 

introduction points gear/wing/tail boom/pylon. Displacement constraint not needed. A 40 percent

propulsion weight increase accommodated. Recommend: additional flutter analysis transonic with 

nonlinear, check ASE and flutter with a pitch controller in the loop

Unconventional aft deck stiffening alternative

• As an alternative, remove the aft deck 26 percent thickening (or leave if desired for fuel volume) 

remove metallic aft deck structure; instead, greatly stiffen the aft deck pitch by increasing the 

pylon height as described in Section 3.6.4 and Figure 197. The aft fuselage could also be more 

arbitrarily reshaped for greater height, and thereby pitch stiffness, without changing its crosssectional area.

• Identify structural mass concentrations and investigate whether stresses can be reduced with 

design changes (like the above) and redesign the FEM for weight savings. See if reducing or 

eliminating metallic attachments can reduce weight and cost.

• Redesign the MLG to be mounted to the bottom of the wing and to the fuselage. Eliminate the 

MLG cut-out in the wing structure.

• Re-run flutter analyses with updated, lighter FEM; and optimize pylon height (pitch) and shape 

(width) of height blend into aft deck for V-tail support (mostly torsion stiffness).

6.3 N+2 Shaped Sonic Boom Testing and Analysis

• Devise and test Glenn 8x6 ft Mach control improvements, needing only movement of model 

support strut (no model or RF1.0 Rail needed until control appears to work) for initial testing.

• Build the 2nd half of the RF1.0 Rail and test it in the final 8x6 check (next).

• Implement 8x6 Mach control improvements with RF1.0 Rail x2 and LM4 model measurements at 

31.8, 42.0, 50, 60 and 70 in. (or largest possible) at Mach1.7; and if time, measure flow at 1.2, 

1.3, 1.4, 1.5 and 1.6 (in this order of priority).
NASA/CR—2015-218719 216
NASA/CR—2015-218719 217

.—Acronym List

AIAA American Institute of Aeronautics and Astronautics

AAPL Aero-acoustic Propulsion Laboratory

ALR Aft-Lift Relaxation

ANSI American National Standards Institute

AoA Angle of Attack

ARMD Aeronautics Research Mission Directorate

BFL Balanced Field Length

BPR Bypass Ratio

CAD Computer Aided Design

CATIA Computer-Aided Three-Dimensional Interactive Application

CFD Computational Fluid Dynamics

CG Center of Gravity

DOE Design of Experiments

EINOx Emission Index for NOx

EIS Entry Into Service

EOC End of Cruise

EPNL Effective Perceived Noise Level

ESF Engine Scale Factor

FAA Federal Aviation Administration

FAR Fuel/Air Ratio

FOD Foreign Object Damage

GE General Electric

GRC Global Research Center

GE GR General Electric Global Research

GT Georgia Tech

GTOW Gross Take Off Weight

IML Inner Mold Line

IRAD Independent Research and Development

LA Linear Actuator

KEAS Knots Equivalent Air Speed (constant Q, dynamic pressure)

KTAS Knots True Air Speed

L/D Lift to Drag Ratio

LM Lockheed Martin

MAC Mean Aerodynamic Chord (often C_ref)

MDAO Multi-disciplinary Analysis and Optimization

MSA Model Support Apparatus

MTOW Max Takeoff Weight

NASA National Aeronautics Space Administration

NATR Nozzle Aeroacoustics Test Rig

NLF Natural Laminar Flow
NASA/CR—2015-218719 218

NRA NASA Research Announcement

OD Outer Diameter

OS Optimum Signature

OEI One Engine Inoperative

OEW Operational Empty Weight

OML Outer Mold Line

OPR Overall pressure ratio

POD Point of Departure

PAX Passenger

PL Perceived Loudness

PLdB Perceived Loudness Decibels

PLR Programmed Lapse Rate

PR Pressure Ratio

QR Quarterly Review

QSST Quiet Small Supersonic Transport

RCD Rapid Conceptual Design

RH Relative Humidity

RMS Root Mean Square

ROC Rate Of Climb

RRLW Rolls-Royce Liberty Works

SF Scale Factor

SFC Specific Fuel Consumption

SLS Sea Level Static

SOA State Of the Art

SOC Start of Cruise

SST Supersonic Transport

TC Technology Configuration

TE Trailing Edge

TOC Top of Climb

TOFL Takeoff Field Length

TOGW Takeoff Gross Weight

TRL Technology Readiness Level

WBS Work Breakdown Structure

ZFW Zero Fuel Weight
NASA/CR—2015-218719 219

.—N+2 NRA—System Level Experimental

Validations for Supersonic Transport Aircraft

Jixian Yao, Mugam Murugappan, Umesh Paliath, Lawrence Cheung, Ivan Malcevic, 

Kishore Ramakrishnan, Nikolai Pastouchenko, and Trevor Wood

GE Global Research

Niskayuna, New York 12309

Steve Martens, Phil Viars, Trevor Tersmette, Jason Lee, Ron Simmons, and David Plybon 

GE Aviation

Cincinnati, Ohio 45215

Executive Summary

This final report covers the entire Phase II Effort. GE Global Research and GE Aviation

supported Lockheed Martin in this Phase II, NASA sponsored ‘N+2 NRA – System Level Experimental

Validations for Supersonic Transport Aircraft’ program.

Based on Lockheed Martin’s updated requirements and targets, GE modified an adaptive cycle

engine propulsion system expected to meet or exceed the environmental goals set by NASA, from

the recent Phase I program. This propulsion system takes advantage of cooled cooling air to extend

the overall pressure ratio of the engine and increase thermal efficiency. A low noise, high

performance exhaust system takes advantage of the innovative adaptive feature, to provide a third

air stream to feed the inverted velocity profile and fluid shield on the exhaust for lower noise

capability. Heat addition is also incorporated in the exhaust to provide augmented transonic thrust.

A limited trade study was performed for various fan pressure ratios. Jet noise assessment played a

role in choosing optimal fan pressure ratio.

The flow-path of the IVP/Shield nozzles tested in Phase I was updated to mitigate the

overexpansion at low power conditions that led to unsteady flow separation. A new exhaust nozzle

configuration was required to fully mitigate this issue. The new exhaust configuration allows the primary 

exhaust to operate in a purely convergent mode as well as a convergent-divergent mode. This change

resulted in higher internal nozzle performance across a wider range of the operational map. Large eddy

simulations of the Phase I cutback configuration show very good comparisons to measured acoustic data 

and Phase II results show a much improved flow field.

Fan noise assessment was performed using a multi-stage fan configuration. Lockheed Martin

supplied an inlet boundary condition. The inflow boundary condition is part of Lockheed Martin’s inlet

CFD results, at flight Mach of 0.3 of isolated engine configuration. Fan was set at take-off condition.

In the Phase II continuation, a range of continued and new tasks are pursued and accomplished.

The NPSS cycle model was extended to capture transient effects and modified for use as an open

loop transient-dynamic model.

A scale model corresponding to the improved sideline and cutback configurations was designed

and fabricated. Scale model testing at GRC shows much improved acoustic performance, with an

elimination of the unsteady separation seen in Phase I, although some broadband shock noise like

component is seen, likely coming off the core cowl expansion surface.

The scale model exhaust nozzle acoustic test was performed at GRC. No evidence of the unsteady

separation was seen in the measured Phase 2 acoustic farfield acoustic data. The three stream nozzle

resulted in lower noise levels than the reference fully mixed equivalent nozzle. The effect of the shield

was measured in the farfield acoustics as a reduction in the higher frequency ranges. A single chevron

design was tested on the shield nozzle and on the portion of the primary nozzle not covered by the shield.
NASA/CR—2015-218719 220

No combination with the chevrons resulted in overall (EPNL) noise reduction, although significant low

frequency benefit was measured with the chevrons. The azimuthal variation around the three stream

nozzle was measured and showed the lowest noise was achieved with the shield aligned with the

microphones.

A range of levels of fidelity of CFD from steady RANS, SAS, to DES and to LES was performed

on the Phase I exhaust configuration to find the proper fidelity level of simulations for the nozzle flow

features to reduce cost of LES simulations. LES simulations were also applied to Phase II nozzle

design, and the results were compared to the test, as well as to results of other lower fidelity simulations.

In the Phase II continuation, Lockheed Martin also provided a modified design of the inlet. Analysis

was performed of the Lockheed Martin supersonic inlet coupled with the multi-stage fan mentioned

above. The flow features of the installed configuration were explored. The acoustic analysis of the

installed configuration was also accomplished and compared to the de-coupled analysis. The CFD

process for large-scale simulations was developed and verified.

Due to insufficient wake mixing captured by RANS prediction, an effort was made to establish a

correction factor using LES simulations and data of GE internal investigations. Both the fan IGV wakes

and the inlet strut wakes were analyzed and the correction factors established.

A detailed CAA investigation was carried out for noise transmission through the inlet duct. The farfield projection is then analyzed using the FWH methodology. These analyses provide input to various

models for system noise evaluations.
1 INTRODUCTION

GE Global Research and Aviation supported Lockheed Martin’s contract from NASA on the N+2 System 

Level Experimental Validations for Supersonic Transport Aircraft. GE’s support included propulsion 

system support through the modification of the advanced adaptive cycle propulsion system to meet the 

vehicle requirements and environmental goals. The overall goals are shown in Figure 1. They cover 

sonic boom, airport noise, fuel efficiency, and emissions. The sonic boom, cruise speed, payload, and 

range were essentially used by Lockheed Martin to set the propulsion requirements that GE used to design 

the propulsion system. The airport noise, cruise emissions, and fuel efficiency were targets that GE 

assessed based on the propulsion system developed. 

Figure 1 NASA Supersonic Goals. 

The baseline propulsion system was an adaptive cycle engine with VAATE Phase III technology 

assumptions with an approximate 2018 TRL 6 technology availability date. The adaptive engine 

architecture includes adaptive cycle features such as: 

 Adaptive Cycle Features 

 Cooled Cooling Air (CCA) with modulated turbine cooling 

 Axisymmetric Plug (Axi-Plug) Exhaust with Inverted Velocity Profile, a Fluid Shield, 

and Chevrons 

 Heat Addition 

Together these technologies provide a propulsion system anticipated to meet the vehicle requirements and 

environmental goals. 

GE also updated the flow path of axisymmetric plug nozzle with an inverted velocity profile and a fluid 

shield around 180-deg of the exhaust that was tested in Phase I. This includes the jet noise reduction 

technologies that are synergistic with the adaptive cycle propulsion system. RANS CFD was run for a 

number of cycle conditions to ensure good aerodynamic performance over the cycle. The internal nozzle 

NASA/CR—2015-218719 221
performance was calculated at each condition. Large Eddy Simulation was completed for the Phase I 

cutback configuration and limited runs were made with the Phase II nozzle at a cutback cycle condition. 

The supersonic inlet was also combined with an existing multi-stage fan configuration that was previously 

tested to look at the acoustic impact of the wakes from the center-body struts and distortion from the open 

auxiliary inlet doors. 

2 PROPULSION SYSTEM

2.1 PROPULSION SYSTEM OVERVIEW

The technology assumptions for this N+2 propulsion system were adjusted from a TRL 6 date of 2025 for 

the N+3 model to a TRL 6 date of 2018. The N+2 engine is a lower technology version of the NASA 

N+3 adaptive cycle engine concept delivered in 2009. The major impact of the lower technology level 

was designing to lower core temperatures, resulting in an increase in weight. Also, for the N+2 program 

Lockheed Martin switched to a tri jet configuration and the engine was resized to the higher thrust 

requirements resulting in an increase in FPR and a larger engine size. In the end, the propulsion system is 

still designed to meet the N+2 airport noise and emissions goals. 

2.1.1 TECHNICAL DESCRIPTION

2.1.1.1 ADAPTIVE CYCLE ENGINE PROPULSION SYSTEM

The advanced Adaptive Cycle Engine (ACE) uses VAATE Phase II level technologies with a TRL 6 date 

of 2018. The engine architecture includes adaptive cycle features. 

2.1.1.2 COOLED COOLING AIR (CCA) ADVANCED THERMAL MANAGEMENT SYSTEM (TMS) 

The CCA system, enables the engine to be designed at a higher overall pressure ratio (OPR) for improved 

thermal efficiency, by keeping the critical blade cooling air down to low levels. The CCA system reduces 

cooling flow requirements and enhances the high-pressure turbine (HPT) blade life. The system operates 

by removing a small portion of hot compressor discharge (CDP) air and ducting it out to a Surface Cooler 

heat exchanger in a bypass duct. 

2.1.1.3 ADVANCED LOW NOX COMBUSTOR

As part of this program, an advanced low NOx combustor concept was assumed, leveraging work from 

the NASA Supersonic Low NOx Combustor contract studies. Meeting the N+2 cruise emission goal of 

EINOx = 10 g/kg fuel will be a challenging goal for the supersonic N+2 program. 

To achieve the N+2 NOx goal, an advanced technology development targeting an N+2 timeframe is 

necessary to reduce combustor residence time, cooling flow, pilot, and fuel/air mixing times. 

2.1.2 EXHAUST SYSTEM

The exhaust system design is very important for supersonic vehicles since the nozzle performance is 

critical to the efficiency of the propulsion system as well as enabling jet noise technologies. The basic 

exhaust concept is a variable A8 and A9 axi-plug exhaust with a number of technologies added to 

improve performance and acoustics. 

NASA/CR—2015-218719 222
2.1.2.1 VARIABLE GEOMETRY FEATURES

The exhaust has variable A8 and A9 (throat and exit area, respectively) capability through the use of a 

translating plug and cowl. The translating cowl also provides for a thrust reversing system. 

2.1.3 NPSS TRANSIENT CYCLE MODEL

2.1.3.1 INTRODUCTION

Under this task, the NPSS Cycle Model of an Adaptive Engine delivered under the GE Subcontract to 

LMCO for the NASA NRA N+2 System Validation Study was modified for use as an open loop 

transient-dynamic model. 

2.1.3.2 MODEL DEVELOPMENT

Transient models are used for assessment of engine operation in the development of the engine and 

aircraft controls and to assess system operational limits. A transient model is critical for to engine and 

aircraft development but is often delayed until late in the program due to the complexity of modeling 

issues and the lack of component detail required for an accurate simulation. Having a transient model, 

even with approximate information about the engine, allows important control and engine response issues 

to be addressed early in the program when configuration and component technology changes are easier 

and cheaper. 

The initial transient modeling is focused on low frequency requirements that meet most engine 

development needs. The simulation time steps are generally consistent with the sensor sampling rate and 

the dynamic response of geometry and fuel control hardware. A 10 to 20 ms time step is typically 

adequate. This time step allows the simulation to capture the movement of bleed valves, variable stators, 

fuel control action, and acceleration of rotors, heat transfer between the metal and gas path and the impact 

of flight condition changes on engine fuel burn and thrust response. The simulation ignores fast pressure 

or temperature perturbations, shock movements and acoustic level responses. These issues can generally 

be modeled on an average basis for most engine design and control development activities, ignoring the 

higher frequency content. 

The first step in creation of the transient-dynamic model was to implement this low frequency capability. 

An engine control is created using this low frequency model and a control system model is eventually 

incorporated into the transient engine simulation. For this effort, open loop inputs of controlled values 

such as variable geometry and fuel flow vs. time are used to exercise the model. 

Transient engine simulation result in different combinations of rotor speeds, component operating lines 

and flow conditions in the engines than those encountered in steady state operation. A key part of any 

transient model development is validation of the component models over the extended range of operation 

that will be encountered transiently. In this study, no component changes were required because the 

original Steady State simulation utilized components models that considered future transient use and he 

open loop studies. For example, the model included off-schedule stator effects for all components. 

New transient specific information was created based on the system studies and similar engines. Inertias 

were obtained from the mechanical system studies and engine cross-section and weight. For heat transfer 

effects, typical values from similar engines were used heat transfer coefficients with surface area and 

NASA/CR—2015-218719 223
metal mass values from system studies. Basic validation studies were performed to validate the simulation 

behaved as expected based on past engine experience. 

Dynamic engine simulation refers to high frequency response addressed on an average basis and 

considered negligible for the normal transient system issues. However, these high frequency events can be 

important in studying engine stability, stall, blow-out response, engine-aircraft interaction, engine-control 

feedback or engine failure modes. High frequency perturbations in inlet temperature or pressure and fuel 

flow or geometry pulses are typical events where a dynamic model can be useful. To address these events 

the model must be run at a time step consistent with the time scale of the perturbation and the time scale 

of the transfer of that perturbation across the component models in the engine. For detailed component 

design studies, this the time scale may be the transition across an individual blade row. For most engine 

simulations, the time scale is based on the component models where the dynamic effects are considered. 

In the inlet or nozzle this can be somewhat larger than the time step required for a multi-stage compressor 

or turbine and much larger than a single stage compressor. Typically the time step must be shorter than 

the time required for an inlet boundary condition change to traverse the component. This can be based on 

the overall volume of the component and the time required for changes in mass flow at the entrance to 

affect the exit mass flow or on the length of the component and the time required for a pressure or 

temperature perturbation at the component entrance to affect the component model exit value. The key 

modeling differences in for the components that include high frequency volume dynamics are: 

Conventional Transient Component Model 

- Flow In = Flow Out (No Mass Retained in Component) 

- P,T In = P,T Out = P,T in Component 

Dynamic Component Model 

- Flow In – Flow Out = Change in Mass in Component Volume (Zero Steady State) 

- P,T In vs. Out vs Component Can Vary (Equal Steady State) 

In a conventional component model, continuity is satisfied by the simplifying assumptions of the model. 

The dynamic component model addresses the conditions where these simplifying assumptions are not true 

and the resulting impact on the component and engine response. For a short period of time, the increase 

in flow or pressure at the entrance to a component will not impact the exit pressure. An increase in 

entrance flow will increase the mass in the component volume and may also result in an increase in the 

component volume P and T. However, without the simplifying assumption of the conventional transient 

model, the exit flow is calculated based on the downstream boundary conditions and the new component 

values. 

For a small change in flow going into a relative large component volume, this delay in the response can 

be long. For ground based power generation engines with a large inter-cooler, this difference can be large 

enough that it must be considered for low frequency operation and control design. For these very large 

volumes a simplified first order lag in the response across the component is often used to increase the 

accuracy of low frequency models. For high frequency events the emptying and filling of the component 

volume and the change P, T in the component and the boundary conditions must be modeled explicitly. 

For a dynamic model, the entire simulation must be run at the time step required to address the response 

of the most limiting component while other components can continue to run with conventional 

assumptions. The initial time step is selected based on the more limiting of the characteristic time to 

empty the component volume at the operating flow rate and the time for a pressure change to traverse the 

component. However, numerical stability often requires the time step to be smaller than this value 

depending on the number of volumes and other response characteristics of the simulation. 

NASA/CR—2015-218719 224
The test cases to show the impact of volume dynamics that follow are for a single volume element but the 

model delivered to NASA includes examples for multiple volumes. 

2.1.3.3 MODEL DEMONSTRATION

Figure 2 shows a planar pressure pulse applied to both model the conventional transient and dynamic 

model. The impact on the engine response is show in Figure 3. Note that the change in core speed is small 

enough that it would not be noticed or considered important for most studies. 

Figure 2 Pressure Perturbation – Conventional Transient vs Volume Dynamics 

Figure 3 Impact of Time Step on Model Response 

Figure 3 also shows the impact of different time steps on the dynamic model response. Only inlet pressure 

is perturbed but the volume temperature responds briefly to the change in pressure. Figure 4 shows the 

0.5 psi

0.01

NASA/CR—2015-218719 225
very small change in temperature during the start and end of the pressure pulse as the volume equilibrates. 

Figure 5 shows the core speed response for the conventional transient model and the model with Volume 

dynamics. Both those the slight slow drop in core speed during the low pressure period and recovery to 

the original speed as the original pressure resumes. With volume dynamics the impact of the change in 

temperature at the start and end of the pressure step can be seen in the speed response. Even though this is 

obviously negligible for most engine studies, it can be important for stability analysis in combination with 

the high frequency pressure information. Figure 6 shows an inlet temperature pulse and the corresponding 

change in the volume pressure at the start and end of the pulse. Since the temperature change occurs for 

the conventional and volume dynamic model, the core speed response is the virtually the same. 

Figure 4 Volume Temperature Response to Pressure Step Change 

Figure 5 Core Speed Response to Pressure Step Change 

0.2

0.01

NASA/CR—2015-218719 226
Figure 6 Dynamic Model Response to Temperature Step Change 

Figure 7 HPC Inlet Response to Temperature Step Change 

Figure 7 shows the change in pressure at the HPC inlet in response to the temperature pulse. The primary 

pressure impact is due to the change in corrected rotor speed due to the temperature pulse but the volume 

dynamic model has a small delta at the start and end of the pulse. The HPC inlet temperature response is 

virtually the same. 

Figure 8 shows the model response to a slow inlet pressure sine wave input. The response is similar to the 

step change but now there a more noticeable response to the pressure changes in the conventional 

transient model. The differences due to volume dynamics are significant but remain small compared to 

the base levels. 

0.5

0.2 4

0.01

NASA/CR—2015-218719 227
Figure 8 Response to Sinusoidal Planar Wave an Engine Inlet 

The frequency of the pressure input causes a significant difference in the response of the engine. A range 

of frequencies were tested on the model. The results for low frequency showed little impact of the volume 

calculation as expected. Figure 9 shows that, as the frequency increases, the volume model captured the 

expected attenuation. 

Figure 9 Frequency Impact Sinusoidal Planar Wave Attenuation 

2.1.3.4 SUMMARY

As a result of this task, NASA has a transient and dynamic capable NPSS model for the N+2 engine 

implemented using typical GE practice and a set of test cases and sample studies to use as an example for 

future efforts. 

2.1.4 PHASE 2 SYSTEM NOISE ASSESSMENT

Jet noise estimates were made for the cutback and sideline noise certification points for three fan pressure 

ratios. Figure 10 shows the estimated margin to Stage 3 for the total jet noise for the cutback 

configuration for a cutback certification point from a take-off trajectory provided by Lockheed Martin. 

The same take-off trajectory was assumed for all three fan pressure ratio engine cycles. This is not 

completely accurate since if the different fan pressure ratio engines were installed and the aircraft sized 

and reflown different trajectories would result. The required thrust for this trajectory is shown on the 

figure and for this relatively low power condition the margins for each fan pressure ratio is fairly close 

and essentially the same for this level of estimate. Although at higher power settings, clearly the lowest 

fan pressure ratio engine results in the highest noise margin. 

0.5

0.05

0.005

Mean pressure 

attenuation constant 

with planar wave 

frequency

Alternating pressure attenuation 

increases with increasing planar 

wave frequency

0.2

0.2 0.1

NASA/CR—2015-218719 228
Figure 10 Margin to Stage 3 for Cutback for Jet Noise Only. 

Figure 11 shows the jet noise margin to Stage 3 for the sideline trajectory. Again this sideline condition 

is constant for all three fan pressure ratio power hooks. At this higher power condition the lower fan 

pressure ratio clearly has the highest margin to Stage 3. 

Figure 11 Margin to Stage 3 for Sideline for Jet Noise Only. 

Taking into account the additional margin which would be realized when including the landing point and 

the reduction due to the other engine and airframe noise components it is anticipated that this engine 

design should meet the N+2 noise goal for cumulative margin to Stage 3. 

Margin to Stage 3 (EPNdB)

Single Engine thrust (lb)

Cutback

Cutback Thrust Required

5000

5

Increasing FPR

SL Margin to Stage 3 (EPNdB)

Single Engine Thrust (lb)

Sideline

Sideline Thrust Required

5000

5

Increasing FPR

NASA/CR—2015-218719 229
2.1.5 PHASE 2 CONTINUATION SYSTEM NOISE ASSESSMENT

A system noise assessment including the fan inlet radiated and jet noise components only was made and 

indicates the current status is expected to meet the NASA N+2 community noise goals. Airframe noise 

and other engine noise components are not included in this assessment. No margins have been applied as 

this is a conceptual study only and no guarantees or commitments are implied. This assessment indicates 

that the fan noise component is important, especially at the cutback condition. A strong suggestion to 

take a more in depth and detailed look at the fan and inlet is made. 

The results of the further refined fan and inlet computational aeroacoustics analysis to be discussed later 

in this report, including LES strut wake calculations and tone propagation through the inlet struts and the 

effect of the auxiliary door were applied to the fan inlet estimate in this section and the impact was less 

than 1 dB at each point. 

3 AEROACOUSTIC NOZZLE

3.1 POD MODAL ANALYSIS OF JET PLUME FROM THE PHASE I NASA – IVP TESTS

A Proper Orthogonal Decomposition was performed on the 2D streamwise and 3D cross sectional PIV 

data at two operating conditions. 

3.1.1 POD BACKGROUND

Proper orthogonal decomposition (POD) is a modal reduction technique for extracting or separating large 

scale structures from background turbulence1. The mathematical theory for POD is based on the spectral 

theory of self-adjoint operators, which are widely used in turbulent theories2-4. This technique provides an 

optimal basis for the modal decomposition of an ensemble of functions, which in essence allows the 

extraction of the dominant features (i.e., coherent structures) embedded in an infinite-dimensional process 

with only a finite number of modes. Mathematically, Lumley indicated that the coherent structure as that 

structure which has the largest mean square projection on the velocity field. This essentially leads to 

solving an integral Eigen value problem with the cross correlation tensor as its kernel. The solution to this 

problem leads to extracting the dominant modes (or flow structures) in the flow field. For the present 

study POD is used to study the evolution of jet plume. 

3.1.2 PIV SET UP

The origin of the coordinate system was the jet centerline at the common nozzle plane. The plane of the 

laser sheet was vertical, centered on the jet axis for the stream-wise PIV. The first FOV extended from the 

fluid shield nozzle downstream. The model was mounted such that the fluid shield was on the bottom of 

the rig to allow laser sheet to illuminate this flow. Due to the model being designed to align with the 

overhead microphone array, the light sheet crossed in a plane oriented 25° to the symmetry plane of the 

nozzle. The laser sheet was offset from centerline roughly 5mm to minimize reflection to the cameras. 

The PIV system was traversed axially 5 times in increments of 14”, creating a 1” overlap between 

acquisitions, providing a total scanned field of 98”. The Cross-stream PIV had a nominal field of view of 

14” by 17” (360mm x 430mm) per acquisition point. Data was acquired at four axial stations: x = 7.25”, 

10.875”, 14.5”, 21.75”, but for the current report, POD analysis is only presented at the nearest nozzle 

location. PIV image pairs were acquired to disk at a rate of 2Hz. Time delays between the matching 

image pairs was adjusted to make the highest expected velocity produce an 8-pixel displacement on the 

cameras. 400 image pairs were recorded for each plane of data, requiring 3.5 minutes to acquire a single 

NASA/CR—2015-218719 230
plane of data. Subsequent to the acquisition, images were processed using multi-pass correlations with 

local distortion and, in the case of the cross-stream data, phased-only filtering to minimize the effect of 

the nozzle image in the background. The final pass of correlation was done on a 32x32 pixel region with 

50% overlap. 

3.1.3 RESULTS

Figure 12 shows the mean U velocities in the stream-wise plane near the nozzle exit for both the 

conditions in region 1 (closest to the nozzle/First FOV). The color scale is set the same for both these 

cases. The shield is at the bottom. All three streams could be observed on the lower half. The outer-most 

stream is the shield stream which spans the lower half (180 degrees) of the nozzle. The center body 

blocks the laser light sheet from entering the fan and core streams on the top half, hence only the flow 

field past the center body is resolved. The separation region at the exit of the fan stream could be 

observed in both these contours. Comparison of both these cases indicates that the separation region 

extends further for the sideline case as compared to the cutback condition. Also observable are the shock 

cells in the sideline condition. 

Figure 12 Mean Streamwise, U, Velocity Contours, (a) Cutback, (b) Sideline. 

Figure 13 and Figure 14 show the 1st U and V POD modes for the cutback and sideline conditions. The 

POD modes are normalized. The contours are displayed so that positive is red and blue is negative 

velocity. The highest POD energy is contained in the structures on the top half of the nozzle. This is 

expected, since the top half of the nozzle diffuses faster and hence generates larger structures in the POD. 

Large scale activity is also observed in the region spanned by the separation. The relative energy 

contained in these structures is much weaker in the sideline case as compared to the cutback case. Due to 

the higher momentum and larger lateral jet spread of the fan stream for the cutback case the V POD 

modes show large scale activity that extend further downstream. 

a b

NASA/CR—2015-218719 231
Figure 13 1st (a) U and (b) V POD Mode for the Cutback Condition. 

Figure 14 1st (a) U and (b) V POD Mode for the Sideline Condition. 

In order to better understand the modal dynamics near the separation region. POD analysis was 

performed in a small region just spanning the separation. Figure 15 and Figure 17 shows the mean 

stream-wise velocity contour, 2D TKE and cumulative energy distribution of the cutback and side line 

condition respectively. Figure 16 and Figure 18 shows the first 3 U and V POD modes in the zoomed 

region for both cases. The modal energy distribution is found to asymptote in both these cases indicating 

that the modal content is statistically resolved in this region. A number of features could be observed from 

these charts. First, the only observable modal activity is on the inside shear layer enclosing the separation 

region in both the cases. Since the separation region is shorter for the cutback condition, the fan and core 

streams mix quicker which lead to more distinct modal structures in U POD modes in the cutback 

condition as compared to the sideline case (compare 3 distinct structures in U POD from Figure 16 c and 

e as compared to 2 structures in Figure 18 a, c, e). Evidence of increasing structure size with downstream 

distance is observed in the cutback condition. For the sideline condition, the lower momentum core as 

compared to the higher momentum fan was found to diffuse quickly and mix with the separated hot fan 

stream. This led to more coherent modal structures on the top half as compared to lower half. The hot fan 

stream with higher NPR was found to maintain its momentum for longer distances which suppressed the 

a b

a b

NASA/CR—2015-218719 232
jet spread and mixing leading to weaker modal structures (compare the top structures of Figure 18 to the 

bottom ones). 

Figure 15 Setpoint 12244 (a) U mean Contours, (b) TKE Contours (c) Cumulative Energy Distribution. 

Figure 16 a, c and e: first 3 U POD Modes; b, d and f: first 3 V POD Modes. 

0 50 100 150 200 250 300 350 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Modes lambda/sum(lambda)

a b c

a b

c d

e f

NASA/CR—2015-218719 233
Figure 17 Setpoint 10070 (a) U mean Contours, (b) TKE Contours (c) Cumulative Energy Distribution. 

Figure 18 a, c and e: first 3 U POD Modes; b, d and f: first 3 V POD Modes. 

The downstream evolution of the nozzle was also studied for the cutback condition. Figure 19 shows the 

mean U, V and TKE contours for region 2 (downstream of region 1). Clearly the distinction of the 

different streams is not observed, since the streams are starting to mix. The wake from the center body is 

0 50 100 150 200 250 300 350 400

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Modes lambda/sum(lambda)

a b c

a b

c d

e f

NASA/CR—2015-218719 234
still noticeable in the U contours along the jet centerline. The V mean Contours appear noisy due to small 

magnitude and small number of instantaneous fields used to reconstruct them. The TKE shows higher 

magnitude on the unshielded side as compared to the shielded side in this location. This occurs due to the 

faster diffusion of the jet in the unshielded side. Figure 20 shows the first 3 U and V POD modes. 

Interestingly, modal activity is only observed on the top half (unshielded side). The bottom half that 

contains the shielded side shows very weak modal activity for both the U and V components. This is 

congruent with the higher TKE in the top half shown in Figure 19. This is expected since, the orthogonal 

basis (i.e. modes) provides the optimal resolution of the TKE (i.e. captures those dominant features that 

are associated with high TKE). Furthermore, the top half POD magnitudes are much higher than the 

bottom half hence, on the same color scale bottom half appears to be overwhelmed. In order to support 

this, POD analysis was performed on only the bottom half. Figure 21 shows the first 3 U and V POD 

Modes on the bottom half of the measurement region. Clearly the distinct structures were observed that 

are similar to the top half modal content from whole FOV (Field of View) POD. This confirms the fact 

that the modal structures are formed at the high shear or velocity gradient region, but the visualization of 

these features might not be obvious depending on the choice of the region chosen for POD analysis. 

Figure 22 shows the mean U, mean V and TKE in the fourth region downstream from the nozzle exit. The 

effect of the shield has started to decay and hence the top and bottom appear symmetric in the mean and 

turbulence contours. POD analysis was also performed in this region. Figure 23 shows the 3rd U and V 

POD modes. As expected, modal structures appear to be nearly symmetric of the same size. 

Figure 19 Region 2 (a) U mean Contours, (b) V mean Contours, (c) TKE Contours. 

a b c

NASA/CR—2015-218719 235
Figure 20 First 3 U and V POD Modes for Full Region 2, M1= Mode 1, M2=Mode 2, M3=Mode 3. 

Figure 21 First 3 U and V POD Modes for Bottom Half of Region 2, M1= Mode 1, M2=Mode 2, 

M3=Mode 3. 

U V

M1

M2

M3

U V

M1

M2

M3

NASA/CR—2015-218719 236
Figure 22 Region 4 (a) U mean Contours, (b) V mean Contours, (c) TKE Contours. 

Figure 23 Third U and V POD Modes for Region 4 for cutback condition. 

Figure 24 shows the mean W, mean V and TKE at the first cross sectional plane (X=184 mm) for setpoint 12244. The shield is again at the bottom, but the whole field of view was rotated counter clockwise. 

Clearly the dominant W mean velocity component appears converged as compared to the lower 

magnitude mean V velocity component. Higher TKE was found to be on the unshielded side at this nozzle 

location. The first 4 W mean velocity POD modes are shown in Figure 25. A couple of features could be 

observed in these contours. First, the higher TKE region on the unshielded side is found to have organized 

modal activity as compared to the shielded side. Second, the first two W mean velocity component POD 

modes appear to have a shorter spatial wavelength as compared to POD modes 3 and 4 on the unshielded 

side. Third, the modal content appears noisy in all the modes and is expected to improve with a higher 

number of snap shots. 

Figure 24 Mean W (a) and V (b) Contours and TKE contours (c) for Region 4. 

a b c

a b

a b c

NASA/CR—2015-218719 237
Figure 25 First 4 W POD Models for Axial Cross Section at Z=184 mm. 

3.1.4 SUMMARY

A POD analysis was conducted on the IVP/shield nozzle at two operating conditions. One corresponded 

to sideline geometry and other was cutback geometry and operating condition. A separation region was 

found on the divergent side of the hot nozzle section in both the cases. The separation region was found to 

extend farther for the sideline condition as compared to the cutback condition. Modal activity was found 

at the shear and high velocity gradient regions. Near the nozzle exit, both the stream-wise and cross 

section POD show stronger modal activity on the unshielded region as compared to the shielded region. 

Higher TKE is expected to be observed farther downstream in the shielded zone as the streams mix, but 

the effect of shield will eventually decay asymptoting to symmetric jet profile. The was observed in 

region 4 (downstream of the nozzle exit) where the Mean, TKE and POD modes appear to be gaining 

symmetry across the jet axis. 

High Modal activity was also observed in region encompassing the separation zone. More number of 

distinct modal structures is observed in the cutback condition as compared to the side line in the inner 

shear layer encompassing the separation zone. This occurred due to the shorter separation zone and 

higher mixing in the region encompassing the separation in the cutback back condition. Though the POD 

modes and low velocity mean components and TKE appear noisy in several regions due to smaller 

number of snap shots, we expect that the observed trends will still hold. 

3.2 EXHAUST NOZZLE CONFIGURATION 

The Phase I nozzle design focused on test hardware to prove the benefits of the inverted velocity profile 

and fluid shield concepts. A notional geometry was developed and interchangeable primary nozzle 

inserts were used to set different operating conditions. 

a b

c d

NASA/CR—2015-218719 238
CFD analysis of the sideline and cutback configurations showed a strong normal shock in the nozzle due 

to the large area ratio and relatively low pressure ratio. This behavior was suspected to be the source of a 

relatively high frequency noise source seen in the test results (Figure 26). The primary goal of Phase II 

was to refine the nozzle design and mitigate this noise source. 

Figure 26 SPL Data Showing Noise Source from Flow Separation near 1 kHz. 

3.2.1 PHASE II EXHAUST DESIGN 

During the Phase II effort, efforts were made to add fidelity and realism into the nozzle aerodynamics. 

Geometry changes were limited to only those that could be made with translating actuation of a realistic 

full scale actuated nozzle. The engine cycle was updated to represent that of a three-engine aircraft. The 

primary exit area (A9) was allowed to vary independently of A8, as was the IVP exit area. The fluid 

shield geometry was left fixed. Table 1 shows the A8 and A9 features for each stream. 

Table 1 Nozzle Area Features. 

Stream A8 A9

Primary Variable Variable

Plug (IVP) Fixed Variable

Fluid Shield Fixed Fixed

10 100 1000 10000

SPL (dB)

Frequency (Hz)

CB, Flight, 60 deg

Increasing NPR

10 dB

NASA/CR—2015-218719 239
A notional chevron design was applied to the nozzle to supplement the acoustic benefits of the inverted 

velocity profile and the fluid shield. The focus of the chevron design effort was to increase turbulence 

and mixing in the near-field in order to increase entrainment and decrease turbulence downstream. 

Chevrons were only placed on the primary nozzle exhaust not surrounded by the fluid shield and on the 

outer surface of the fluid shield stream. 

Figure 27 and Figure 28 show, quantitatively, the impact of chevrons on these parameters. TKE is 

increased within two diameters of the nozzle exit but is reduced thereafter which is intended to reduce 

low-frequency noise. The increased mixing results in a significant but short-lived increase in mass flow 

entrainment. 

Figure 27 Axial Distribution of Average Turbulent Kinetic Energy. 

Figure 28 Axial Distribution of Ambient Flow Entrainment. 

Steady-state CFD analysis shows good thrust performance over the entire range of conditions and seems 

to point to better acoustic performance as well. 

NASA/CR—2015-218719 240
3.3 RANS AND DES SIMULATIONS

3.3.1 PHASE I SIMULATION APPROACH

The primary objective of this subtask was to conduct a simulation fidelity study of the Phase I nozzle 

geometry. Through an analysis of various simulation approaches and meshes of various grid resolutions, 

the necessary CFD requirements can be determined for capturing the features of interest in the nozzle 

flow. In this case, the primary benchmark for comparison is the experimental visualizations which 

showed flow separation from the inner nozzle lip and its associated flow unsteadiness. A secondary aim 

is to capture the far-field tones generated by the flow unsteadiness. 

All of the CFD cases examined in this subtask were completed using ANSYS/CFX, version 13.0. This 

software package contains all of the necessary CFD models and capabilities necessary for the study: 

structured and unstructured grid capability, steady and unsteady RANS, Scale-Adaptive Simulation (SAS) 

turbulence model, as well as Detached Eddy Simulation (DES) models. These features allowed for 

convenient comparisons of results between model outputs, and using ANSYS/CFX also took advantage 

of prior CFD best practices developed from GE Global Research and GE Aviation. 

The meshes for the CFD cases discussed below were generated using Pointwise Gridgen version 15.17. 

The structured meshes were created for a 180-degree sector surrounding one-half of the nozzle geometry, 

and symmetry boundary conditions were used on the dividing plane. The outlet plane was placed 30.9 

fan diameters downstream, and the far-field boundary was placed 11 fan diameters from the centerline at 

the exit. 

Figure 29 Computational domain for the Phase I nozzle geometry. 

To study the effect of grid quality on the resulting solution, varying mesh resolutions were applied to two 

regions of the domain (Figure 30). The nozzle region consisted of all internal passages of the nozzle 

before the exit, and the plume region located just downstream of the center-body. Three basic versions of 

the mesh were created. The baseline mesh resolved both the nozzle and the plume region of the domain 

with a total of 24.8 million mesh elements. The coarse mesh used only half of the elements in the nozzle 

region and 2/3 of the elements in the plume region compared to the baseline. In the nozzle refined mesh, 

more than twice the number of elements was applied to the nozzle region, while sacrificing elements from 

the plume region to maintain a relatively similar grid count compared to the baseline mesh. In all three 

NASA/CR—2015-218719 241
cases, the mesh near the lip line just downstream of nozzle exit was kept constant, in order to resolve the 

shear layers from the shield, inner and outer streams. 

Mesh Name Nozzle Region Plume Region Comments

Coarse 0.6 M 8.5 M Total: 19.8 M

Baseline 1.2 M 13 M Total: 24.8 M

Nozzle Refined 2.7 M 8.5 M Total: 24.3 M 

These three meshes were then used in ANSYS/CFX to study the effects from various simulation 

approaches. Details from the steady RANS, unsteady SAS, and DES calculations are found in sections 

3.3.1.1 - 3.3.1.3 below, but the ultimate findings indicated that DES calculations using the baseline and 

nozzle refined grids were necessary to resolve the separation from the inner nozzle lips. 

Although LES and higher fidelity approaches using more conservative, resolved meshes could also be 

applied to capture the correct separation phenomena, the objective of this study is to determine the 

minimum necessary fidelity required in the current situation. With mesh counts of approximately 25 

million on a 180-degree sector grid, several RANS and DES calculations can be run within a reasonable 

time frame. Various CFD approaches and nozzles designs can then be tested to select the optimum 

combination. 

Figure 30 Schematic of the near nozzle mesh, and closeup of the nozzle region for the baseline Phase I 

nozzle geometry. The grid density in the nozzle region and plume region were modified for the coarse 

and nozzle refined meshes. 

NASA/CR—2015-218719 242
All of the RANS, unsteady SAS, and DES simulations were conducted using the wall function method, 

based on the work of Launder and Spalding (listed as “automatic near wall treatment” in ANSYS/CFX). 

Near the shock region on the outer nozzle, the near wall spacing used y+

values approximately ~20-25 in 

the nozzle refined grid, and y+values approximately ~40 in the baseline grid. Upstream of the nozzle 

exit, towards the contraction region of the nozzle, the near wall spacing y+

values were approximately 

~150 in the nozzle refined grid, and y+ ~300 in the baseline grid. This places the first grid point inside 

the log-law region, existing from about 10 < y+< ~1,000. 

The axial grid spacing was the determining factor in resolving the shock region, and hence, the point of 

separation from the nozzle lip. Although finer near wall meshes could be used with lower y+ values, the 

study indicated that increased near wall resolution alone would likely be unable to capture the separation 

behavior. 

3.3.1.1 STEADY RANS

Steady RANS calculations were first performed to determine the overall structure of the flow. Using the 

baseline grid with approximately 25M elements, the CFX calculation was set up using the operating 

conditions given above and the Shear Stress Transport (SST) turbulence model with default parameters. 

Outlet boundary conditions were applied to the exit surface, and Entrainment boundary conditions were 

applied at the radial far-field boundaries. At the ambient inlet surface, a small free-stream velocity of 10 

m/s was included to enhance the robustness of the calculation. 

Figure 31 Velocity contours of the steady RANS solution using the baseline grid. 

The CFX solution was run for almost 30,000 iterations until the RMS mass and momentum residuals 

were below 10−4. The results shown in Figure 31, illustrate the velocity contours near the nozzle exit. 

Contrary to the experimental visualizations, this solution shows the flow separation near the outer nozzle 

lip on both the shield side and the non-shield side. In addition, the steady RANS solution does not 

capture any of the unsteady fluctuations associated with the separated flow region. Thus, using steady 

RANS is unlikely to resolve the primary features of the flow and accurately match the experimental 

measurements. 

3.3.1.2 UNSTEADY RANS WITH SAS MODEL

The next level of simulation involved using unsteady RANS with the Scale Adaptive Simulation (SAS) 

model to resolve unsteady flow features in the simulation. The SAS model, developed by Menter et al, 

improves upon the unsteady RANS formulation by introducing a von Karman length scale into the 

NASA/CR—2015-218719 243
turbulence equation. This length scale is dynamically adjusted to resolve turbulent structures in the 

unsteady flow-field, providing “LES-like” behavior in specific regions of the domain. 

The default SAS formulation in CFX, also used in this steady, couples the two-equation SST model with 

a dynamic model for the von Karmen length scale LvK. This appears as an additional source term in the 

transport equation for ω, the turbulence eddy frequency. The length scale LvK depends on the turbulent 

kinetic energy κ and the scalar invariant S of the strain rate tensor via the relation 

LvK = κS/U′′,

where U′′ is the magnitude of the velocity Laplacian. More details on the formulation can be found in the 

CFX-Solver theory guide and the papers by Menter et al, and Egorov & Menter. 

Figure 32 Velocity contours from the unsteady RANS calculation with SAS model (coarse grid) 

This formulation was applied on both the coarse and baseline meshes of the Phase I nozzle geometry. 

The initial flow field was provided from the steady RANS calculation of section 3.3.1.1, and allowed to 

run until all transients had disappeared from the computational domain. The simulations were run on 64 

cores of the local GRC Linux cluster. 

The SAS simulations using the coarse mesh returned solutions similar to the steady RANS results of 

section 3.3.1.1. Flow separation from the outer nozzle lips was visible, but the flow remained attached to 

the inner nozzle lips (see Figure 32). Better results were obtained using the SAS model on the baseline 

mesh. As shown in Figure 33, the baseline mesh captured the proper flow separation from the inner 

nozzle lip and flow attachment at the outer nozzle lip. However, the separated flow region near the 

nozzle exit only exhibited low-frequency undulations in the jet near field, and none of the high frequency 

unsteadiness seen in the experimental measurements. Thus, it was concluded that an even higher fidelity 

approach was necessary to capture the jet dynamics of the Phase I nozzle. 

NASA/CR—2015-218719 244
Figure 33 Successive instantaneous velocity fields from the unsteady RANS with SAS model on the 

baseline mesh. 

3.3.1.3 DETACHED EDDY SIMULATIONS

To provide more realistic simulations of the near field turbulent structures in the Phase I nozzle geometry, 

Detached Eddy Simulations (DES) were explored using ANSYS/CFX. In this approach, both RANS and 

Large Eddy Simulation (LES) methods are combined in a single formulation. The typical DES model 

uses the RANS turbulence model inside attached and mildly separated boundary layers, and LES in 

massively separated regions, where the large scale turbulent structures are the same size as the 

characteristic geometry. 

Combining the two approaches is possible by having the CFD algorithm switch between the SST RANS 

model to an LES model in regions where the turbulent length scale Lt

is larger than the local grid 

spacingΔ. In the two equation SST RANS model, this alters the dissipation term in the k-equation in the 

following manner: 

ε = β∗kωFDES , where FDES = max ( Lt

CDESΔ

, 1)

The default CDES in ANSYS/CFX is given as CDES = 0.61. The effect of altering this parameter is 

discussed later in this section. 

For all DES simulations conducted in this study, the Delayed Detached Eddy Simulation (DDES) model 

was used. The DDES model avoids the problem of grid induced separation, where the boundary layer can 

separate at arbitrary locations depending on the grid spacing. DDES further modifies the switching 

function FDES to explicitly force RANS behavior inside the wall boundary and LES behavior away from 

the wall. 

The DES approach was applied to the Phase I nozzle geometry, this time on the coarse, baseline, and 

nozzle refined meshes. For the baseline mesh, separation was seen to occur at the correct location near 

the inner nozzle lips, and the proper flow attachment at the outer nozzle lips. Instantaneous snapshots of 

the velocity flow field also showed that fine-scale turbulent fluctuations were better resolved in the DES 

compared to the SAS simulations of section 3.3.1.2 (see Figure 34). When compared to the flow field 

visualizations captured by experiments (Figure 35), the mean velocity profiles showed good agreement. 

Note that the experimental visualization was captured at operating point SP12244, which includes a freestream co-flow of Mach 0.30, which is not accounted for in the DES result. 

NASA/CR—2015-218719 245
Figure 34 Successive instantaneous snapshots from the DES velocity flow field using the baseline mesh. 

Figure 35 Comparison of the stream-wise U velocity between the DES calculation (upper half) and the 

experimental visualization (lower half). 

The effect of the grid resolution was also examined by comparing the results from the coarse, baseline, 

and nozzle refined mesh. The grid density in the interior nozzle regions doubles from the course to 

baseline mesh, and more than doubles again for the nozzle refined mesh. This difference is apparent in 

the overall flow features seen in Figure 36. In both the baseline and the nozzle refined grid, the separated 

flow region occurs at the inner nozzle lip radial location, while the coarse grid shows the flow separation 

at the outer nozzle lip. 

Figure 36 Comparison of the instantaneous velocity flow fields from the DES calculation using the 

coarse, baseline, and nozzle refined grids. 

NASA/CR—2015-218719 246
The unsteady fluctuations in the jet near field were also examined quantitatively. A set of probes was 

inserted downstream of the nozzle in the nozzle refined DES calculation, and the time history of the 

pressure and velocity variables was recorded for each one of the probes. An example of the probe 

locations is shown in Figure 37. Probe 4 is located outside of the jet exhaust plume, probe 4A is located 

at the shield stream shear layer, and probe 4B is located closest to the centerline near the inner nozzle lip 

shear layer. A Fourier Transform of the pressure signal at location 4B showed unsteady flow fluctuations 

causing a peak near 3 kHz. However, the absence of any such peak at the probe 4A and probe 4 locations 

suggested that these unsteady flow fluctuations are mostly hydrodynamic in nature, and do not propagate 

to the far-field as acoustic noise. Similar results were seen for the probes located on the no-shield side of 

the jet. 

Figure 37 Top: Location of the measurement probes in the nozzle refined DES computation. Bottom: 

Pressure spectra from the probes located at position 4B (left), 4A (center), and 4 (right). 

Taking the DES calculation on the baseline geometry, we were also able to explore the effect of the 

CDES parameter on the overall solution. By lowering the parameter from the default value CDES=0.61, 

it was hoped that more “LES-like” behavior could be seen in the solution, and velocity/pressure 

fluctuations in the near field would be transferred to the acoustic far-field. In addition to the default value 

of 0.61, two other values were explored on the same grid: CDES=0.51 and CDES=0.31. 

Comparing the overall velocity contours in Figure 38, little difference in the flow structures could be seen 

at the various CDES values. The flow separation points remained largely unchanged, and the near field 

flow structures were also similar between the DES calculations. However, the differences among the 

simulations were visible upon examining the DES blending function in Figure 39. This function, which is 

equal to 1.0 in the RANS region and 0.0 in the LES region, showed some small variations depending on 

NASA/CR—2015-218719 247
the value of CDES. As the value of CDES was lowered from 0.61 to 0.31, the blending function 

displayed greater regions of LES behavior inside the nozzle passages, particularly near the nozzle 

contractions before the exit. However, aft of the nozzle exit, little difference was seen in the region of 

LES applicability. 

Figure 38 Comparison of the velocity fields from the DES simulation using a CDES parameter of 0.31, 

0.51, and 0.61 (default). 

Figure 39 Comparison of the DES blending function from the computations using CDES=0.31, 0.51, and 

0.61. Red contours indicate RANS regions while blue contours indicate LES regions. The black arrows 

on the left indicate regions where the LES region was more dominant in the CDES=0.31 calculation. 

3.3.2 PHASE I SIMULATION SUMMARY

From the various RANS and DES calculations performed as a part of this subtask, the best practices for 

nozzle CFD could be determined. Listed in order of increasing simulation fidelity, the following CFD 

approaches were applied to the Phase I N+2 nozzle: 

1. Steady RANS using SST 

2. Unsteady RANS with SAS model 

3. Unsteady DDES 

The quality of the meshes required for the simulations was also examined by testing a baseline, coarse, 

and nozzle refined version of the computational grid. For the steady RANS simulations on the baseline 

grid, it was found that the flow separation points from the nozzle lips were incorrectly predicted. Using 

unsteady RANS with the SAS turbulence model, the baseline grid was able to correctly capture the flow 

NASA/CR—2015-218719 248
separation, however, the unsteady flow features of the nozzle exit did not compare favorably with 

experimental measurements. Finally, using the DES approach on the baseline and nozzle refined grid 

captured both the flow separation locations and near field flow unsteadiness in the jet. 

Figure 40 provides a summary of the results from the Phase I simulations. 

Figure 40 Summary of the steady RANS, SAS, and DES computations done for the Phase I nozzle 

geometry. 

3.3.3 PHASE II CONFIGURATION SIMULATION APPROACH

Using the information obtained from the Phase I CFD simulations, the Phase II nozzle geometry could be 

simulated in a similar fashion. The objective for this subtask is to confirm that the changes in nozzle 

geometry removed the separated flow region from the inner nozzle lip exit. 

Two DES simulations were conducted for the Phase II study. The first simulation used a baseline mesh, 

constructed in a similar manner to the Phase I geometry, and the default CDES value in CFX. The second 

simulation refines the inner nozzle region and also uses a smaller value of CDES, in order to encourage a 

larger LES-like zone. The ultimate grid counts for the baseline and refined meshes were 29.5 million and 

41.2 million elements, respectively. 

Mesh Name Total Size CDES

Baseline 29.5 M 0.61

Refined 41.2 M 0.31

NASA/CR—2015-218719 249
Results for both meshes are plotted in Figure 41. From the velocity contours in the DES solutions, we 

can see that flow separation has been eliminated from the inner nozzle lips in both simulations. The 

overall agreement in the velocity and Mach number fields between the baseline and the refined grid also 

indicates good grid convergence in the final DES solution. 

Figure 41 Comparison of the velocity field for the DES computations of the Phase II geometry using the 

baseline grid (left) and refined grid (right). 

It should be noted that these lower values of the CDES parameter were tested with the primary goal of 

measuring the sensitivity of the solution to the blending function used in the DES model. A number of 

previous studies by Shur et al (1999) and Travin et al (2006) have examined the general sensitivity of 

DES to different RANS-LES blending functions as well. In this particular case, the CDES parameter and 

mesh spacing were appropriate adjustment quantities to quickly assess the sensitivity. The results of the 

above test, along with the results of section 3.3.1.3, indicate that the DES simulations were insensitive to 

changes in the RANS-LES blending function, and the default CDES value can be used. 

Simulations of the corresponding Phase II nozzle geometry using LES showed unsteadiness in the outer 

shear layer due to an interaction of a shock with the separated boundary layer from the outer nozzle lip. 

To see if the DES solution could capture this unsteady phenomenon, a transient pressure fluctuation was 

applied at the primary stream inlet: 

P − P∞ = 0.2 P∞ exp (−

(t − t0

)

2

tw

2

)

For this case the characteristic width of the Gaussian pulse was tw = 0.01 seconds, and the reference 

pressure was P∞ = 63,125 Pa. Applying this boundary condition produced a transient shear layer 

instability in the outer stream, as shown in Figure 42. However, the growth of this instability was not 

self-sustaining, and eventually decayed at large times. When compared to the LES behavior, we then 

concluded that a forced impulse at the inlet was not sufficient in the DES to create an acoustic feedback 

loop which would sustain an oscillatory motion at the outer nozzle lip. 

NASA/CR—2015-218719 250
Figure 42 Successive instantaneous snapshots of the DES computation of the Phase II geometry after a 

Gaussian impulse was applied to the inlet of the primary stream. 

3.4 LES SIMULATIONS

3.4.1 PHASE I CONFIGURATION SIMULATION APPROACH

3.4.1.1 FLOW PREDICTION

A LES prediction capability has been developed at GE Global Research Center based on the code 

(FDL3DI) developed by Gaitonde & Visbal5. The code solves the Navier-Stokes equations. It uses a 

sixth-order accurate compact finite difference scheme for spatial discretization, and has the option to use 

the second-order Beam-Warming or the fourth-order Runge-Kutta time stepping methods for temporal 

integration. The higher-order accurate algorithm is desirable because this minimizes the dispersion and 

dissipation errors for accurate long time integration. Filters of different orders of accuracy (from secondto tenth-order) and the Smagorinsky subgrid scale model are implemented in the solver. This allows the 

solver to run in either full LES or implicit LES modes. For the current study, all simulations are 

performed with the use of implicit LES technique. In addition, the code uses a multi-block overset 

gridding structure to handle complex geometries. This allows a straightforward modeling for realistic 

engine geometries. For parallel computation, MPI is employed to handle communication between 

cores/processors. 

3.4.1.2 FFOWCS WILLIAMS HAWKINGS APPROACH

Having solved for the near field fluctuations in the jet plume using LES, the far-field noise prediction are 

made using a permeable surface Ffowcs Williams and Hawkings (FWH) solver. This projects the nearfield fluctuating quantities on the FWH integration surface to a far field observer location. This alleviates 

the need to numerically solve for the flow quantities all the way to the far field, resulting in significant 

resource savings. The FWH control surface is defined as a conical surface just outside the jet plume, such 

that no significant large-scale noise generating sources go across the surface as seen in Figure 43. The far 

field solution is computed based on surface integrals of the flow quantities evaluated on the control 

surface. This approach is superior to traditional acoustic analogy approach, which requires volume 

integrals. The frequency domain formulation of the FWH solver is used in the present work. The 

approach used in the current work is the same as that followed by Strelets et.al.6 

NASA/CR—2015-218719 251
Figure 43 Schematic of FWH surface definition for single flow nozzle configuration. 

Details about the validation of the current LES approach and its ability to capture the acoustics effects of 

nozzle geometry are presented in Paliath et.al7. The FWH solver had been validated for configurations 

with no forward flight effect. This approach was extended recently to include flight effects so as to enable 

far-field acoustic predictions for some of the configurations in the current work. But detailed validation of 

this extension is pending. 

3.4.2 SIMULATION RESULTS

Simulations were performed for the reference nozzle and the GE IVP/shield nozzle. Simulations were 

conducted both with and without forward flight. Figure 44 shows a typical mesh used in the computation. 

The grid topology and count for the simulations was developed based on best practices developed earlier. 

A conical FWH surface is chosen, where the time accurate unsteady flow field was sampled. The surfaces 

were not closed on either the upstream or downstream ends. The grid spacing at the FWH surface is 

specified so as to resolve acoustic waves up to a Strouhal number of 5. The LES simulation was started 

with zero initial flow-field. Flow was allowed to reach a fully developed state before sampling the 

unsteady flow field. Farfield acoustic predictions are made, using the FWH formulation, at observer 

locations matching the experiment, using the data sampled on the FWH surface. Multiple FWH surfaces 

were evaluated to ensure acoustic prediction convergence. 

Figure 44 LES grid topology for reference nozzle. 

3.4.2.1 REFERENCE NOZZLE

The overset grid topology used is shown in the Figure 44. The grid was designed for a Strouhal number 

resolution of 5. The grid count for the current configuration was approximately 28 million points. The 

simulation was run on 2000 cores on GE’s internal cluster. Acoustic time sample of approximately 250 

time units was used to calculate the spectra. One time unit is the time for a fluid particle to travel 1 nozzle 

NASA/CR—2015-218719 252
diameter at the jet exit speed. LES simulations were performed with and without flight effects. Figure 45

show a comparison of the time accurate instantaneous flow fields. Far field acoustic predictions are made 

at 4 observer locations and comparison with data are shown in Figure 46 and Figure 47. It is seen that the 

LES methodology is able to predict the spectral shape in both downstream and sideline directions. The 

large-scale low frequency noise peaks at the downstream directions are over predicted by approximately 

2-3 dB, while the small-scale high-frequency noise in the upstream direction is under predicted by about 

3-4 dB. It is also seen that the LES based approach is able to capture the reduced broadband spectra due 

to forward flight. But over prediction of very low frequency part of the spectra is observed though. 

Figure 45 Axial velocity contours for reference nozzle a) Mf= 0.0 b) Mf= 0.34. 

Figure 46 Far field acoustic predictions at 4 different downstream angles for the reference nozzle with 

Mf= 0.0. 

F, Hz

SPL (1/3rd Octave) dB

103 10 55 4

60

65

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Data Pred

59.30

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88.60

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119.20

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146.10

NASA/CR—2015-218719 253
Figure 47 Far field acoustic predictions at 4 different downstream angles for the reference nozzle with 

Mf= 0.3. 

3.4.2.2 IVP/SHIELD NOZZLE

The overset grid topology was once again leveraged to define the computational domain and better 

represent the nozzle with the IVP and fluid shield. The grid was once again designed to achieve similar 

spectral accuracy as for the reference nozzle configuration. The grid count for the three stream nozzle 

was considerably larger at 85M pts. Numerical simulations were performed for 2 operating conditions, 

with and without forward flight. The simulations were once again run on approximately 2,000 cores. The 

total run time, to obtain a fully developed flow field and for flow sampling, was approximately 2.5 weeks. 

The run time was larger for the case with forward flight as it started with zero initial velocity. Figure 48

shows the axial flow field along an azimuthal plane for the case without forward flight. Signs of flow 

separation are seen near the nozzle exit, similar to observations in experimental the PIV data. Data was 

sampled on four different FWH surfaces of varying radial extent to ensure convergence in prediction. The 

integral FWH approach was then used to predict the far-field spectra at 4 different observer angles. Figure 

49 shows the acoustic prediction comparisons with data for the case with no forward flight. Once again it 

is seen that the LES based approach does a reasonable job of capturing the spectral shapes and peaks 

across all observer angles. In addition the numerical approach also picks up a tonal signature being 

radiated in the downstream direction, possibly related to the flow separation near nozzle exit. 

F, Hz

SPL (1/3rd Octave) dB

103 10 65 4

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119.20

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Data Pred

146.10

NASA/CR—2015-218719 254
Figure 48 Axial velocity and density gradient contours for cutback nozzle at Mf= 0.0. 

Figure 49 Far field acoustic predictions at 4 different downstream angles for the cutback nozzle at Mf= 

0.0. 

F, Hz

SPL (1/3rd Octave) dB

103 10 65 4

70

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59.30

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5 dB

NASA/CR—2015-218719 255
Figure 50 shows plots of the reference nozzle noise level minus the IVP/shield nozzle for the four angles 

shown previously for the measured data as well as the predicted value for cases with and with external 

flow. The trends for the no external flow case, M=0, is predicted extremely well in both amplitude and 

shape. For the case with external flow the trends are not correct at the low frequency, this is the first time 

LES has been used with external flow and further investigation is needed to ensure the FWH 

methodology is being implemented correctly. 

Figure 50 Noise Delta Between Reference Nozzle and IVP/Shield Nozzle, Prediction and Measured 

Values, (a) 59.3 deg, (b) 88.6 deg, (c) 119.2 deg and (d) 146.1 deg. 

3.4.3 PHASE II LES

Aeroacoustic analysis of the Phase II design is in progress using Large Eddy Simulation (LES) with an 

unstructured flow solver from Cascade Technologies (CTI). A coarse grid of the phase II cutback 

condition was created in Gridgen using a blocked structure. This grid was then adapted by splitting cells 

and creating hanging nodes to resolve the critical regions of the flowfield. The final grid count is 78 

million points. 

The three engine streams (primary, IVP, and shield) were driven with total pressure and total temperature 

boundary conditions per the engine cycle model. A wall model was used to more accurately predict wall 

shear and boundary layer growth. A co-flow (simulated flight speed) Mach number 0.3 was applied to 

the ambient boundaries with temperature of 296K and pressure of 99.4kPa. 

-20

-15

-10

-5

0

5

100 1000 10000 100000 Ref Minus Nozzle SPL (dB)

Frequency (Hz)

Cutback, Ref Minus 3-Stream Nozzle, 59.3 deg

LES Prediction Measured Data

Filled - Static Open - M=0.3

-20

-15

-10

-5

0

5

10

100 1000 10000 100000 Ref Minus Nozzle SPL (dB)

Frequency (Hz)

Cutback, Ref Minus 3-Stream Nozzle, 88.6 deg

LES Prediction Measured Data

Filled - Static Open - M=0.3

-14

-12

-10

-8

-6

-4

-2

0

2

4

100 1000 10000 100000 Ref Minus Nozzle SPL (dB)

Frequency (Hz)

Cutback, Ref Minus 3-Stream Nozzle, 119.2 deg

LES Prediction Measured Data

Filled - Static Open - M=0.3

-15

-10

-5

0

5

10

100 1000 10000 100000 Ref Minus Nozzle SPL (dB)

Frequency (Hz)

Cutback, Ref Minus 3-Stream Nozzle, 146.1 deg

LES Prediction Measured Data

Filled - Static Open - M=0.3

a b

c d

NASA/CR—2015-218719 256
The LES solution shows regions of unsteadiness not predicted by the RANS solution, including on the 

inner and outer surfaces of the primary nozzle, as seen in the instantaneous density gradient shown in 

Figure 51. Figure 52 shows the instantaneous temperature and velocity in the flowfield near the nozzle. 

No shocks or regions of gross separation are predicted, which is an improvement over the behavior of the 

Phase I nozzle. This is expected to result in reduced noise levels for both the LES analysis and the scale 

model testing. Note this solution is not fully developed throughout the entire computational domain, but 

in the near nozzle region it is fully developed. 

Figure 51 Instantaneous density gradient showing development of mixing structures. 

Figure 52 Instantaneous velocity (top) and temperature (bottom). 

Figure 53 shows the time averaged mean velocity in the near nozzle flowfield and Figure 54 shows the 

fluctuating velocity in the same region. 

NASA/CR—2015-218719 257
Figure 53 Time-averaged velocity. 

Figure 54 RMS velocity (m/s). 

3.5 PHASE II CONTINUATION LES 

NASA/CR—2015-218719 258
The LES simulations for the Phase II geometry were run at GE Aviation, where the primary LES code for 

acoustics is CharLES from Cascade Technologies, Inc. This code solves the compressible Navier-Stokes 

equations on unstructured grids using a highly-scalable, second-order accurate control volume based 

finite volume method. Flux is computed at each control volume face by blending a non-dissipative 

central flux and a dissipative upwind flux. The blending parameter is computed locally based on the grid 

quality and varies spatially to minimize the introduction of numerical dissipation and dispersion where 

grid quality is good, and to prevent the introduction of numerical instabilities where grid quality is less 

than ideal. The explicit Smagorinsky sub-grid scale model is utilized to model the effects of unresolved 

turbulence on the flow-field, and shocks are captured through a second-order ENO scheme by surgically 

introducing dissipation where needed. To further improve simulation accuracy, wall modeling is used to 

account for the effects of boundary layer development on the overall solution. The above combination of 

low-dissipation schemes allows for high-fidelity LES simulations of complex geometries. 

3.5.1 SIMULATION RESULTS

Simulations were performed for the baseline GE Phase II cutback geometry as well as the cutback nozzle 

with a trimmed primary-stream throat. Both simulations were performed at an ambient Mach number of 

0.3 to aid in solution speed. Figure 55 shows a representative mesh for these runs, which was generated in 

ICEM. Initial grid resolution was set to accurately resolve the nozzle geometry as well as to add 

sufficient boundary layer grid for use of the CharLES wall modeling, but the volume away from the 

nozzle walls was left as coarse as possible. The Cascade mesh adaption tool was then used to refine the 

remaining volume with relatively isotropic hexahedral cells, while introducing hanging nodes that the 

CharLES solver can handle. This approach allows a high concentration of mesh in regions of interest 

while limiting the amount of unnecessary mesh resolution outside of the important areas of the flow field. 

Like the Phase I LES, a conical FWH surface was used to sample the unsteady flow field and the 

simulation was started with a zero initial flow-field and allowed to fully develop before sampling. Far 

field acoustic predictions were made with the with observer locations set to mimic the experimental setup. 

Again, multiple FWH surfaces were investigated to ensure acoustic prediction convergence 

Figure 55 LES Grid Topology for Phase II Continuation LES 

3.5.2 BASELINE CUTBACK NOZZLE

The unstructured grid used for the baseline cutback LES simulation is shown in Figure 55. The grid had a 

total node count of ~76M. The simulation was run on roughly 2000-9000 cores on the AFRL’s Spirit 

supercomputer. Acoustic sampling was performed at a time step of 1.7E-08 seconds for a total of ~1.49M 

time steps (about 0.0253 seconds). 

NASA/CR—2015-218719 259
Figure 56 shows a comparison of the scale model data, to be presented in the next section, with the LES 

farfield prediction for the cutback geometry and condition, at an angle of 59 deg. The data is shown for 

the range of cycle conditions tested, referenced with the primary nozzle pressure ratio. In the data some 

broad humps are seen in the 8 kHz range for some of the mid-range primary nozzle pressure ratios. The 

LES results also show this broad hump at approximately the same frequency range. This relatively broad 

noise hump is likely the result of a broad band shock noise from the shocks on the cowl of the IVP 

nozzle. 

Figure 56 Comparison of LES Acoustic Prediction and Experimental Data at 59.4 deg at Model Scale. 

3.6 PHASE 2 CONTINUATION EXHAUST NOZZLE TEST

The exhaust nozzle test was conducted at NASA GRC in the AAPL/NATR facility. Two geometric 

configurations were available along with a chevron configuration for the primary nozzle exit, where not 

covered by the shield, as well as for the shield nozzle. 

Figure 57 and Figure 58 show photographs of the three-stream nozzle installed at the NASA facility. 

60

65

70

75

80

85

90

95

100

105

100 1000 10000 100000 SPL (dB)

Frequency (Hz)

59.4 deg

2.604 2.29

2.016 1.835

1.709 1.592

1.557 1.486

LES

5 dB

NASA/CR—2015-218719 260
Figure 57 Photo of Three-Stream Nozzles Installed at NASA Facility. 

Figure 58 Photo of Three-Stream Nozzle Installed in NASA Facility. 

3.6.1 PHASE 1 VS PHASE 2 DATA COMPARISON

Figure 59 shows a comparison of the Phase 1 and Phase 2 cutback configurations and conditions. The 

model sizes and conditions are not the same between the two phases so the levels should not be compared 

exactly. The Phase 1 data had an unsteady separation in the flowfield that was generating an extra noise 

source, seen in the forward angles in the OASPL plot and in the 8 – 12 kHz frequency range in the 

spectra. Clearly this has been mitigated in the Phase 2 configuration. 

NASA/CR—2015-218719 261
Figure 59 Phase 1 vs Phase 2 Scale Model Test Results for the Cutback Geometry and Condition. Scale 

Model Data at with Different Nozzle Sizes and Conditions, Shield Aligned with the Microphones. 

Figure 60 shows Phase 1 and Phase 2 comparisons of the single engine jet noise EPNL values as a 

function of mixed velocity, for the cutback configuration. In the Phase 1 figure the unsteady separation 

issue is seen at lower mixed velocities. The data is compared to data taken with a round conic nozzle run 

at the same mixed velocity and scaled to provide equivalent thrust levels. At high mixed velocities the 

three-stream nozzle is quitter than the reference configuration. For Phase 2 the three-stream nozzle is 

quieter at all velocities. 

Figure 60 Comparison of Jet Noise EPNL Levels for Phase 1 and Phase 2 Cutback Geometries. Vehicle 

and Propulsion System Different Between Phase 1 and 2, Shield Aligned with the Microphones. 

Figure 61 shows the detailed farfield noise comparisons for the cutback configuration at the cutback cycle 

conditions compared to the reference nozzle. The PNL plot shows that the three-stream nozzle is quieter 

than the reference at angles aft of 80 deg. The levels are the same at the forward angles. The spectra 

0 20 40 60 80 100 120 140 160 180 OASPL (dB)

Directivity Angle (deg)

CB Cycle Condition, M=0.3, Scale Model Data

Phase 1 Phase 2

2 dB

10 100 1000 10000 100000 SPL (dB)

Frequency (Hz)

CB Cycle Condition, M=0.3, Scale Model Data, 60 deg

Phase 1 Phase 2

5 dB

10 100 1000 10000 100000 SPL (dB)

Frequency (Hz)

CB Cycle Condition, M=0.3, Scale Model Data, 90 deg

Phase 1 Phase 2

5 dB

10 100 1000 10000 100000 SPL (dB)

Frequency (Hz)

CB Cycle Condition, M=0.3, Scale Model Data, 150 deg

Phase 1 Phase 2

5 dB

80

82

84

86

88

90

92

94

96

98

100

1000 1100 1200 1300 1400 1500 1600

EPNL Single Engine

Vmix (ft/sec)

Cutback Configuration, Flight

No Miniburner

Ref No Miniburner

80

85

90

95

100

105

110

1100 1200 1300 1400 1500 1600 1700 EPNL (EPNdB)

Vmix (ft/sec)

CB, M=0.3, 0 deg

Ref

CB

Phase 1 Phase 2

2 dB

2 dB

100 ft/sec 100 ft/sec

NASA/CR—2015-218719 262
show that the three-stream nozzle is quieter over most frequencies, especially below approximately 5 

kHz. 

Figure 61 Full Scale Jet Noise Data for Cutback Configuration and Cycle Compared to Reference Nozzle, 

with Flight Simulation, Shield Aligned with the Microphones. 

Figure 62 shows Phase 1 and Phase 2 comparisons of the single engine jet noise EPNL values as a 

function of mixed velocity, for the sideline configuration. In the Phase 1 figure the unsteady separation 

issue is seen at lower mixed velocities. At high mixed velocities the three-stream nozzle is quieter than 

the reference configuration. For Phase 2 the three-stream nozzle is quieter at all velocities. 

Figure 62 Comparison of Jet Noise EPNL Levels for Phase 1 and Phase 2 Sideline Geometries. Vehicle 

and Propulsion System Different Between Phase 1 and 2, Shield Aligned with the Microphones. 

Figure 63 shows the detailed farfield noise comparisons for the cutback configuration at the sideline cycle 

conditions compared to the reference nozzle. The PNL plot shows that the three-stream nozzle is quieter 

than the reference at angles aft of 120 deg. The spectra show that the three-stream nozzle is quieter over 

0 20 40 60 80 100 120 140 160 180 PNL (PNdB)

Directivity Angle (deg)

CB, M=0.3

Ref CB

5 PNdB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

CB, M=0.3, 90 deg

Ref CB

5 dB

25

30

35

40

45

50

55

60

65

70

75

10 100 1000 10000 SPL (dB)

Frequency (Hz)

CB, M=0.3, 130 deg

Ref CB

5 dB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

CB, M=0.3, 150 deg

Ref CB

10 dB

75

80

85

90

95

100

1000 1100 1200 1300 1400 1500 1600

EPNL Single Engine

Vmix (ft/sec)

Sideline Configuration, Flight

No Miniburner

Ref No Miniburner

70

75

80

85

90

95

100

105

800 900 1000 1100 1200 1300 1400 1500 1600 1700 EPNL (EPNdB)

Vmix (ft/sec)

SL, M=0.3, 0 deg, with shield

Reference

SL

Phase 1 Phase 2

5 dB 5 dB

100 ft/sec 100 ft/sec

NASA/CR—2015-218719 263
most frequencies, especially below approximately 5 kHz. At forward angles the three-stream nozzle is 

sometimes louder than the reference nozzle. 

Figure 63 Full Scale Jet Noise Data for Sideline Configuration and Cycle Compared to Reference Nozzle, 

with Flight Simulation, Shield Aligned with the Microphones. 

Figure 64 shows full scale data for the cutback configuration with flight conditions at M=0.3 for all of the 

cycle conditions tested. The PNL plot shows the peak noise directivity is at relatively side angles and 

only moves forward at the highest nozzle pressure ratios, where the broadband shock noise really picks 

up. The spectra at 60, 90 and 130 deg show fairly smooth characteristics, although some of the mid-range 

cycle conditions have some very broad ripples at 8 and 12 kHz. These are thought to be the remnants of 

the shock like phenomena discussed previously. Although for the current commercial application the 

operating conditions around noise sensitive operations may only be slightly supersonic, traditional 

broadband shock noise does not become evident until pressure ratios fairly above supercritical conditions. 

This is another benefit of the IVP, broadband shock noise really doesn’t show up until fairly supersonic 

conditions are reached. 

Figure 64 PNL and Spectra at Three Angles for the Cutback Configuration Including Entire Cycle Tested, 

Simulated Flight Conditions with M=0.3, Shield Aligned with Microphones. 

0 20 40 60 80 100 120 140 160 180 PNL (PNdB)

Directivity Angle (deg)

SL M=0.3

Ref SL

5 PNdB

10 100 1000 10000 SPL (dB)

Directivity Angle (deg)

SL M=0.3, 90 deg

Ref SL

10 dB

10 100 1000 10000 SPL (dB)

Directivity Angle (deg)

SL M=0.3, 130 deg

Ref SL

10 dB

10 100 1000 10000 SPL (dB)

Directivity Angle (deg)

SL M=0.3, 150 deg

Ref SL

10 dB

0 20 40 60 80 100 120 140 160 180 PNL (PNdB)

Directivity Angle (deg)

Cutback Configuration, M=0.3, Shield Aligned

NPR Increasing

10 PNdB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

Cutback Configuration, M=0.3, Shield Aligned, 60 deg

NPR Increasing

10 dB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

Cutback Configuration, M=0.3, Shield Aligned, 90 deg

NPR Increasing

5 dB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

Cutback Configuration, M=0.3, Shield Aligned, 130 deg

NPR Increasing

10 dB

NASA/CR—2015-218719 264
Figure 65 shows full scale data for the sideline configuration with flight conditions at M=0.3 for all of the 

cycle conditions tested. The trends are similar for the cutback configuration. 

Figure 65 PNL and Spectra at Three Angles for the Cutback Configuration Including Entire Cycle Tested, 

Simulated Flight Conditions with M=0.3, Shield Aligned with Microphones. 

Figure 66 shows full scale data for the cutback configuration with static conditions for all of the cycle 

conditions tested. The PNL plot shows the peak noise directivity is at aft angles and only moves forward 

at the highest nozzle pressure ratios, where the broadband shock noise really picks up. The spectra at 60, 

90 and 130 deg show nice smooth characteristics. Although for the current commercial application the 

operating conditions around noise sensitive operations may only be slightly supersonic, broadband shock 

noise does not become evident until pressure ratios fairly above supercritical conditions. This is another 

benefit of the IVP, broadband shock noise really doesn’t show up until fairly supersonic conditions are 

reached. 

Figure 66 PNL and Spectra at Three Angles for the Cutback Configuration Including Entire Cycle Tested, 

Static Conditions, Shield Aligned with Microphones. 

0 20 40 60 80 100 120 140 160 180 PNL (PNdB)

Directivity Angle (deg)

Sideline Configuration, M=0.3, Shield Aligned

10 PNdB

NPR Increasing

10 100 1000 10000 SPL (dB)

Frequency (Hz)

Sideline Configuration, M=0.3, Shield Aligned, 60 deg

10 dB NPR Increasing

10 100 1000 10000 SPL (dB)

Frequency (Hz)

Sideline Configuration, M=0.3, Shield Aligned, 60 deg

10 dB NPR Increasing

10 100 1000 10000 SPL (dB)

Frequency (Hz)

Sideline Configuration, M=0.3, Shield Aligned, 130 deg

NPR Increasing

10 dB

0 20 40 60 80 100 120 140 160 180 PNL (PNdB)

Directivity Angle (deg)

CB, M=0, Shield Aligned

NPR Increasing

5 PNdB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

CB, M=0, Shield Aligned, 60 deg

10 dB NPR Increasing

10 100 1000 10000 SPL (dB)

Frequency (Hz)

CB, M=0, Shield Aligned, 115deg

NPR Increasing

10 dB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

CB, M=0, Shield Aligned, 150deg

NPR Increasing

10 dB

NASA/CR—2015-218719 265
Figure 67 shows full scale data for the sideline configuration with static conditions for all of the cycle 

conditions tested. The trends are similar for the cutback configuration. 

Figure 67 PNL and Spectra at Three Angles for the Sideline Configuration Including Entire Cycle Tested, 

Static Conditions, Shield Aligned with Microphones. 

Figure 68 shows the effect of the shield with the sideline configuration on the EPNL. At this high level 

metric the effect on the EPNL is fairly small. Figure 69 shows the detailed shield effect at the sideline 

condition. The PNL plot shows the shield has a benefit over all angles. The SPL spectra show the shield 

results in the lower noise at the mid-range frequencies, where the shield is expected to reflect noise. 

Figure 68 EPNL vs Vmix Showing Effect of the Fluid Shield for the Sideline Configuration with Flight 

Simulation, Shield Aligned with the Microphones. 

10 100 1000 10000 SPL (dB)

Frequency (Hz)

SL, M=0, Shield Aligned, 60 deg

10 dB

NPR Increasing

10 100 1000 10000 SPL (dB)

Frequency (Hz)

SL, M=0, Shield Aligned, 90 deg

10 dB NPR Increasing

10 100 1000 10000 SPL (dB)

Frequency (Hz)

SL, M=0, Shield Aligned, 130 deg

10 dB NPR Increasing

0 20 40 60 80 100 120 140 160 180 PNL (PNdB)

Directivity Angle (deg)

SL, M=0, Shield Aligned

NPR Increasing

5 PNdB

75

80

85

90

95

100

105

1100 1200 1300 1400 1500 1600 1700 1800 EPNL (EPNdB)

Vmix (ft/sec)

SL, M=0.3, 0 deg

With Shield Without Shield

5 EPNdB

NASA/CR—2015-218719 266
Figure 69 Full Scale Jet Noise Data for Sideline Configuration and Cycle with and without the Fluid 

Shield, with Flight Simulation, Shield Aligned with the Microphones. 

Figure 70 shows the effect of chevrons with the sideline configuration on the EPNL. At this high level 

metric the effect on the EPNL is fairly small, with no significant benefit seen. Figure 71 shows the 

detailed chevron effect at the sideline condition. The PNL plot shows the chevrons have a benefit at aft 

angles. The SPL spectra show the chevrons result in lower noise at the low frequencies, but can increase 

noise at the higher frequencies. These results show the difficulty with chevrons nozzles that interact with 

the freestream, great care must be taken with the amount of vorticity generated to balance this low 

frequency benefit and high frequency penalty. 

Figure 70 EPNL vs Vmix Showing Effect of the Chevrons for the Sideline Configuration with Flight 

Simulation, Shield Aligned with the Microphones. 

0 20 40 60 80 100 120 140 160 180 PNL (PNdB)

Directivity Angle (deg)

SL M=0.3 With Shield Without Shield

5 PNdB

10 100 1000 10000 SPL (dB)

Directivity Angle (deg)

SL M=0.3, 90 deg

With Shield Without Shield

10 dB

10 100 1000 10000 SPL (dB)

Directivity Angle (deg)

SL M=0.3, 60 deg

With Shield Without Shield

10 dB

10 100 1000 10000 SPL (dB)

Directivity Angle (deg)

SL M=0.3, 130 deg

With Shield Without Shield

10 dB

75

80

85

90

95

100

105

1100 1200 1300 1400 1500 1600 1700 EPNL (EPNdB)

Vmix (ft/sec)

SL, M=0.3, 0 deg, with shield

SL

SL PHV

SL SCHV

SL ACHV

5 EPNdB

NASA/CR—2015-218719 267
Figure 71 Full Scale Jet Noise Data for Sideline Configuration and Cycle with and without Chevrons, 

with Flight Simulation, Shield Aligned with the Microphones. 

Figure 72 shows the effect of the azimuthal angle with the sideline configuration on the EPNL. At this 

high level metric the effect on the EPNL is fairly small, but the case with the shield aligned with the 

microphones generally has the lowest EPNL value. Figure 73 shows the detailed azimuthal angle effect 

at the sideline condition. The PNL plot shows that with shield aligned with the microphones the benefit is 

seen at the peak noise radiation angles. The SPL spectra show the shield results in the lower noise at the 

mid and high frequencies, where the shield is expected to reflect noise. The broad ripples discussed 

previously at 8 and 12 kHz are also suppressed with the shield aligned with the microphones. 

Figure 72 EPNL vs Vmix Showing Effect of the Azimuthal Angle for the Sideline Configuration with 

Flight Simulation, Shield Aligned with the Microphones, Opposite the Microphones and aligned with the 

edge of the Shield. 

0 20 40 60 80 100 120 140 160 180 PNL (PNdB)

Directivity Angle (deg)

SL, M=0.3

Base Prim Chv Shield Chv Both Chv

5 PNdB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

SL, M=0.3, 60 deg

Base Prim Chv Shield Chv Both Chv

10 dB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

SL, M=0.3, 90 deg

Base Prim Chv Shield Chv Both Chv 10 dB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

SL, M=0.3, 130 deg

Base Prim Chv Shield Chv Both Chv 5 dB

1000 1100 1200 1300 1400 1500 1600 1700 EPNL (EPNdB) - Single Engine Jet Noise

Vmix (ft/sec)

Sideline Configuration, M=0.3

Shield Aligned

Shield Opposite

Edge of Shield

5 EPNdB

NASA/CR—2015-218719 268
Figure 73 Full Scale Jet Noise Data for Sideline Configuration and Cycle Showing the Effect of the 

Azimuthal Angle for the Sideline Configuration with Flight Simulation, Shield Aligned with the 

Microphones, Opposite the Microphones and aligned with the edge of the Shield 

4 NUMERICAL SIMULATIONS OF DECOUPLED FAN DOMAIN

A multi-stage fan rig is chosen for the NASA N+2 Phase II program to assess the fan noise. Numerical 

simulations of this fan rig are then performed using unsteady RANS (URANS) to provide information for 

fan noise assessment. This report has all the necessary details of the numerical simulations. The details 

include CFD domain, geometry setup, meshing, operating and boundary conditions, CFD process, 

convergence check, and post-processing. These details are organized in subsections of this report. 

4.1 CFD DOMAIN, MESH, AND BOUNDARY CONDITIONS

The CFD domain includes all the blade rows except for the structural members of the rig at the rear of 

the two streams. This is a full-annulus model that includes all the blades and vanes contained in the blade 

rows, with the intention of simulating the distorted flow that enters from the upstream boundary that 

connects to the supersonic inlet. The LM inlet center-body line is re-scaled slightly to match the fan rig 

hub profile for a smooth transition. The inlet is also scaled down to match the fan rig scale. 

The operating condition for the fan CFD is at a take-off condition. The inflow boundary condition is from 

CFD of the inlet, generated by Lockheed Martins. Inlet CFD is on unstructured mesh, shown in Figure 74. 

All five flow variables and two turbulence variables (TKE and Omega) are taken from the inlet solution at 

above-mentioned AIP. This flow condition is then mapped onto fan domain. Fan CFD is done with GE’s 

in-house turbomachinery code TACOMA, which is a URANS code with structured mesh driven by dualtime stepping algorithm for unsteady calculations. The main numerical scheme in TACOMA is a JSTstyle 3-stage Runge-Kutta explicit scheme with multigrid and other convergence techniques. 

0 20 40 60 80 100 120 140 160 180 PNL (PNdB)

Directivity Angle (deg)

SL M=0.3

Shield Aligned

Shield Edge

Shield Opposite

5 PNdB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

SL M=0.3, 60 deg

Shield Aligned

Shield Edge

Shield Opposite 10 dB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

SL M=0.3, 90 deg

Shield Aligned

Shield Edge

Shield Opposite

10 dB

10 100 1000 10000 SPL (dB)

Frequency (Hz)

SL M=0.3, 130 deg

Shield Aligned

Shield Edge

Shield Opposite

10 dB

NASA/CR—2015-218719 269
Figure 74 LM inlet grid and AIP location. 

The mesh for the fan rig is a structured multi-block topology. Care is taken to provide enough resolution 

for the expected flow physics, such as wake migration. Proper tip and hub clearances are included, and 

the relative clocking of stator rows and rotor rows are also checked and placed accordingly. Proper grid 

spacing in between two adjacent blade rows is also placed in both axial and tangential directions for 

smooth transition of flow features. Total mesh size is about 600 million cells. 

The inflow boundary condition, taken from the Lockheed Martin inlet solution is illustrated in Figure 75. 

This represents the distorted flow, where the three strut wakes and wakes from the open auxiliary door are 

shown. Two clean inflow conditions are also derived from this condition. One is a circumferentially 

averaged condition, labeled as “clean inflow”. The other is a face-averaged clean condition, labeled as 

“very-clean inflow”. Both clean flow conditions retain the endwall boundary layers.

Figure 75 Inflow boundary condition mapped to fan domain. 

PTA

PS

MABS

RHO swirl Radial angle

NASA/CR—2015-218719 270
4.1.1 STEADY-STATE CALCULATION

A steady-state calculation is necessary to establish the basis for unsteady simulations. A mixing-plane 

approach is adopted in between two adjacent blade rows. This calculation ensures the fan is at the correct 

operating condition in exit corrected flow. 

4.1.2 CONVERGENCE OF UNSTEADY CALCULATIONS

It’s important to make sure the unsteady calculations have good convergence. Convergence checks for 

unsteady calculations should consider multiple aspects rather than a typical error level check. The first 

rule of thumb is to allow ample time for a fluid particle to pass the entire domain before convergence is 

checked. Using an averaged velocity and the overall axial length of the domain, the time scale can be 

calculated, and compared against the physical time step for the unsteady flow calculation to determine 

how many physical time steps it takes for one particle to pass through. Usually, 4-5 passings is a good 

measure for the time to establish a converged flow. 

Once ample time has passed, a check is performed on the usual error terms of the equations. Beyond the 

error check, other critical flow properties also need to be checked for proper convergence. The inlet and 

exit massflows, blade torque, and loss in the domain are examined as well. Figure 76 shows the 

convergence check of these variables in the blade rows that are near the exit boundary of the CFD 

domain. These trends show that the flows in all components are properly converged. The solution is 

extracted after convergence to record blade surface pressure and flow variables at other locations for one 

revolution. 

Figure 76 Unsteady flow calculation convergence check. 

4.1.3 UNSTEADY RESULTS

The unsteady calculations are carried out on NASA’s HPC facility, using 1620 cores, with careful loadbalancing. Three major calculations are performed, distorted, clean, and very-clean inflow conditions. 

These calculations are necessary for the acoustic assessment. Figure 77 illustrates the inflow PTA map 

corresponding to the three conditions. The results of the three calculations are compared in Figure 78, in 

which the snapshots of the static pressure distribution upstream of the IGV are compared. With distorted 

flow, the static pressure map shows the effect from the combined strut (upstream) and fan (downstream). 

NASA/CR—2015-218719 271
Figure 77 PTA (abs total pressure) of inflow conditions. 

Figure 78 Static pressure maps with clean and distorted inflow conditions. 

4.1.4 DETAILED RESULTS WITH DISTORTED INFLOW

The details on the flow field are then investigated, especially for the distorted inflow condition. First the 

flow in the main stream, upstream of the combined IGV is shown in Figure 79. Rotor 1 shocks interact 

with the combined IGV and project a complex, non-uniform pattern upstream. The shock impingement 

and reflection are well captured. 

Figure 79 Rotor 1 Shock and IGV Interaction 

distorted clean very clean

NASA/CR—2015-218719 272
The combined strut/IGV has a thick wake that travels further downstream, and interacts with Rotor 1, and 

can be seen in the Rotor 2 domain as well. These flow features are well captured, and are important to 

resolve for the aero-acoustic assessment which is based on the CFD results. See illustration in Figure 80. 

Figure 80 Rotor1/Rotor2 interaction. 

4.1.5 POST-PROCESSING AND CONCLUSIONS

Three major unsteady simulations (distorted inflow, clean inflow, and very-clean inflow) have been 

carried out on the multi-stage fan rig for the acoustic assessment of the flow distortion effects. These 

calculations capture the flow features in great detail which in turn enables higher confidence in acoustic 

calculations. These are large-scale calculations that are time-consuming but deliver high-fidelity results. 

Unsteady pressure of all the blades in all blade rows are recorded for the extent of one fan revolution. 

Also recorded are all the flow variables in-between any adjacent blade rows for one revolution. Most 

importantly, all the flow variables are recorded upstream to the fan rig for one-revolution for the 

processing of noise projection and comparison under these three different inflow conditions. 

5 WAKE SOURCE CORRECTION USING LES SIMULATION OF WAKE DECAY

5.1 MOTIVATION

Blade wake prediction is important to study interaction effects with downstream blade rows. Both 

aero and acoustic performance of the downstream blade row is impacted due to unsteady wake migration. 

Typical two equation Reynolds Average Navier Stokes (RANS) models under-predict momentum mixing 

in the wake, predicting a deeper, narrower wake impinging the downstream blade. This typically results in 

an overestimation of the tonal noise source generated due to interaction with the aft blade row. Large 

Eddy Simulation (LES) offers an attractive alternative which predicts better mixing as it directly resolves 

turbulent structures responsible for flow mixing. GE has internally developed and validated a LES based 

approach for predicting blade wake evolution. This approach is applied to predict the wake evolution of 

the strut wake and the IGV wake as they evolve and interact with the downstream blade row. This 

NASA/CR—2015-218719 273
improved wake prediction is compared to the RANS based wake prediction to estimate the correction 

factor that should be applied when estimating the tonal noise source for these two wake interaction 

problems. 

5.2 NUMERICAL APPROACH

The LES solver used is the same solver described in the earlier section (3.4), where it was used 

for exhaust noise prediction. Details of the solver are given in that section. Significant developments 

where made in the solver to enable handling the blade wake problem. This included incorporating buffer 

zone boundary conditions and an ability to specify turbulent boundary condition at the inlet. 

Reynolds number for fans in gas turbine engines are typically of the order of a million or more and 

performing a complete LES or DNS computation of the full fan blade is prohibitively expensive. 

Therefore a zonal LES approach is adopted, where a portion of the blade trailing edge of a linear blade is 

simulated, along with the wake convection region. This requires non-trivial boundary condition 

specification at the edge of the computational domain. This is achieved by using the sponge boundary 

condition at the spanwise, azimuthal and exit boundaries and driving them to values from a RANS 

computation of the full blade. In order to provide unsteady disturbances in the blade boundary layer, time 

accurate turbulence information from a pre-computed flat plate boundary layer is superimposed on the 

RANS mean flow. This approach is only an idealization since the blade is not a flat plate, but seems to 

work quite well for wake prediction. A schematic of the computational domain is shown in Figure 81. 

Figure 81 Zonal LES approach, with pre-computed boundary layer turbulence 

This approach was validated on a GeNX cascade blade design. Wake profiles predicted at 0.6 chords 

downstream of the trailing edge is shown in Figure 82. LES predicted wake shape is in excellent 

agreement with measurement data, while the RANS based wale prediction is deeper and narrow, as 

expected. This validates the zonal LES approach and provided GE a great cost effective tool for wake 

prediction which is a huge improvement over traditional RANS based methods. 

NASA/CR—2015-218719 274
Figure 82 Wake profile at 0.6chods downstream of the trailing edge. 

5.2.1 SIMULATION SETUP: 

5.2.1.1 IGV WAKE SIMULATION: 

As discussed in the earlier section only a fraction of the IGV blade is simulated in the LES calculation to 

limit the computational cost of the simulation. To determine the spanwise extent of the computational 

domain two factors were looked at. First the wake strength (acoustic source) at the trailing edge of the 

IGV was examined. Figure 83 shows the spanwise variation of the wake at four different radial locations. 

It is observed that the wake strength, i.e., the width and depth of the wake does not vary significantly 

across most of the outer span region. Next, the rotor loading is examined to evaluate the region of 

dominant acoustic response. It is found that the flow in the tip region is supersonic, which means the 

acoustic response will be tip dominated. Based on these factors the spanwise extent of the calculation 

was chosen to be between the streamlines that pass through from 60% span to 95% span on the IGV. We 

do not go to 100% span to avoid tip effects. 

Figure 83 RANS based prediction of wake strength near trailing edge of the IGV 

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To determine the portion of the IGV blade that should be included in the simulation, the blade loading 

was examined at the two spanwise boundary locations. The inlet location is then chosen by using the zero 

pressure gradient criterion as shown in Figure 84. This is done to enable the use of turbulent boundary 

condition from a separate flat plate calculation as an inlet condition to the current computational domain. 

A schematic of the two block computational domain is shown in Figure 85. It should be noted that the 

axial gap between the IGV and rotor one is less than an IGV chord and we are really in the near wake 

evolution region. The sponge boundary condition is used at the exit and span wise locations and the 

solution is driven to the predictions based on a full blade RANS calculation. The azimuthal extent is one 

IGV passage and periodic boundary conditions are imposed at the azimuthal boundaries. 

Figure 84 Blade loading at spanwise boundaries of computational domain. Inlet boundary chosen based 

on pressure gradient being close to zero 

Figure 85 Schematic of two block computational domain for IGV wake simulation 

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The grid size for the two block computational domain was ~65M points. The computational domain was 

split into 512 parts and the calculation was run on 512 cores on NASA computer system Pleiades. A 

separate flat plate boundary layer calculation was initially run internally at GE to get a fully developed 

solution. The Reynolds number based on the momentum thickness was calculated at the inlet location to 

block 1 using the RANS solution for either side of the blade. These values were then used to choose two 

axial locations on the flat plate LES calculations which had the closest match to the Reynolds based 

momentum thickness. Time accurate data was collected at these two axial locations for three time units. 

One time unit is defined as the time taken for the flow to go through one IGV chord (at 95% span). This 

turbulent data was superimposed along with the RANS based mean flow to provide the inlet boundary 

condition for the C-block (block around the TE of the IGV blade). 

The computational domain was first initialized using the RANS solutions and then run to a fully 

developed state. It took approximately 5 time units of run time to remove all transients. Data was then 

sampled along a full axial plane located at ~ 85% gap between the IGV and rotor 1. This was done to 

avoid going into the sponge boundary zone near the exit of the computational domain. Data was sampled 

for 10 time units. 

5.2.1.2 STRUT WAKE SIMULATION: 

Once again only a fraction of the strut blade is simulated in the LES calculation to limit the computational 

cost of the simulation. It was noted that the strut geometry had very little variation in the spanwise 

direction and was essentially a 2D blade. In addition the wake evolution distance, or the gap between the 

strut trailing edge and the IGV leading edge was significantly larger than in the previous case. To limit 

the problem size for such a large evolution region the spanwise extent was limited to be between the 

streamlines that pass through from 70% span to 98% span on the strut. This was the minimum spanwise 

extent needed to accommodate the specification of the sponge boundary layers at the spanwise 

boundaries. 

Next, the blade loading on the strut was examined at these radial locations, to once again determine the 

location for the inlet boundary (portion of strut to be included in computation). A schematic of the two 

block computational domain is shown in Figure 86. It should be noted that the axial gap/ wake evolution 

region is significantly larger in this case as opposed to the IGV wake simulation. Once again the sponge 

boundary condition is used at the exit and span wise locations and the solution is driven to the predictions 

based on a full blade RANS calculation. Unlike the IGV wake simulation, given the low blade count for 

the strut, the azimuthal extent of the computational domain is not one strut passage. To maintain the 

computational cost to manageable levels the azimuthal extent was limited to one IGV passage and sponge 

boundary/RANS conditions were imposed at the azimuthal boundaries as well. 

In spite of these limiting choices on the computational domain the grid size for the strut wake calculation 

was ~125M grid points. The computational domain was split into 1024 parts and the simulation was 

carried out on 1024 cores on the NASA computer system Pleiades. An examination of the RANS 

predicted boundary layer on the strut showed no significant turbulence. Hence in this case, only the 

RANS based mean flow was used to specify the inlet boundary condition and no additional inlet 

turbulence boundary condition was used. The simulation was run for 25 time units to obtain a fully 

developed solution with all initial transients removed. Data was then sampled at an axial location 

corresponding to ~90% of the gap. Data was sampled for 50 time units. 

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Figure 86 Schematic of two block computational domain for strut wake simulation 

5.3 POST-PROCESSING AND RESULTS

The flow variables sampled near the exit plane for the IGV and strut wake calculations was time averaged 

to obtain the mean flow prediction based on the LES approach. The wake profile at various spanwise 

locations was compared to the wake profiles predicted by the RANS calculation. Figure 87 shows the 

comparison of the LES vs. RANS predicted strut wake at 85% span location near the IGV leading edge. It 

can be seen that the strut wake is not of significant strength, primarily due to the large evolution distance. 

In addition, the LES approach predicts a marginally higher mixing which translates to a slightly smaller 

prediction of the wake depth in the far wake region. 

Figure 87 Comparison of RANS vs. LES based prediction for the strut wake at IGV leading edge (85% 

span location) 

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To characterize the acoustic source strength for the tone noise generation mechanism based on the wake 

profile the following steps are followed for both the RANS and LES data sets. 

 Multiple constant theta lines are drawn on the 2D axial plane where data was sampled as shown 

in Figure 88. 

 Along each constant line, data was probed at 256 equally spaced points. 

 A spatial FFT was done along each line to extract the harmonics at 1st, 2nd & 3rd BPF 

 The source strength (V2D) was approximated to be equal to the combination of the azimuthal 

perturbation ( Vtheta) and axial perturbation (Vz). 

Figure 88 Estimation of wake strength at rotor leading edge using spatial FFT at multiple spanwise 

locations 

This process was repeated on the data sampled from both the IGV wake evolution and strut wake 

evolution simulation. The spanwise variation of the estimated acoustic source strength for the IGV wake 

interaction with rotor 1 is shown in Figure 89. The x-axis is chosen to be the 10log10 (V2D), to provide an 

estimate in the dB scale. It is seen that, as expected, the LES based prediction of a more mixed out wake 

results in a reduction in the estimated tonal noise source at the 1st three harmonic frequencies. Comparing 

the RANS vs. LES predictions, the estimated reduction is observed to be ~ 0.36dB, 0.58dB and 1.41dB 

for the 1st, 2nd and 3rd BPF tones respectively. 

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Figure 89 Comparison of 1st, 2nd & 3rd BPF source strength due to IGV wake interaction with rotor one. 

Figure 90 shows a similar comparison between the RANS based and LES based prediction of acoustic 

source strength of the strut wake interacting with the IGV. The first thing to note here is that the absolute 

values are much smaller than what was observed in the IGV wake case. This is again as expected since 

the IGV wake strength at rotor one leading edge is much higher that the strut wake strength at the IGV 

leading edge. This is because of the large gap between the strut and the IGV allowing for a much more 

mixed out strut wake before it interacts with the IGV. In addition, the LES based wake mixing predicts 

that the acoustic signature of the strut wake – IGV interaction tones should be ~ 1.18dB, 2.34dB & 

6.58dB lower than what is predicted by RANS at the 1st , 2nd and 3rd BPF tones respectively. 

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Figure 90 Comparison of 1st, 2nd & 3rd BPF source strength due to strut wake interaction with IGV. 

These correction factors will be applied to the Strut wake – IGV interaction tones and the IGV wake - 

rotor one interaction tones, when computing the system noise. 

6 NUMERICAL SIMULATIONS OF COUPLED AIRCRAFT/FAN DOMAIN

6.1 MOTIVATION OF THE INSTALLED CFD 

Putting the aircraft and engines in a mutual flowfield is not currently a common practice even though 

the benefit of doing so is obvious, both to the airframers and to the engine manufacturers. This is 

primarily due to the barriers of information sharing between commercial companies under increasingly 

intense competitive pressure, which is a justifiable cause. This is where NASA-led programs can bridge 

the gap by joining the aircraft and engines in research programs like N+2 for aerodynamic and acoustics 

evaluations. 

One of the purposes of performing the installed CFD within the scope of the N+2 program is to 

exercise the CFD tools and identify the aspects that CFD excels and the areas that are still in need of 

improvement. This task in the N+2 Phase II Extension is just the first step in maturing the CFD 

capability. Large-scale numerical simulations for aircrafts and for engines have significantly different 

requirements in terms of parallel efficiency and accuracy of prediction. Aircraft CFD software needs to be 

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efficient in a “strong-scaling” fashion since the CFD code needs to deal with the entire aircraft domain.

On the other side, the current industrial CFD software used in engine companies has the benefit of “weakscaling” due to the nature of turbomachinery which consists of many rows of rotors and stators and for 

each row, identical or similar blade/vane passages. Internal flows in turbomachinery, however, has the 

added requirement to capture the inlet and outflow conditions accurately for each row, or the stagematching can easily go wrong with multi-stage compressors and turbines. 

It is a challenge to apply GE’s turbomachinery CFD code to be able to handle very large domains like 

external flows around aircraft, not due to numerical algorithms but due to data/control structure of GE’s 

turbomachinery CFD code. 

The second purpose of this task is to evaluate the fan face noise level in the installed environment. The 

operating condition for the installed CFD is determined to be at take-off condition, details of the 

conditions are presented in the following sections. A logical outcome of the noise level evaluation is to 

compare the installed noise level to the results that was obtained in Phase II where a fan-alone CFD was 

performed. The report has a section for this comparison. However, due the design evolution of the 

nacelle/inlet geometry, the comparison is not directly back-to-back. Also the difference between the 

standalone fan calculation and the coupled calculation are quite visible for conditions at fan face. This 

certainly highlights the necessity of coupled simulations. Detailed discussion of the comparison is given 

in the following sections. 

6.2 CFD DOMAIN, GEOMETRY MODEL, MESH AND BOUNDARY CONDITIONS

Geometry model for installed CFD simulation consists of a multi-stage fan rig, and the aircraft model 

developed by Lockheed Martin. CFD domain is composed of the fan rig domain described in Section 4.2. 

The location of AIP (Aero-dynamic Interface Plane) is kept at the same location as in Phase II, and inlet 

portion of the domain described in Section 4.2 is replaced with nacelle / wing / fuselage model. The 

aircraft model has been scaled and positioned to match the size and orientation of the fan rig portion of 

the domain. The inlet hub center body has been slightly re-shaped to provide for smooth transition to fan 

rig domain. 

6.2.1 GEOMETRY

Aircraft geometry model developed by LM is shown in Figure 91. Several details considered of no 

importance for fan/inlet CDF simulation have been modified to allow for simpler CFD model. Since the 

engine is shielded from the top by the presence of the wing, features above the wing are either simplified 

or eliminated. The engine at the plane of symmetry has been removed from the model as was the 

stabilizer at the back side of the wing. In both cases, appropriate smoothing of the fuselage and the top of 

wing was performed to obtain closed geometry model. The simplified fuselage model is show in Figure 

92. 

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Figure 91 Lockheed Martin aircraft model 

Figure 92 Simplified aircraft model. 

6.2.2 CFD DOMAIN

The CFD domain passes through aircraft symmetry plane. Outer boundaries of the domain are set as 

shown in Figure 93. 

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Figure 93 CFD Domain boundaries. 

6.2.3 MESH OF THE AIRCRAFT

A manual meshing process was carried out to ensure proper grid resolution in areas of important 

geometric features. Attention was given to the engine nacelle, pylon, and wing. Farfield boundaries are 

placed far enough to eliminate potential boundary condition influences on the flow around the nacelle. 

Total grid points in the aircraft domain are 250 million. 

Finite element mesh for the installed CFD consists of the fan rig portion of the domain and inlet-fuselage 

part of the domain. Mesh for the fan-rig part of the domain has been kept consistent with the Fan/Inlet 

CFD part of this program, and is described in the section 4.1 of this document. Compared to the mesh 

described in Section 4.1, the inlet region has been removed and replaced with inlet-fuselage mesh. The 

inlet fuselage mesh is a multi-block structured mesh suitable for flow the GE flow solver TACOMA. The 

mesh is generated using ANSYS ICEMCFD meshing software. Care has been taken to properly resolve 

the AIP interface surface to ensure a smooth transition to the fan-rig part of the domain which is 

important for unsteady CFD analysis. Relative resolution in all three grid directions does not exceed 1:2. 

Standard design meshing practices commonly applied to turbomachinery CFD have been applied with 

respect to strut leading and trailing edge resolution, nacelle lip and wing leading edge areas. To ensure 

proper flow feature resolution, most of the mesh is concentrated in the inlet / duct / underwing zones 

which are the focus of the CFD and acoustics analyses. 

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Figure 94 Inlet – fuselage mesh: Engine surface mesh details 

Special care has been taken to ensure smooth transition between mesh regions, and to optimize grid 

quality. Mesh quality has been checked through a standard set of quality checks including determinant, 

minimum angle, and stretching ratios, and the mesh has been iteratively updated until the quality checks 

satisfied GE standard design guidelines. Mesh features physically resolved the boundary layers for all 

surfaces, and care has been taken to control wall cell size distribution off the walls as well as mesh 

stretching. Total mesh size for the fuselage – inlet domain is approximately 200 million nodes. Details of 

the inlet-fuselage mesh are shown in Figure 94 (nacelle), Figure 95 (nacelle lip and strut details), Figure 

96 (fuselage details) and Figure 97 (details of the interior of the mesh). 

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Figure 95 Inlet – fuselage mesh: Nacelle lip and strut details 

Figure 96 Inlet – fuselage mesh: Aircraft and engine details 

A

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Figure 97 Inlet – fuselage mesh: Details of the interior mesh. 

6.2.4 Y+ OF THE AIRCRAFT MESH

Wall boundary layers need to be resolved properly for any potential interaction of viscous effects. Figure 

98 shows the statistics of the y+’s of all the non-slip walls of the aircraft/nacelle model. Average y+ 

ranges from 1.7 to 5, with deviation from 0.7 to 1.4. All y+’s are certainly below 30, and a synthesized 

wall function mode is used in CFD solver to properly resolve the viscous layers. 

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Figure 98 y+ distribution of all surfaces in the aircraft domain: All within 30. Spalding wall function is 

used (combined sublayer and log-law). 

6.2.5 BOUNDARY CONDITIONS OF THE INSTALLED CFD 

A take-off condition is designated for the installed CFD simulations. Aircraft angle of attack is 8.1 

degrees, flight Mach number is 0.38, as communicated from LM’s design team. The condition is 

equivalent to airspeed of 246.8 KEAS (251.4 KATS). Similar to the numerical simulations that LM had 

been performing for the nacelle/inlet (in 2012, with flight Mach number of 0.3), a blowing wind tunnel 

condition is used for the far-field pressure boundary condition, in which the static pressure is set as one 

atmosphere. 

The engine axis is parallel to the reference aircraft axis. The engine has zero degree of toe angle and zerodegree of tilt angle as the current installation model has indicated. These are the conditions that were 

considered in determining the far-field boundary condition for the aircraft CFD, as illustrated in Figure 

99. 

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Figure 99 Farfield flow condition for the aircraft model 

6.3 STEADY-STATE SOLUTION OF FLOW AROUND THE AIRCRAFT

GE’s turbomachinery flow solver, TACOMA, needs to be tested with the aircraft domain before 

commencing the coupled calculation with the engine. The aircraft domain, as described in earlier sections 

of the report, consists of the aircraft and the exterior surfaces of the engine nacelle and the forward 

portion of the interior surfaces. The interface between the aircraft domain and the engine domain is set 

downstream of the supporting struts inside the nacelle. 

The shakedown was performed on a coarse mesh, which is 1/8 of the size of the full mesh by taking 

every other grid point out from the full mesh. This technique ensures the integrity and topology of the full 

mesh, and saves time for the trials. 

The steady-state run with full mesh was then performed. The proper flow rate at the aircraft/engine 

interface was specified for the steady-state run. The purpose of the steady-state run is to (a) establish flow 

of the external domain, which needs to be done before taking on the fully coupled unsteady calculation 

and, in doing so, saves significant amount of run time compared to starting the fully coupled unsteady run 

from a uniform solution of the external domain; and (b) establish proper flow condition at the interface to 

the engine. 

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Figure 100 Illustration of interface between aircraft domain and engine domain 

The most significant flow feature in this high AoA condition is the significant favorable shielding effect 

of the wing to the engine nacelle due to the location of engine installation. The engine is completely 

shielded from the large AoA effect. Under the wing, the flow goes into the engine nacelle in a parallel 

fashion alleviating severe FWD nacelle lip separation if otherwise exposed to the high AoA condition. 

Figure 101 shows the streamlines approaching the engine nacelle. The set-back installation of the engine 

benefits from the under-wing re-direction of the approaching flow with 8.1-deg AoA. 

A closer look at the flow near the FWD cowl of the nacelle reveals a very small separation zone of a 

limited extent of the inlet circumference (near the keel location). The crown portion of the nacelle has no 

separation due to closer proximity to the underside of the wing, see Figure 102. Also shown in this figure 

are Mach number, static pressure, and flow vectors. There is a mild static pressure distortion in the 

circumferential direction at the nacelle lip. This pressure distortion is attenuated as the flow goes through 

the nacelle. The flow around the aux door is also different at crown than at keel of the nacelle due to the 

crown portion’s close interaction with the wing.

Figure 101 Wing has very favorable shielding effect on the engine nacelle. Static pressure and streamlines 

are shown. 

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Figure 102 Close examination of flow around the nacelle lip. Plot from left to right are: Mach#, static 

pressure, Mach#, velocity vectors 

As flow progresses inside the nacelle, the mild circumferential pressure distortion at the FWD cowl lip is 

attenuated. See plots in Figure 103. The flow is not completely uniform in the circumferential direction, 

but there is no concern of significant distortion in the circumferential direction. The wakes from the aux 

door convect downstream into the fan domain, so do the strut wakes as well. In all three plots of this 

figure, one can see the flow features above the wing. That is a predominant wing vortex, details of which 

are shown later. 

Static pressure and absolute Mach number contours at a location that’s in the FWD cowl of the nacelle, 

upstream of the aux door are shown in Figure 104. The mild static pressure distortion we’ve seen in 

Figure 102 at the lip of FWD nacelle cowl is attenuated. The pressure and Mach number field represents 

the flow around the three struts in the nacelle. There are visible differences of the flow around each of the 

strut, shown both on the pressure and Mach number contours. Higher Mach number is seen around the 

strut that’s closest to the wing. This is mainly due to the higher pressure level near the underside of the 

wing. 

We also observed a swirl distortion on the order of -5 to 5 degrees at the lip of the FWD nacelle, shown in 

Figure 105. This swirl is driven by the static pressure field described earlier. This swirl distortion is 

attenuated as the flow progresses further downstream toward the aux door location. Again, since the 

pressure distortion vanishes, so does the swirl distortion. 

Figure 103 Cross-section of flow in the AFT cowl of the nacelle 

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Figure 104 Flow details at upstream of the aux door 

Figure 105 Swirl details at FWD cowl lip, and at middle of the aux door 

The open aux door certainly is creating interesting flow features as the flow is entering the engine nacelle 

through these doors and merging with the main flow that comes from entrance of the nacelle. Under the 

wing, at the location where the engine is installed, due to the presence of the fuselage, there is a 

significant cross flow under the wing, as illustrated in Figure 106 on the left. This cross flow moves 

around the engine nacelle, and enters the nacelle through the open aux doors. As the cross flow enters, it 

creates a one-per-rev swirl distortion. Remember that the swirl distortion in the FWD cowl has attenuated 

before flow reaching the aux door. The swirl distortion ranges from -8 to 8 degrees at the location close to 

the trailing edge of the struts in the inlet. Figure 106 has both contour plots of tangential velocity and 

swirl from the perspective of the engine. The radial penetration of this swirl distortion, however, is 

limited the shear-layer that is created by the aux door. This is a favorable effect since the main flow path 

of the engine is shielded from the distorted flow. 

Flow around the “hinge” to which the struts are attached on is also examined. A pair of vortices is 

observed at each hinge location. These vortices roll up due to the local geometry and two streams of flow 

merging, one stream is from the FWD cowl of nacelle, and the other is the flow that comes in from the 

open aux door. Figure 107 shows the PTA (absolute total pressure) signature of the shear layer, the 

vortices, and strut wakes that are going to enter the turbomachinery. 

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Finally, an external flow feature that cannot be missed is the large delta-wing vortex that’s on the upper 

surface of the wing on the wing leading edge. This vortex is far from where the engine is installed, and 

does not have visible interactions to cause flow distortion to the engine. Figure 108 gives the view from 

both the upper surface and lower surface of the wing, illustrating the wing vortex location and trajectory, 

and the relative separation of the vortex and flow entering the engine. 

Figure 106 Explanation of elevated swirl distortion level in the AFT cowl 

Figure 107 Roll-up of a pair of vortices associated with the strut and aux door 

Figure 108 Streamlines illustrating the above-wing vortex 

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A summary of flow features is as follows: 

- The wing has significant favorable shielding effect to nacelle; 

- Nacelle only has a minor lip (FWD cowl) separation in a limited sector angle 

- Pressure and swirl distortion in FWD cowl of nacelle attenuate before flow entering AFT 

cowl 

- A swirl distortion is seen at fan face, -8 to 8 degrees, due to cross flow under the wing 

that enters the nacelle via the open aux door 

- Fan will see reduced swirl distortion due to IGV and rotation effect; 

- Pressure distortion does not seem to be significant 

- Wing vortex does not interact with flow entering engine 

6.4 SOLUTION OF COUPLED AIRCRAFT/FAN SIMULATIONS

A proper physical time step is chosen to resolve the unsteadiness of blade-row interaction of the 

turbomachinery. Two hundred (200) time steps per blade passing are used for this purpose. The choice of 

time step is appropriate for resolving major BPF tone for acoustic purposes. This time step is sufficiently 

small to be effective for any unsteady flow features in the aircraft domain if there is any. 

6.4.1 CONVERGENCE CHECK

A total of 17 fan revolutions have been performed to obtain good convergence. TACOMA uses dual-time 

stepping for unsteady flow calculations. The inner iteration is converged to a 3-order magnitude drop of 

the error. Overall convergence is checked using measures of massflow rate of each blade row, the torque 

of rotors, and the loss in the blade region as well. 

As an additional assurance of convergence, many probes are placed in the domain, both internally and 

externally. Examination of the probe readings indicated that the flow is properly converged. Probes are 

placed around the nacelle, the aux doors, above and under the wing (tracking for wing vortex 

unsteadiness), and inside the nacelle at fan face. 

6.4.2 CHECK FOR UNSTEADY FLOW FEATURES IN AIRCRAFT DOMAIN

Probes places in the external (aircraft) domain do not have readings that suggest flow unsteadiness. The 

far-field boundary condition is steady at 8.1-deg angle-of-attack. The grid resolution and time step are 

effective to capture any unsteady feature, but there is none observed. The unsteadiness mainly comes 

from the fan/inlet interaction. This interaction has become insignificant at the entrance of the nacelle, 

where the pressure oscillation is only 0.04 psi, peak-to-peak. 

6.4.3 UNSTEADY FLOW AT FAN FACE

The unsteadiness at fan face (location of upstream of IGV’s) is more significant. The main fan rotors 

receive relatively clean flow, with only three strut wakes. And thanks to the wing shielding effect, the fan 

is not adversely affected by the 8.1-deg aircraft angle-of-attack at take-off. Compared to the steady-state 

flow in Figure 103, the aux door shear layer now shows the influence of the IGV, which is downstream of 

the cut-plane, as shown. 

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Figure 109 Mach number contours at fan face: range 0-0.54 (blue to red) 

The flow properties at the inlet/fan interface plane, which is upstream of the fan IGV, are depicted in 

Figure 110, including total pressure (absolute), static pressure, swirl, and entropy. The aux door shear 

layer, the hinge vortices, and the inlet strut wakes are clearly visible. The higher swirl distortion is limited 

by the shear layer, stay near the casing. The static pressure distribution is a result of fan rotor1 shock 

waves interacting with the IGV vanes. This interaction is seen in Figure 111, where the fan rotor-1 shock 

wave is reflected multiple times inside the IGV passage, and propagates upstream of the IGV. Higher 

levels of pressure variations are seen in the out-board portion of the IGV, this is due to the increased 

shock strength of the fan tip, intensifying the interaction. 

Figure 110 Flow properties at the AIP plane, an interface between the inlet and the fan domain, upstream 

of the fan IGV's 

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Figure 111 Static pressure on IGV surfaces, showing the fan rotor-1 shock reflecting and propagating 

upstream 

Figure 112 shows more details of the flow in the main flow path, this is taken at a location downstream of 

the interface place (AIP), as shown in Figure 110. The location is at the inlet to the IGV of the main 

stream. Total pressure, static pressure, axial velocity and swirl angles are shown. The improved 2013 inlet 

design has made this possible. When the splitter is bathed in the aux door shear layer, the fan would only 

see a thickened casing boundary layer. This benefits the fan performance and the noise generation. 

Figure 112 Flow properties of main stream, at inlet to combined IGV. 

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The circumferential swirl distortion is attenuated by the IGV’s in the fan stream. This is one of the major 

benefits of IGV’s. The swirl distortion coming to the main stream is not a large one, only ranging from -2 

to 2 degrees. See Figure 113 for a snapshot of the swirl profiles at a near casing radius. The distortion is 

clearly visible. Downstream of the IGV the per-rev type circumferential distortion is eliminated. 

Figure 113 Swirl profile (unsteady snapshot) at inlet and exit of the main stream IGV near casing 

6.5 COMPARISON OF INSTALLED CFD RESULTS WITH DECOUPLED CFD RESULTS OF PHASE II

The design improvement of the nacelle/inlet since 2012 is worth noting. Overall, the 2013 design reduces 

the interactions, improves fan performance, and reduces noise level as well. 

The aux door location change has resulted in a more out-board location of the loss core associated with 

the shear layer. The more out-board location aligns with the splitter of the fan domain very well, so that 

the splitter is bathed in the shear layer. See Figure 114 for total pressure profile (radial) comparison. This 

alignment mitigates the risk of a stronger shear layer (stronger due to the shear layer being less mixed out 

than the 2012 design) being ingested by the fan. The 2012 design has a more FWD aux door location, 

which results in a mis-alignment of shear layer with splitter, but it has a better mixed state when flow 

enters the fan. It also has a thicker casing boundary layer. The centerbody profile is also improved, and 

this improvement has reduced total pressure loss in the hub boundary layer. 

At the main stream rotor-1 inlet, the radial profiles of the absolute total pressures are compared in Figure 

115, also compared are the Mach number profiles. In the 2012 design, the loss core is ingested by the fan 

rotor-1, which would generate additional loss. Also, due to the tip section of the fan rotor receiving a 

more energized flow (higher PTA of 7% immersion), the fan tip would have a strong shock compared to 

the 2014 results. The main stream fan rotor does not have tip clearance, and this also contributes to higher 

fan loading. The outcome of this flow difference could explain why the 2014 design, though coupled with 

the aircraft, actually has a lower fan tone noise level at AIP. Added to this phenomenon, the main stream 

IGV wakes with the new design are shallower than the wakes from the 2012 design, and this further 

reduces the IGV wake and fan rotor interaction, hence reducing noise, see Figure 116. 

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Figure 114 Radial location of the aux door shear layer 

Figure 115 Radial profiles of total pressure and Mach number at main stream rotor-1 inlet 

Figure 116 Main stream IGV wake comparison: Mach number profile in circumferential direction near 

main stream rotor tip (R=18.6in). Blue is new design (2013), Red is old design (2012) 

6.6 SUMMARY OF INSTALLED CFD RESULTS

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a) Both macro and microscopic flow features are thoroughly examined in the interest of various level 

of interactions; 

b) Based on the conditions we chose, there was no unsteady flow observed in the external, aircraft 

domain; 

c) Aircraft wing provides a significant shielding effect to the engine at high angle-of-attack 

conditions; the engine only receives mild level of per-rev-type distortion; 

d) The circumferential distortions are effectively attenuated by the IGV’s in the fan domain;

e) Inlet design change has led to better coupling of the aircraft and the engine, reducing the 

interactions of the inlet and the fan components. 

7 ACOUSTICS ANALYSES AND FAN NOISE ESTIMATES

7.1 ACOUSTICS ANALYSES 

7.1.1 INTRODUCTION

Computed results from the unsteady fan CFD analysis (Section 4) were used to assess the acoustic 

impact on the fan noise that can radiate out the inlet and the cowl doors (when open). This assessment 

provides an initial look at the influence of fan inflow distortion on fan noise. In the sections below, we 

will briefly overview the analytical methodology used in performing this assessment, and describe the 

results obtained. First, the effect of the inlet strut wakes on the fan noise is assessed (Section 7.1.3) 

followed by the impact of installation (Section 7.1.4). The objective of this study is to qualitatively 

determine whether strut-fan interaction or fan inflow distortion noise potentially dominates over the other 

components of fan noise (IGV-Fan and rotor-rotor interaction) so as to prioritize subsequent efforts in 

designing a low-noise fan for this supersonic aircraft configuration. 

Note that this assessment does not include broadband noise sources which may be evaluated at a 

future date. Propagation of fan tones upstream through the inlet and cowl doors is described in Section 

7.2. Finally we will provide recommendations for engine noise reduction technologies of greatest 

potential interest for this program as well as follow-on studies that could be undertaken to reduce the 

analytical uncertainties in the current assessment prior to testing a technology demonstrator. 

7.1.2 METHODOLOGY

The analytical approach follows that employed during Phase I of this program. The full unsteady 

RANS CFD solution is taken at the Inlet–Fan interface station and harmonics are extracted by temporal 

Fourier Transforms evaluated at fan blade passing frequencies (R1, R2) as well as R1-R2 interaction tone 

frequencies. The spatial distribution of acoustic energy is then decomposed into circumferential Fourier 

modes and projected onto an array of propagating duct modes, as described by the equation below 

This allows us to effectively filter out the hydrodynamic and evanescent energy and integrate only the 

propagating acoustic power in the duct to provide a relevant assessment of the observable fan noise. This 

assessment assumes the inlet duct is effectively unchanging in annular geometry and extends far upstream 

 = 1 1 1 2 2 2

 = 1 1 − 2 2

 ′( , ) = ( , ) 

 ( − )

 , 2, 

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such that mode cutoff ratios remain unchanged. It also assumes that the flow in the duct is uniform, 

which is a reasonable approximation at the interface plane. In reality, some evanescent energy will cut-on 

and the inlet struts will further scatter and reflect a portion of the acoustic energy before the fan noise can 

radiate to the far-field. However, we expect the first-order effects in comparing different fan 

configurations is suitably represented by comparing trends of in-duct acoustic power levels in this fashion 

prior to performing a full analytical propagation to the far-field. 

7.1.3 RESULTS – UNINSTALLED FAN NOISE

In Figure 117, an instantaneous view of the pressure at the Inlet—Fan interface station is shown for 

both the clean and distorted inlet configurations. The influence of the 3 non-equally spaced inlet struts is 

clearly visible, and the peak pressure fluctuation (spatially) at lines of constant radius is noticeably higher 

for the distorted case. 

Figure 117: Instantaneous pressure at interface station. (a) Clean inlet; (b) Distorted inlet with struts and 

open cowl. 

Performing the acoustic mode decomposition analyses outlined in Section 7.1.2, the in-duct tone 

PWL for upstream propagating acoustic waves are calculated for each fan tone frequency of interest. 

Figure 118 shows the total upstream propagating, cut-on in-duct PWL for all interaction tones 

corresponding to each blade harmonic index. In Figure 118, the nomenclature is iFjA for the ith R1 and jth

R2 harmonic, respectively, and this is adopted through the entire section. Also, for all the acoustics results 

to be presented below, the term “Clean” actually corresponds to the “very-clean” inflow condition 

described in Section 4.2, the “Circ” configuration has a radial profile distortion due to the 

circumferentially uniform shear layer created by the opening of the auxiliary door (the “clean” CFD 

condition described in Section 4.2). The “Distorted” configuration refers to the circumferentially nonuniform conditions prescribed by the strut wakes. 

Three observations can be drawn from Figure 118 as follows: 

a b

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1. First, the upstream radiated tone noise increase due to the shear layer generated by opening the 

auxiliary door is minimal (at least in a tone OAPWL sense). The effect of the aux door opening is 

however, significant for acoustic radiation to the far-field as will be shown later. However, at this 

stage, we are merely referring to the interaction noise caused by the shear layer impinging on the 

fan. This noise increase is expected to be minimal since the opening of the door creates a 

circumferentially uniform shear layer (at least in the CFD model) that does not contribute to noise 

increase. Any noise increase can only be attributed to radial loading profile changes on the 

fan/IGV brought about by the total pressure distortion of the ingested shear layer. 

2. Second, the inclusion of strut wakes increases noise as expected, but only by a small amount (1.1 

dB). Thus the dominant sources of fan noise lie in the fan itself (e.g. fan loading, IGV-R1 

interaction etc.) rather than installation or distortions upstream. 

3. Thirdly, the R1 blade passing tone and it’s interactions with R2 dominate the tone noise spectrum. 

While the CFD temporal resolution is fine enough to resolve up to 5 times the blade passing 

frequency of R1, spatial resolution is sufficient to only reasonably resolve the 1F0A tone (20 

points per wave axially). Higher harmonics are under-resolved. Thus the large excursion of the 

1F0A tone in Figure 118 can partly be attributed to numerical dissipation of higher frequency 

tones. 

Because of the dominance of the 1F0A tone, the OAPWL for all fan tones is negligibly impacted by 

the inclusion of higher frequencies in this CFD/acoustic assessment. 

Figure 118: R1-R2 interaction tone in-duct PWL summed over R2 harmonic index. 

The effect of the strut wakes on fan noise can be cross-checked simply by comparing the strengths of 

the strut and IGV wakes as shown in Figure 119 where the strut wakes just upstream of the IGV are 

compared to the IGV wakes just upstream of R1. The IGV wakes are clearly deeper but the strut wakes 

are wider. An estimate of the total unsteady vortical energy hitting the fan can be derived as shown 

below: 

5db

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 Thus, the IGV wakes dominate the vorticity ingested by the fan and any noise increase due to 

addition of IGV wakes is small as shown in Figure 118. 

Figure 119: Comparison of strut and IGV wakes near R1. 

A breakdown of the interaction tone power levels at the first two R1 frequencies is shown in Figure 

120. The 1F0A tone dominates over all others including other 1F interaction tones. As mentioned 

previously, part of the reason for this dominance is insufficient numerical resolution of higher 

frequencies. 

Figure 120: R1-R2 interaction tone in-duct PWL. 

D(wake energy) ≈ 20log10[(0.1*.05)/(0.036*.25)] = -5.1 dB

D(blade count) = 20log10 (17/3) = +15.1 dB

D(source power) = D(wake energy) + D(blade count) = 10 dB in favor of IGV

5db

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However, a closer examination of unsteady pressures at the IGV-R1 interface and just upstream of the 

IGV shows that the 1F0A tone is significantly louder than the interaction tones even near the 

turbomachinery, Figure 121. The unsteady pressures in this case are not acoustic, however they are an 

indication of acoustic levels of the various tones. It is possible that the transonic portion of R1 acts as a 

significant source of transmission loss to acoustics propagating forward from R2.

Figure 121: SPL for fan tones near R1. 

Finally, the modal contributions to the 1F0A tone are shown in Figure 122. The dominant tones are 

m=28 and m=-23 corresponding to the R1 blade passing tone scattered by the IGVs. For the case without 

circumferential distortion, all the azimuthal modes shown (in red) correspond to the Tyler-Sofrin theory 

for scattering of the blade passing tone by the IGVs. For the case with distortion, because of the nonuniform spacing of the struts, additional modes are observed in the decomposition. The increase in noise 

at the distorted condition is seen across all modes implying a change in the source rather than 

propagation. However, the increase is small and is consistent with Figure 118. 

Figure 122: Azimuthal mode content for 1F0A tone. 

From Figure 122, only the m=28 and m=-23 modes at the 1F0A frequency need be considered for 

propagation to the far-field. 

5db

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7.1.4 RESULTS – INSTALLED FAN NOISE

The acoustic assessment of fan tones at the interface plane is identical for the installed calculations. 

Due to better matching of the inlet and fan designs, (see Section 0), the installed configuration is expected 

to be quieter. This is confirmed in Figure 123 and Figure 124 where the reduction in the 1F0A tone at the 

Inlet-Fan interface plane is 2.2 dB. 

Figure 123: R1-R2 interaction tone in-duct PWL summed over R2 harmonic index for installed 

configuration. 

Figure 124: R1-R2 interaction tone in-duct PWL for installed configuration. 

5db

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7.2 FAN NOISE TRANSMISSION THROUGH INLET

7.2.1 KEY FEATURES OF INLET GEOMETRY FOR ACOUSTIC TRANSMISSION

With the fan face acoustics quantified via unsteady CFD simulations, it is of interest to assess fan 

noise propagation upstream and scattering by the inlet struts. Two configurations, one with the struts, and 

one without, but both with the auxiliary door open are assessed to quantify the scattering effect of the 

struts. The aux door is open in both cases because it is expected that this condition would be noisier. 

Figure 125 shows a schematic of how the fan noise transmits upstream. The aux door opening has an 

area that is slightly more than 50% of the inlet area upstream, hence it is critical to model noise radiation 

through the door opening. The other feature of the inlet transmission problem is that the struts are nonidentical and non-axisymmetric (Figure 126). This ideally requires a full annulus model to accurately 

capture the scattering into low-order azimuthal modes that could propagate more efficiently through the 

inlet duct. However, because the grid requirements for propagation are rather severe and the struts are 

only slightly non-uniform, a single blade passage model assuming identical struts was ultimately used. 

This would slightly degrade the accuracy, however, the first order effect of strut scattering should be 

captured. 

Figure 125: Schematic of fan noise upstream transmission. 

Transmitted 

through inlet

Escapes 

via aux door

Noise

from fan

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Figure 126: Inlet Struts (aft looking forward). 
7.2.2 CAA MODEL AND BOUNDARY CONDITIONS

Because the aux door is open, it is necessary to model the external flow around the nacelle to 

obtain the correct entrainment into the inlet. The geometry model for noise transmission analysis has been 

derived from the inlet part of the installed CFD model described in Section 0. The model consists of 

inlet/nacelle. Geometry updates related to details around strut hinges are as described in Section 6.2.3. 

The only addition relative to the model used for installed CFD is the presence of a hub surface at the 

extension of the inlet center body. This feature is used to preclude potential numerical difficulties with the 

CAA model at the axis of rotation. The radius of the surface has been set at 0.1 inch and the surface is 

modeled as an inviscid wall. The model used is illustrated in Figure 127. 

Figure 127: CAD model for noise transmission analysis. 

The domain exit in the core region is set at the same aerodynamic interface plane described in 

Section 6.2. Compared to the installed CFD analysis, the model does not include the pylon, wing and 

fuselage. The far-field domain boundary has been set per standard GE aero acoustics practice with the 

inlet placed far upstream. A second exit (in the external flow) is placed at a convenient location just far 

enough downstream of the auxiliary door. Two different inlet models have been generated for the purpose 

of this calculation: nacelle only model without struts and corresponding hinges, and model including the 

struts. The no-strut model has been modeled as a 200 periodic sector while the strut model has been 

modeled as a 1200 periodic sector. The domain boundaries and CFD domains for both strut and nacelle 

only model are shown in Figure 128. 

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Figure 128: Noise transmission model: domain boundaries for no-strut and strut domains. 

A schematic of the boundary conditions used is shown in Figure 129. A far-field boundary condition 

similar to those used in past GE studies on propellers and open rotors is employed at radial boundary with 

the static pressure held at ambient. This boundary is sloped radially outward to ensure unambiguous 

inflow. Far upstream, the inflow is purely axial at the flight Mach number of 0.3 with static flow variables 

at standard day conditions. The artificial inviscid hub surface upstream of the inlet is not expected to 

significantly alter the acoustic behavior of the inlet (since acoustic energy is concentrated near the casing) 

or the steady flow characteristics with axial inflow. On the external flow exit, ambient static pressure is 

imposed with a steady non-reflecting boundary condition. On the internal exit (at the Inlet-Fan interface 

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plane, also referred to sometimes as “fan-face” in this report), the radial static pressure profile from the 

2012 de-coupled CFD analysis is enforced to entrain the correct mass flow. 

Figure 129: Domain for CAA analysis. 

The CAA propagation is performed using a linearized Navier-Stokes solver for turbomachinery 

acoustics. First a steady flow analysis of the inlet with the aux door open is performed. This forms the 

base flow about which the linearized acoustic calculation is performed. Then, the perturbation variables 

extracted from the fan face acoustic processing (Section 7.1.3) are imposed via a 3-D non-reflecting 

boundary condition that allows upstream propagating acoustic modes to travel into the inlet domain while 

minimizing numerical reflections. The acoustic solver has been validated previously for ducted as well as 

unducted fan tone noise predictions [12, 13]. A simplified 1-D non-reflecting boundary condition is used 

at the exterior inlet whereas a far-field boundary condition is used at the radial boundary. At the exterior 

exit, the 3-D non-reflecting boundary condition is again used to maintain consistency with the interior exit 

boundary. While the 1-D boundary conditions can generate numerical reflections when acoustic waves 

are incident, they are located sufficiently far away from the sources near the nacelle. Furthermore, the 

grid is coarsened rapidly away from the nacelle as shown in Section 7.2.3, dissipating any acoustic waves 

propagating towards the boundary and reflecting back towards the sources. 

Acoustic propagation to the far-field is handled by sampling the near-field sources on a surface 

enclosing the nacelle and propagating using the Ffowcs-Williams and Hawkings equation. This process 

will be described in detail in Section 7.3.1. 

7.2.3 MESH DETAILS

Multi-block structured meshes have been generated for both nacelle-only and strut models. The CFD 

mesh has same block topology as the nacelle region of the installed mesh described in section 6.2.3. Care 

has been takes for proper boundary layer resolution of all wall surfaces, as well proper nacelle lip and 

strut leading and trailing edge resolution per GE compressor aero design practice. For the purposes of 

acoustic sensitivity studies, several acoustic–level refined meshes have been generated by varying mesh 

resolution in axial, radial (span-wise) and circumferential direction. Mesh resolution details for each level 

Inlet

M=0.3

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of refinement for both models are shown in Figure 130. Details of the mesh cross section for no-strut and 

strut models are shown in Figure 131 and Figure 132 respectively. 

Nacelle Only Model (20 degree sector)

Refinement level Axial (nacelle-core exit) Radial Circumferential Size (million cells)

Base 1300 128 48 35

Refinement 1 1300 272 48 47

Refinement 2 1800 272 48 62

Nacelle and Strut Only Model (120 degree sector)

Refinement level Axial (nacelle-core exit) Radial Circumferential Size (million cells)

Base 800 100 184 64

Refinement 1 1026 192 232 129

Refinement 2 1400 216 272 195

Figure 130: Mesh resolution data for nacelle only and nacelle-strut model. 

Accurate propagation of high frequency acoustic waves over long distances is a challenge 

numerically, particularly for standard turbomachinery solvers like the one used in this analysis. A key 

factor that affects accuracy is how well the acoustic waves are resolved. The mesh resolution was chosen 

so as to satisfy a minimum requirement of 20 points per wave in the axial and circumferential directions 

for a nominal axial wavelength of 1.6”, and nominal circumferential mode order of 28 for the 1F0A tone. 

Even so, perceptible changes in the far-field power were observed with grid refinement. Thus, 

quantitative comparisons of strut scattering must be performed only at consistent grid resolution. 

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Figure 131: Cross-section of the CFD mesh for nacelle only model. 

Figure 132: Strut and hinge details of the nacelle – strut noise transmission model. 

7.2.4 STEADY FLOW

The steady CFD is performed at the conditions listed in Section 7.2.2. Figure 133 shows a crosssection of the flow Mach numbers for a coarse mesh check-out run. Entrainment of mass flow through the 

auxiliary door increases the Mach number at the fan face. A lip separation is also observed due to the low 

incidence sensitivity of the sharp lip designed for supersonic cruise conditions. Fortunately even though 

the predicted bubble is long, it did not destabilize the linearized acoustic propagation calculations for this 

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geometry. But note that this lip separation was substantially smaller in the installed analysis, hence it is 

not expected to be a significant acoustic source or destabilizing effect in reality either. 

Figure 133: Inlet takeoff Mach number distribution (no-struts). 

 

Figure 134 shows the circumferentially averaged Mach number profiles as a function of span at 

the Inlet-Fan interface plane. Since the static pressure this plane is the same as the decoupled 2012 CFD 

analysis, the Mach number profiles should be identical. The differences at the hub and case are likely due 

to the boundary layer interference losses that are absent in the no-strut simulation shown. Overall, the 

agreement is acceptable and the steady flow in the inlet and exterior is consistent with the decoupled fan 

analysis from which acoustic signals are extracted for the propagation calculation. 

Fan 

face

Lip separation

Inlet takeoff Mach number distribution NASA/CR—2015-218719 311

Figure 134: Fan face circumferentially averaged flow profiles. 
7.2.5 CAA ANALYSIS

After the steady flow analyses are complete (with and without struts), the fan face perturbation 

variables corresponding to the dominant acoustic modes for the 1F0A tone (Section 7.1.3) with and 

without struts are extracted to be input to the CAA calculation. Only the m=28 and m=-23 azimuthal 

modes are considered because of their dominance. Note that the perturbations can contain both 

propagating and evanescent acoustic energy although only the upstream propagating part is reported in 

Section 7.1.3. 

7.2.5.1 TRANSMISSION WITHOUT STRUTS

Figure 135 and Figure 136 show the real part of the unsteady pressure corresponding to the m=28 and 

m=-23 acoustic modes at the 1F0A frequency from the CAA analysis. Red and blue contours indicate 

positive and negative unsteady pressures respectively with the distance between two red or blue contours 

being the wavelength in the direction of propagation. The need for high grid resolution is apparent in the 

number of wavelengths an acoustic wave must travel from the fan face before it reaches the inlet lip. 

Several interesting features can be observed in the analysis. Both figures show that the most intense 

acoustic radiation emanates from the auxiliary door at approximately 60 ̊-70 ̊ from the inlet axis, 

validating the decision to model the auxiliary door. The direction of the dominant beam depends on 

diffraction around the side AB of the door opening as shown in Figure 137, refraction by the mean flow 

entrained into the aux door and reflections from the forward end of the door opening. For m=-23, the 

radiation issuing from the auxiliary door bifurcates into two beams whereas the radiation through the inlet 

is more axial compared to m=28. This difference in behavior between m=28 and m=-23 could be related 

to the azimuthal and radial mode content of the two modes drawing from acoustic radiation from typical 

commercial fan inlets [14].

Finally, it is worth noting that the acoustic field is azimuthally uniform due to absence of struts. 

NASA/CR—2015-218719 312

Figure 135: Real part of unsteady pressures for m=28 mode (1F0A tone). 
Figure 136: Real part of unsteady pressures m=-23 mode for 1F0A tone 

(same contour levels as Figure 135).

Figure 137: Real part of unsteady pressures for m=28 mode (1F0A tone), with detail around auxiliary 

door opening. 

7.2.5.2 TRANSMISSION WITH STRUTS

For the geometry with struts, the dominant modes are again m=28 and m=-23 for the 1F0A tone 

(Section 7.1.3). Since there is only a small axial overlap between the struts and the auxiliary door, the 

presence of the struts is not expected to qualitatively change the physics observed in the previous analysis 

without struts. However, the amount of energy scattered into the duct can change as can the fraction 

radiating through the aux door. 

Figure 138 shows the unsteady pressure contours at a plane mid-way between two struts for the m=28 

mode (only). Recall that the struts were made uniform, affording a single passage simulation at a 

manageable mesh size (195 MM elements, Figure 130). This picture is qualitatively similar to Figure 135

except that there is less structure near the inlet lip (possibly due to superposition of multiple scattered 

A

B

NASA/CR—2015-218719 313
azimuthal modes). However, there is significant variation in the azimuthal direction due to scattering by 

the struts as shown in Figure 139. There are acoustically active spots, followed by zones of silence. Since 

the far-field pattern is non-uniform, noise deltas due to strut scattering will be computed by averaging the 

far-field power circumferentially. 

Figure 138: Real part of unsteady pressures for m=28 mode (1F0A tone) with struts. Cutting plane 

location is mid-way between struts. 

Figure 139: Real part of unsteady pressures for m=28 mode (1F0A tone) with struts. Cutting plane 

location is over aux door, aft looking forward. 

m=28

Strut hinge

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7.3 NOISE PROPAGATION TO FAR-FIELD

7.3.1 PROCEDURE

To project the solution to the far-field, a permeable formulation of the Ffowcs-Williams and 

Hawkings (FWH) equation is used. The amplitude and phase of the unsteady acoustic pressure is 

extracted on the cylindrical surface surrounding engine inlet shown in Figure 135. The extracted solution 

is replicated with the appropriate phasing over the full annulus, and decomposed into a Fourier series for 

different azimuthal modes to take into account the scattering by the struts when applicable. Given the 

frequency of the mode, the time history of the unsteady pressure, velocities, and density is built on the 

cylindrical surface. This is then projected to the far-field using the FWH formula by integration over the 

cylindrical surface for a specified frequency, ω, and a set of observer locations: 

A frequency domain formulation of the FWH equation is used here and is derived in [12]. In the 

above formula, subscript zero denotes the mean flow, Mr

is the projection of the mean flow Mach number 

in the direction of the observer, and the hat denotes the time Fourier time transform. This formulation has 

been validated for canonical cases and used extensively for open rotor tone noise prediction within GE 

[12].

A sensitivity study has been performed on the length of the time sample, time sampling rate, and 

spatial resolution of the sampled solution on the cylindrical surface and the position of the FWH surfaces. 

It was found that the solution converges and becomes insensitive to the choice of the parameters for the 

sampling rate of 10 points per time period of the acoustic wave, and time history of 10 periods. Spatial 

sampling should be done on a grid fine enough to allow at least seven points per acoustic wave length 

both axial and azimuthal directions. The effect of FWH surface location is described in Section 7.3.2.1. 

7.3.2 FAR-FIELD RESULTS

A sideline array of observers at a 450 meter distance was used to obtain the far-field SPLs. The 

far-field observers are located starting at 20 ̊ from the inlet axis and ending at 160 ̊. Between 30 ̊ and 

150 ̊, the observer locations are spaced every 5 ̊. 

7.3.2.1 FWH SENSITIVITIES

It is found that the far-field PWL is particularly sensitive to grid resolution and FWH sampling 

locations. Figure 140 shows the far-field PWL for the m=28 mode (no struts, base grid) as a function of 

where the acoustic sources are sampled (Figure 135). In Figure 140, increasing surface number 

corresponds to increasing volume of sources sampled and each surface is offset by 1” (~60% of the 1F0A 

tone acoustic wavelength) radially and axially. It is interesting to note that the far-field PWL displays an 

oscillatory behavior. It is possible that this happens because of phase interference effects due to a large 

number of wavelengths being sampled especially on the radial surface. Notice also that the amplitude of 

NASA/CR—2015-218719 315
the PWL oscillation is decreasing and the far-field PWL appears to be converging to a constant value with 

sampling location. 

 In order to obtain a more robust estimate of the far-field PWL that is not subject to such 

numerical uncertainties, the far-field power levels are averaged successively over increasing number of 

surfaces (the red line in Figure 140). This value does not oscillate much (less than 0.5 dB) over the entire 

range of surfaces averaged. While the highest value of this averaged line (most acoustic power) 

corresponding to averaging over 3 surfaces is used for comparison purposes to the configuration with 

struts, this highlights the level of uncertainty in the assessment. 

Figure 140: Far-field PWL for m=28 mode (1F0A tone) without struts for baseline grid. 

Sensitivity to grid is shown in Figure 141. Here, the complex pressures corresponding to m=-23 and 

m=28 modes are added in the far-field prior to averaging over 3 FWH surfaces. There is a 1.7 dB noise 

increase by going to a more refined grid and a further increase of 0.9 dB with additional grid refinement 

(Figure 130). Obviously, more grid is required to achieve grid convergence, however, matching this 

resolution on the strut model is already challenging. Since our objective is to quantify the scattering effect 

of the struts, further mesh refinement was not attempted. 

Thus, comparison of the strut scattering effects is accurate only when the underlying mesh resolution 

is identical. However, as Figure 130 shows, a direct comparison is not possible. The closest match is to 

compare the finest grid on the struts (Refinement level 2) with an average of the base and the first level 

refinement on the no-strut geometry as quantified by the resolutions in the axial, circumferential and 

radial directions within the nacelle. 

Figure 141: Grid sensitivity of far-field PWL for 1F0A tone for no-strut CAA. 

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For the case with struts, the far-field SPL is azimuthally varying due to strut scattering. Hence the farfield power is averaged azimuthally for each FWH surface location. Contrary to the oscillatory behavior 

observed in Figure 140 (no struts), the far-field PWL is monotonically reducing with increasing surface 

number (Figure 142). In Figure 142, both m=28 and m=-23 modes are added with appropriate phase. 

The fact that the curve is monotonically decreasing is an indication that we are losing acoustic power due 

to grid dissipation with increasing FWH surface distance for the strut mesh. Thus, the far-field results for 

the struts use the predictions from the FWH surface closest to the geometry. Unlike the no-strut case, 

averaging to reduce phase interference errors is not needed because an azimuthal average is inherently 

performed. 

Figure 142: Far-field PWL for 1F0A tone with finest strut mesh. 

To summarize, the following process is used to arrive at the far field SPL/PWL: 

1. Add m=-23 and m=28 mode far-field complex pressures at each observer location to account 

for interference effects. 

2. For the strut scattering case, average resulting far-field power over the azimuth on the closest 

FWH sampling surface. In the case without struts, no such averaging is required. Rather, an 

average is performed over three FWH surfaces to smooth spatial jitters in the far-field 

directivity due to FWH surface sampling sensitivities in the source field. 

3. For the no-strut case, a further average over the baseline and first level refined grids is 

performed to match the grid resolution of the finest strut mesh. 

7.3.2.2 EFFECT OF STRUT SCATTERING

In both the strut and no-strut cases, we report only the highest far-field PWL obtained using the above 

process (Figure 143). Strut scattering actually lowers far-field acoustic power by 1.3 dB. This is a little 

counter-intuitive because normally one would expect scattering to conserve power or potentially increase 

it due to more efficient radiation caused by generation of lower order azimuthal modes. However, the 

struts do add more shear layers to the flow, and it is possible that there is some dissipation of acoustic 

energy. In any case, the reduction in power is only slight and is well within the uncertainties of the 

assessment. 

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Figure 143: Far-field PWL due to strut scattering (1F0A tone). 

Figure 144 shows the far-field directivity for the two geometries. The peak near 60 ̊-70 ̊ is the strong 

beam radiating through the auxiliary door. Without struts, the radiation drops steeply away from the peak 

particularly towards the inlet. With struts, the directivity is flatter, and more energy beams through the 

inlet axis. As a consequence, even though the PWL for the strut case is lower, the flatter directivity could 

cause the EPNL levels due to strut scattering to be higher.

Figure 144: Far-field SPL as a function of inlet angle. 

7.3.2.3 ACOUSTIC POWER SPLIT BETWEEN AUX DOOR AND INLET 

Since the dominant beam of radiation issues through the aux door, it is of interest to assess the effect 

of closing the door on the far-field noise. In reality, closing the door would reduce the mass flow to the 

fan, and the steady flow inside the inlet would change. The resulting effect on noise however, is expected 

to be beneficial. An accurate assessment of the flow field and acoustic change would entail a new 

NASA/CR—2015-218719 318
CFD/CAA calculation. However, the effect of noise radiating out through the inlet only can be 

qualitatively assessed by repeating the FWH propagation by neglecting the sources on the radial FWH 

surface and the axially aft surface. Such a calculation shows that the power radiating through the inlet is 

46% of the total power, i.e. almost equal power radiates through the aux door as well as the inlet. 

We previously mentioned that the area of the aux door opening is approximately 50% of the inlet 

area. Therefore the intensity of sound escaping through the aux door is twice that emanating from the 

inlet. In addition, this noise radiates almost normal to the flight direction. Thus, this is the portion of the 

inlet geometry that likely contributes the most to EPNL. Hence, any efforts on noise reduction in the inlet 

should first focus on attenuating acoustics upstream of the aux door by say acoustic liners, and then 

adding treatment further upstream. 

7.4 CONCLUSIONS

Results from fully-coupled unsteady RANS-based CFD predictions were post-processed to assess fan 

tone noise for a counter-rotating fan configuration subject to inflow distorted by the presence of inlet 

struts and a translatable inlet cowl to entrain exterior flow when opened. The analysis shows that the 

upstream radiated noise is governed by the fan system sources rather than interaction with the inlet struts. 

Improving the inlet design and installation under the wing allows for better matching between the inlet 

and the fan, reducing the noise generated. 

The noise generated by the fan in the un-installed scenario was propagated upstream through the 

inlet and to the far-field using a linearized tone CAA analysis. Results show that a little more than 50% of 

the acoustic power radiates through the aux door at an angle of approximately 60 ̊-70 ̊ from the inlet axis. 

The inlet struts merely direct more energy through the inlet resulting in a flatter directivity. While the tone 

PWL is reduced slightly (within the fidelity of the numerical assessment), the EPNL change due to strut 

scattering is likely smaller than the observed far-field PWL reduction (possibly even a penalty to system 

noise) on account of the more uniform directivity. Application of acoustic treatment in the inlet duct and 

axial positioning of the aux door is thus likely to have significant impact on noise transmission. 

It is important to note that these predictions were obtained by extending a standard tone noise 

analysis process for ducted and unducted turbomachinery to a challenging problem of high frequency 

acoustic propagation over long distances. Thus it is critical to thoroughly validate the results of this 

analysis to improve confidence in the ability of the predictions to guide low noise fan design both in 

terms of source reduction and transmission. In addition, the following need to be addressed to obtain a 

more accurate picture of installed fan noise levels: 

 Prediction of broadband noise levels generated by the fan leveraging current GE broadband noise 

prediction tools for fan-OGV interaction. 

 Tone scattering due to installation effects (which would be an extension of the current analysis to 

include the installed geometry). 

Finally, the results of this analysis suggest that low noise fan design efforts for this application should 

be pursued along the following directions:

 Fan inlet radial profile tailoring to reduce tip shock strengths. 

 Designing the IGV to increase rotor noise transmission loss and reduce IGV wake strengths. 

 Application of acoustic treatment in the inlet duct and nacelle aft of cowl door opening in 

conjunction with axial re-positioning of the aux door further forward, and variations in door 

opening/geometry. 

NASA/CR—2015-218719 319
8 SUMMARY

This report covers the activity in Phase II of the N+2 NRA – System Level Experimental Validations for 

Supersonic Transport Aircraft. Three main activities were included in this portion, an updated conceptual 

design of a propulsion system to meet the N+2 goals with technologies appropriate with the associated 

timeframe, a redesign of the flow lines for the inverted velocity profile nozzle with a fluid shield, and an 

assessment of the noise impact of the Lockheed Martin supersonic inlet with a multi-stage fan module. 

The proposed engine concept incorporated advanced technologies such as Cooled Cooling Air (CCA) 

with modulated turbine cooling flow, variable area bypass injector (VABI), Axi-Plug inverted velocity 

profile (IVP) exhaust with a fluid shield and chevrons, and heat addition. The maximum T4 of the engine 

was set to meet the cruise NOx requirement. 

Testing of the exhaust nozzle with the inverted velocity profile and a fluid shield around 180 deg of the 

exhaust in Phase I indicated that the primary nozzle flowpath was too divergent at lower power settings 

for quiet operation. The flowpath was redesigned to mitigate the unsteady separation seen due to the 

overexpansion. This resulted in the adoption of the dual model nozzle, where the nozzle can operated in a 

purely convergent mode as well as a convergent-divergent mode. Large Eddy Simulations of the Phase I 

reference and IVP/shield nozzle provided very accurate prediction of the farfield acoustics. LES of the 

Phase II geometry indicates a much cleaner flowfield at the cutback conditions, although it did pick a tone 

and an additional primary nozzle flowpath was fabricated and tested. 

A scale model corresponding to the improved sideline and cutback configurations was designed and 

fabricated. Scale model testing at NASA GRC shows much improved acoustic performance, with an 

elimination of the unsteady separation seen in Phase I, although some broadband shock noise like 

component is seen, likely coming off the core cowl expansion surface. 

The scale model exhaust nozzle acoustic test was performed at NASA GRC. No evidence of the unsteady 

separation was seen in the measured Phase 2 acoustic farfield acoustic data. The three stream nozzle 

resulted in lower noise levels than the reference fully mixed equivalent nozzle. The effect of the shield 

was measured in the farfield acoustics as a reduction in the higher frequency ranges. Single chevron 

designs were tested on the shield nozzle and one the portion of the primary nozzle not covered by the 

shield. No combination with the chevrons resulted in overall (EPNL) noise reduction, although 

significant low frequency benefit was measured with the chevrons. The azimuthal variation around the 

three stream nozzle was measured and showed the lowest noise was achieved with the shield aligned with 

the microphones. 

A less-expensive alternative to LES was found. This alternative approach, grid-proper DDES can indeed 

capture the flow separation in the Phase I nozzle. URANS and SAS failed to capture this significant flow 

development. Sufficient grid resolution is needed for the DDES to be able to predict this flow feature. 

Key learning of the proper grid resolution was to resolve shock wave with 6 points across shock width, 

and a wall y+ less than 40 in the shock impingement region. 

Noise correction factors were established for acoustics assessment using RANS/URANS predictions in 

which the wakes from inlet struts or IGVs were not mixed properly. The correction was based on LES 

wake decay which matched available wake decay test results.

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Analysis was performed of the Lockheed Martin supersonic inlet coupled with a multi-stage fan 

configuration. The simulations were carried out in two steps. In step 1 (Phase II), Lockheed Martin 

supplied an inlet/boundary condition with inlet auxiliary doors open, downstream of the inlet center-body 

and struts. And the decoupled calculation was performed for the fan component using the pre-determined 

inflow boundary conditions. In step 2 (Phase II extension), a new inlet design was provided by Lockheed 

Martin. The fan, nacelle, and the aircraft are coupled in the installed configuration. A take-off condition 

of Mach 0.38 and 8.1-deg angle-of-attack is simulated in the installed configuration. It is found that, due 

to the engine location, the wing provides a significantly favorable shielding effect to the engine. The large 

angle-of-attack (8.1 degrees, take-off condition) did not affect the nacelle and fan significantly. The 

under-wing cross-flow does, however, introduce a strong swirl distortion, which is ingested by the fan via 

the open aux door. The swirl distortion mainly stays within the near-casing stream, no the effect to main 

flow fan blades is minimal. The struts and IGVs in the fan domain attenuate the swirl distortion 

effectively. 

The inlet design evolution in Phase II extension has improved the aerodynamic interaction with the fan. 

The acoustics analysis is carried out for the installed CFD result. The improvement of main fan tip flow 

has resulted in reduced overall fan tone noise level. 

Results from fully-coupled unsteady RANS-based CFD predictions were post-processed to assess fan 

tone noise for a counter-rotating fan configuration subject to inflow distorted by the presence of inlet 

struts and a translatable inlet cowl to entrain exterior flow when opened. The analysis shows that the 

upstream radiated noise is governed by the fan system sources rather than interaction with the inlet struts. 

Improving the inlet design and installation under the wing allows for better matching between the inlet 

and the fan, reducing the noise generated. 

The noise generated by the fan in the un-installed scenario was propagated upstream through the inlet and 

to the far-field using a linearized tone CAA analysis. Results show that a little more than 50% of the 

acoustic power radiates through the aux door at an angle of approximately 60 ̊-70 ̊ from the inlet axis. 

The inlet struts merely direct more energy through the inlet resulting in a flatter directivity. While the tone 

PWL is reduced slightly (within the fidelity of the numerical assessment), the EPNL change due to strut 

scattering is likely smaller than the observed far-field PWL reduction (possibly even a penalty to system 

noise) on account of the more uniform directivity. Application of acoustic treatment in the inlet duct and 

axial positioning of the aux door is thus likely to have significant impact on noise transmission. 

A cumulative system noise assessment including the fan inlet radiated and jet noise components was 

made, indicating the NASA community noise goals for N+2 should be achievable with this propulsion 

system. 

9 SUGGESTED FUTURE WORK

Based on the work conducted in this program some suggested follow-on activities are included: 

 Flowfield testing of exhaust nozzle including schlieren measurements to look for shocks on the 

IVP cowl. 

 Exhaust Nozzle fine tuning of the core cowl geometry to eliminate the shocks seen. 

 More detailed fan and inlet integration studies and noise estimates 

o More detailed analysis with inlet treatment 

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o Propuslion/Airframe Integration Studies to look at noise reflection and shielding, 

depending on location 

 Wind Tunnel test with the supersonic inlet concept with auxiliary doors to validate the high 

fidelity assesments 

 Fan wind tunnel acoustic test. 

10 ACKNOWLEDGEMENTS

This work was funded by the NASA N+2 Supersonic program administered by Peter Coen at the NASA 

Langley Research Center under prime contract (Lockheed Martin) NNC10CA02C. The authors 

recognize numerous individuals from both GE Global Research and GE Aviation, and Lockheed Martin 

for their technical guidance. 

11 REFERENCES

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vol.17, p307-314, 1994.

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dual flow nozzle,” AIAA 2011 -2881

8. J. Yao, P. Cargill, D. G. Holmes, S. Gorrell, “Aspects of Numerical Analysis for Unsteady Flows 

in Aircraft Engines”, Invited paper AIAA-2010-1603, 48th AIAA Aerospace Sciences Meeting 

Including the New Horizons Forum and Aerospace Exposition, 2010

9. J. Yao, S. Gorrell, A. Wadia, “High-Fidelity Numerical Analysis of Per-Rev-Type Inlet 

Distortion Transfer in Multistage Fans - Part II: Entire Component Simulation and 

Investigation”, ASME Turbo Expo 2008: Power for Land, Sea and Air, June 2008, Berlin, 

Germany, GT2008-50813, Also published on J. of Turbomachinery, Vol 132, No 4, Oct. 2010

10. J. Yao, S. Gorrell, A. Wadia, “High-Fidelity Numerical Analysis of Per-Rev-Type Inlet 

Distortion Transfer in Multistage Fans - Part I: Simulations with Selected Blade Rows”, ASME 

Turbo Expo 2008: Power for Land, Sea and Air, June 2008, Berlin, Germany, GT2008-50812, 

Also published on J. of Turbomachinery, Vol 132, No 4, Oct 2010

11. J. Yao, S. Gorrell, “A Time-Accurate CFD Analysis of Inlet Distortion Induced Swirl in 

Multistage Fans”, 43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, AIAA 

Paper 2007-5059.

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12. Sharma, A., and Chen, H., “Prediction of Tonal Aerodynamic Noise from Open Rotors” Journal 

of Sound and Vibration, Vol. 332, No. 16, pp. 3832–3845, 2013.

13. Sharma, A., Richards, S.K., Wood, T.H., and Shieh, C.M., “Numerical Prediction of Fan Tone 

Interaction Noise from High Bypass Aircraft Engines,” AIAA Journal, Vol. 47, No. 12, pp. 2866-

2878, 2009.

14. Tam, C.K.W., Parrish, S.A., Envia, E., and Chien, E.W., “Physical Processes Influencing 

Acoustic Radiation from Jet Engine Inlets”, Journal of Fluid Mechanics, Vol. 725, pp. 152-194, 

2013.

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predictions", AIAA Paper 2003-0767, 2003

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the DESIDER Project”, Second Symposium on Hybrid RANS-LES Methods, Corfu, Greece, 

2007

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NASA/CR—2015-218719 325

.—Development of Design Capabilities Within the

NASA/LMCO N+2 Supersonics Effort

Juan J. Alonso, Francisco Palacios, Trent Lukaczyk, and Gerald Carrier*

Stanford University

Stanford, California 94305

Abstract

This report describes the efforts of the Stanford University (SU) Team during the entire Phase II of the

Supersonic Validations Study, pursued in collaboration with the Lockheed-Martin Corporation. Contained

in this report is a description of our efforts in the development of cutting-edge, high-fidelity techniques for

the design of supersonic aircraft that must match a particular equivalent area distribution at pre-specified

lift coefficients and while retaining high aerodynamic performance. The report focuses on the

description of the complete capability including the flow analysis, grid adaptation technique, the

formulation of an adjoint solver for the equivalent area distribution (at different azimuth angles), the

optimization strategy (gradient based, and response surface modeling with Gaussian process regression)

and the practical shape design process of the supersonic aircraft proposed by LMCO. This shape design

effort is challenging due the complexity of the geometric details involved that require a full unstructured

direct and adjoint flow solver with grid adaptation capabilitiesto capture the propulsion systemeffects and

the shock and expansion waves in the near-field. It is important to highlight that the application of the

continuous adjoint methodology to the equivalent area objective function is novel, and the technique

developed in this project includes filtering methodologies to manage the gradient inaccuracies while

enhancing the response surface model. Moreover, even though in our earlier work we had relied on

LMCO’s capabilities to compute the ground sonic boom metrics, in this report we include the results of

our own computations of sonic boom loudness metrics, and we make a number of observations about the

computed sonic boom intensities.

*Visiting Research Engineer from Onera/Meudon, France.
Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 High-fidelity flow analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1 Solver description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.2 Adaptive mesh refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3 Gradient computation and design variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3.1 Gradient evaluation using control theory . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3.2 Adjoint method applied to engine simulations . . . . . . . . . . . . . . . . . . . . . . . 10

3.3 Design variable definition, and mesh deformation . . . . . . . . . . . . . . . . . . . . . 13

3.4 Managing gradient inaccuracies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 Optimization framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.1 Gradient based optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4.2 Surrogate Based Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4.3 Response surface modeling with Gaussian process regression . . . . . . . . . . . . . . . 17

4.4 Methodology for surrogate based optimization . . . . . . . . . . . . . . . . . . . . . . 20

4.5 Python package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5 Numerical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.1 Grid adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.2 Shape optimization maintaining equivalent area . . . . . . . . . . . . . . . . . . . . . . 23

5.3 Unconstrained surrogate-based optimization . . . . . . . . . . . . . . . . . . . . . . . . 27

5.4 Recovering boom performance after including structurally-motivated changes . . . . . 27

6 Sonic boom assessment of the different aircraft configuration geometries . . . . . . . . . . . . 29

6.1 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6.2 Description of the methods and tools used for ground propagation of sonic booms . . 32

6.3 Flight conditions used for the sonic boom analysis . . . . . . . . . . . . . . . . . . . . 34

6.4 Set-up and verification of the sonic boom evaluation process . . . . . . . . . . . . . . . 35

6.5 Summary of the sonic boom evaluation results for the different configurations investigated 41

7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

NASA/CR—2015-218719 326
1 Introduction

As part of the NASA Supersonics Project N+2 effort (Supersonic Validations) and in collaboration with

LMCO, SU had agreed to develop and demonstrate an adjoint-based capability for the inverse shape design

of supersonic configurations that must match a number of target equivalent area distributions at different

azimuth angles (chosen for their beneficial ground-boom properties). The long-term intent is to incorporate

such a capability into a generic multi-disciplinary analysis and optimization (MDAO) framework that, in

addition to aerodynamic performance and sonic-boom considerations, accounts for other important aspects

of the design in order to reach a balanced, realizable, low-boom aircraft. The main objective of our effort

during Phase II of this program has been to improve and test the techniques developed during Phase I of

the project. In particular, the starting point was a preliminary version of an adjoint solver that was able to

produce sensitivities (gradients) of cost functions derived from the aircraft equivalent area distributions with

respect to arbitrary numbers of design variables that parameterize the shape of the aircraft. Owing to the

properties of adjoint solvers, the entire vector of sensitivities (the gradient) can be obtained with a single flow

solution followed by a single adjoint solution, thus leading to large computational efficiencies. To analyze both

the performance and the boom loudness, high-fidelity CFD analyses were performed all the way to a nearfield location (typically between 2 and 3 body lengths beneath the aircraft) and then the pressure signature

was extracted at a number of azimuthal locations (see Fig. 1). Based on classical linearized supersonic

aerodynamics theory[1], these signatures were converted into equivalent area distributions that can then be

compared to target distributions known to have good loudness characteristics. In order to carry out design

/ optimization exercises, the shape of the aircraft is parameterized using arbitrary Free Form Deformation

(FFD) control boxes so that we are able to alter the aircraft configuration to minimize drag (while maintaining

lift and ensuring that the pitching moment is no worse than that we started with) and ensure that the aircraft

equivalent area distributions do not deviate from the target by more than a permitted amount. In contrast,

Figure 1: Schematic of CFD-based Supersonic Boom Propagation Simulations

design approaches that are based directly on the minimization of sonic boom loudness measures on the

ground can lead to difficult design spaces with multiple local minima and sharp discontinuities as was shown

by Chung et al. [2]. Additionally, equivalent area analyses for low boom have a well-established history,

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have led to design approaches for supersonic aircraft, and have been used extensively by Lockheed Martin

to shape this design and to generate the favorable pressure signature it has today. Starting with a baseline

adjoint implementation, the following features have been developed/implemented during the course of Phase

II: equivalent area design at multiple azimuthal locations, integration of the engines (and engine inflow and

exhaust) in the simulation and design process, grid adaptation during the analysis and optimization processes

to capture the engine effects, and substantial improvements in the computational infrastructure (see Fig. 2)

and design process (gradient based, and response surface modeling with Gaussian process regression). In

addition, we have used a loudness computational capability that is separate from the at LMCO in order to

better understand the differences in equivalent area distributions (and their derivatives) that can be tolerated

without significant changes in the perceived loudness of the ground boom. In particular, this report focuses

Figure 2: Overall methodology for supersonic low-boom shape optimization (gradient-based method, and

response surface modeling with Gaussian process regression)

on:

• The description of direct flow solver (including the grid adaptation methodology),

• The formulation of the adjoint solver for low-boom design (based on cost functions related to the

equivalent area distribution at different azimuthal angles),

• The description of the response surface modeling with Gaussian process regression,

• The results of various demonstration optimization runs that we have been pursuing and that leverage

all other components, and

• The calculation of loudness metrics of the various computed ground booms.

As in our Phase I report, this document does not focus on the details of the unstructured flow solver that

we are using [3], or the optimization algorithms that drive the results presented.

2 High-fidelity flow analysis

The solution of challenging multi-disciplinary problems involves the interaction between separate simulation

modules that represent different physical models and procedures. In order to avoid problems with efficiency

and integration, it can be beneficial for the modules to share a common set of numerical solution algorithms

NASA/CR—2015-218719 328
and a code architecture. Additionally, adjoint equations might need to be solved for each system, and the

entire problem may require an infrastructure for shape design (shape parameterization, grid deformation,

and optimization algorithms) to be available. In supersonic low-boom design, the ability to integrate multiple

physics, parameterize the resulting system, and design/optimize the outcome is of utmost importance and

must be accomplished with minimum effort. In this work we use the open-source solver SU2 developed

in the Aerospace Design Laboratory at Stanford University. SU2is a general purpose partial differential

equation solver equipped with tools for optimal shape design including flow and adjoint solvers, free-form

mesh deformation, goal-oriented adaptive mesh refinement, and a constrained optimization environment.

These tools are wrapped in the Python language to efficiently manage the input and output of data and the

exchange of information between the different modules in the SU2suite. In order to overcome challenges

and develop a lasting infrastructure for future efforts, the basic philosophy in the development of the SU2

framework has been to ensure:

• An open-source model: while the Aerospace Design Laboratory (ADL) at Stanford University has

provided the basic formulation with a reasonable set of initial capabilities, we would like to see contributions from the community to further enhance the capabilities of the suite, and we will ensure that

all of these developments are available to all users in the future.

• Portability: SU2 has been developed using ANSI C++ and only relies on widely-available, wellsupported, open-source software including OpenMPI, Metis, and Python. As such, SU2

is able to

run on any computing platform for which a C++ compiler is available.

• Reusability and encapsulation: SU2is built so that the main concepts (geometry, grids, solution

algorithms, numerical implementations, etc.) are abstracted to a very high-level. This abstraction

promotes reusability of the code and enables modifications without incorrectly affecting other portions

of the modules in the suite.

• Performance: we have attempted to develop numerical solution algorithms that result in high-performance

convergence of the solver in SU2. Although some level of performance is traded for reusability and

encapsulation, the loss in performance is minor.

• Gradient availability: for many applications (optimization, response surface formulations, and uncertainty quantification, among others) it is important to obtain gradients of the responses computed by

SU2to variations of, potentially, very large numbers of design parameters. For this reason, SU2relies

on adjoint solver implementations that can be used to compute the necessary gradients. In addition,

these adjoint solutions can be used to compute functional-driven mesh adaptation techniques.

Using this philosophy within SU2, we are able to develop both Finite Volume Method (FVM) or FEM solvers,

their corresponding adjoint systems, and, if needed, multi-physics solvers that can combine both approaches.

The use of a clearly-structured set of classes allows for the easy identification of the main pieces of the

code that will need to be re-implemented or enhanced for new models without interfering with the main

code. A library of numerical schemes and linear solvers reduces the development time required for a new

implementation (e.g., agglomeration multigrid, line-implicit relaxation, and goal-oriented grid adaptation are

generic capabilities provided by SU2that can be reused in many numerical simulations.) Additionally, the

solver structure and parallelization methodology are shared by all the members of the suite. It is important

to highlight that the ability to easily integrate these solvers ensures that new features or updated models

can be included without affecting the main infrastructure and with a reasonably low degree of difficulty.

2.1 Solver description

SU2is built to enable vertical integration with optimizers and to reduce the amount of user overhead required

for setup. There are five levels of components in the optimization control architecture, and most rely on

Python scripts to modify the configuration input, execute lower-level components and post-process any

resulting data. To simplify and shorten overhead time during problem setup, all levels start from a common

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configuration file, which is modified as needed when passed to lower levels. Listed in order from lowest to

highest, these levels are:

• Core tools - These contain all of the SU2 binary executables, which are the core tools of the suite.

As input, they take a custom format ASCII configuration file. For output, they write data such

as integrated forces, moments and other objectives to an iteration history file, field data to files for

plotting, or deformed, adapted, or decomposed meshes in the native format, for instance.

• Solution decomposition/recomposition - Many of the core solvers (i.e., SU2 CFD, SU2 MDC, SU2 GPC,

SU2 MAC) can operate in parallel on a partitioned mesh. The management of pre- and post-processing

tasks such as mesh decomposition and plot-file merging for the output data (and for other tasks) is handled by the ’parallel computation.py’ and ’parallel deformation.py’ Python scripts (’parallel adaptation.py’

is under development).

• Sensitivity analysis - This level manages the pre- and post-processing needed for calculating performance sensitivities with respect to user-specified design variables. Both continuous adjoint and finite

differencing approaches have been implemented, and the discrete and hybrid adjoint approaches are

under development. For the adjoint approach, both a direct and adjoint solution are computed, and the

resulting adjoint surface sensitivities must be projected into the design space during a post-processing

step. In the case of finite differencing, multiple-but-independent evaluations of the direct problem are

required before the performance sensitivities can be calculated.

• Design evaluation - For easier integration with optimization packages, SU2 has a design management

class that wraps a black box around the previous components and only takes design vectors for input.

This interprets special configuration file options for design variables which allow it to set up mesh

deformation. When it receives a design vector from the optimizer, it then executes mesh deformation,

direct solution, and sensitivity analyses as needed, and then finally returns performance data. As it

operates, it archives restart and plot data in an organized folder structure, which may be useful for

secondary analyses or debugging. Evaluations of multiple design requests can be submitted in parallel

if the resources are available, for example on a high-performance computing cluster.

• Design optimization - Single-objective design optimization is the highest level of architecture that we

have developed at the moment. Two optimization strategies have been adopted for use with SU2. The

first is gradient-based optimization using SciPy’s SLSQP optimizer, which adds complexity by requiring

separate function handles for the objective function, constraints and their sensitivities. The second is

surrogate based optimization, where an in-house gradient-enhanced Gaussian Process Regression based

optimizer is used.

The core tools of the SU2suite are the C++ modules, a brief description of each binary is presented below:

• SU2 CFD (Computational Fluid Dynamics Code) - Solves direct, adjoint (steady or unsteady) problems for the Euler, Navier-Stokes and Reynolds-Averaged Navier-Stokes (RANS), plasma, free-surface,

electrostatic, etc., equation sets. SU2 CFD can be run serially or in parallel using MPI. It uses either

the FVM or FEM approaches and an edge-based structure. Explicit and implicit time integration

methods are available with centered or upwinding spatial integration schemes. The software also has

several advanced features to improve robustness and convergence, including residual smoothing, agglomeration multigrid, linelet and low-speed preconditioning, and Krylov space methods for solving

linear systems.

• SU2 GPC (Gradient Projection Code) - Computes the partial derivative of a functional with respect

to variations in the aerodynamic surface. SU2 GPC uses the surface sensitivities computed using

SU2 CFD, the flow solution and the definition of the geometric design variables to evaluate the derivative of a particular functional (e.g. drag, lift, etc.) with respect to a given design parameter.

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• SU2 MDC (Mesh Deformation Code) - Computes the geometric deformation of surfaces within the

computational mesh and the surrounding volumetric grid. Once the type of deformation is defined,

SU2 MDC performs the grid deformation using different strategies. Three-dimensional deformations

use a method called Free Form Deformation (FFD), while two-dimensional problems typically use

bump functions, such as Hicks-Henne.

• SU2 MAC (Mesh Adaptation Code) - Performs grid adaptation using various techniques (including

goal-oriented) based on the analysis of a converged flow, adjoint or linearized solution to strategically

refine the mesh about key flow features.

• SU2 DDC (Domain Decomposition Code) - Partitions the specified volumetric grid for use with several

of the other core tools when performing simulation or design in parallel. SU2 DDC is built around the

METIS [4] software that will identify and assign nodes to each processor for achieving load balancing

with minimal communication (edge cuts). Once that information is received, SU2 DDC prepares the

communication between nodes on different partitions and generates the appropriate computational grid

partitions required by the other core tools for executing in parallel.

• SU2 PBC (Periodic Boundary Code) - Creates ghost cells in the computational domain for performing

simulations with periodic boundary conditions and outputs a new mesh containing the proper communication structure between periodic faces. This module must be run prior to SU2 CFD for any

simulation the uses such boundary conditions.

• SU2 SMC (Sliding Mesh Code) - Creates ghost cells in the computational domain for performing simulations with sliding surfaces and outputs a new multi-zone mesh containing the proper communication

structure between sliding interfaces. As in the case of SU2 PBC, this module must be run prior to

SU2 CFD.

2.2 Adaptive mesh refinement

In combination with the apriori-adapted grid, an adaptive mesh refinement procedure has been implemented

to capture the effects of the propulsion system on the near-field pressure distributions. Using this method,

an existing mesh can be dynamically modified by the code to improve the accuracy of the solution without

an excessive increase in computational effort. In particular, we are interested in the impact of the details

of the engine flows (inflow and exhaust) on the sonic boom signal. The procedure we have implemented [5]

uses anisotropic adaptation of the grid based on an edge binary system that identifies the right division for

each element. This methodology is important to maintain the coherence in the division of the common faces

between control volumes. Some of the main important characteristics of this adaptation strategy are:

• Robust adaptation. The method should allow multiple adaptation cycles, using classical two-dimensional

and three-dimensional finite volume elements (tetrahedra, hexahedra, pyramids and wedges).

• Fully automated and easy to use.

• No “hanging” nodes: the final grid should be conforming.

A method based on a flexible element division has been implemented in addition to tetrahedral and hexahedral division methods which might not give optimum results in some cases. The tetrahedral division

procedure is based on the detection of edge division patterns that are prescribed in the code. However,

the anisotropic division of tetrahedra can significantly deteriorate the quality of the mesh. This is a serious problem when directional flow-field features are present. The main challenge of hexahedral division

methodologies is the so-called refinement propagation problem (the buffer zone between an adapted element

and the non-adapted grid is greater than one cell.) This occurs when more than one set of edges in the

same hexahedron are marked for adaptation non-uniformly and “hanging” nodes are not allowed. To prevent the refinement propagation, the implementation uses a flexible element division technique [5]. Once an

NASA/CR—2015-218719 331
Figure 3: Allowed hexahedral divisions. The last division requires a new vertex in the middle of the hexahedron.

Figure 4: Allowed pyramidal divisions: a

total number of 16 combinations are possible.

edge-based structure is in place, the allowed hexahedral division is identified. Fig. 3 shows the seven divisions/combinations implemented in the code. The last division is a special division with a vertex inserted in

the middle of a marked hexahedron. The special hexahedral division into pyramids is required to connect the

adapted with the non-adapted grid. Furthermore, each pyramid will later be divided into tetrahedrons (see

Fig. 4). The error estimate of integral outputs of partial differential equations can be used as goal-oriented

grid adaptation indicators [6, 7]. These techniques produce good (and even optimal) numerical grids for the

accurate estimation of an output functional. To illustrate the main idea behind this technique, suppose that

a nonlinear function J(U) (e.g. heat flux, temperature, or pressure distributions on a body surface) is to be

evaluated, where U is the exact solution of a set of nonlinear equations R(U) = 0. Given an approximate

solution, U ̄, we define u as the error of the solution, u = U ̄ − U, and linearize both the nonlinear equation

and the functional as follows:

R(U ̄) = R(U + u) ≈

∂R

∂U u, (1)

and

J(U ̄) = J(U + u) ≈ J(U) + ∂J

∂U u. (2)

This can be re-written as Au ≈ f, where A = ∂R/∂U, f = R(U ̄), and J(U) ≈ J(U ̄)−g

T u where gT = ∂J/∂U.

If u satisfies the primal equation and v satisfies the dual or adjoint equation then AT v = g. Hence,

J(U) ≈ J(U ̄) − v

T

f ≈ J(U ̄) − v

T R(U ̄), (3)

where J(U ̄)−v

T R(U ̄) is a more accurate estimate for J(U) than J(U ̄). This computable correction vT R(U ̄)

is the sensor for our goal-oriented adaptation. This goal-oriented adaptation, and a more classical featurebased (solution gradient) grid adaptation has been used in this project to increase the accuracy of the solution

without an excessive increase in computational effort.

3 Gradient computation and design variables

In this project two optimization techniques has been used: Gradient Based Optimization (GBO), and Response Surface Modeling (RSM) with gradient-enhanced Gaussian Process Regression (GPR). Both methods

rely on the calculation of sensitivities of the objective and constraints in the design space. In the following

subsections we will examine the use of adjoint formulations to perform sensitivity analyses, and the definition

of the shape design variables.

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3.1 Gradient evaluation using control theory

The adjoint method was originally applied to aerodynamics by Jameson [8], adapting ideas originally formulated by Lions [9] on optimal control of systems governed by partial differential equations (PDEs). The

adjoint equations can be conveniently formulated in a framework to calculate the sensitivity of a given objective function J (f for the RSM problem) to parameters α in a problem governed by the set of equations which

can be represented by G(U, α) = 0, where U is the solution. The additional computational cost of solving the

adjoint problem is generally of the order of one additional flow solution, and the adjoint variables represent

the sensitivities of J to changes in all of the parameters that define the problem at every point in the domain.

There are two main ways to pose the adjoint equations. First, we often speak of a discrete adjoint method,

in which the discretized governing equations are used to derive the adjoint equations. Secondly, we often use

the continuous adjoint method, in which the adjoint equations are derived from the analytical PDEs. The

discrete and continuous approaches are found to have relative advantages and disadvantages over each other.

In theory, a discrete method can handle PDEs of arbitrary complexity without significant mathematical

development and can treat arbitrary functionals. However, this method requires the evaluation of discrete

Jacobians, which we denote as D

D

to distinguish from their continuous alternatives ∂

∂

, and there are two

main ways to do this. The first is to analytically derive these terms from the discretized forms of the flow

residuals and then develop code based on this knowledge, and the second it to use algorithmic Automatic

Differentiation, either via source code transformation[10] or operator overloading[11]. The former approach

requires significant development, more than that generally required in the continuous method[12], while the

latter is computationally expensive, with large memory storage requirements. In comparison, the continuous

adjoint requires significant theoretical development but is better connected to the underlying physics, can

be solved using a numerical method independent of the flow solution scheme, and, what is more important

form the shape design perspective, it provides a closed formula based on a surface integration to evaluate

the functional sensitivity. The objective of this section is to describe the way in which we quantify the influence of geometry modifications on either the pressure distribution on the aircraft surface or at an arbitrary

location within the domain of interest (namely the near-field, which can be used to derive the equivalent

area distribution). Conventional adjoint implementations are typically aimed at reducing a cost function

computed from the pressure distribution on the surface that is being modified [13, 14]. In our current work,

apart from the traditional functionals defined on the airplane surface (drag, lift, etc.), we will use two objective functions that depend on the near-field pressure distribution: the pressure coefficient distribution on

the near-field [15], and the equivalent area distribution calculated from this pressure distribution. The fluid

domain Ω is bounded by a disconnected boundary δΩ which is divided into a “far-field” component, Γ∞,

and a solid wall boundary, S. Ω has been further divided into two subdomains Ωi and Ωo separated by the

“near-field” boundary Γnf . Note that Γnf will remain fixed throughout the optimization process, but the

solid surface S will change as needed to meet the optimization criteria. A typical optimization problem seeks

the minimization of a certain cost function J with respect to changes in the shape of the boundary S. We

will concentrate on functionals defined as integrals over the solid surface S, and integrals over the near-field

boundary Γnf ,

J =

Z

S

~d · (P ~nS) ds +

Z

Γnf

g(x, P) ds =

Z

S

jS ds +

Z

Γnf

jnf ds, (4)

where P is the value of the pressure, ~nS is a normal vector to the solid surface S pointing outside the

domain, g(x, P) is a function that depends on the spatial coordinates and the pressure at the near-field, and

~d is an arbitrary constant vector that we will define later on. The goal is to compute the variation of the

above functional caused by arbitrary (and multiple) deformations of S. Upon an infinitesimal deformation

δS of the control surface S along the normal direction ~nS, the cost function varies due to the changes in the

solution induced by the deformation:

δJ =

Z

S

~d · δP ~nS ds +

Z

Γnf

∂g(x, P)

∂P δP ds +

Z

S

(

~d · ∇~ P)δS ds. (5)

In the last expression we note that the objective function variation depends on the linearized steady Euler flow

equations, ∇· ~ (

~δF) = 0, where F~ are the convective fluxes. And we resort to the adjoint state Ψ = (ψ1, ~φ, ψ5)

NASA/CR—2015-218719 333
to tackle the variation of the flow variables δP in the objective function:

δJ =

Z

S

(

~d · ~nS)δP ds −

Z

S

(~nS · ~φ)δP ds +

Z

Γnf

∂g(x, P)

∂P δP ds −

Z

Γnf

∆Ψ ~nnfA~



δU ds

+

Z

S



~d · ∇~ P + (∂n~v · ~nS) θ + ∇S(~v θ)



δS ds, (6)

where ∆Ψ = Ψi − Ψo is the difference between the values of Ψ above and below the near-field boundary, A~

is the jacobian matrix of the fluxes, and θ = (ρψ1 + ρ~v · ~φ + ρHψ5). Finally, the following adjoint system

should be solved

A~T· ∇~ Ψ = 0. (7)

where the boundary equations will be discussed below. Using a pressure base functional on the surface, the

vector ~d is defined as

~d =









1

C∞



(cos α cos β,sin α cos β,sin β), CD Drag coefficient,



1

C∞



(− sin α, cos α, 0), CL Lift coefficient,

(8)

where α is the angle of attack, β is the angle of sideslip, C∞ =

1

2

v

2

∞ρ∞Az, and v∞ and ρ∞ denote the

infinity values of velocity and density. Using this particular definition of ~d, the boundary condition for the

adjoint equations are:

~nS · ~φ = ~d · ~nS, on ΓS (9)

Apart from surface pressure based functionals, in this work the equivalent area distribution is used in the

optimization problems. The equivalent area distribution Ae(x, θ) at a particular azimuthal angle θ is given

by

Ae(x, θ) = Z x

0

C(P − P∞)(x − t)

1/2

dt, (10)

where C = 4

√

2βr

γP∞M2∞

, and the line integral is evaluated on a near-field line characterized by an azimuthal

angle θ. In order to pose an inverse design problem whereby we attempt to match a target equivalent area

distribution, the logical approach is to formulate the problem using a least-squares minimization formulation.

Let’s minimize the weighted sum of the square of the differences between the computed equivalent area

and the target equivalent area at N different points xi (i = 0, ..., N − 1), and different azimuthal angles

k = 0, ..., M, with weights ωik, and target equivalent areas At(xi, θk). The objective function can be written

as:

J =

X

M

k=0

N

X−1

i=0

ωik [Ae(xi, θk) − At(xi, θk)]2, (11)

The variation of Eq. 11 is given by:

δJ =

X

M

k=0

N

X−1

i=0

2ωik [Ae(xi, θk) − At(xi, θk)] δAe(xi, θk). (12)

On the other hand, the variation of the equivalent area, Ae(x, θ), with respect to the pressure yields:

δAe(x, θ) = Z x

0

C(x − t)

1/2

δP dt. (13)

Using this last expression it is possible to separately evaluate the variation of the non-zero equivalent areas

at the chosen points as follows:

δAe(xi, θ) = X

i−1

j=0

Z xj+1

xj

C(xi − x)

1/2

δP dx, (14)

NASA/CR—2015-218719 334
where, for the sake of simplicity, we have substituted the dummy variable of integration t by x. Using the

variation of the equivalent area that we have computed in Eq. 12, it is possible to rewrite the variation of

the objective function as:

δJ =

X

M

k=0

N

X−1

i=0



2ωik [Ae(xi

, θk) − At(xi, θk)]X

i−1

j=0

Z xj+1

xj

C(xi − x)

1/2

δP dx



 , (15)

where, to simplify the notation, we will use:

∆Ae(xi, θk) = 2Cωik [Ae(xi, θk) − At(xi, θk)] . (16)

Rearranging the integrals in Eq. 15, it is possible to write the variation of the objective function as

δJ =

X

M

k=0

N

X−2

j=0

Z xj+1

xj

N

X−1

i=j+1



∆Ae(xi, θk)(xi − x)

1/2



δP dx. (17)

Using this final expression, the admissible adjoint boundary condition that eliminates the dependence on the

fluid flow variation in the inverse equivalent area shape design problem is:

∇~ Ψ



~nnf · A~



= h(x, θ, P), on Γnf (18)

and the function h(x, θ, P) is defined at each discrete azimuthal angle θk as

h(x, θk, P) =







0 , if −L < x < x0, PN−1

i=j+1 ∆Ae(xi

, θk)(xi − x)

1/2

, if x0 ≤ x ≤ xN−1,

0 , if xN−1 < x < L,

where xj is the closest discrete lower value to x.

3.2 Adjoint method applied to engine simulations

The simulation of propulsion systems in SU2is based on the specification of the fan face Mach number at the

inflow surface (located somewhere close to the actual location of the fan face), and total nozzle temperature

and pressure in the exhaust surface (typically located inside the exit nozzle.) These boundary conditions

are imposed in the direct problem using a method based on the local characteristics (see Fig. 5). In the

direct problem, characteristic-based boundary conditions are imposed at the outer boundaries where the

fluid states are updated depending on the sign of the characteristic speeds, or eigenvalues, given by the wellknown diagonalization of the flux Jacobian matrix. Incoming characteristics correspond to the propagation

of information into the flow domain, and at these locations, physical boundary conditions, such as mass flow,

stagnation conditions, or back pressure, are prescribed. Outgoing characteristics correspond to information

propagation out of the domain, and numerical boundary conditions are imposed at these locations that

extrapolate the characteristic variable information from within the domain, often using Riemann invariants.

However the adjoint implementation is far from trivial [16] and requires a non-standard treatment of adjoint

term on the boundaries. Let us return to the integral over the boundaries that was assumed to vanish with

the appropriate choice of adjoint boundary conditions. This integral will indeed vanish if the scalar integrand

is satisfied at every point on the boundary:

Ψ

T



A~ −

 ̄ ̄I~ub



· ~n δU = 0. (19)

Therefore, we seek the adjoint state, Ψ, at the boundary that eliminates any contribution from this integral

to the variation of the functional through the satisfaction of the preceding expression. A common strategy

for the removal of this integral is the imposition of a homogeneous adjoint boundary condition, Ψ = 0.

NASA/CR—2015-218719 335
Figure 5: Boundary conditions in the direct simulation of an engine.

Table 1: Number of physical (P) and numerical (N) boundary conditions required for the direct and adjoint

problems in three dimensions (the state vector, U, has five components).

Supersonic Inflow Subsonic Inflow Subsonic Outflow Supersonic Outflow

Direct 5 P, 0 N 4 P, 1 N 1 P, 4 N 0 P, 5 N

Adjoint 0 P, 5 N 1 P, 4 N 4 P, 1 N 5 P, 0 N

While this does force Eqn. 19 to be true, it may be an over-specification and does not take into account the

mathematical character of the equations at the boundary. Before further consideration, we will revisit the

handling of boundary conditions in the direct problem and the relationship between the direct and adjoint

characteristic behavior. The sign of the characteristic velocities has been flipped in the adjoint problem,

causing characteristic information to propagate in the reverse direction. This sign reversal occurs due to

the integration by parts procedure during the adjoint derivation. The result is that the required type of

boundary conditions for the direct and adjoint problem are also reversed, i.e. physical conditions in the direct

problem become numerical ones in the adjoint problem and vice-versa, and the reversal implies the existence

of a complementary set of well-posed conditions for the adjoint problem. This relationship is summarized

for inflow and outflow boundaries in Tab. 1.

Based on this idea, it is possible to propose suitable physical adjoint boundary conditions involving the

characteristic speeds based on a manipulation of Eqn. 19 after introducing the diagonalization of the flux

Jacobian (see Fig. 6). Recent work by Hayashi et al, advances the notion of characteristic-based boundary

conditions for the adjoint equations by connecting the direction of information propagation to the imposition

of physical conditions at the boundaries in the direct problem. That is to say, rather than impose conditions

using the characteristic variables, one should consider the realizable perturbations in the flow solution, δU,

allowed at the boundaries given the particular choice of physical boundary conditions in the direct problem.

The latter derivation approach will now be further detailed and extended in a systematic fashion. Consider

the boundary, Γ∞, to be split into a subsonic inflow and subsonic outflow region for the direct problem. At

Figure 6: Boundary conditions in the adjoint simulation of an engine.

NASA/CR—2015-218719 336
the inflow, 4 physical conditions can be prescribed, and for clarity in the derivation, we will choose to specify

mass flow (ρ and ~v) at this boundary. At the outlet boundary, the typical physical condition of back pressure

(p) is imposed. The type of physical conditions chosen for the direct problem has therefore constrained the

allowable perturbations at the boundaries. More specifically, we have prescribed δρ = δ~v = 0 at the inlet

and δp = 0 at the outlet. Therefore, to derive the suitable adjoint boundary conditions for this situation,

we must introduce these primitive variable perturbations into Eqn. 19:

Ψ

T



A~ −

 ̄ ̄I~ub



· ~n∞ MδV = 0, (20)

where M =

∂U

∂V is the transformation matrix from conservative to primitive variables and δV = {δρ, δ~v, δp}

T

is the vector of primitive variable perturbations. After taking the transpose of Eqn. 20 and evaluating

(A~ −

 ̄ ̄I~ub) · ~n∞ M in three dimensions, the expanded result can be written as







δρ

δu

δv

δw

δp







T 











vn

ρnx

ρny

ρnz

0

uvn

ρunx + ρvn

ρuny

ρunz

nx

vvn

ρvnx

ρvny + ρvn

ρvnz

ny

wvn

ρwnx

ρwny

ρwnz + ρvn

nz

~v 2

2

vn

ρHnx + ρuvn

ρHny + ρvvn

ρHnz + ρwvn

1

γ−1

[γ(~v · ~n) − (~ub · ~n)]



















ψρ

ψρu

ψρv

ψρw

ψρE







= 0, (21)

where vn = (~v − ~ub) · ~n∞ as a shorthand, ~n∞ = {nx, ny, nz}

T, and ~v 2 = u2 + v2 + w2

. In this format,

physical boundary conditions for Ψ are easily recovered by imposing the constraints on the flow perturbations

resulting from the choice of direct boundary conditions and then manipulating the remaining expressions.

For example, at the inlet, the imposition of mass flow forces δρ = δ~v = 0, and therefore, the terms associated

with the products in the first four rows of Eqn. 21 are all zero. As δp is the only allowable perturbation in

the flow variables remaining, in order to completely remove contributions from the boundary integral the

following expression must be satisfied

δp φ~ · ~n∞ +

ψρE

γ − 1

[γ(~v · ~n) − (~ub · ~n)]= 0, (22)

and because δp is arbitrary, we recover the admissible physical boundary condition at the mass flow inlet:

ψρE = −

γ − 1

γ(~v · ~n) − (~ub · ~n)

φ~ · ~n∞. (23)

Using the same derivation approach, physical boundary conditions for the adjoint variables at the outlet can

be found. At the outlet, only δp = 0, and all other flow variable perturbations are allowable. This eliminates

any contributions from the final row of the expression in Eqn. 21, and leaves a system of four equations with

five unknowns from the first four rows of Eqn. 21 that must be set equal to zero in order to remove any

dependence on the remaining perturbations. Choosing ψρE free variable and solving the other four equations

in terms of it gives the following four physical boundary conditions:

ψρ = ψρE 

γ e (~v · ~n)

(~v − ~ub) · ~n +

~v 2

2



, (24)

ψρu = −ψρE 

γ e nx

(~v − ~ub) · ~n + u



, (25)

ψρv = −ψρE 

γ e ny

(~v − ~ub) · ~n + v



, (26)

ψρw = −ψρE 

γ e nz

(~v − ~ub) · ~n + w



, (27)

where e = E −

~v 2

2

is the internal energy per unit mass. While they also pursue a characteristic form of the

adjoint equations to provide numerical boundary conditions, we will use the values for the free variables (φ~

NASA/CR—2015-218719 337
Figure 7: Nested Free-Form Deformation example (volume, main, and secondary boxes are shown).

and ψρE) found at the boundaries that satisfy the discretized adjoint Euler equations within the domain.

Other sets of subsonic inflow and outflow conditions for the adjoint equations can be derived using the

same approach above when choosing different types of physical boundary conditions for the direct problem

(such as prescribing stagnations conditions at an inlet). One must simply introduce the perturbation of the

chosen quantities and the appropriate transformation matrix from the conservative variables into Eqn. 19

and solve for the admissible conditions in the same manner. Supersonic inlet and outlet boundaries are

straightforward in comparison. At a supersonic inlet, all of the flow variables can be prescribed as physical

boundary conditions in the direct problem (all characteristics are incoming) which means that none of the

adjoint variables will have prescribed values in the adjoint problem. This approach assumes that δU = 0 due

to the direct problem boundary conditions, and therefore, Eqn. 19 is automatically satisfied. In the case of a

supersonic outlet, no flow variables can be specified in the direct problem (all characteristics are outgoing),

so all of the adjoint variables can be prescribed with Ψ = 0 being the choice that exactly satisfies Eqn. 19.

3.3 Design variable definition, and mesh deformation

As we have seen, using the continuous adjoint methodology, SU2can compute the variation of an objective

function with respect to infinitesimal surface shape deformations in the direction of the local surface normal

at points on the design surface. While it is possible to use each surface node in the computational mesh as

a design variable capable of deformation, this approach is not often pursued in practice. A more practical

choice is to compute the surface sensitivities at each mesh node on the design surface and then to project

this information into a design space made up of a smaller set (possibly a complete basis) of design variables.

This procedure for computing the surface sensitivities is used repeatedly in a gradient-based optimization

framework in order to march the design surface shape toward an optimum through gradient projection and

mesh deformation. In three dimensions, a Free-Form Deformation (FFD) strategy [17, 18] has been adopted.

Here an initial box encapsulating the object (wing, fuselage, tails, pylons, nacelles, etc.) to be redesigned is

parameterized as a B ́ezier solid. A set of control points are defined on the surface of the box, the number

of which depends on the order of the chosen Bernstein polynomials. The solid box is parameterized by the

NASA/CR—2015-218719 338
following expression

X(u, v, w) = X

l

i=0

Xm

j=0

Xn

k=0

Pi,j,kB

l

i

(u)B

m

j

(v)B

n

k

(w), (28)

where l, m, n are the degrees of the FFD function, u, v, w ∈ [0, 1] are the parametric coordinates, Pi,j,k are

the coordinates of the control point (i, j, k), and Bl

i

(u), Bm

j

(v) and Bn

k

(w) are the Bernstein polynomials.

The Cartesian coordinates of the points on the surface of the object are then transformed into parametric

coordinates within the B ́ezier box. Control points of the box become design variables, as they control the

shape of the solid, and thus the shape of the surface grid inside. The box enclosing the geometry is then

deformed by modifying its control points, with all the points inside the box inheriting a smooth deformation.

With FFD, arbitrary changes to the thickness, sweep, twist, etc. are possible for the design of any aerospace

system. Once the deformation has been applied, the new Cartesian coordinates of the object of interest

can be recovered by simply evaluating the mapping inherent in Eq. 28. To increase the flexibility of the

definition of three-dimensional design variables, a nested Free-Form Deformation (FFD) capability has been

implemented in Phase II of this project. The key idea of this methodology is the use of a set of nested FFD

boxes to explore the design space, with each FFD box corresponding to a different objective (see Fig. 7).

• The volume box (which embeds the entire aircraft surface) is useful for rotating the entire geometry,

adjusting the area of the different sections or the total length of the aircraft.

• The main box (which embeds the main wing, fuselage, tail, etc.) is useful for redefining the camber

and thickness of the wing, or applying some deformations like twist, sweep, etc.

• The secondary box (in small localized areas) is useful for removing shock waves in those areas (e.g.,

shocks induced by the nacelles and pylons).

3.4 Managing gradient inaccuracies

Our derivations of the adjoint equations for shape design rely on an assumption of smoothness of the adjoint

variables. Discontinuous flow features like shocks violate this assumption and can result in high-frequency

oscilations or noise in the surface sensitivities which introduce errors that decorrelate the objective and

gradients. These errors can be important in both guiding gradient-based optimization techniques and in the

construction of surrogate models that benefit from the calculation of the gradient to reduce the total number

of functions evaluations required for a pre-specified accuracy level.

A key assumption in gradient-enhanced Response Surface Modeling is that the correlation of all input

information can be modeled by a covariance function. Typically, when performing gradient enhanced RSM

with Kriging or GPR, an exact correlation model is used to relate the function and its gradients[19]. As

identified by Dwight[20], violations of this model have adverse effects on the quality of the fit.

Decorrelation of objectives and gradients can affect gradient-based optimization as well. However, many

modern methods approximate Hessians with an under-fitting surrogate model, such as a quadratic polynomial, which expresses a weak assumption of the behavior of the data in small regions. Along with various

relaxation techniques, this makes GBO more robust, but not insensitive, to gradient errors.

A key problem that we have identified, explored and addressed during this project is the quality degradation of optimization methods that occurs when using function and gradient data that are decorrelated

because they possess varying levels of “physical” exactness [21].

Here we define physical exactness as the ability of the numerical model to describe the real physical

flow. We include the governing flow equations, adjoint equations, and even finite differencing in this set of

numerical models. Each numerical model has limits of physical exactness, based on, for example, numerical

scheme or step size. In this limit, we describe a digit of precision as “physically representative” if an engineer

would use it to make a decision between two candidate designs. Experience is largely a determining factor in

setting the maximum level of physical representativity. These factors will negatively affect the performance

NASA/CR—2015-218719 339
of a response surface and can hamper the convergence of SBO and GBO optimizations without methods to

manage gradient inaccuracies.

For example, we apply a gradient-enhanced GPR technique to training data from a NACA0012 test case

with two Hicks-Henne bump functions on the top and bottom of the airfoil at mid-chord. In Fig.8 and

Fig.9, we generate response surfaces for drag and lift with 20 latin hypercube sampled training data with

continuous adjoint-based gradients. We then compare the effect of changing the amount of noise the response

surface is allowed to model. For reference we compare the fits to a response surface generated from a 10x10

grid of direct solution data only. The data shows that if a noise of 1e3 (in dimensions of the design variable)

is allowed in the response surface, a reasonable quality fit is achieved with 3.3% mean estimation error in

the lift objective, and 1.2% mean estimation error in the drag objective. If noise is not allowed, or restricted

to be very low, then the accuracy of the response surface degrades significantly. A methodology that we

have derived for this work allows a certain level of noise in the gradients to ensure both the accuracy of

the resulting fit and the beneficial effects of exploiting the gradient information during the construction of

response surface models.

0.02 0 −0.02 −0.01 0 0.01 0.02

0.02

0.025

0.03

0.035

0.04

0.045

X1 (1/c)

Surfaces of Drag Coefficient

X2 (1/c)

CD

0.02 0 −0.02 −0.01 0 0.01 0.02

0

0.1

0.2

0.3

0.4

0.5

X1 (1/c)

Surfaces of Lift Coefficient

X2 (1/c)

CL

Direct Surrogate

Adjoint Samples

Adjoint Surrogate σ:1e−3

(a)

−0.02 −0.01 0 0.01 0.02

−0.02

−0.01

0

0.01

0.02

X1 (1/c)

X2 (1/c)

Contours of Drag Coefficient

−0.02 −0.01 0 0.01 0.02

−0.02

−0.01

0

0.01

0.02

X1 (1/c)

X2 (1/c)

Contours of Lift Coefficient

Direct Surrogate

Adjoint Samples

Adjoint Surrogate σ:1e−3

(b)

−0.02 −0.01 0 0.01 0.02

−0.02

−0.01

0

0.01

0.02

X1 (1/c)

X2 (1/c)

Contours of Drag Coefficient Sensitivity to X1

−0.02 −0.01 0 0.01 0.02

−0.02

−0.01

0

0.01

0.02

X1 (1/c)

X2 (1/c)

Contours of Lift Coefficient Sensitivity to X1

Direct Surrogate

Adjoint Samples

Adjoint Surrogate σ:1e−3

(c)

Mean Errors: Lift Objective: 3.3%; Lift Gradient: 10.9%; Drag Objective: 1.2%; Drag Gradient: 3.4%

Figure 8: Example RSM with Adjoint Gradients, Noise Tolerence = 1e-3

0.02 0 −0.02 −0.01 0 0.01 0.02

0.02

0.025

0.03

0.035

0.04

0.045

X1 (1/c)

Surfaces of Drag Coefficient

X2 (1/c)

CD

0.02 0 −0.02 −0.01 0 0.01 0.02

0

0.1

0.2

0.3

0.4

0.5

X1 (1/c)

Surfaces of Lift Coefficient

X2 (1/c)

CL

Direct Surrogate

Adjoint Samples

Adjoint Surrogate σ:1e−10

(a)

−0.02 −0.01 0 0.01 0.02

−0.02

−0.01

0

0.01

0.02

X1 (1/c)

X2 (1/c)

Contours of Drag Coefficient

−0.02 −0.01 0 0.01 0.02

−0.02

−0.01

0

0.01

0.02

X1 (1/c)

X2 (1/c)

Contours of Lift Coefficient

Direct Surrogate

Adjoint Samples

Adjoint Surrogate σ:1e−10

(b)

−0.02 −0.01 0 0.01 0.02

−0.02

−0.01

0

0.01

0.02

X1 (1/c)

X2 (1/c)

Contours of Drag Coefficient Sensitivity to X1

−0.02 −0.01 0 0.01 0.02

−0.02

−0.01

0

0.01

0.02

X1 (1/c)

X2 (1/c)

Contours of Lift Coefficient Sensitivity to X1

Direct Surrogate

Adjoint Samples

Adjoint Surrogate σ:1e−10

(c)

Mean Errors: Lift Objective: 5.5%; Lift Gradient: 50.8%; Drag Objective: 4.8%; Drag Gradient: 12.8%

Figure 9: Example RSM with Adjoint Gradients, Noise Tolerence = 1e-10

NASA/CR—2015-218719 340
4 Optimization framework

We have used two optimization approaches in this work. The flow chart in Fig. 10 compares the eXtended

Design Structure Matrices (XDSMs) of the two approaches [22]. Central to both approaches is the physical

model, in this case the SU2

suite, which accepts design variables, performs the various analyses (deformation,

direct and adjoint solutions) and returns performance variables and their derivatives. The first approach,

Gradient Based Optimization (GBO), directly drives the physical model. The second approach, Surrogate

Based Optimization (SBO), uses a response surface model constructed using Gaussian Process Regression

(typically enhanced with gradient information) to guide the physical model. The intent of this additional

complexity is to reduce function evaluations and wall time required to perform global optimization. The

approach has been programmed in Python, which easily interfaces with the SU2suite.

Figure 10: Comparison of Optimization Frameworks.

4.1 Gradient based optimization

Compared to SBO, gradient based optimization can require fewer function evaluations in higher-dimensional

design spaces at the cost of performing local optimization. In this study, we use a sequential quadratic

programming (SLSQP) optimizer built into the Scientific Python (scipy) toolbox [23, 24], a well-established

open-source software for mathematics, science and engineering. The SciPy library provides many userfriendly and efficient numerical routines for the solution of non-linear constrained optimization problems,

such as conjugate gradient, Quasi-Newton or sequential least-squares programming algorithms.

At every iteration, the SLSQP code chooses a search direction based on the constrained optimization of

a second-order least-squares response surface (the quadratic program). It then performs a line search in this

direction to find a point that satisfies first- and second-order optimality conditions. At each major iteration

the response surface is updated with the BFGS update rule. Convergence is declared when imporvement of

the objective, or norms of the gradients and Hessian fall below a tolerance.

The SciPy routines require as inputs the values and gradients of the objective functions as well as the

chosen constraints, but not necessarily in a sequential order. Thus the physical model must also keep track

of what simulations have been run to avoid unnecessarily repeating an analysis. This is handled by the SU2

suite’s optimization wrappers.

NASA/CR—2015-218719 341
4.2 Surrogate Based Optimization

To address the high computational cost of the CFD simulations and to enable global design space optimization, we are using Response Surface Modeling (RSM) with gradient-enhanced Gaussian Process Regression

(GPR) [25]. Gradient information available from an adjoint solution is used to increase the accuracy of the

RSM at lower computational cost. With the solution of one flow and one adjoint simulation, we are able to

provide 1 + d pieces of information to the response surface model. This is much more efficient than running

1 + d flow solutions to yield approximately the same amount of information.

A large body of work has been built around RSM approaches, especially using a stochastic modeling

technique known as Kriging[26, 27] and its gradient enhanced relative known as Co-Kriging[2, 19]. GPR is

a superset of Kriging, and is formulated by conditioning a probability distribution over random functions.

In general both GPR and Kriging result in the same mathematical fitting models[27, 28]. However, GPR is

built within the context of machine learning, which allows more flexibility when dealing with complex design

spaces[29].

Response surface models can be used in what is known as Surrogate-Based Optimization (SBO) (see

Sec.4.4) to search for optimum designs in a global design space. To generate an initial response surface, a

set of designs are sampled using Design of Experiment (DOE) techniques such as Latin Hypercube Sampling. Because the locations of these points are not dependent on each other, they can be run in parallel,

dramatically reducing wall-clock time if the resources are available. After this initial sample, additional

design points are chosen using an Infill Sampling Criterion (ISC). It is common to take advantage of the

uncertainty information available from stochastic response surface models like GPR to efficiently add points

that attempt to improve the accuracy of the model near regions of optimal design[30].

An equally large body of work has been built around surrogate-based optimization. Forrester provides a

thorough summary of different infill sampling criterion[31]. Several studies describe its application to aircraft

design problems [32, 33, 34, 35]. In previous work, we explored the use of ISCs to further improve the SBO

process using a hybrid infill sampling criteria with expected improvement and estimated optimum[25].

4.3 Response surface modeling with Gaussian process regression

Gaussian Process Regression is a super-set of Kriging. It approaches regression from a Bayesian standpoint

by conditioning a probabilistic function to training data[36]. For example, it can be shown that in the case

where the probabilistic prior is assumed to be a Gaussian process with a stationary zero mean, the resulting

model matches that of Simple Kriging (SK). Because GPR is posed as a conditioning problem and not an

expected error minimization problem, it may have more flexibility when handling poorly behaved design

spaces, such as discontinuities as found by Chung[2], or in our case noisy response functions. This is one of

the key motivations for our exploration of Gaussian Process Regression.

Following the derivation given by Rasmussen[36], Gaussian Process Regression is approached by conditioning a multivariate normal distribution

f ∼ N (μ, [σ]), (29)

where f is a normally distributed function with mean vector μ and standard deviation matrix [σ]. In this

work, we take a uniformly zero mean vector, and populate the standard deviation matrix with a covariance

submatrix k that is a function of training and estimated data:



fp

f

∗

k



∼ N 0,



k(xp, xq) k(xp, x∗

j

)

k(x

∗

k

, xq) k(x

∗

k

, x∗

j

)

 ,

{ fi(xi)|i = 1, ..., n } , { f

∗

t

(x

∗

t

)|t = 1, ..., m }.

(30)

The notation (·)

∗

is used to distinguish the estimated data from the training data. Additionally, index

notation is used to describe the sub-blocks of the covariance matrix, where k(xp, xq) would be equivalent to

the matrix kpq. There are n training point vectors x with function values f(x), and m estimated data point

vectors x

∗ with function values f∗

(x

∗

).

NASA/CR—2015-218719 342
Of the data, we do not know the estimated function values f

∗

. We do know the training data locations

x and function values f(x), as well as the desired estimated data locations x

∗

. Following Rasmussen’s

derivation [36], we condition the normal distribution with the data we do know

f|x

∗

, x, f ∼ N (f

∗

, V[f

∗

]), (31)

which allows us to identify useful relations for estimating a function fit,

f

∗

k = k(x

∗

k

, xq) k(xp, xq)

−1 fp

V[f

∗

k

] = k(x

∗

k

, x∗

j

) − k(x

∗

k

, xq) k(xp, xq)

−1 k(xp, x∗

j

)



k

(32)

where V[f

∗

] is the covariance of the estimated value f

∗

. These are the relations needed for coding a

GPR program. Rasmussen provides an example algorithm that simplifies these relations by using Cholesky

decomposition [36]. The covariance function models the spatial correlation between data points. It is chosen

based on the types of functions that are going to be modeled. Highly-smooth or weakly-smooth functions

would be examples of different types of functions that would require different choices of covariance functions.

A common covariance function is the Gaussian function of the Euclidean distance between points:

k(xp, xq) = k(p, q) = θ

2

1

exp −

1

2θ

2

2

X

d

z=1

(pz − qz)

2

!

{pi, qi,

∂

∂xi

| i = 1, ..., d},

(33)

where d is the number of dimensions, and p and q are the position vectors chosen from the design space

x. There are two degrees of freedom in the covariance function. These are known as hyper-parameters. In

terms of their effect on the function fit, the nominal variance θ1 is a measure of the amount of variance

allowable between points, and the length scale θ2 is a measure of the range of influence of a point.

Modeling the influence of gradients on the fit involves adding information to the covariance matrix. This

requires finding a covariance function to model the correlation between points and derivatives. One approach

to do this is shown for Co-Kriging by Chung[37] by deriving the covariance functions from the definitions of

variance and derivative. Another approach suggested by Papoulis[38] and used for gradient enhanced GPR

by Solak[39] exploits the theorem that the linear operation of an expected value is the expected value of the

linear operation. The result from either approach is the same. To include gradient information in the fit,

simply take the derivatives of the covariance function:

k



∂p

∂xv

, q=

∂k(p,q)

∂xv







q

k



p,

∂q

∂xw



=

∂k(p,q)

∂xw







p

k



∂p

∂xv

,

∂q

∂xw



=

∂

∂xw



∂k(p,q)

∂xv







q









p

.

(34)

This is where the assumption of a correlation model between function value and its gradient is made. It is a

natural and powerful assumption. However, because there will be d-times more gradient information than

function values, inaccurate gradients can override the behavior of the fit.

The gradient information must be packed into the covariance matrix. This can be done by updating the

definition for the covariance function as follows:

k(p, q) →





k(p, q) k



p,

∂q

∂xw



k



∂p

∂xv

, qk



∂p

∂xv

,

∂q

∂xw





 fp →



fp

∂fp

∂xd



, (35)

where submatrix k(p, q) has dimension n × n, and submatrix k



∂p

∂xv

,

∂q

∂xw



has dimension n d × n d.

A useful extension of this formulation is estimating the gradients of the response surface given only

objective information. This simply involves omitting the blocks associated with the training data gradients

NASA/CR—2015-218719 343
but keeping those associated with the estimated data gradients. Given a reasonable amount of objective

data, this can be used to build an analytic estimate of the gradients in the design space for those data.

While the curse of dimensionality unfortunately constrains this method to low-dimensional design spaces,

it is still useful for generating an accurate reference when evaluating the errors of the various sensitivity

analysis methods.

We are able to model several types of noise within the GPR framework. Allowing noise can relax the

assumption of exact correlation between objective and gradient information. The effect on the response

surface will have the form:

f

∗

N (x) = f

∗

(x) + , (36)

where  is a noise model. Adding noise to our model requires us to update our covariance matrix structure:

[σ] = [k] + [kN ], (37)

where [k] is the full covariance matrix for functions and gradients, and [kN ] is the noise component of the

covariance matrix.

A simple but useful model is an independent identically-distributed Gaussian noise with zero mean and

given variance. This will only affect the self-correlated covariance terms along the diagonal of [kN ]. The

noise covariance matrix will then take the form:

[kN ] = 

θ

2

3

In0

,n0 0n0

,m0

0m0

,n0 θ

2

4

Im0

,m0



, (38)

where I and 0 are identity and zero matrices with n

0 = n(1 + d) and m0 = m(1 + d). Note we have allowed

two separate noise hyper-parameters for the function values and gradients. Adding this diagonal component

to the covariance matrix relaxes the requirement that the fit exactly honors the training data. Depending

on the magnitude of the noise hyper-parameter, the fit will be allowed to stray a certain distance away from

the data. This will allow us to model noise in the gradients due to inaccuracies from mesh refinement or the

particular sensitviy method.

Steps are taken to improve numerics and generality of the method by scaling the data based on the initial

LHC sample. The sampled objective function range, and design variable bounds are linearly scaled according

to:

f = f

0

· fref + fof f s.t. [min(f), max(f)] → [0, 1]

xz = x

0

z

· xz,ref + xz,of f s.t. [min(xz), max(xz)] → [0, 1] , z = 1, ..., d

∂f

∂xz

=

∂f0

∂x0

z

·

fref

xz,ref

.

(39)

Several benefits are realized from scaling the data past improving the condition of the covariance matrix, if we

can assume the response surface is smooth with a nominal amount of variation. First, we can approximately

say the variation of data is brought to be the same order of magnitude. This allows us to assume isotropy

of variation in each dimension and reduce the number of length scale parameters to one. This significantly

reduces the computational cost of hyperparameter learning. Second, this allows us to claim that the scaled

magnitude of the noise parameters for f and ∂f

∂x are of the same order of magnitude. This is important since

the value of the noise in the gradients is difficult to estimate a-priori. Finally, it makes the problem robust

to more design problems, where different design parameters of different scales can be mixed without having

to learn separate length scales.

To use the covariance function, the hyper-parameters θ1−4 must be chosen. Different values will yield

different fits, each being a different view of the data. We present the method of tuning the required hyperparameters by maximizing Marginal Likelihood[36].

Marginal Likelihood measures how well a given set of hyperparameters describes the training data.

Finding its argument maximum is a common way to select hyperparameters for GPR. It can be defined

mathematically with:

log p(fp|xp, θ) = −

1

2

f

>

p

[σ]

−1

fp −

1

2

log |[σ]| − n

2

log 2π, (40)

NASA/CR—2015-218719 344
Sample by Expected 

Improvement

Sample by Estimated 

Optimum

Estimated Optimum 

Converged?

YES NO

Expected Improvement 

Converged?

YES NO

Hyperparameter Tuning

Objective Function

Constraint Function

High Fidelity Simulation

Mesh Deformation

Direct Solution

Adjoint Solutions

Initial Sample Locations

Convergence

Adaptive Refinement

Genetic Algorithm

Gradient Based Optimizer

New Sample Location

Figure 11: Surrogate Based Optimization Process.

where θ is a vector of hyper-parameters. Maximizing the marginal likelihood is itself an optimization problem.

This problem can be solved with a gradient based optimizer, however the space is not gauranteed to be convex.

This study used modified Newton’s method with multiple starting locations chosen with Latin Hypercube

sampling to account for multiple local minima.

In order to avoid honoring the gradient information over the objective function information, we constrain

the noise hyperparameters according to

θ3 < θ4. (41)

If we had not scaled the gradient data to the same order of magnitude as the objective data, this constraint

would not be appropriate. The constraint is especially important in a problem with inaccurate gradient

information, as the presence of high-dimensional gradients can overpower the objective data and result in a

response surface with high error at the training data locations.

4.4 Methodology for surrogate based optimization

We have developed a surrogate based optimization approach that follows the basic process flow shown in

Fig. 11. A summary of this process is first described, and detail on modifications to the traditional process

are then given. Our approach to surrogate based optimization involves three sampling stages - first an initial

sample, second an efficient global refinement, and last a refinement of the estimated minimum. These stages

are designed to efficiently dissect the design space and attempts to yield the best estimate of the optimum

only in the last sample. The main motivation of this construction is to build in a sense of convergence that

is similar to that of gradient-based optimization.

In the first stage, the design space is sampled with a set of well-spread initial design points chosen with

Latin Hypercube (LHC) sampling restricted to the hypercube,

x

l

k < xk < xuk

, k = 1, ..., d. (42)

NASA/CR—2015-218719 345
The initial x = 0 design is included with this sample as the first point. Given enough computational

resources, this first phase is nicely parallelizable as each design point can be run independently. The number

of samples to take should depend on the expected complexity of the design space. For the problems explored

in this study, two samples per dimension has worked well.

Second, the surrogate model is refined using an Infill Sampling Criteria (ISC) based upon Expected Improvement (EI). This sampling criteria is well known for its ability to leverage both estimated function value

and uncertainty to balance design space exploration (targeting global accuracy) and exploitation (targeting

local optimality)[31, 40]. Expected improvement is commonly expressed as:

E [I(x)] = E [ max(fmin − F, 0) ]

= (fmin − f

∗

)Φ 

fmin−f

∗

s

∗



+ s

∗φ



fmin−f

∗

s

∗



.

(43)

In the above, Φ(·) and φ(·) are the standard normal distribution and density functions, s

∗ = V[f∗

(x)]

is the estimated variance of the response surface, and fmin is the minimum of the current training point

sample.

If additional information in the form of constraints is available in the problem, it can be added to the

infill sampling criterion by searching in locations of high probability of feasibility. Following the method

described by Shimoyama[41], this is done by building a second surrogate model and evaluating the expected

improvement, conditioned by the probability of feasibility,

P[c(x) < 0] = φ



c

∗

s

∗

c



, (44)

where the constraint is formulated as c(x) < 0, and c

∗

is the estimated constraint value with estimated

variance, s

∗

c

.

In our experience, the expected improvement infill sampling criteria often added training data near the

box boundaries and corners of the design space. This is inefficient because at least half of a training point’s

region of influence is wasted modeling the response surface outside the boundaries. To encourage sampling

inside the design space, we apply a penalty function,

B(x) = 1 − exp −

1

2

min 

xk − x

l

k

b

2

k

,

x

u

k − xk

b

2

k

, k = 1, ..., d , (45)

which is a Gaussian function of the distance from the nearest hypercube boundary. In this study, the lengthscale bk is set to 0.1(x

u

k − x

l

k

), or 1% of the hypercube dimension k’s length. The above penalty function is

greatly simplified in the non-dimensional space constructed in the previous section.

Combining the Expected Improvement, Constraint Penalty, and Boundary Penalty results in the first

infill sampling criterion:

ISC1(x) = E [I(x)] · P[c(x) < 0] · B(x)

xnew = x | max (ISC1(x)),

(46)

The response surface is refined by sampling f(xnew) and c(xnew) in this way until the criterion converges to

a small value,

R1(i) = ISC1(xi) < T1, (47)

where T1 is a specified tolerance.

While indeed efficient, our experience with GPR-based SBO has suggested that after expected improvement has converged to some preset value, it becomes difficult to discover the sharp and narrow wells that

develop in its surface. For this reason, it can be more efficient to switch to an infill sampling criteria based

on pure exploitation.

Thus the final phase of our method’s design space sampling is based on refining around the estimated

deterministic optimum of the surrogate model:

ISC2(x) = f

∗

(x),

xnew = x | min (ISC2(x)), s.t. c∗(x) < 0.

(48)

NASA/CR—2015-218719 346
This procedure continues until the measured improvement of the design sample converges to a small value

R2(i) = max ( fbest − fi, R2(i − 1) ) < T2, (49)

or until the sampling stalls after not finding an improvement for a predetermined number of iterations. In

the above equation, T2 is another specified tolerance, and fbest is the minimum of the training data that

came before fi. We use similar monitors on the change in location of the best point, and the norm of the

gradient of the best point, but often the function value of best point converges the fastest.

Both the second and third sampling stages use a two-part optimization of the surrogate model. First a

genetic algorithm is used to find a reasonable estimate of the global minimum. This estimate is fed as the

initial guess to a gradient based optimizer, which returns a better estimate of the global minimum. This

process requires several thousand function evaluations of the response surface, but is far less expensive than

evaluating an additional high-fidelity design point.

4.5 Python package

The above methods have been implemented in a python package called ”pyGPR” to allow for greater portability. The package api is built in a similar architecture to the XDSM flow chart in Fig. 10. The basic steps

for using the package involve defining ”Models” for each objective or constraint. Each Model is defined by

a Kernel, and a set of Training data. Code listing 1 below shows an example of defining one such model. In

Listing 1: pyGPR Model Definition

Y,DY = Func Y (X)

Train = pyGPR. T r ai ni n g (XB,X,Y,DY)

Ke rnel = pyGPR. K e r n el s . Gaussian ( Train )

Model Y = pyGPR. Modeling ( Ke rnel )

this listing, training data locations X are chosen using latin hypercube sampling, Y and DY are the objective

and gradients at X, and XB is a matrix defining the upper and lower bounds of the design space.

The function and model are then used to execute the sampling strategy described earlier. The present

code implements adaptive refinement using expected improvement and estiamted minimum and has been

abstracted such that it can accept multiple constraints constraints. Listing 2 shows an example of starting this

process. Monitoring of the optimization process is provided with convergence plots. An example convergence

Listing 2: pyGPR Sampling Execution

Sample = pySBO . Sampling (Model Y , Func Y , [ Model C1 , Model C2 ] , [ Func C1 , Func C2 ] )

Sample . Optimize ( )

plot shown in Fig.12 monitors the reduction in expected improvement, as well as the change in best training

point objective, gradient, and location. It shows here that the objective function was converged to 1e-6% in

27 function evaluations. This plot is from the optimization example presented in Sec.5.3.

5 Numerical results

5.1 Grid adaptation

The gradient-based adaptation methodology was also tested with highly-complex geometries. For example,

the adaptation techniques were used to study the effects of propulsion integration for the Lockheed N+2

NASA/CR—2015-218719 347
Figure 12: Example Surrogate Based Optimization Convergence Plot

aircraft geometry (M∞ = 1.7, angle of attack 2.1

◦

). Here the final target was to evaluate the pressure

signature in the near-field, and two levels of solution adaptation were used to capture minor effects due to

the propulsion. In Fig. 13 and Fig. 14 the baseline and two levels of adaptation are shown. The baseline

mesh has 1.3 million nodes, while the adapted grids have 1.5 and 1.8 million nodes, respectively. The effect

of the adaptation is also reflected in the near-field pressure signature, plotted at different azimuthal angles

in Fig. 15, which shows minor differences. The effects of propagating the resulting near-field signatures at

varying levels of adaptation are presented in Sec. 6.

5.2 Shape optimization maintaining equivalent area

The objective of this particular problem is to demonstrate the ability of the adjoint formulation to enable

design optimization while maintaining a target equivalent area distribution across multiple azimuths [25].

More specifically, a gradient-based, multi-objective optimization of the Lockheed N+2 design was performed

(M∞ = 1.7, angle of attack 2.1

◦

). This was done for 9 iterations with azimuths varying from 0◦to 60◦

(pressure disturbances above 60◦ do not reach the ground) and the coefficient of drag was reduced from

0.00875 to 0.00850, a 3% reduction. The ability to compute sensitivities is a fundamental capability and

very useful output of SU2. Fig. 18 and Fig. 19 show the sensitivities of CD and CL with respect to variations

of the geometry in the local normal direction. The magnitude of the surface sensitivity is related to changes

in the cost function caused by changes in geometry, and designers can use this sensitivity information to

determine appropriate parameterizations of the configuration prior to optimization. Figs. 16 and 17 show a

comparison of the near-field pressure distributions, and Fig. 20 and Fig. 21 show a comparison of equivalent

areas between the baseline and final design. The plots for equivalent area distribution show at most a 2%

change from the target. The overlay of the baseline and final geometry in Fig. 22 shows that the upper wing

surface was flattened and the lower fuselage deformed inwards in order to reduce the drag. To compensate

for the fuselage effect on the equivalent area, the lower wing surface was deformed downwards as well. A

comparison of surface pressures for the design study is given in Fig. 23, and contours of the density and

adjoint density fields are shown in Fig. 24 and Fig. 25.

NASA/CR—2015-218719 348
Figure 13: Gradient-based mesh refinement for the Lockheed N+2 aircraft geometry (M∞ = 1.7, AoA 2.1

◦

,

with engine propulsion effects), showing the change in the Mach number contours.

Figure 14: Gradient-based mesh refinement for the Lockheed N+2 aircraft geometry (M∞ = 1.7, AoA 2.1

◦

,

with engine propulsion effects), showing the change in the grid.

NASA/CR—2015-218719 349
X coordinate (m)

C

p

0 20 40 60 80 100

-0.01

-0.005

0

0.005

0.01

Azimuthal angle 0 deg (baseline).

Azimuthal angle 0 deg (cycle 0).

Azimuthal angle 0 deg (cycle 1).

X coordinate (m)

C

p

0 20 40 60 80 100

-0.01

-0.005

0

0.005

Azimuthal angle 34 deg (baseline).

Azimuthal angle 34 deg (cycle 0).

Azimuthal angle 34 deg (cycle 1).

Figure 15: Near-field pressure signatures with different levels of grid adaptation.

Figure 16: Baseline near-field pressure signatures. Figure 17: Final near-field pressure signatures.

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Figure 18: Drag sensitivity (M∞ = 1.7, AoA 2.1

◦

),

including propulsion effects.

Figure 19: Lift sensitivity (M∞ = 1.7, AoA 2.1

◦

),

including propulsion effects.

Figure 20: Baseline target equivalent area distributions.Figure 21: Final target equivalent area distributions.

Figure 22: Baseline and final N+2 geometry comparison.Figure 23: Baseline and final N+2 surface pressures.

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Figure 24: Contour plot of density for the N+2 baseline configuration.

Figure 25: Contour plots of density adjoint variable

for the N+2 baseline configuration and equivalent

area as objective function.

5.3 Unconstrained surrogate-based optimization

An unconstrained, surrogate-based drag optimization was performed on the N+2 configuration using the

9 Free-Form Deformation control points placed on the upper wing surface (see Fig. 26). A comparison

of the convergence history with a gradient-based optimization of the problem is shown in Fig. 27. The

surrogate-based optimization procedure was able to discover a 4.6% reduction in drag in 20 fewer iterations

than a gradient-based optimization. The first 21 design points are chosen by latin hypercube sampling and

are independent. They can be simulated simultaneously given enough computing resources, which further

reduces the wall time spent optimizing. The six adaptive refinement iterations after the initial sample show

that the response surface allows rapid convergence to the minimum. The use of an expected improvement

sampling criteria also allows us to claim with reasonable certainty that this is the global minimum within

the box bounds of the problem. A comparison of the geometry change between the baseline and final design

in Fig. 28 shows that the drag reduction was accomplished by reducing the thickness of the wing.

5.4 Recovering boom performance after including structurally-motivated changes

In the course of this project, a Lockheed Martin structures team suggested design changes for the aircraft

outer mold line to strengthen the wing-body joint and General Electric provided an updated engine nacelle

geometry. These changes required the re-optimization of the aircraft in order to attempt to recover the

original target equivalent area distribution and drag. To accomplish this, we used the developed design

methodology to recover the boom performance after including the structurally-motivated and engine modifications to the baseline aerodynamic shape. This calculation was used as the final demonstration of our efforts

in Phase II. If Fig. 29 a comparison of the initial design (1044-1) and the modified design (1044-3b) is shown.

Note that LM 10440-3b was modified to take advantage of better wing-body blending and improved load

paths through the aft strut. The engine nacelle/nozzle designs were also updated based on work by GE. The

result, as described above, was a modification of the ground boom signatures that significantly increased the

boom loudness. The purpose of this optimization was to perform an inverse design that attempted to recover

the equivalent area distributions (at a number of azimuthal locations) of the original design, denominated

LM 1044-1. The optimization problem is defined at Ma=1.7, AoA = 2.1deg, H = 50,000 ft, and the objective

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Figure 26: Location of the design variable (control

points of the FFD box).

Figure 27: Comparison of unconstrained drag GBO

and SBO.

Figure 28: Original and deformed N+2 surfaces, unconstrained drag SBO.

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Figure 29: Comparison of 1044-1 and 1044-3 geometries.

is to recover 1044-1 target equivalent area distribution (computed at 2 body lengths), while maintaining the

value of the original lift coefficient. A total of 74 Free-Form Deformation (FFD) design variables (see Fig. 30)

were used. The optimization history is shown in Fig. 31, and Fig fig:OptimizationHistory. In particular,

different sets of design variables have been used: 12DV (tail), 24DV (tail, aft deck), and 74DV (tail, aft

deck, main wing, fuselage, un-scaled). When 74 design variables were used it was possible to obtain a 85.5%

reduction in the equivalent area objective (a 100% reduction would have implied the exact recovery of the

original equivalent area distributions), with an increase in the lift of 1.8% and 1.1% in drag. By considering

the calculated value of the lift-curve slope, the increase in drag is almost entirely attributable to the increase

in lift of the resulting design. However, the drag may be further minimized by an optimization with Ae and

lift constraints that was not attempted here. The resulting geometry is presented in Fig. 33, in the new

configuration the main wing dihedral has been increased, the trailing edge de-cambered, the tail angle of

attack increased near root, and the fuselage volume has been increased. The formulation developed in this

work allows a multiple azimuth formulation maintaining the off-track performance which is fundamental in

this kind of problem. In Fig. 34 the equivalent area at different azimuth angles is shown, note that for angles

between 0 and 40 degrees the target equivalent area has been recovered. Note that disturbances radiated

at azimuths greater than 55 degrees do not contribute to the boom loudness. In Fig. 35 a detailed contour

map of the Mach number is presented. As final step of the optimization process the ground signature has

been computed and compared with the target configuration. The results of this comparison are presented

in Fig. 36, Fig. 37, and Fig. 38, in all the cases the optimized geometry has the shocks in the same location

(and with a similar amplitude) than in the target configuration.

6 Sonic boom assessment of the different aircraft configuration

geometries

6.1 Objective

The achievement of a low-boom configuration is one of the primary objectives of these design efforts. The

adjoint-based sonic boom minimization performed using the SU2 CFD code used a set of target equivalent

area distributions to achieve a low sonic boom signature in the cruise condition. The target equivalent arNASA/CR—2015-218719 354
Figure 30: 1044-3b Design Parameterization. A total of 74 free-form deformation control points available.

Figure 31: Optimization history (equivalent area error).Figure 32: Optimization history (force coefficients).

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Figure 33: Baseline and optimized shape (74 Design variables)

Figure 34: Equivalent area distributions at different

azimuth angles.

Figure 35: Initial and final surface Contours

(wing detail).

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Figure 36: Comparison of ground

signatures (azimuth angle 0 deg.).

Figure 37: Comparison of ground

signatures (azimuth angle 30 deg.).

Figure 38: Comparison of ground

signatures (azimuth angle 50 deg.).

eas used for this optimization were defined to recover the low-boom characteristics of a previously-designed

configuration. Although, this optimization successfully reduced the difference between the actual and the

target equivalent area distributions, a perfect match was not obtained. The differences in the equivalent

area distributions motivate a careful evaluation of the actual loudness of the ground-propagated sonic boom

achieved by the final optimized configuration, and a comparison with the sonic boom of the other configurations that have been investigated at Stanford University over the reporting period (Phase II) with the SU2

code.

6.2 Description of the methods and tools used for ground propagation of sonic

booms

Simulating the sonic boom is an intricate task involving complex physical phenomena and very different

length scales. Accurate modeling of sonic boom first requires an adequate prediction of the acoustic source:

the aerodynamic pressure disturbances generated in the close vicinity of the aircraft. In this region, the

aerodynamic flow is governed by the non-linear Euler equations and it includes three-dimensional features

with scales proportional to the aircraft length. Therefore, CFD methods are perfectly suited and necessary

to predict these near-field aerodynamic perturbations that create the sonic-boom. Because the prediction of

the sonic boom requires propagating this pressure signal from the near field down to the ground over tens of

kilometers, several hundred aircraft lengths, conventional CFD methods are inadequate to perform this long

distance propagation. They would require a tremendous number of mesh points and would eventually fail

to capture important effects occurring during the propagation (such as molecular relaxation). Therefore, a

specifically-tailored acoustic code is necessary to carry out this long-distance propagation of the sonic boom

signal through the standard atmosphere, which in these calculations is assumed to be stratified and include

the standard temperature and density variations with altitude. A suite of ONERA sonic boom evaluation

methods and tools have been used for this portion of the study. This method is based on a three-layer

approach, as illustrated in Figure 39. Layer 1 corresponds to the near-field aerodynamic flow prediction with

CFD. Layer 3 is the atmospheric propagation of the sonic boom with an acoustic code, while layer 2 ensures

a natural matching between the near-field aerodynamic data and the native inputs of the acoustic code.

Layer 1: Near-field aerodynamic calculation by CFD

First, the aerodynamic pressure field in the near field of the aircraft flying in the supersonic cruise condition

is calculated by solving the three-dimensional steady Euler equations using the SU2 CFD code. To obtain

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Figure 39: ONERA Sonic Boom evaluation methodology

an accurate evaluation of the near-field aerodynamic pressure perturbations, careful attention must be paid

to the quality of the CFD mesh which must have features adapted to the flow physics of these perturbations

traveling along flow characteristics. The computational domain of this CFD calculation typically extends

between twice and four times the aircraft length below the aircraft.

Layer 2: Multipole matching method

The CFD aerodynamic pressure field is extracted on a cylinder surrounding the aircraft, aligned with the

flow direction, whose radius is a user-specified parameter varying between one half to one body length. The

pressure perturbations on this cylinder are then post-processed using the multipole decomposition method

originally introduced by Plotkin and Page [42, 43] and applied by Salah El Din [44]. This decomposition

method proceeds through a development of the near-field pressure signature on the cylinder according to the

azimuthal variable Φ. Thereby, it allows to rebuild a Whitham function equivalent, at long distance, to the

pressure perturbation generated by the aircraft, while accumulating the diffraction effects associated with

the non-axisymmetric near-field flow. The ground signature computed from this equivalent rebuilt Whitham

function is observed to converge much faster with the matching distance between the CFD and the acoustic

theory (i.e. the radius of the cylinder) than the ground signature from a direct CFD/acoustic match (without

the use of the multipole expansion). This justifies the use of this second layer which, in addition to providing

a theoretically-correct near-field/far-field match, greatly reduces the CFD grid size needed for the near-field

computation, thus saving significant computing time.

Layer 3: Atmospheric non-linear acoustic propagation method

The ground signature is computed by propagating the near-field aerodynamic pressure perturbations matched

with the multipole decomposition method (layer 2) with the acoustic propagation code TRAPS. TRAPS

[45, 46] is a dedicated sonic boom propagation code based on inviscid non-linear acoustic theory. It uses

a ray-tracing approach to account for the refraction phenomena occurring during propagation through a

stratified atmosphere with vertical temperature and density gradients, and to evaluate the extent of the

primary carpet, i.e. the width of the corridor underneath the aircraft trajectory directly affected by the

sonic boom. Along each acoustic ray, a signal aging process (first-order correction to linearized supersonic

theory) is used to predict the evolution of the shape of the sonic-boom pressure signal. The ground pressure

signature is finally obtained by post-processing the previous aged pressure signal to identify and locate the

different shocks with the equal-area rule [45] and by applying a multiplication factor of 2.0 to account for a

perfect reflection on the ground. Finally, from this ground sonic boom pressure signature (having infinitely

steep shocks), corrections for rise time are applied to each shock composing the signature. A hyperbolictangent shape is introduced for the actual shocks and the rise time is assumed to be inversely proportional

to the pressure jump. From this corrected ground signature, a spectral analysis is performed and classical

sonic boom metrics such as ASEL, CSEL and PLdB can be calculated. The process of calculating SEL and

PLdB sonic boom metrics follows the procedure described by Shepherd and Sullivan in [47] and is depicted

in Figure 40.

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Figure 40: PLdB calculation process from ground propagated (inviscid) pressure signature

6.3 Flight conditions used for the sonic boom analysis

All sonic boom evaluations presented hereafter have been performed using the results of SU2 CFD calculations

(Euler) obtained for a Mach number of 1.7 and an angle of incidence of 2.1 degrees, corresponding to the

cruise conditions and a lift coefficient of about 0.136. The CFD meshes used for these calculations were

meshes specifically generated for sonic boom analysis in term of density and extent. A view of the mesh and

the CFD pressure field in the symmetry plane is given in Figure 41.

Figure 41: View of the Euler CFD mesh and CFD solution in the symmetry plane. The rightmost figure

shows the full extent of the mesh (in the symmetry plane)

The changes in the aerodynamic forces with angle of attack, calculated using two different meshes (for

configuration LM 1044-3b) are presented in Figure 42.

Two flight altitudes have been considered for the evaluation of sonic boom: the start-of-cruise (SoC) at

48,200 feet and the end-of-cruise (EoC) at 57,000 feet.

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Figure 42: Aerodynamic polars for the LM 1044-3b configuration. The results using two meshes are compared. Only the finest mesh (sonic boom mesh) has been used for the present sonic boom analyses.

6.4 Set-up and verification of the sonic boom evaluation process

Prior to presenting the results obtained for the ground propagated sonic booms of the different designs investigated recently at SU, some verifications and the set-up of the ONERA sonic boom calculation procedure

are described in this section. The effects of the major parameters or options offered by the different tools

composing the complete sonic boom evaluation process are presented. To illustrate these effects, the CFD

solution of the LM 1044-3b configuration is used and we focus more precisely on the effects of:

• The distance at which the near-field pressure is extracted in the SU2 CFD solution;

• The multipole decomposition and the number of Fourier modes used to reconstruct the data;

• The rise time of shocks in the ground sonic boom signature on the Decibel metrics.

The first analysis conducted was used to evaluate the quality of the CFD solution in the near-field by

extracting the CFD pressure on cylinders of increasing radii, aligned with the free stream and surrounding

the aircraft, as illustrated in Figure 43. The evolution of the pressure signal with distance from the aircraft,

under the flight track (Φ = 0 deg) and off-track at azimuth (Φ = 45 deg) is shown in Figure 44 and Figure

45. By plotting ∆P ×

p

R/L, an indication of the diffusion of the forming shocks in the signature can be

obtained. Indeed, if the signal were fully axisymmetric, it would remain unchanged. Therefore any decrease

in the amplitude of the compression in the signal with R can only be due to numerical dissipation or threedimensional effects (non-axisymmetry). A non-negligible reduction of the strongest shock (under-track) can

be observed. Although it is not directly possible to evaluate the contribution of the non-axisymmetric effects

on this reduction, an indication is given by looking at the evolution of the signal at other azimuthal angles. In

this case, it appears clearly that the overpressure associated with this shock wave (wing leading edge shock)

is also reduced at azimuthal angles 15deg and 30deg. This tends to indicate that numerical dissipation is

the main reason for the decrease of this shock. This situation illustrates the typical compromise to be made

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Figure 43: Cylinders on which the aerodynamic near-field pressure is extracted from the SU2 CFD solution.

The radius (R) of these cylinders varies from 0.5 to 2.5 times the aircraft length (L)

when choosing the distance at which the pressure is extracted from the CFD solution: if extracted too far,

the CFD solution is affected by the spurious numerical dissipation of shocks; if extracted too close, the

near-field solution is still far from being locally axisymmetric (three-dimensional effects are still important),

which is the assumption made by the acoustic propagation code.

The multipole matching method offers an interesting solution to help solve this dilemma in choosing the

right distance for extracting pressure in the CFD solution. Indeed, by accounting for the three-dimensional

effects occurring between the cylinder where he pressure is extracted in the CFD solution and the farfield (by using the asymptotic behavior of each azimuthal mode), this method allows to actually extract

the CFD data very close to the body, typically R/L = 1.0, where the CFD solution does not yet suffer

from significant spurious attenuation of shocks. The advantage of using this multipole matching method

is directly illustrated in Figure 44 which shows the propagated ground sonic boom calculated with data

extracted from the CFD at different distances R/L, with and without use of the method, respectively. The

faster convergence of the ground signature with distance of extraction of the near-field pressure is observed

when the multipole method is used to generate the inputs for the acoustic code TRAPS. When using the

multipole decomposition method, the number of (azimuthal) Fourier modes used to reconstruct the signal

must be chosen by the user. A perfect reconstruction would require the use of an infinite number of modes,

which is practically impossible. Usually, the number of modes that can be used is limited by the appearance

of numerical oscillations during the treatment of high-order modes. In the present case, using 20 modes was

not possible because of the numerical oscillations produced by the method. However, as illustrated in Figure

45 and Figure 46, the convergence of the reconstructed near-field signal with increasing numbers of modes is

fast and the use of 10 modes proved to be sufficient. Therefore, this parameter has been frozen to 10 during

the remainder of the study.

Figure 49 summarizes, for the start-of-cruise conditions, the effect of the distance of extraction R/L on

the under-track ground-propagated sonic boom signatures obtained with the multipole matching method

(for the LM 1044-3b configuration). Figure 50 gives the off-track evolution of the ground-propagated sonic

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Figure 44: Visualization of the evolution of the near-field pressure signal under-track as distance from the

body increases from 0.5L to 2.0L. Configuration LM 1044-3b

Figure 45: Evolution of the near-field pressure with distance from the aircraft (radius of the cylinder on

which pressure is extracted from the CFD solution). Under-track (left) and off-track at azimuth 45deg.

Direct extraction results (top) and results of multipole matching extrapolation (bottom) are presented.

boom signatures for the same configuration, using the exact same evaluation procedure. The evolution of

the overpressure with azimuthal angle exhibits a typical decrease. However this overpressure decrease is also

accompanied by a decrease of the mean rise time of the initial shock that can also be detected in these results

(which will have an adverse impact on the PLdB and other decibels metrics). Note that the results in Figure

NASA/CR—2015-218719 362
Figure 46: Influence of the distance at which the pressure is extracted in the CFD solution on the under-track

ground sonic boom signatures. Left: direct matching from the CFD results to the acoustic code; Right: use

of multipole matching method (with 10 Fourier modes).

Figure 47: Influence of the number of modes used in the multipole matching method applied on near-field

pressure data extracted at R/L = 0.5. Under-track results (left) and at azimuth 45deg (right).

50 correspond to the direct output of the TRAPS propagation code and the dissipative effects occurring

during the propagation through the atmosphere are not captured in these results. This rise time effect

actually comes from the careful low-boom design of the aircraft nose to produce a progressive compression

instead of a strong initial shock.

The pressure signatures obtained with the TRAPS propagation code shown in Figure 50 have been postprocessed in term of decibels metrics. The results are presented in Figure 51 and Figure 52 which show the

evolution with azimuth angle (over the complete primary carpet) of ASEL, CSEL and PLdB. Figure 51 shows

the influence of the addition of a physical rise-time to the TRAPS invicid results. The impact is negligible

for the CSEL metric which is mostly sensitive to low-frequencies, but is important on the ASEL and PLdB

metrics which are reduced by almost 10 dB while accounting for a rise time modeled by a simple empirical

tanh-shaping of each shock in the TRAPS signatures. The coefficient actually used in this empirical model

(number of ms per psf of the shock overpressure) does have an impact on the decibel evaluations, but it is

not as important as whether a correction is made or not (going from 1 ms per psf to 2 ms per psf reduces the

PLdB by about 1 dB, under-track). The value of 1 ms per psf has been retained for the rest of the study.

Finally, since the use of the multipole method has been shown to significantly reduce the impact of the choice

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Figure 48: Influence of the number of modes used in the multipole matching method applied on near-field

pressure data extracted at R/L = 2.0. Under-track results (left) and at azimuth 45deg (right).

Figure 49: Influence of the distance of extraction of pressure data in the CFD solution on the under-track

ground sonic boom signature with the use of the multipole matching method (10 Fourier modes)

of the distance of extraction of the near-field pressure in the CFD solution, the remaining sensitivity of the

ground signature to this parameter R/L has an impact on Decibels metrics, as it is shown in Figure 52. This

proves the high sensitivity of these metrics evaluation to different parameters of the sonic boom evaluation

process.

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Figure 50: Ground-propagated sonic boom signatures under-track and at different azimuthal angles (offtrack). Results obtained from pressure data extracted at R/L = 2.0 and using the multipole matching

method (with 10 Fourier modes)

Figure 51: Effect of the assumption made for the empirical rise time correction of the ground pressure signal

on the ASEL, CSEL and PLdB metric over the full primary carpet (under- and off-track).

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Figure 52: Effect of the extraction distance of the near-field CFD pressure on the ASEL, CSEL and PLdB

metric over the full primary carpet (under- and off-track).

Figure 53: Effect of mesh adaptation (in the region of the engines) on the ground-propagated sonic boom of

configuration LM 1044-3b (multipole matching method).

6.5 Summary of the sonic boom evaluation results for the different configurations investigated

Presentation of the different aircraft configurations studied

The geometry differences between the three configurations investigated are shown in Fig. 54 and correspond

to:

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Figure 54: Comparison of the geometries of the 3 aircraft configurations studied: LM 1044-1, LM 1044-3b

(before optimization) and LM 1044-3b-opt (optimized to recover the equivalent area of LM 1044-1).

• LM 1044-1;

• LM 1044-3b;

• LM 1044-3b after sonic boom optimization (equivalent area target),

Effect of the cruise angle

The effect of cruise altitude on the ground sonic boom signatures propagated with the TRAPS code are

illustrated in Figure 55 for the LM 1044-1 configuration.

Analysis of the configuration effect on the near-field and ground propagated sonic boom

The comparison of the evolution of near-field pressure signatures extracted from the SU2 CFD solution for

the three configurations investigated are shown in Figure 57 (under-track) and Figure 58 (off-track).

The configuration effects on the ground propagated sonic boom signatures are presented in Figure 59.

These results confirm the efficiency of the optimization started from the initial LM 1044-3b to recover the

ground sonic boom of LM 1044-1. The effects on the ASEL, CSEL and PLdB metrics are illustrated in

Figure 60. The analysis of these configuration effects has to be made in light of the relatively high sensitivity

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Figure 55: Ground sonic boom signature for configuration LM 1044-1 under-track and off-track for start-ofcruise (left) and end-of-cruise (right) altitudes.

Figure 56: Under-track and off-track PLdB evolutions with azimuthal angle for configuration LM 1044-1 for

start-of-cruise and end-of-cruise altitudes.

of these metrics to various parameters in the simulation process. Indeed, differences of the order of 1 dB are

observed from one configuration to the other, which is really close to the reasonably achievable accuracy of

these evaluations. The analysis of CSEL clearly shows the convergence of -3b OPTIM results towards the

-1 configuration results. However, the ASEL and PLdB results analysis are not as straightforward, since the

behavior evolves with the azimuth angle. It should also be noted that the present results show an advantage

of the -1 configuration over the -3b configuration in term of the ASEL and CSEL metrics, but a slight

drawback in terms of PLdB.

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Figure 57: Comparison of the under-track near-field pressure for the three aircraft geometries investigated

at different distances from the aircraft.

7 Conclusions

In this work we have improved the methodology for the simulation and optimal shape design of supersonic

aircraft using objective functions derived from the equivalent area distribution (and within the framework

of an adjoint methodology). We have also implemented a new methodology for the development of gradientenhanced Gaussian Process Regression surrogate models for MDO including noise tolerance. A grid adaptation methodology has been implemented, and the engine effects have been introduced in the direct and

adjoint problem with the implementation of new engine (and adjoint-engine) boundary conditions for powered simulations / optimizations. Careful calculations of the effect of these configuration optimizations on

the actual loudness metrics for the ground sonic boom have been performed and presented, and shows that

the recovery of equivalent area distributions has the desired effect on the reduction of the calculated loudness

metrics, although further work to define more effective equivalent area error measures should be pursued.

Finally the entire methodology has been tested in different equivalent-area based design problems.

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Figure 58: Comparison of the near-field pressure at azimuth 30deg (left) and 50deg (right) for the three

aircraft geometries investigated at different distances from the aircraft.

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Figure 59: Ground-propagated sonic boom for the three investigated configurations under-track and for three

off-track azimuth angles.

Figure 60: Comparison of PLdB, A- and C-SEL metrics over the primary carpet for the three aircraft

configurations

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