NASA/CR–2016-219201 

Application of Deterministic and Probabilistic 

System Design Methods and Enhancements of 

Conceptual Design Tools for ERA Project 

Final Report 

Dimitri N. Mavris and Jeff S. Schutte 

Georgia Institute of Technology, Atlanta, Georgia 

https://ntrs.nasa.gov/search.jsp?R=20160007420 2018-12-02T14:19:29+00:00Z
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National Aeronautics and 

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Hampton, Virginia 23681-2199 under Contract NNL12AA12C 

 

MAY 2016 

NASA/CR–2016-219201 

Application of Deterministic and Probabilistic 

System Design Methods and Enhancements of 

Conceptual Design Tools for ERA Project 

Final Report 

Dimitri N. Mavris and Jeff S. Schutte 

Georgia Institute of Technology, Atlanta, Georgia 

 
Available from: 

NASA STI Program / Mail Stop 148 

NASA Langley Research Center 

Hampton, VA 23681-2199 

Fax: 757-864-6500 

Acknowledgment 

This report documents a three year study conducted for the National Aeronautics and Space 

Administration, Aeronautics Research Mission Directorate, Integrated System Research Program, 

Environmentally Responsible Aviation (ERA) Project. This report was prepared under NASA 

Contract NNL12AA12C “Application of Deterministic and Probabilistic System Design Methods 

and Enhancements of Conceptual Design Tools for ERA Project” from March 2012 through March 

2015. This study was conducted by the Aerospace Systems Design Lab, School of Aerospace 

Engineering, Georgia Institute of Technology in Atlanta, GA. Georgia Tech is thankful for the 

opportunity to conduct system level assessments in order to help NASA meet the goal of 

simultaneously meeting ERA’s N+2 targets for fuel burn, noise and emissions reductions. Georgia 

Tech the leadership provided by the project manager Dr. Fayette Collier, the NRA COTR Dr. 

Frank Gern and other members of the NASA system assessment team Craig Nickol, Andy Hahn 

and Doug Wells. 

The Principal Investigator for Georgia Tech was Prof. Dimitri Mavris. The Co-PI was Dr. Jeff 

Schutte. The following individuals were technical contributors to this report: Dr. Jimmy Tai, Mr. 

Russell Denney, Dr, Hernando Jimenez, Dr. Brian Kestner, Mr. Chris Perullo, Mr. Carl Johnson, 

Dr. Hongjun Ran, Dr. Elena Garcia, Dr. Jon Sands, Mr. Turab Zaidi, Mr. Kyung Hak Choo, Mr. 

Adam Siegel, Mr. Gregory Busch, Mr. Michael Bozeman, Mr. Eric Fuerst, Mrs. Caroline Ingram, 

Ms. Mathilde Deveraux, and Mr. Michael Anderson.

The use of trademarks or names of manufacturers in this report is for accurate reporting and does not 

constitute an official endorsement, either expressed or implied, of such products or manufacturers by the 

National Aeronautics and Space Administration.
Table of Contents 

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

1.1 Background ...................................................................................................................... 1

1.2 Program Description ........................................................................................................ 2

1.2.1 Task 1: Identify Technology Collectors ................................................................... 2

1.2.2 Task 2: Model Technology Collectors in EDS ......................................................... 2

1.2.3 Task 3: Model and Assess ERA Technologies ......................................................... 3

1.2.4 Task 4: LTO and Cruise Emission Predictions ......................................................... 3

1.2.5 Task 5: Probabilistic Analysis of Technology Collectors and Portfolios ................. 3

2.0 Technology Portfolios ......................................................................................................... 3

2.1 Technology Report ........................................................................................................... 8

2.2 Reference Technology Collector ...................................................................................... 9

2.3 ERA Technology Portfolio............................................................................................. 10

2.4 N+2 Technology Portfolio ............................................................................................. 10

3.0 Aircraft Modeling ............................................................................................................. 11

3.1 EDS Background ............................................................................................................ 11

3.2 Aircraft Classes .............................................................................................................. 12

3.3 Engine Modeling ............................................................................................................ 15

3.3.1 Direct Drive ............................................................................................................ 15

3.3.2 Geared Fan .............................................................................................................. 16

3.3.3 Open Rotor .............................................................................................................. 36

3.4 Aircraft Configurations .................................................................................................. 54

3.4.1 Vehicle Sketch Pad ................................................................................................. 54

3.4.2 Tube and Wing ........................................................................................................ 55

3.4.3 Hybrid Wing Body .................................................................................................. 61

3.4.4 Over Wing Nacelle ................................................................................................. 77

3.4.5 Mid Fuselage Nacelle ............................................................................................. 78

3.4.6 Box Wing ................................................................................................................ 84

3.5 Vehicle Coefficients for Fleet Assessment .................................................................... 93

4.0 Technology Modeling ....................................................................................................... 94

4.1 Technology Assessment Process .................................................................................... 94

4.2 Technology Compatibility Matrix (TCM) ..................................................................... 95

4.3 Technology Impact Matrix (TIM) .................................................................................. 96

4.3.1 Vehicle - Input vector generation ........................................................................... 97

iii
4.3.2 DoE Generation for Surrogate Training– Procedure ............................................ 100

4.4 Advanced Combustor Modeling .................................................................................. 102

4.4.1 Emissions Prediction Methodology ...................................................................... 103

4.4.2 Description of Combustor Technology Levels ..................................................... 104

4.4.3 ModelCenterTM Environment ................................................................................ 105

4.4.4 1-D Flow Model .................................................................................................... 108

4.4.5 Model Tuning Process .......................................................................................... 109

4.4.6 Incorporation of Emissions Model into EDS ........................................................ 121

5.0 Surrogate Model Generation ........................................................................................... 122

5.1 Surrogate Modeling Methodology ............................................................................... 122

5.2 Surrogate Modeling Process Description ..................................................................... 125

5.3 Surrogate Model Results .............................................................................................. 126

6.0 Probabilistic Analysis ..................................................................................................... 127

6.1 Literature Review of Probabilistic Assessment Methods and Technology Impacts .... 127

6.1.1 Introduction and Background ............................................................................... 127

6.1.2 Analytical Approaches .......................................................................................... 128

6.1.3 Numerical Approaches .......................................................................................... 129

6.2 Overview of Methodology ........................................................................................... 142

6.3 Implementation of Flexible and Extensible Probabilisitic Analysis and Representative 

Results ..................................................................................................................................... 143

7.0 Results ............................................................................................................................. 147

7.1 RTC/ITD Data .............................................................................................................. 147

7.2 Multi-Objective Genetic Algorithm for N+2 Technology Pareto Frontier .................. 164

7.2.1 Motivation for Multi-Objective Optimiztion ........................................................ 164

7.2.2 Formulation of the Technology Portfolio Problem ............................................... 166

7.2.3 Implementation of the Multi-Objective Genetic Algorithm for the Technology 

Portfolio Problem ................................................................................................................ 169

7.2.4 Characterization of the Technology Tradespace ................................................... 171

7.2.5 Comparison of Vehicle Concepts’ Technology Tradespace with Pareto-Optimal 

Sets 178

7.3 Technology Portfolio Performance Comparison to ERA Goals .................................. 189

7.4 Comparison of ERA Technologies to Goal .................................................................. 197

8.0 References ....................................................................................................................... 200

9.0 Appendices ...................................................................................................................... 207

9.1 Appendix A: EDS Surrogates Response Names, Descriptions, and Units .................. 207

iY
9.2 Appendix B: EDS Vehicle Surrogate Fit Statistics ...................................................... 208

9.2.1 LSA Vehicle Fit Statistics ..................................................................................... 208

9.2.2 LSA OWN Vehicle Fit Statistics .......................................................................... 210

9.2.4 LTA Vehicle Fit Statistics .................................................................................... 212

9.2.6 LTA MFN Vehicle Fit Statistics ........................................................................... 214

9.2.8 LTA Box Wing Vehicle Fit Statistics ................................................................... 216

9.2.10 LTA HWB Vehicle Fit Statistics .......................................................................... 218

9.2.11 LSA UDF (Open Rotor) Vehicle Fit Statistics ..................................................... 220

9.2.12 LTA ....................................................................................................................... 221

9.2.13 HWB UDF (Open Rotor) Vehicle Fit Statistics ................................................... 222

 

Y
List of Figures 

Figure 1. NASA Subsonic Transport System Level Metrics .......................................................... 2

Figure 2. Technology Portfolio Venn Diagram .............................................................................. 4

Figure 3. Technology Portfolios Impact on Metrics ....................................................................... 5

Figure 4. Environmental Design Space (EDS) ............................................................................. 11

Figure 5. EDS 1995 Baseline Calibration Process ........................................................................ 13

Figure 6. Design Mission Profile .................................................................................................. 14

Figure 7. Cycle Optimization Trends............................................................................................ 21

Figure 8. Constrained Cycle Design Space Using EDS ............................................................... 22

Figure 9. Constrained Cycle Design Space Using Surrogate Models .......................................... 23

Figure 10. Constrained Cycle Design Space for RTC .................................................................. 25

Figure 11. Constrained Cycle Design Space for N+2 Technology Package ................................ 27

Figure 12. Constrained Design Space for N+2 Technology Package with Intercooler ................ 29

Figure 13. Fuel Burn vs. Noise Highlighting FPR Variation ....................................................... 31

Figure 14. Fuel Burn vs. Noise Highlighting FCDO Variation .................................................... 32

Figure 15. Fuel Burn vs. Noise Highlighting Extraction Ratio Variation .................................... 33

Figure 16. Fuel Burn vs. Noise Pareto Frontier ............................................................................ 34

Figure 17. SLS Thrust vs Fan Diameter ....................................................................................... 35

Figure 18. Cruise TSFC vs FPR Highlighting Fuel Burn Variation ............................................ 36

Figure 19. Open Rotor Modeling Methodology ........................................................................... 38

Figure 20. EDS Counter Rotating Propeller Map ......................................................................... 40

Figure 21. EDS Counter-rotating propeller map comparison to F31/A31 data ............................ 40

Figure 22. Propfan Weight Estimation [General Electric Company 1977] .................................. 43

Figure 23. GE Representative Open Rotor Improvements [Niskode 2010] ................................. 45

Figure 24. EDS Open Rotor High Speed Cruise Performance ..................................................... 45

Figure 25. EDS Open Rotor Takeoff Performance ....................................................................... 46

Figure 26. Projected Open Rotor Fuel Burn Benefit [Niskode 2010] .......................................... 46

Figure 27. Measured SPL versus Frequency for Mach Number of 0.2 at 12, 67.7, 100% Power 

and Geometric Angle of 90°. ........................................................................................................ 49

Figure 28. SPL versus Frequency for an Observer Angle of 90 Degrees and Variations in 

Number of Blades for the Fore Rotor ........................................................................................... 50

Figure 29. SPL versus Frequency for an Observer Angle of 90 Degrees and Variations in 

Circulation Index .......................................................................................................................... 50

Figure 30. Comparison of CRPFAN and NASA Experimental Data Source Noise for Observer 

Angle of 90° Pre-Parametric Study .............................................................................................. 51

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Figure 31. Comparison of CRPFAN and NASA Experimental Data Source Noise for Observer 

Angle of 90° Post-Parametric Study ............................................................................................. 52

Figure 32. Example VSP Wireframe and Shaded Model ............................................................. 55

Figure 33. Tube and Wing Configuration ..................................................................................... 56

Figure 34. RJ RTC VSP Model .................................................................................................... 59

Figure 35. SSA RTC VSP Model ................................................................................................. 59

Figure 36. LSA RTC VSP Model ................................................................................................. 60

Figure 37. STA RTC VSP Model ................................................................................................. 60

Figure 38. LTA RTC VSP Model ................................................................................................. 61

Figure 39. VLA RTC VSP Model ................................................................................................ 61

Figure 40. Hybrid Wing Body Configuration ............................................................................... 62

Figure 41. Boeing 0009A HWB Configuration [Bonet 2011] ...................................................... 63

Figure 42. OREIO HWB Configuration [Pitera 2011] ................................................................. 64

Figure 43. Comparison of Actual to Modeled 0009A Geometry, right image from [Bonet 2011]

....................................................................................................................................................... 65

Figure 44. Comparison of APEX Aero to 0009A values from [Bonet 2011]............................... 66

Figure 45. APEX Integration into EDS ........................................................................................ 66

Figure 46. HWB FLOPS Implementation Geometric Layout [Nickol 2009] ............................... 68

Figure 47. Aft Body Weight Accounted for by Wing Weight W4 Term [Nickol 2009] .............. 68

Figure 48. HWB VSP Model ........................................................................................................ 69

Figure 49. (Left) Primary Structure for FEM Modeling and (Right) Primary Structure with 

Overlay of Nastran Doublet Lattice Aerodynamic Paneling for Specified Geometry ................. 72

Figure 50. (Left) Primary Structure for FEM Modeling and (Right) Primary Structure with 

Overlay of Nastran Doublet Lattice Aerodynamic Paneling for Original Inputs ......................... 72

Figure 51. MATLAB Figures Generated for the Specified Geometry ......................................... 73

Figure 52. OREIO Weights Comparison for Different Analyses. [Gern 2013] ........................... 74

Figure 53. MATLAB Figures Generated for the Original Files ................................................... 75

Figure 54. Over Wing Nacelle Configuration ............................................................................... 78

Figure 55. Over Wing Nacelle Reference Geometry from [Hahn 2010] ...................................... 78

Figure 56. Concept of MFN Aircraft ............................................................................................ 79

Figure 57. NASA ERA Primary Mission Requirements (from [Manglesdorf 2012]) .................. 80

Figure 58. NASA ERA Reserve Mission Requirements (from [Manglesdorf 2012]) .................. 80

Figure 59. Calibration of ATF Engine for MFN Reference Aircraft ............................................ 83

Figure 60. Concept of Box Wing Aircraft .................................................................................... 84

Figure 61. Box Wing Aero and Weight Estimation FlowChart.................................................... 90

Yii
Figure 62. Open Wing System for Modeling Box Wing in APEX .............................................. 91

Figure 63. Calibration of Ultra Fan Engine for BXW Reference Aircraft ................................... 92

Figure 64. Technical Approach Process Chart ............................................................................. 95

Figure 65. Technology Compatibility Matrix (TCM) depiction showing the various relationships 

assumed present between ERA technology pairs. ........................................................................ 96

Figure 66. Example of the three point estimates of technology impacts for various ERA vehicles.

....................................................................................................................................................... 97

Figure 67. NOx Emissions Prediction Model ............................................................................. 103

Figure 68. Talon II Emissions Relative to CAEP/6 .................................................................... 105

Figure 69. Chemical Reaction Network (CRN) of Rich, Quick-Quench, Lean (RQL) Combustor 

(Screenshot of CHEMKIN GUI) ................................................................................................ 106

Figure 70. Experimental Results of Unmixedness Parameter Collected by Sturgess et al. ........ 107

Figure 71. Droplet Model ........................................................................................................... 108

Figure 72. Sensitivity Study Results for Model Tuning Parameters .......................................... 111

Figure 73. Calibration Result of Target Engines ........................................................................ 114

Figure 74. EINOx Predictions for Calibrated High OPR Engines .............................................. 115

Figure 75. Relationship between T3 and P3 with Intercooler Effect ........................................... 116

Figure 76. Results of Complete Model ....................................................................................... 117

Figure 77. EDS Implementation Test Results ............................................................................. 122

Figure 78. Surrogate Modeling Process ...................................................................................... 123

Figure 79. Neural Network Conceptual Diagram ....................................................................... 124

Figure 80. Probabilistic analysis interface dashboard in ERA systems analysis calculator ....... 146

Figure 81. Pareto frontier of non-dominated technology combinations – data point greyscale and 

markers for feature differentiation purposes ............................................................................... 172

Figure 82. Pareto frontier of technology combinations indicating number of technologies present 

in each solution ........................................................................................................................... 176

Figure 83. Pareto frontier of technology combinations with greyscale indicating Euclidean 

distance to N+2 goal vector (Left) and ideal objective vector (Right) ....................................... 177

Figure 84. Vehicle concepts for Pareto-Optimal set comparison ............................................... 179

Figure 85. Superposition of Pareto sets for vehicle concepts in the objective space .................. 181

Figure 86. Superposition of vehicle concept Pareto set convex hull projections in NOX percent 

reduction vs fuel burn percent reduction .................................................................................... 182

Figure 87. Pareto set clustering associated with identified influencing technologies - GF ........ 185

Figure 88. Pareto set clustering associated with identified influencing technologies - UDF ..... 186

Figure 89. Trends in the number of technologies per solution in Pareto optimal sets - GF ....... 187

Figure 90. Trends in the number of technologies per solution in Pareto optimal sets - UDF .... 188

Yiii
Figure 91. LSA T&W Technology Portfolio Performance ........................................................ 189

Figure 92. LSA OWN Technology Portfolio Performance ........................................................ 190

Figure 93. LSA OR Technology Portfolio Performance ............................................................ 191

Figure 94. LTA T&W Technology Portfolio Performance ........................................................ 192

Figure 95. LTA MFN Technology Portfolio Performance ......................................................... 193

Figure 96. LTA BW Technology Portfolio Performance ........................................................... 194

Figure 97. LTA HWB Technology Portfolio Performance ........................................................ 195

Figure 98. LTA HWB OR Technology Portfolio Performance .................................................. 196

Figure 99. ERA Comparison to Fuel Burn Goal ......................................................................... 197

Figure 100. ERA Comparison to Noise Margin Goal................................................................. 198

Figure 101. ERA Comparison to Emissions Goal ...................................................................... 199

 

i[
List of Tables 

Table 1. Technology Categories ..................................................................................................... 5

Table 2. Airframe Lightweight Structural and Sub-System Technologies ..................................... 6

Table 3. Airframe Aerodynamic Technologies .............................................................................. 6

Table 4. Airframe Noise Technologies ........................................................................................... 6

Table 5. Engine Fuel Burn Technologies ....................................................................................... 7

Table 6. Engine Emission Technologies ......................................................................................... 7

Table 7. Engine Noise Technologies .............................................................................................. 8

Table 8. RTC Baseline Technologies ............................................................................................. 8

Table 9. RTC Technologies and Impacts ........................................................................................ 9

Table 10. ERA Technologies ........................................................................................................ 10

Table 11. N+2 Technologies ......................................................................................................... 10

Table 12: EDS Baseline Vehicles ................................................................................................. 12

Table 13. Common Mission Profile Assumptions Across Seat Classes ....................................... 14

Table 14. Mission Assumptions .................................................................................................... 14

Table 15. Vehicle Matrix .............................................................................................................. 15

Table 16. Engine Specifications for Advanced Direct Drive Architecture .................................. 16

Table 17. Turbomachinery Component Design Points for Advanced Direct Drive Architecture 16

Table 18. Baseline Vehicle Values for All Technology Cycle Selection Study ........................... 18

Table 19. Engine Cycle Design Space for All Technology Cycle Selection Study ..................... 19

Table 20. EDS and Surrogate Model Cycle Optimization Comparison ....................................... 24

Table 21. Cycle Selection for RTC ............................................................................................... 25

Table 22. Cycle Selection for N+2 Technology Package ............................................................. 27

Table 23. Cycle Selection for N+2 Technology Package with Intercooler .................................. 30

Table 24. Sensitivity Variables for Geared Fan Pressure Ratio Study ......................................... 31

Table 25. EDS Open Rotor MDP Setup ....................................................................................... 41

Table 26. EDS Prop Design Comparison ..................................................................................... 44

Table 27. EDS Open Rotor Engine Cycle .................................................................................... 53

Table 28. EDS Open Rotor Noise Comparisons ........................................................................... 54

Table 29. Regional Jet 1995 Baseline ........................................................................................... 56

Table 30. Small Single Aisle 1995 Baseline ................................................................................. 56

Table 31. Large Single Aisle 1995 Baseline ................................................................................. 57

Table 32. Small Twin Aisle 1995 Baseline .................................................................................. 57

Table 33. Large Twin Aisle 1995 Baseline .................................................................................. 58

[
Table 34. Very Large Aircraft 1995 Baseline ............................................................................... 58

Table 35. EDS Validation to Boeing ERA Report ....................................................................... 63

Table 36. EDS Validation to OREIO Study ................................................................................. 64

Table 37. Comparison of APEX Aero to 0009A values from [Bonet 2011] ................................ 66

Table 38. Structural Input File Comparison ................................................................................. 70

Table 39. Parameter Weights Input File ....................................................................................... 71

Table 40. Boeing OREIO Weights Statement [Pitera 2005, Gern 2013] ..................................... 74

Table 41. Vehicle Characteristics of HWB STA RTC with a Geared Fan ................................... 76

Table 42. Vehicle Characteristics of HWB LTA RTC with a Geared Fan ................................... 77

Table 43. Vehicle Characteristics of HWB VLA RTC with a Geared Fan .................................. 77

Table 44. MFN Vehicle and Mission Assumptions ...................................................................... 81

Table 45. Double Deck Fuselage Modeling ................................................................................. 82

Table 46. MFN Reference Aircraft Model Calibration/Validation Results .................................. 83

Table 47. BXW Vehicle and Mission Assumptions ..................................................................... 84

Table 48. Symbols and Units of the Box Wing Weight Estimation Equation .............................. 88

Table 49. BXW Reference Aircraft Model Calibration/Validation Results ................................. 93

Table 50. Talon II Engines in ICAO Data Base ......................................................................... 104

Table 51. Combustor Technology Calibration Factors ............................................................... 110

Table 52. Results for Model Calibration at 30 OPR ................................................................... 113

Table 53. Ranges for DOE Variables ......................................................................................... 119

Table 54. EINOx Neural-Net Regression Independent Variables .............................................. 120

Table 55. EINOx Regression Model Statistics ........................................................................... 121

Table 56. 45 Response Metric Names for ERA Surrogates ........................................................ 126

Table 57. RJ Tube and Wing Geometry ..................................................................................... 147

Table 58. RJ Tube and Wing Vehicle Characteristics ................................................................ 147

Table 59. RJ Tube and Wing Propulsion System Characteristics .............................................. 148

Table 60. SSA Tube and Wing Geometry .................................................................................. 148

Table 61. SSA Tube and Wing Vehicle Characteristics ............................................................. 149

Table 62. SSA Tube and Wing Propulsion System Characteristics ........................................... 149

Table 63. LSA Tube and Wing Geometry .................................................................................. 150

Table 64. LSA Tube and Wing Vehicle Characteristics ............................................................. 150

Table 65. LSA Tube and Wing Propulsion System Characteristics ........................................... 151

Table 66. LSA Over-the-Wing Nacelle Geometry ..................................................................... 151

Table 67. LSA Over-the-Wing Vehicle Characteristics ............................................................. 152

[i
Table 68. LSA Over-the-Wing Propulsion System Characteristics ............................................ 152

Table 69. STA Tube and Wing Geometry .................................................................................. 153

Table 70. STA Tube and Wing Vehicle Characteristics ............................................................. 153

Table 71. STA Tube and Wing Propulsion System Characteristics ........................................... 154

Table 72. STA Hybrid Wing-Body Geometry ............................................................................ 154

Table 73. STA Hybrid Wing-Body Vehicle Characteristics ...................................................... 155

Table 74. STA Hybrid Wing-Body Propulsion System Characteristics ..................................... 155

Table 75. LTA Tube and Wing Geometry .................................................................................. 156

Table 76. LTA Tube and Wing Vehicle Characteristics ............................................................. 156

Table 77. LTA Tube and Wing Propulsion System Characteristics ........................................... 157

Table 78. LTA MFN Geometry .................................................................................................. 157

Table 79. LTA MFN Vehicle Characteristics ............................................................................. 158

Table 80. LTA MFN Propulsion System Characteristics ........................................................... 158

Table 81. LTA Box Wing Geometry .......................................................................................... 159

Table 82. LTA Box Wing Vehicle Characteristics ..................................................................... 159

Table 83. LTA Box Wing Propulsion System Characteristics ................................................... 160

Table 84. LTA Hybrid Wing-Body Geometry ........................................................................... 160

Table 85. LTA Hybrid Wing-Body Vehicle Characteristics ...................................................... 161

Table 86. LTA Hybrid Wing-Body Propulsion System Characteristics ..................................... 161

Table 87. VLA Tube and Wing Geometry ................................................................................. 162

Table 88. VLA Tube and Wing Vehicle Characteristics ............................................................ 162

Table 89. VLA Tube and Wing Propulsion System Characteristics .......................................... 163

Table 90. VLA Hybrid Wing-Body Geometry ........................................................................... 163

Table 91. VLA Hybrid Wing-Body Vehicle Characteristics ...................................................... 164

Table 92. VLA Hybrid Wing-Body Propulsion System Characteristics .................................... 164

Table 93. Comparison of N+2 goals and ideal objective vector ................................................. 177

Table 94. Performance statistics for top 30 (2%) solutions based on proximity to N+2 Goal and 

ideal objective ............................................................................................................................. 178

Table 95. Pareto set ranges and clustering technologies for vehicle concepts ........................... 182

Table 96. Broad impact critical technologies appearing in all Pareto-optimal solutions for all 

vehicle concepts .......................................................................................................................... 188

 

[ii
Nomenclature 

ACD Acoustic Data Module 

ADD Advanced Direct Drive 

ADP Aerodynamic Design Point 

AEDT Aviation Environmental Design Tool 

ANN Artificial Neural Network 

ANOPP Aircraft Noise Prediction Program 

AP Approach 

ASDL Aerospace Systems Design Lab 

ATF Advanced Turbofan 

BPR Bypass Ratio 

BRAINN Basic Regression Analysis for Integrated Neural Networks 

BXW Box Wing 

BWB Blended-Wing-Body 

CAEP Committee on Aviation Environmental Protection 

CDF Cumulative Distribution Function 

CMC Ceramic Matrix Composite 

CRN Chemical Reactor Network 

CRPFAN Counter Rotation Prop Fan Noise, NASA 

CO Cut Out throttle setting 

DDTF Direct Drive Turbofan 

DOE Design of Experiments 

EDS Environmental Design Space 

EINOX Emissions Indices NOx 

EPNdB Effective Perceived Noise in Decibels 

ERA Environmentally Responsible Aviation 

FAR Fuel to Air Ratio 

FEM Finite Element Method 

FCDO Lift-Independent Drag 

FCDI Lift-dependent Drag 

FLOPS Flight Optimization System, NASA 

FPI Fast Probability Integration 

FPR Fan Pressure Ratio 

GF Geared Fan 

GW Gross Weight 

HPC High Pressure Compressor 

HPC High Pressure Turbine 

HWB Hybrid Wing body 

ID Idle throttle setting 

ITD Integrated Technology Demonstrator 

JMP jmp statistical software program 

LDI Lean Direct Injection 

LPC Low Pressure Compressor 

LPT Low Pressure Turbine 

LSA Large Single Aisle 

LTA Large Twin Aisle 

[iii
LTO Landing/Takeoff 

MFN Mid-Fuselage Nacelle 

MLP Multi-Layer Perceptron 

MMC Metal Matrix Composite 

MPP Most Probable Point 

MOGA Multi-Objective Genetic Algorithm 

NASA National Aeronautics and Space Administration 

NSGA Non-dominated Sorting Genetic Algorithm 

NPSS Numerical Propulsion System Simulation 

NOx Nitrous Oxides 

OLH Optimal Latin Hypercube 

OPR Overall Pressure Ratio 

OR Open Rotor 

OWN Over Wing Nacelle 

PFR Plug-Flow Reactors 

PM Powder Metal 

PMC Polymer Matrix Composites 

PSR Perfectly-Stirred Reactors 

RJ Regional Jet 

RPM Rotations per Minute 

RQL Rich Quench Lean 

RTC Reference Technology Collector 

SME Subject Matter Expert 

SLS Sea Level Static 

SSA Small Single Aisle 

STA Small Twin Aisle 

T3 Stage 3 Temperature (Burner Inlet) 

T4 Stage 4 Temperature (Turbine Inlet) 

TBC Thermal Barrier Coating 

TC Technology Collector 

TCM Technology Compatibility Matrix 

TIM Technology Impact Matrix 

TO Take Off 

TOC Top of Climb 

TOGW Take Off Gross Weight 

TRL Technology Readiness Level 

TSFC Thrust Specific Fuel Consumption 

T&W Tube and Wing 

TWR, T/W Thrust to Weight Ratio 

UDF UnDucted Fan (Open Rotor) 

Vapp Approach Velocity 

VLA Very Large Aircraft 

WATE++ Weight Analysis of Turbine Engines, NASA 

WSR, W/S Wing loading 

[LY
1 

1.0 Introduction 

This report documents work done by the Aerospace Systems Design Lab (ASDL) at the 

Georgia Institute of Technology, Daniel Guggenheim School of Aerospace Engineering for the 

National Aeronautics and Space Administration, Aeronautics Research Mission Directorate, 

Integrated System Research Program, Environmentally Responsible Aviation (ERA) Project. 

This report was prepared under contract NNL12AA12C, “Application of Deterministic and 

Probabilistic System Design Methods and Enhancement of Conceptual Design Tools for ERA 

Project”. 

There are three additional reports that cover work performed under this contract as well. 

The first two reports titled “Environmentally Responsible Aviation Systems Analysis Report: 

Technology Portfolio and Advanced Configurations” and “Environmentally Responsible 

Aviation Systems Analysis Report: Technology Portfolio and Advanced Configurations 

Executive Summary” provide details of each of the technologies modeled in this report including 

a general description, TRL values, mapping of the impacts to various codes, values of the 

impacts and the compatibility with other technologies. The third report titled “Application of 

Strategic Planning Process with Fleet Level Analysis Methods and Enhancement of Technology 

Portfolio Assessment Methods for the ERA” uses the vehicles and technologies modeled in this 

report applied to various fleet scenarios and uses the surrogates and probabilistic information 

from this report for the creation of the ERA dashboard, a strategic planning and prioritization 

tool. 

1.1 Background 

The research within this report addressed the Environmentally Responsible Aviation 

(ERA) project goal stated in the NRA solicitation "to advance vehicle concepts and technologies 

that can simultaneously reduce fuel burn, noise, and emissions." To identify technology and 

vehicle solutions that simultaneously meet these three metrics requires the use of system-level 

analysis with the appropriate level of fidelity to quantify feasibility, benefits and degradations, 

and associated risk. In order to perform the system level analysis, the Environmental Design 

Space (EDS) [Kirby 2008, Schutte 2012a] environment developed by ASDL was used to model 

both conventional and unconventional configurations as well as to assess technologies from the 

ERA and N+2 timeframe portfolios. A well-established system design approach was used to 

perform aircraft conceptual design studies, including technology trade studies to identify 

technology portfolios capable of accomplishing the ERA project goal and to obtain accurate 

tradeoffs between performance, noise, and emissions. The ERA goal, shown in Figure 1, is to 

simultaneously achieve the N+2 benefits of a cumulative noise margin of 42 EPNdB relative to 

stage 4, a 75% reduction in LTO NOx emissions relative to CAEP 6 and a 50% reduction in fuel 

burn relative to the 2005 best in class aircraft. 

There were 5 research task associated with this research: 1) identify technology 

collectors, 2) model technology collectors in EDS, 3) model and assess ERA technologies, 4) 

LTO and cruise emission prediction, and 5) probabilistic analysis of technology collectors and 

portfolios. The following section provides the specifics for each of the tasks and associated 

subtasks. 

1
Figure 1. NASA Subsonic Transport System Level Metrics 

1.2 Program Description 

1.2.1 Task 1: Identify Technology Collectors 

In consultation with NASA, ASDL identified conventional and unconventional aircraft 

configurations as technology collectors (TCs) for use in the systems level analysis. There were 5 

aircraft configurations modeled as technology collectors. These aircraft configurations included 

conventional tube and wing (T&W), Hybrid Wing Body (HWB), Over Wing Nacelle (OWN), 

Mid-fuselage Nacelle (MFN) and Box Wing (BXW). Descriptions of the modeling of each of 

these aircraft can be found in section 3.4. There were also 3 engine configurations identified as 

technology collectors, a direct drive turbofan engine, a geared fan engine, and an open rotor or 

unducted fan. Descriptions of the modeling of each of these engines can be found in section 3.3. 

1.2.2 Task 2: Model Technology Collectors in EDS 

In order to perform a systems level assessment of the vehicle configurations identified in 

Task 1, each TC was parametrically modeled in EDS to allow for the evaluation of different 

technology portfolios and optimization of design parameters. The following subtasks were 

performed to generate the EDS models: 

• Develop VSP Models for New Unconventional Configurations (section 3.4.1) 

• Integrate VSP into EDS Framework (section 3.4.3.3) 

• Identify and Integrate Aerodynamic Prediction Codes into EDS (section 3.4.3.3) 

• Identify and Integrate Aircraft Component Weight Sizing Algorithm into EDS (section 

3.4.6.4 and section 3.4.3.4 for HWB centerbody) 

• Develop Engine Models (section 3.3) 

• Develop Vehicle Sizing and Performance Models (section 3.4) 

• Develop Noise Models (section 0 and 3.4) 

• Develop Fleet Coefficient Generator Algorithm (section 3.5) 

2
1.2.3 Task 3: Model and Assess ERA Technologies 

To model and assess ERA technologies, the following subtasks were performed: 

• Identify and Review ERA Technologies (section 2.0) 

• Collect and Compile Technology Data (section 2.1) 

• Select Engine Cycles for Technology Portfolios (section 3.3) 

• Technology Portfolio Assessment and Surrogate Model Generation (section 4.0) 

1.2.4 Task 4: LTO and Cruise Emission Predictions 

To assess the capabilities of advanced combustion technologies to meet the ERA landing 

and take-off (LTO) and cruise emissions metrics, ASDL has developed a semi-analytical model 

to predict emissions during a three-year research effort sponsored by NASA Glenn Research 

Center. This task was to mature the emissions prediction model for advanced combustors to a 

level appropriate for systems-level technology assessments for EDS. The following subtasks 

were performed for the emissions modeling and can be found in section 4.4: 

• Reformulate Chemical Reactor Network Models 

• Reformulate 1-D Flow Models 

• Verify Predictive Capability of Reformulated Models for LTO NOx 

• Extend Emissions Model for Cruise NOx Predictions 

• Improve Emissions Model Integration for Design Space Exploration 

1.2.5 Task 5: Probabilistic Analysis of Technology Collectors and 

Portfolios 

A probabilistic technology assessment was conducted that includes a rigorous, traceable, 

and quantitative assessment of technology benefits which explicitly incorporates uncertainty and 

risk. The following subtasks were performed for the probabilistic assessment and can be found in 

section 6.0: 

• Conduct Literature Review of Uncertainty Quantification 

• Solicit NASA Technology SMEs for Input to Construct Uncertainty Distributions 

• Perform Probabilistic Analysis and Visualize Results 

2.0 Technology Portfolios 

The ERA mission is to conduct system research to bridge the gap between fundamental 

research (TRL 1-4) and product prototyping (TRL 7) thus expanding the viable and wellinformed trade space for vehicle design decisions enabling simultaneous realization of national 

noise, emissions, and performance goals. ERA is focused on N+2 timeframe technologies, those 

that can achieve a TRL of 4-6 by 2020 as shown in Figure 1. The initial list of technologies 

compiled for this report was selected based on the current estimate of TRL and the anticipated 

time to TRL 6 based on work by [Peisen 1999] and modified by direct input from NASA 

technologists, engineers and management. 

Three different portfolios of technologies have been created for this report, the Reference 

Technology Collector (RTC) technology portfolio, the ERA technology portfolio and the N+2 

technology portfolio. Each portfolio builds on the subsequent portfolio, as shown in the Venn 

3
diagram in Figure 2 unless there is an incompatibility in which case a new technology replaces 

one from the previous portfolio. An example would be PRSEUS composite system replacing 

sandwich composites. Since the ERA goals are referenced to 2005 best in class vehicles, some 

additional technologies that are potentially on current aircraft but are not included on the 

reference system have been grouped as the Reference Technology Collector (RTC) baseline 

technologies. These technologies are included to determine the absolute improvements in aircraft 

performance with respect to ERA goals but are not included in the ERA portfolio as they are 

already in the fleet. There should not be an expectation for ERA technology development in 

these areas unless to further improve the SOA. 

Figure 2. Technology Portfolio Venn Diagram 

The ERA technology portfolio includes technologies developed in both phase 1 and 

phase 2 of the ERA project. The phase 2 technologies are referred to as the Integrated 

Technology Demonstrators (ITD). The third group of technologies are the N+2 technologies. 

These were technologies that NASA ERA was not maturing but which were found to be 

potentially available in the N+2 timeframe. As such they represent technologies being matured 

by other government agencies and by industry. While the actual technologies being developed 

outside the ERA project might differ materially from those selected for the N+2 portfolio, their 

impact on the different metrics is most likely captured. For example, there may be several 

potential technologies that reduce flap edge noise. The selection of one over the other would be 

left to the detailed design. For the system assessment study, they can be treated equally since 

they would be modeled to have the same impact on the flap edge noise source. Thus, it is more 

important for the system assessment to capture the impact than the specific technology. An 

example of the expected progression in performance as one advances from RTC to ERA to N+2 

technology portfolios is shown in Figure 3. 

ERA

N+2

RTC

RTC-ERA

RTC-ERA-N+2

ERA-N+2

4
Figure 3. Technology Portfolios Impact on Metrics 

A total of 68 technologies were evaluated across the three technology portfolios. They 

can be grouped by their primary impact into one of the seven following categories: Airframe 

lightweight structure and subsystem technologies, Airframe aerodynamic technologies, Airframe 

noise technologies, Engine fuel burn technologies, Engine noise technologies, Engine emission 

technologies and RTC Baseline Technologies. The number of categories is shown in Table 1. A 

listing of the technologies in each category is shown in Table 2 through Table 8. 

Table 1. Technology Categories 

Number of Technologies Category 

11 Airframe lightweight structure and subsystem technologies 

6 Airframe aerodynamic technologies 

6 Airframe noise technologies 

16 Engine fuel burn technologies 

11 Engine noise technologies 

5 Engine emission technologies 

13 RTC Baseline Technologies 

5
Table 2. Airframe Lightweight Structural and Sub-System Technologies 

Technology ID Name 

T3 Damage Arresting Stitched Composites 

T6 Electro Mechanical Actuator System 

T7 Solid Oxide Fuel Cell APU 

T78 Primary Structure Joining Methodologies 

T79 Damage Tolerant Laminates 

T80 Advanced Sandwich Composites 

T81 Post Buckled Structures 

T82 Out-of-Autoclave Composite Fabrication 

T83 Unitized Metallic Structures 

T84 Tow Steered Composite Structures 

T94 Adaptive Compliant Trailing Edge 

Table 3. Airframe Aerodynamic Technologies 

Technology ID Name 

T10 Hybrid Laminar Flow Control - Suction 

T11 Natural Laminar Flow Control 

T12 Riblets 

T66 Active Flow Control for Tail 

T68 Advanced Aero Wing 

T69 HLFC Discrete Roughness Elements 

Table 4. Airframe Noise Technologies 

Technology ID Name 

T14 Continuous Moldline Link for Flaps 

T15 Flap Fences / Flaplets 

T16 Landing Gear Integration 

T17 Flap Edge Treatment 

T18 Slat Inner Surface Acoustic Liner 

T19 Slat-Cover Filler 

6
Table 5. Engine Fuel Burn Technologies 

Technology ID Name 

T20 Active Compressor Clearance Control 

T21 Active Compressor Flow Control 

T22.1 Compressor Intercooler 

T22.2 Cooled Cooling 

T23 Active Turbine Clearance Control 

T24 Active Turbine Flow Control 

T25 Active Film Cooling 

T26 Advanced Powder Metallurgy (PM) Disk Bonded to a Single 

Crystal (SX) Rim

T28 Advanced Turbine Superalloys 

T29 Ceramic Matrix Composites (CMC’s) 

T32 Highly Loaded Compressor 

T33 Highly Loaded Turbine 

T67 Advanced Engine Components 

T72 Low Interference Nacelle 

T74 Thrust Reversers - Nacelle 

T75 PAI Thrust Reverser 

 Table 6. Engine Emission Technologies 

Technology ID Name 

T61 Active Combustion Control 

T62 Lean Direct Injection (LDI) 

T63 Lightweight CMC Liners 

T64 LPP Combustor 

T65 RQL Combustor 

7
Table 7. Engine Noise Technologies 

Technology ID Name 

T40 Acoustic Splitter 

T41 Advanced Blade Tone Control 

T42 Noise Cancelling Stator 

T47 Fluidic Injection 

T52 - GF Lip Liner (for GF) 

T53 Over-the-Rotor Treatment 

T54 Compound Rotor Sweep for Low Tip Speed Fan 

T56 Soft Vane 

T57 Stator Sweep & Lean 

T59 Variable Geometry Chevrons 

T76 Active Pylon Shaping/Blowing 

Table 8. RTC Baseline Technologies 

Technology ID Name 

T1 Composite Technologies 

T4 Gust Load Alleviation 

T9 Excrescence Reduction 

T27 Advanced TBC Coatings 

T36 + T37 Polymer Matrix Composites (PMC) 

T43 Aft Cowl Liners 

T45 Combustor Noise Plug Liner 

T46 Fixed Geometry Core Chevrons 

T52 - ADD Lip Liner (for ADD) 

T60 Zero Splice Inlet 

T77 Variable Area Nozzle (for GF) 

T92 Blisk Compressors 

T93 Ti-Al Low Pressure Turbine Components 

2.1 Technology Report 

Two additional reports titled “Environmentally Responsible Aviation Systems Analysis 

Report: Technology Portfolio and Advanced Configurations” and “Environmentally Responsible 

Aviation Systems Analysis Report: Technology Portfolio and Advanced Configurations 

Executive Summary” provide details of each of the technologies modeled in this report including 

a general description, TRL values, mapping of the impacts to various codes, values of the 

impacts and the compatibility with other technologies. The technologies that make up each of the 

portfolios is listed in the following sections. 

8
2.2 Reference Technology Collector 

The RTC portfolio is shown in Table 9 with impact and values for each technology. 

Table 9. RTC Technologies and Impacts 

Name Impact Value 

RTC Composite Technologies 

Fuselage Weight -5% 

Bending Material Weight -15% 

Control Surfaces/Spars/Ribs Weight -5% 

Horizontal Tail Weight -15% 

Vertical Tail Weight -15% 

Gust Load Alleviation Gust Load -15% 

Excrescence Reduction Lift independent drag -0.5% 

Advanced TBC Coating HPT Blade/Vane Temperature +150 

LPT Blade/Vane Temperature +150 

Polymer Matrix Composites 

Inlet Nacelle Density -46.9% 

Fan Case Density -48% 

Fan Stator Density -48% 

Bypass Duct Density -48% 

Fan Blade -34.8% 

Aft Cowl Liners 

Bypass Duct Pressure Drop +1.8% 

Fan Discharge Noise Suppression TO -3 dB 

Fan Discharge Noise Suppression AP -3 dB 

Combustor Noise Plug Liner 

Core Noise Reduction at TO -3 dB 

Core Noise Reduction at AP -3 dB 

Fixed Geometry Core Chevrons Core Nozzle Velocity Coefficient -0.5% 

Core Nozzle Perimeter Ratio 1.3 

Lip Liner for ADD 

Fan Inlet Noise Reduction at TO -1 dB 

Fan Inlet Noise Reduction at AP -3 dB 

Zero Splice Inlet Fan Inlet Noise Reduction at TO -2 dB 

Variable Area Nozzle for GF Nozzle Exit Area Variable 

Blisk Compressors Compressor Disk Material Density -10% 

Ti-Al Low Pressure Turbine 

Components 

LPT Stator Material Density -50% 

LPT Forward Blade Material Density -50% 

LPT Aft Blade Material Density -50% 

9
2.3 ERA Technology Portfolio 

Table 10. ERA Technologies 

ERA Technologies 

Damage arresting stitched composites (fuselage and wing) 

Natural laminar flow wing (RJ, SSA, LSA only) 

Active flow control tail 

Advanced aero wing 

Flap edge noise 

Landing gear integration (main and nose) 

Highly loaded compressors 

Lightweight ceramic matrix composite liners (combustor) 

Advanced combustor 

Adaptive compliant trailing edge 

Discrete Roughness Elements 

UHB Engine (Geared fan) 

Advanced engine components (efficiency improvements) 

SMA variable area nozzle 

Soft vanes 

Over the rotor acoustic treatment 

Laminar flow nacelle 

Low interference nacelle 

Advanced PM disk 

Advanced turbine super alloys 

CMC exhaust core nozzle 

Lightweight CMC liners 

Advanced TBC coatings 

CMC turbine vanes

Open rotor engine 

2.4 N+2 Technology Portfolio 

Table 11. N+2 Technologies 

N+2 Technologies 

Active Compressor Clearance Control Active Pylon Shaping/Blowing Acoustic Splitter 

Active Compressor Flow Control Active Turbine Clearance Control Active Film Cooling 

Out-of-Autoclave Composite Fabrication Advanced Sandwich Composites Metallic Structures 

Highly Loaded Turbine Unitized Advanced Turbine Superalloys Lip Liner - GTF 

Compound Rotor Sweep for Low Tip Speed Fan Post Buckled Structures Fluidic Injection 

Electro Mechanical Actuator System Damage Tolerant Laminates PAI Thrust Reverser 

Hybrid Laminar Flow Control - Suction Discrete Roughness Elements Riblets 

Ceramic Matrix Composites (CMC’s) Variable Geometry Chevrons Slat-Cover Filler 

Porous / Absorbent Flap Edge Treatment Advanced Blade Tone Control Solid Oxide Fuel Cell APU 

Primary Structure Joining Methodologies Compressor Intercooler Stator Sweep & Lean 

Slat Inner Surface Acoustic Liner Active Turbine Flow Control Thrust Reversers - Nacelle 

Tow Steered Composite Structures Noise Cancelling Stator Flap Fences / Flaplets 

10
3.0 Aircraft Modeling 

3.1 EDS Background 

The foundation for this systems analysis capability is advanced methods developed at 

ASDL coupled with the integrated aircraft modeling and simulation environment known as EDS. 

EDS is capable of predicting the fuel burn, NOx emissions, and noise metrics in a single 

environment with an automated link to provide necessary data for a fleet level assessment (see 

Figure 4). The majority of EDS analysis components are NASA developed programs which have 

been integrated using the object oriented software, Numerical Propulsion Simulation System 

(NPSS [Lytle, 1999 and Lytle, 2000]). The resulting engine and aircraft models from EDS are 

directly transferrable back to NASA since they are natively NASA software models. EDS is 

capable of modeling the thermodynamic performance (NPSS) of any engine cycle coupled with a 

parametric component map generation tool (CMPGEN [Converse, 1984 and Glassman, 1995]) 

and with a 1-D aeromechanical design/analysis for flowpath and weight estimation purposes 

(WATE++ [Onat, 1979 and Tong, 2002]). This propulsion system simulation is well suited to 

assess the ERA engine technology portfolio and is unique in its ability to match the engine to a 

sized airframe using a simultaneous, multi-design point sizing algorithm develop by ASDL 

[Schutte, 2009]. The propulsion simulation module is coupled with the mission analysis module 

(FLOPS [McCullers, 2009]) in an iterative fashion to ensure that all coupling variables are 

internally consistent and have converged. EDS ensures this convergence and consistency in order 

to provide more accurate mission fuel burn results and more accurate data to the noise prediction 

module (ANOPP [Zorumski, 1982]) to assess acoustic impacts, including the generation of 

engine state tables from NPSS and the resulting aircraft noise flight trajectories for the sized 

vehicle. This data is used within ANOPP to generate the three certification noise values for 

sideline, cutback and approach as well as characteristic NPD curves. 

Figure 4. Environmental Design Space (EDS) 

11
Another important EDS module is the NOx prediction algorithm which comprises a low 

fidelity flow model, implemented in NPSS and integrated with the engine cycle model, and a 

separate Chemical Reactor Network (CRN) implemented in Chemkin. The low fidelity flow 

model provides engine cycle and combustor geometry information to the CRN, and the CRN 

performs the non-equilibrium chemical kinetics calculations to predict the emissions. The current 

emissions prediction model was developed during a three-year research effort sponsored by 

NASA Glenn Research Center (NRA NNX07AO08A). The model has been validated against 

data from the ICAO engine emissions databank [ICAO, 2010], as well as some advanced lowemissions designs such as the Lean-Direct-Inject (LDI) combustor. Finally, EDS is capable of 

direct linkage to a fleet level analysis since it generates the vehicle/engine/noise information 

necessary to provide replacement aircraft [de Luis, 2008]. EDS coupled with ASDL's wellstructured technology assessment approach utilizing deterministic and probabilistic methods 

allow NASA to obtain accurate tradeoffs between performance, noise, and emissions. 

3.2 Aircraft Classes 

The baseline, tube and wing, vehicle classes used as the starting points for this project are 

summarize in Table 12. These tube and wing baseline aircraft systems serve as the calibration 

points for vehicles in each passenger class. The calibration process followed for each passenger 

class is depicted in Figure 5. 

Table 12: EDS Baseline Vehicles 

Passenger Class Airframe Engine 

Regional Jet (RJ) CRJ900 CF34-8C5 

Small Single Aisle (SSA) 737-200 CFM56-7B27 

Large Single Aisle (LSA) 737-800 CFM56-7B27 

Small Twin Aisle (STA) 767-300ER CF6-80C2 

Large Twin Aisle (LTA) 777-200ER GE90-94B 

Very Large Aircraft (VLA) 747-400 PW4056 

12
Figure 5. EDS 1995 Baseline Calibration Process 

The vehicle calibration process begins with collecting data from public domain on the 

propulsion system, aircraft, mission, etc. The sources data include FAA’s type certification 

datasheets, airport planning documents, engine cross section data, three-view drawings, etc. 

Next, the engine model is developed using NPSS and calibrated for both on-design and offdesign (SLS) conditions. The calibrate engine results are tabular engine performance data table, 

propulsion system weight, maximum diameter and length. In parallel of the engine model 

development and calibration exercise, the aircraft model, including mission analysis, is 

developed. The aircraft calibration is performed with calibrated engine performance data. The 

procedure of calibrating the engine first and then calibrating the aircraft model is crucial to 

establish a well-grounded model. 

The mission segments assumed for the design mission profile are taxi-out, take-off, 

climb, cruise, descent, land, and taxi-in with a reserve mission, as depicted in Figure 6. The 

10,000 foot cruise segment is needed in order to extract performance data needed to calculate 

vehicle performance coefficients for fleet assessments. Step cruise is also performed during the 

cruise segment. The common elements of the mission profile for all vehicles classes are 

summarized in Table 13, and the mission profile elements that are specific to each vehicle class 

are summarized in Table 14. 

Gather Public 

Domain Information

Engine 

Mission

Develop Engine

Model

Calibrate Engine

Model

Develop Aircraft

Model

Calibrate Aircraft

Model

EDS 1995 Baseline

Vehicles

etc.

Aircraft

13
Figure 6. Design Mission Profile 

Table 13. Common Mission Profile Assumptions Across Seat Classes 

Parameter Value 

Baggage Weight per Passenger (lbs) 30 

Weight per Passenger (lbs) 180 

Reserve Cruise Altitude (ft) 18,000 

Reserve Cruise Mach 0.3 

Reserve Cruise Mach 0.3 

Reserve Hold Time (min) 30 

Table 14. Mission Assumptions 

Mission Profile Elements RJ SSA LSA STA LTA LQ 

Design Range (nmi) 1,980 3,330 2,960 5,920 7,530 7,060 

Design Cruise Mach 0.80 0.78 0.78 0.80 0.84 0.85 

Design Payload (lbs) 18,060 26,880 37,170 54,810 64,050 87,570 

No. of First Class Passengers 0 8 12 24 24 32 

No. of Business Class Passengers 0 0 0 0 54 76 

No. of Tourist Class Passengers 86 120 165 237 227 309 

Reserve Range (nmi) 100 200 200 200 200 200 Descent and land Step Cruise

Taxi outTakeoff to M=0.3

Climb (min fuel)

Climb (min fuel)

Cruise @ Design M#

Taxi in

Cruise at 10,000 ft

Design Range

Cruise @ Design M#

Reserve Range

Reserve 

Altitude = 

18,000

Reserve

Cruise M= 0.3

with 30 min hold

Cruise altitudes optimized by FLOPS

14
In addition to the tube and wing (T&W) configuration, unconventional configurations 

were also modeled. The unconventional airframes include hybrid wing body (HWB), over-wingnacelle (OWN), and mid-fuselage-nacelle (MFN), and boxed wing (BXW) configurations. 

Another combinatorial factor is the engine architectures modeled which are the advanced direct 

drive (ADD), geared fan (GF), and open rotor (OR) architectures. Table 15 shows the airframepropulsion combinations modeled for each vehicle seat class. 

Table 15. Vehicle Matrix 

RJ SSA LSA STA LTA VLA 

T&W – ADD T&W – ADD T&W – ADD T&W – ADD T&W – ADD T&W – ADD 

T&W – GF T&W – GF T&W – GF T&W – GF T&W – GF T&W – GF 

 T&W – OR 

 HWB – ADD HWB – ADD HWB – ADD 

HWB – GF HWB – GF HWB – GF 

 HWB – OR 

 OWN – ADD MFN – ADD 

OWN – GF MFN – GF 

 BXW – GF 

Only a subset of the vehicle listed in Table 15 were included for surrogate generation (see 

section 5.0). These include the T&W – GF, T&W – OR and OWN – GF in the LSA class and the 

T&W – GF, HWB – GF, HWB – OR, MFN – GF and BXW – GF in the LTA class. 

3.3 Engine Modeling 

The values of specific assumptions for the RTC and ITD engines can be found in the 

Propulsion System Characteristics tables located in section 7.1. 

3.3.1 Direct Drive 

The Advanced Direct Drive (ADD) engine represents an improved version of 

conventional turbofans in terms of both bypass ratio and overall pressure ratio. It maintains the 

traditional architecture with the low pressure turbine directly driving the fan. The engine 

technology level modeled for the ADD engine within EDS is anchored to the CFM LEAP-X for 

the single aisle thrust class (LSA) and the GE GEnX for the twin aisle thrust class (LTA). The 

higher bypass ratio will allow for savings in specific fuel consumptions and the higher overall 

pressure ratio represents gains in efficiency. An increase in allowed turbine inlet temperature or 

overall pressure ratio will result in larger allowable bypass ratios. The advanced direct drive 

baseline, which represents approximately 2010 level technology and from which the N+2 

technologies will be integrated into, operates at a fan pressure ratio of about 1.5, an SLS 

uninstalled overall pressure ratio around 45, and bypass ratios of about 10-12. The ADD engines 

are predicted to have up to 12% improvement in fuel efficiency, 50% reduction in emissions, and 

15 EPNdB margin on FAA Stage IV noise regulations [McKay 2009]. GE proposes an advanced 

15
configuration operating at a bypass ratio of around 9, which could yield around 13 EPNdB 

margin on FAA Stage IV noise regulations and around 16% improvement in fuel burn [Hughes 

2008]. A recent study from NASA suggests the Large Single Aisle (LSA) configuration would 

have a 15% improvement in TSFC over the 1995 baseline engine [Guynn 2011], which agrees 

with the EDS baseline model prediction of 14.5%. Some key engine specification parameters and 

assumptions for each engine class (LSA and LTA) are summarized in Table 16, and the 

turbomachinery component design assumptions are summarized in Table 17. 

Table 16. Engine Specifications for Advanced Direct Drive Architecture 

Engine Specifications LSA Engine LTA Engine 

Thrust Class (SLS, lbs) 25,180 83,260 

Bypass Ratio (ADP) 10.1 12.4 

Overall Pressure Ratio (ADP) 46.7 42.6 

Fan Diameter (in) 69.4 123.5 

Compressor Stages (Fan/LPC/HPC) 1/4/10 1/3/10 

Turbine Stages (HP/LP) 2/7 2/7 

Number of Fan Blades 18 18 

Table 17. Turbomachinery Component Design Points for Advanced Direct Drive Architecture 

Component Design Point (ADP) LSA Engine LTA Engine 

Fan Polytropic Efficiency 0.919 0.925 

Fan Pressure Ratio 1.55 1.50 

LPC Polytropic Efficiency 0.933 0.904 

LPC Pressure Ratio 1.39 1.26 

HPC Polytropic Efficiency 0.905 0.926 

HPC Pressure Ratio 22.0 23.0 

HPT Adiabatic Efficiency 0.899 0.925 

HPT Pressure Ratio 4.47 4.26 

LPT Adiabatic Efficiency 0.900 0.933 

LPT Pressure Ratio 6.81 7.80 

3.3.2 Geared Fan 

3.3.2.1 Cycle Selection Studies 

Cycle optimization is an important consideration for technology assessment. As shown in 

[Hall 2007], the optimization of the cycle for a given technology package may result in about a 

4% reduction in engine TSFC. However, depending on the technology package being analyzed, 

the optimal cycle may drastically change. For example the optimal OPR on an engine using 

current technology is much lower than on an engine that has advanced cooling technologies. 

Further technologies such as an intercooler may push the optimal OPR even higher. In 1999, 

Kurzke proposed using different numerical optimizers wrapped around an engine cycle code to 

find the optimal cycle of a derivative engine [Kurzke 1999]. More recent studies have used 

methods such as genetic algorithms integrated with an engine cycle code to optimize the cycle 

and vehicle design simultaneously [Rallabhandi 2008, Ghenaiet 2010]. Other studies by NASA 

16
have explored how optimal cycles differ based on the weightings of the multi- objective function 

used to perform the optimization [Berton 2010]. 

As the number of technologies and the resulting potential technology packages becomes 

large, the selection of the optimal cycle for multiple potential technology packages becomes 

challenging. For the NASA ERA project, there are 68 potential technologies being considered. 

The number of permutations of technology combinations becomes too large to manage 

effectively if each combination were to be investigated on its own. Performing a direct engine 

cycle optimization on each potential package is virtually impossible. Even if a smaller subset of 

technology packages is chosen, the computational burden of performing the optimization using 

the cycle analysis code directly remains large. This is especially true when the analysis code 

(EDS) is really several codes integrated in a multi-disciplinary optimization (MDO) framework. 

Such environments can assess multiple vehicle characteristics such as fuel burn, emissions, and 

noise. Furthermore, as assumptions about the impacts of the technologies change, the 

corresponding optimal cycle may also change. Therefore two cycle selection studies were 

performed for the geared fan. Although the metrics of interest for the NASA ERA project are 

fuel burn, noise, and NOx, both studies focused only on cycle optimization relative to fuel burn 

and noise. The first study [Kestner 2011, Kestner 2012] looked at all available technologies 

applied to a LTA T&W vehicle. To enable faster cycle optimization and analysis of the 

technologies, surrogate models trained on the higher fidelity analysis codes were used. The 

second study examined cycle selection for the ERA ITD technology package applied to a HWB 

vehicle with variation in some parameters to understand certain sensitivities. It was thought that 

applying the geared fan to the HWB with its better aerodynamics and noise shielding might 

result in a different cycle preference. 

3.3.2.1.1 All Technology Portfolio on LTA T&W 

Due to the large number of design (technology and cycle) variables it is nearly 

impossible to query the entire design space. In the present analysis 68 technologies being 

investigated in addition to the cycle parameters that are being varied. The end result is a 

design space that consists of more than 100 independent input variables. Even if one were to 

simply evaluate only the minimum and maximum values for a given variable there are more 

than 1.26 * 1030 permutations. As a result this work employs surrogate modeling techniques to 

sample the design space in a flexible and relatively rapid manner. Section 5.0 details the 

methodology used to create surrogate models. 

The baseline model for a large twin aisle configuration aircraft in EDS is based on a Boeing 777-

200 with General Electric GE90-94B engines. The relevant cycle parameters in addition to 

vehicle fuel burn and noise margin for the baseline vehicle are shown in Table 18. Similar to the 

ERA project, the fuel burn reduction in the subsequent runs will be compared to this baseline 

value. On the other hand, all noise results will be compared against stage 4 noise stringency 

levels per the ERA goals. 

17
Table 18. Baseline Vehicle Values for All Technology Cycle Selection Study 

Parameter Value 

Inputs 

FPR 1.58 

LPCPR 1.26 

HPCPR 20 

T4max, R 3450 

Gear Ratio 1 

Extraction Ratio 1.09 

Outputs 

Fuel Burn, lbs 246519 

Noise Margin, dB 11.3 

SLS BPR 8.3 

SLS OPR 40.5 

3.3.2.1.1.1 Technology Design Space 

The technology design space is defined data formally recorded into the Technology 

Interaction Matrix and Technology Compatibility Matrix respectively (see sections 4.2 and 4.3). 

These matrices provide both traceability and transparency to the technology modeling and 

auditing process. Using these matrices, a technology design space can be defined. It is important 

to note that while there are more than 100 inputs into EDS for this study, each input can be 

labeled as either a “technology” or “cycle” variable. The majority of inputs fall into the 

“technology” category. These variables are set as a function of the technology package that has 

been selected. Furthermore, the “technology” inputs are for the most part invariant with respect 

to the chosen engine cycle. In order to apply a technology package the correct input vector must 

be selected using the TIM and TCM tables. 

3.3.2.1.1.2 Cycle Design Space 

The cycle design space is defined through the inputs that fall into the “cycle” category. 

These are design variables such as engine FPR and OPR. Ranges are established for these 

variables by understanding historical trends, other published studies, and assessment of current 

industry capabilities. The significant cycle parameters were determined to be bypass ratio (BPR), 

overall pressure ratio (OPR), maximum turbine inlet temperature, gear ratio, and extraction ratio. 

Due to the nature of gas turbine thermodynamics there exists an optimum fan pressure ratio for a 

given jet velocity ratio. The jet velocity ratio is defined as the ratio of the core nozzle jet velocity 

divided by the bypass nozzle jet velocity [Guha 2001]. Effectively the engine cycle designer may 

chose two of the three design variables of FPR, BPR, or jet velocity ratio. The third will be a 

resulting fallout of the cycle. For simplicity of calculation the EDS environment has been set up 

to take in FPR and extraction ratio with BPR as a resulting calculated parameter. Extraction 

ratio, defined as the ratio of the total pressure of the bypass stream relative to the core stream, is 

representative of the total potential energy in the two streams. Since jet velocity ratio is also 

indicative of the energy in the two streams, the two parameters may be used interchangeably in 

engine design. The benefit allowing the BPR to be calculated is that the resulting engine will be 

near-optimal with additional fine tuning allowed by the use of extraction ratio. If the designer 

were to input FPR and BPR it would be far easier to select designs that are far from the optimum 

FPR-BPR relationship and as a result many analysis runs would be wasted. 

Over the last few decades there has been an increase in the OPR of in-production engines 

[Young 2002]. This upward trend has been driven primarily by improvements in material 

18
temperature limits in the compressor and turbine, improvements in compressor performance, as 

well as improvements in turbine blade cooling technology. With a higher OPR, there is an 

increase in thermal efficiency and corresponding reduction in TSFC and fuel burn, which is a 

driving motivation for this study. The current 2010 maximum OPR is around 45; this is the value 

used for the RTC geared turbofan cycles. As a guideline, the anticipated OPR for the 2025 N+2 

simulations could range from conservative estimates of approximately 45 to more aggressive 

values closer to 60 [Hall 2007]. However, the choice of the optimal OPR depends on the 

technologies integrated into the engine. Cost and risk may also enter into the choice of OPR; 

however, neither metric is considered in this report. 

OPR is a function of the fan, LPC, and HPC pressure ratios. To increase propulsive 

efficiency, the BPR on next generation engines needs to increase. As a result, the FPR must be 

continuously lowered to maintain optimum propulsive efficiency, with some estimates of 

pressure ratio as low as 1.25 [Guha 2001]. Given the potentially lower FPR, higher values of 

OPR will be realized through higher pressure ratio LPC and/or HPC. 

Similar to OPR, T4 has increased over past few decades from values of 2950R in the 

1970s to around 3450R on a GE90 [Young 2002]. The primary enablers for this have been the 

development of high temperature materials and improved cooling techniques which allow higher 

T4 without incurring a large penalty in system TSFC because of an increase in required turbine 

cooling flows. As the design T4 becomes higher, assuming required cooling flows remain 

constant, the TSFC can be reduced. However as either OPR or T4 increase, assuming constant 

metal temperature, the required turbine cooling flow to maintain the metal temperature has to 

also increase which has a negative impact on TSFC. Further improvements in turbine materials 

such as CMC or cooling technology may enable T4 up to potentially 3950R [Young 2002] 

without incurring significant cooling flow penalties. However, because of the likelihood of a 

high OPR design, the maximum T4 considered in the N+2 timeframe may be more conservative. 

Since the gear is a fairly new technology for turbofan applications, there is not a large 

historical database to understand trends in gear ratio. Using current industry trends as well as 

similar studies [Kurzke 2009] the gear ratio design space was chosen to range from 2.25 to 3.0. 

Similarly, the design space for extraction ratio was quantified using similar studies. Previous 

studies by NASA assumed a value of 1.25 [Guyann 2009], although this number may result in 

too large of an engine for the LTA class. However to capture potential interactions with other 

cycle parameters, the design space of extraction ratio was chosen to vary around this point. The 

cycle design space explored for this study is defined by the variables in Table 19. 

Table 19. Engine Cycle Design Space for All Technology Cycle Selection Study 

Variable Min Max 

FPR 1.25 1.55 

LPCPR 1.35 1.6875 

HPCPR 23 30 

GearRatio 2.25 3.00 

Max T4, R 3300 3600 

Extraction Ratio 1.1 1.3 

3.3.2.1.1.3 Anticipated Trends 

One of the benefits of using surrogate models is the ability to generate partial derivative 

information of each response relative to the design inputs. The resulting partial derivative space 

19
for the cycle optimization DOE is shown in Figure 7. Running across the bottom of Figure 7 are 

the engine cycle design parameter from Table 19, and running up the y-axis are the response 

variables of interest: fuel burn, noise margin, maximum T3, and fan diameter. Recall that T3 and 

fan diameter are constraints on the design space. At the intersection of each response and 

independent variable lies a box which contains the partial derivative of a given response relative 

to a given input design variable. For example, noise margin decreases linearly with FPR 

increases. 

Immediately apparent by examining Figure 7 is that FPR is the largest driver on both the 

objective metrics and the constraints. Decreasing FPR increases fuel burn and reduces noise (via 

increased noise margin). This effect is due to the correlation between FPR and BPR. Increasing 

BPR reduces the jet velocity and reduces noise. Increasing BPR also increases propulsive 

efficiency which reduces TSFC; however, as BPR increases the nacelle size and engine weight 

also grow, which creates a second order effect as seen in the FPR vs. fuel burn relationship in 

Figure 7. The increased weight and drag outweigh TSFC gains. One can see increasing OPR 

helps to offset the fuel burn increases from decreasing FPR too far. As LPCPR or HPCPR 

increase the engine OPR will also increase. Increases in OPR will increase engine TSFC due to 

increased thermal efficiency. An extremely important secondary effect is the increase in core 

specific power that is enabled by higher OPR engines. By making the core more thermally 

efficient, it can provide similar power output with less flow. Flow is a primary driver on both 

size and weight in the core. Smaller cores will reduce the overall engine size for a given fan 

diameter. The end effect will be the shift in the FPR vs. fuel burn ‘bucket’ to the area of lower 

FPR; thereby enabling further noise and fuel burn reductions 

Relative to pressure ratios, T4, gear ratio, and extraction ratio have smaller derivatives 

relative to the responses. In general to obtain lower fuel burn, T4, gear ratio, and extraction ratio 

would tend to the upper end of the design space. T4 does not have any noticeable effect on noise 

margin. In fact T4 is a secondary driver on thermal efficiency. 

Although cycles with high OPR and BPR are generally desired, not all of the cycles in 

the design space are feasible because of violation of certain constraints. Cycles which have a T3 

that exceeds the maximum temperature capability of the materials in the HPC would result in a 

drastic reduction in the useful life of the engine. Increasing the pressure ratio of the engine via an 

increase in FPR, LPCPR, or HPCPR will result in an increase in the maximum cycle T3. 

Additionally, for tube and wing aircraft engines having diameters much larger than the wing 

clearance would result in significant potential system redesign at significant cost and risk. Thus 

cycles that violate either of these constraints would be considered infeasible. These are only two 

of many potential design constraints and are used to illustrate how constraints effect the resultant 

optimal cycle 

20
Figure 7. Cycle Optimization Trends 

3.3.2.1.1.4 Cycle Optimization using EDS 

EDS was used to directly simulate 300 sample engine cycles. While this is a somewhat 

time intensive process relative to using surrogate models, the purpose is to provide a secondary 

validation of the surrogate model results. The results of the cycle simulations in Figure 8 will be 

subsequently compared to the results of the exact same study using surrogate models in place of 

EDS, shown in Figure 9. The EDS results of the 300 case DOE are shown in Figure 8 with fuel 

burn plotted against noise margin for each case. Each point in Figure 8 represents a set of engine 

cycle settings. All of the points in Figure 8 have the same technology package applied. In other 

words, only the engine cycle variables in Table 19 differ between the points. The points 

highlighted in blue violate the somewhat arbitrary fan diameter constraint of 130 in. which is 

similar to the diameter of a GE90. The points highlighted in red violate the maximum T3 limit of 

1850R, which is the limit of advanced PM disk materials. Given these constraints, the cycle 

which best optimizes both fuel burn and noise margin has been highlighted as the “Best 

Constrained” point on Figure 8. Certain aircraft configurations such as an over the wing nacelle 

may not have as strict a fan diameter constraint as a traditional wing mounted nacelle. The 

resultant engine cycle used on these aircraft could potentially be different and is highlighted as 

the “Best T3 Constrained” on Figure 8. Relative to the “Best Constrained” cycle, this cycle has a 

significant increase in the noise margin with minimal fuel burn penalty. For reference, a “Best 

Unconstrained” point is highlighted also. That point is selected by picking the point that has the 

largest noise margin value and whose fuel burn is within about 1% of the minimum fuel burn 

case. 

21
Figure 8. Constrained Cycle Design Space Using EDS 

3.3.2.1.1.5 Cycle Optimization using Surrogate Models 

Figure 9 below shows the constrained design space for running the 300 case DOE using 

the surrogate models to analyze the design space. The feasible design space for the surrogate 

runs has a similar shape as that of the EDS runs. Similar to the EDS DOE runs, the “Best 

Constrained”, “Best T3 Constrained”, and “Best Unconstrained” cases were selected as 

highlighted on Figure 9. 

22
Figure 9. Constrained Cycle Design Space Using Surrogate Models 

3.3.2.1.1.6 Cycle Optimization Comparison 

Table 20 compares the chosen cycles using each method and calculates the difference 

between the cycle inputs and estimated outputs. The selected “Best Compromise” and “Best T3 

Constrained” cycle is the same DOE case using both models. The “Best Constrained Case” 

picked a different optimal cycle. For the two constrained cases the difference in the resultant fuel 

burn, OPR, and BPR estimates between the EDS and surrogate model are within 1%. The noise 

margin estimates were within 0.6 dB. The resultant optimal cycle chosen in an unconstrained 

design space, however, differs. All metrics are within 10% of each other, but relative to the EDS 

results, the differences are large. This is because the optimal solution is at the edge of the design 

space explored with the surrogate models. These results validate that a surrogate model can be 

used to make fairly accurate estimates of the optimal engine cycle for a constrained design space. 

However, the surrogate model approach can only be used to make order of magnitude estimates 

if the resulting cycle is at the edge of the design space. 

23
Table 20. EDS and Surrogate Model Cycle Optimization Comparison 

3.3.2.1.1.7 Technology Package Evaluation 

Using the same surrogate models, the optimal cycles for different technology packages 

can be quickly selected. Using only 300 cases, the resultant fuel burn and noise margin design 

space shown in Figure 8 and Figure 9 are sparsely populated. The surrogate models allow for a 

larger DOE, which will better populate the design space, with minimal additional CPU burden. 

To demonstrate this capability, the surrogate models were used to optimize the cycle for three 

different technology packages. The first technology package is the RTC. The second technology 

package is a subset of the N+2 technology package used in the validation cases. The third 

package augments the N+2 technology package with an intercooler between the LPC and HPC. 

In addition to increasing the specific power of the engine core, an intercooler has the potential of 

allowing significantly higher OPR engine cycles without having to drastically increase the 

temperature capability of the materials. 

3.3.2.1.1.8 RTC Evaluation 

The results for the RTC package are shown in Figure 10. The feasible design space for 

the RTC is drastically smaller than that of the N+2 technology package, discussed subsequently 

and shown in Figure 11. This is due to the maximum allowable T3 of only 1750R, thereby 

significantly increasing the T3 constrained area. Similar to the validation cases, a “Best 

Constrained”, “Best T3 Constrained”, and “Best Unconstrained” case are selected. 

EDS Surr Diff EDS Surr Diff EDS Surr Diff

FPR 1.4 1.4 0.0% 1.36 1.36 0.0% 1.43 1.45 1.4%

LPCPR 1.58 1.58 0.0% 1.52 1.52 0.0% 1.52 1.67 9.9%

HPCPR 27.8 27.8 0.0% 29.6 29.6 0.0% 30 28.5 -5.0%

T4max, R 3552 3552 0.0% 3543 3543 0.0% 3450 3533 2.4%

Gear Ratio 2.63 2.63 0.0% 2.78 2.78 0.0% 2.63 2.95 12.2%

Extraction Ratio 1.28 1.28 0.0% 1.25 1.25 0.0% 1.2 1.19 -0.8%

Fuel Burn Reduction 31.1% 31.4% -0.2% 30.7% 31.0% 0.3% 32.3% 33.0% 0.7%

Noise Margin, dB 29.2 28.6 0.6 dB 31.8 31.3 0.5 dB 27.1 25.5 1.6 dB

SLS BPR 16.3 16.4 0.6% 19.7 19.9 1.0% 14.7 14.2 -3.4%

SLS OPR 55.1 55.2 0.2% 52.7 52.5 -0.4% 59.5 63.9 7.4%

Best Constrained Best T3 Constrained Best Unconstrained

Input Output

24
Figure 10. Constrained Cycle Design Space for RTC 

The three highlighted cycles have been summarized in Table 21. Both the T3 constrained 

cycles have SLS OPR values of 45 whereas the unconstrained cycle has a SLS OPR of 65 and a 

6% lower fuel burn relative to the constrained cycles. This highlights the need for technologies 

that enable higher OPRs. The “Best T3 Constrained” cycle has a lower FPR and higher BPR than 

the “Best Constrained” cycle with an almost a 5 dB increase in noise margin. This highlights that 

going to different aircraft configurations or investing in technologies that shift the fan constraint 

may enable significantly lower FPR cycles with drastic increase in noise margin. 

Table 21. Cycle Selection for RTC 

Best Constrained Best T3 Constrained Best Unconstrained

FPR 1.42 1.33 1.43

LPCPR 1.401 1.36 1.67

HPCPR 25.03 29.3 29.8

T4max, R 3438 3529 3454

Gear Ratio 2.75 2.78 2.89

Extraction Ratio 1.23 1.29 1.23

Fuel Burn Reduction -22.50% -22.70% -27.10%

Noise Margin, dB 24.2 29.7 24.3

SLS BPR 15.6 21.5 13.9

SLS OPR 45.5 45 64.9

Inputs Outputs

25
3.3.2.1.1.9 N+2 Technology Package 

Using the same surrogate model as was used in the last section, an N+2 level technology 

package can be evaluated. Figure 11 below shows the constrained design space for both the RTC 

(green triangles) and N+2 (blue diamonds) technology packages. For the same cycle design 

space, the N+2 design space is shifted down and to the right. The downward shift is largely due 

to the drag and weight reduction technologies. The rightward shift is a result of the inclusion of 

the noise technologies. The design points constrained by the fan diameter and T3 constraints are 

highlighted in blue and red, respectively. The feasible design space for the N+2 technology 

package is significantly larger than the RTC package. The T3 constraint shifted downwards 

because of the increase temperature materials in the HPC. The fan diameter constraint shifted to 

the right because of incorporation of weight and drag reduction technologies which reduce the 

thrust requirement for the engine. The reduced thrust engine enables a lower FPR engine to meet 

the engine diameter requirement. 

As done in the previous sections, “Best Constrained”, “Best T3 Constrained” and “Best 

Unconstrained” cycles have been identified and are shown in Table 22. To capture the benefits of 

cycle optimization, the cycles chosen in Table 21 were run for the N+2 technology package. The 

results of these cycle runs are labeled as RTC Cycle in the table. Assuming the cycle remains 

constant when moving from a RTC to N+2 technology package, the N+2 “Best Constrained” 

technology package results in approximately a 7% reduction in fuel burn and a 2.5 dB increase in 

noise margin relative to the RTC “Best Constrained” technology package. Further optimization 

of the cycle enables an additional 2.8% reduction in fuel burn and a 3 dB increase in noise 

margin. The fuel burn reduction is primarily a result of the increase in OPR while the noise 

margin increase is a result of a lower FPR. Similar trends can be seen for the “Best T3 

Constrained” case. One observation noted is that the “Best T3 Constrained” cycle for the N+2 

technology package has an OPR of only 53. Because of how the DOE was setup combinations of 

high OPR and low FPR would require extrapolation of the surrogate model and were not 

explored. 

26
Figure 11. Constrained Cycle Design Space for N+2 Technology Package 

Table 22. Cycle Selection for N+2 Technology Package 

27
3.3.2.1.1.10 N+2 Technolgy Package with Intercooler 

As shown in the previous sections, as OPR increases, fuel burn decreases. Unfortunately, 

the maximum potential increase in OPR is constrained by the temperature limits of the materials 

in the HPC. An intercooler, which is a heat exchanger using fan bypass air to cool the core 

airflow between the LPC and HPC, is a technology which enables a further increase in OPR 

without requiring a further increase in material temperature capability. There are, however, 

significant performance issues related to the intercooler such as potentials for large pressure 

drops in both the core and bypass streams as well as the heat exchanger weight which is 

estimated to be on the order of the LPC [Gronstedt 2009]. 

Using similar assumptions of fan bypass bleed flow, pressure drops, heat exchanger 

effectiveness, and weights as [Gronstedt 2009], the surrogate model was used to estimate the 

cycle design space for an N+2 technology package with an intercooler. Figure 12 shows the 

constrained design space for both the RTC and an N+2 technology package with an intercooler. 

The T3 constrained space is pushed further down such that except for a few points the T3 

constrained space has virtually disappeared. This is a result of the choice of cycle design space

and will be discussed later. Relative to the other N+2 technology package, the fan diameter 

constrained space remains unchanged. Since the explored design space is no longer T3 

constrained, there are no “Best Constrained” and “Best T3 Constrained”. The “Best 

Unconstrained” cycle can be chosen using the “Best Compromise” process from earlier

sections. Unlike previous sections, the “Best Unconstrained” cycle is now feasible. 

Results of this cycle are shown in Table 23 under the “Best Compromise” column. 

Three other cycles have been selected for comparison: the RTC cycle, the N+2 cycle, and a 

N+2 intercooled cycle that minimizes fuel burn. The RTC and N+2 cycles are the “Best 

Compromise” cycles chosen from the analysis in the previous sections. Using the N+2 “Best

Compromise” cycle settings, the intercooler offers the potential for only minimal fuel burn 

benefits. With the RTC “Best Compromise” cycle the integration of an intercooler actually

increases the fuel burn. Using the N+2 intercooled “Best Compromise” cycle, there is a 

potential for a 2.3% fuel burn reduction. This fuel burn gain is offset by a 2.3 dB reduction in

noise margin. The “Best Fuel Burn” cycle offers a further 1% fuel burn reduction with an 

additional 3 dB reduction in noise margin. 

28
Figure 12. Constrained Design Space for N+2 Technology Package with Intercooler 

As noted throughout the paper, the highest OPR cycles resulted in the best fuel burn. The 

“Best Compromise” intercooled cycle only had an OPR of only 60 because the chosen design 

space used to train the surrogate models did not include cycles with OPR much greater than 60 

for which values of FPR was less than 1.5. The T3 for this case was only 1700F which indicates 

there is significant margin available to increase OPR before the maximum T3 constraint is hit. 

The surrogate model can be used to estimate potential performance of the cycle in these high 

OPR and low FPR regions; however, since the model was not trained on data in that region, 

caution must be taken with the extrapolated results, especially if the trends are significantly 

different than expected. Two points were estimated to see if there is potential to obtain 

significant fuel burn reduction from the higher OPR without the large noise penalty incurred 

from selecting a higher FPR cycle. Using cycle chosen for the N+2 “Best Fuel Burn”, the FPR 

was lowered to 1.38 and the LPCPR was increased to maintain OPR. When this case was 

run using the surrogate model, the fuel burn estimates were on the same order as the N+2

intercooled “Best Compromise” case with the noise margin estimates similar to the case with 

FPR equal to 1.38. The next cycle estimated assumed a FPR equal to 1.38, but raised the

LPCPR to obtain an OPR on the order of 75. In this case, the surrogate estimated that there 

was potential for a further 3% fuel burn reduction from operating at OPR around 75. To

validate, these claims, the “Low FPR Extrapolated” and “Low FPR/High OPR Extrapolated” 

case were run through EDS. For these two cases, the difference in fuel burn estimates between

EDS and the surrogates were within 0.5%. EDS also estimated that the fuel burn difference 

between these two cycles was about 2.5%, thus validating that the surrogate models can be

used to make cycle estimates outside of the region for which they were trained. An interesting 

observation is that the T3 for both these cases remained around 1700 F. This is because as the

29
LPCPR is increased, the temperature difference in the intercooler becomes larger, increasing 

heat transfer, and resulting in the core temperature exiting the intercooler to remain 

approximately constant. If HPCPR is constant, the higher OPR from an increase in LPCPR 

will not increase the cycle T3 significantly whereas T3 increases proportionally to increases in 

HPCPR assuming everything else is fixed. Thus for intercooled cycles with the same FPR, a 

cycle with a larger LPCPR will enable a cycle with a larger OPR and in turn more potential 

fuel burn benefit than a cycle with a larger HPCPR. This indicates that for an intercooled 

cycle, larger values LPCPR instead of HPCPR are desired. As the cycle moves to higher 

values of OPR by increasing LPCPR, minimum HPC size, not maximum T3, may be the OPR 

limiting constraint. For smaller thrust class engines, the minimum HPC size constraint may limit 

the maximum OPR of intercooled cycles to be much lower than values shown in this study 

thus limiting the intercooler potential. 

Table 23. Cycle Selection for N+2 Technology Package with Intercooler 

3.3.2.1.2 ITD Technology Portfolio on LTA HWB 

This second cycle selection study was performed to understand the impact of FPR on fuel burn 

and noise for the geared fan installed on a LTA HWB. The technology portfolio used for the 

study was the ITD portion of the ERA portfolio. The following parameters were varied for this 

study to capture uncertainties around the geared fan; FPR, extraction ratio to vary the BPR, 

FCDO to capture uncertainty from nacelle and installation drag and inlet/nacelle material density 

to capture uncertainty in nacelle weight. The maximum and minimum value of each of the 

parameters is listed in Table 24. Several graphs were constructed to display the sensitivity of fuel 

burn and noise as each of the variables was parametrically varied. 

30
Table 24. Sensitivity Variables for Geared Fan Pressure Ratio Study 

Variable Min Max 

FPR 1.3 1.5 

Extraction Ratio 1.05 1.45 

FCDO 0.99 1.03 

Inlet/Nacelle Density 0.1 0.5 

Figure 13 shows fuel burn vs noise with the different FPR values highlighted (only values 

of 1.3, 1.35, 1.4, 1.45 and 1.5 were used). This graph shows that the minimum fuel burn occurs 

at a FPR of 1.45 for the study. A reduction in noise of 7dB could be achieved by lowering the 

FPR to 1.3, but this would be at the expense of a fuel burn increase of 2.75%. 

Figure 13. Fuel Burn vs. Noise Highlighting FPR Variation 

 

31
Figure 14 is the same chart of fuel burn vs noise but now with changes to lift-independent 

drag (FCDO) highlighted. It shows that there is very little impact on noise from changes in drag. 

There is the expected change in fuel burn of 3-4% from a change in drag from 99% to 103%. The 

fuel burn is also more sensitive to changes in FCDO as FPR is decreased due to the larger engine 

and nacelle wetted area that results. 

Figure 14. Fuel Burn vs. Noise Highlighting FCDO Variation 

 

32
Figure 15 is again the same graph of fuel burn vs noise but now with changes in the 

extraction ratio (ratio of core nozzle to bypass nozzle inlet pressure). From this graph it can be 

seen that there is an extraction ratio for each FPR that minimizes fuel burn. The impact of 

extraction ratio on noise is much greater for higher FPR than lower fan pressure ratios from a 

range of 1.5 dB for a FRP of 1.3 to a range of 5 dB for a FPR of 1.5. 

Figure 15. Fuel Burn vs. Noise Highlighting Extraction Ratio Variation 

 

33
An additional 2000 data points were added with randomly selected FPRs between 1.3 and 

1.5 to generate Figure 16. This graph was generated to define the Pareto frontier for fuel burn 

and noise. It shows that the best FPR is between 1.42 and 1.5 which will achieve a fuel burn with 

0.25% of lowest value while minimizing noise. 

Figure 16. Fuel Burn vs. Noise Pareto Frontier 

 

34
Figure 17 shows SLS uninstalled thrust vs fan diameter for the same data points in Figure 

16. It show that for the geared fan, a fan diameter of around 130 inches will be required for 

engines generating 60,000 lbf of thrust. This is about half the thrust that would be generated by a 

similarly sized direct drive engine with today’s fan pressure ratios. This points to the possibility 

that there may be a thrust capability limit where the geared fan is no longer practical. Either the 

engine would be too large and the drag and weight penalties would overwhelm and TSFC 

benefits from a lower FPR or the FPR would be raised to the point that the advantages of a 

geared fan over a direct drive architecture would no longer be applicable. 

Figure 17. SLS Thrust vs Fan Diameter 

 

35
 

The final graph of the study, Figure 18, uses the same data points as the previous two 

graphs but displays minimum cruise TSFC vs FPR while highlighting fuel burn. It again shows 

that minimum fuel burn occurs in the FPR range of 1.42-1.45. The lowest TSFC occurs at the 

lowest FPR, but has a high fuel burn due to increases in engine weight and drag. This 

demonstrates that ERA’s ITD goal of 15% TSFC reduction may be at odds with its overall goal 

of 50% fuel burn reduction. The TSFC goal may need to be sacrificed in order to achieve the fuel 

burn goal. 

Figure 18. Cruise TSFC vs FPR Highlighting Fuel Burn Variation 

3.3.3 Open Rotor 

3.3.3.1 Performance Modeling Approach 

Modeling the open rotor requires synthesis of public domain data and analysis codes with 

available modeling data and result to establish a firm baseline configuration. Once a baseline is 

verified and established, technologies can be applied in the same manner as other engine 

architectures. The next few sections describe the establishment of a baseline along with major 

modeling assumptions. 

36
3.3.3.1.1 Overview 

The open rotor (GE36) was flight tested in the late 1980’s and early 1990’s by GE and a 

wealth of information exists in the detailed design reports that accompanied the demonstration 

[Hoff 1990, GE Aircraft 1987]. These reports contain detailed information about the open rotor 

configuration, general performance, and noise characteristics and serve as an important departure 

point for establishing a baseline for any current study, including the work presented herein. More 

recent work has sought to fuse a combination of test data and analytically generated engine and 

airframe performance in order to assess the combined benefits of the integrated engine and 

airframe system [Guynn 2011, Berton 2011]. These studies offer important insight into the 

installed benefits of an open rotor architecture, but are tied a specific engine cycle and aircraft 

configuration. In essence, they are predictive estimates of fixed designs. 

Modeling work under this work has focused on modeling the open rotor engine 

components in significant detail for use in a multidisciplinary environment [Bellocq 2010].The 

referenced work focuses heavily on the engine and lacks a specific methodology to assess the 

installed vehicle performance or noise. Nonetheless, Georgia Tech has incorporated many of the 

engine modeling features and recommendations directly into this assessment. Recent NASA 

work has also focused on creating parametric models of the open rotor engine architecture 

[Hendricks 2011], specifically the focus is on multi-design point engine sizing. This work, more 

than any other, has served as the basis for the open rotor model being developed in EDS. 

In order to include the open rotor in EDS’ assessment capabilities a development road 

map was created to identify required modifications to the EDS framework. An overview of the 

long-term development roadmap for open rotor to be included into EDS is shown in Figure 19 

and is discussed below. 

The development roadmap to fully address configurations employing open rotor 

propulsion system starts with the designer defining the initial counter rotating propeller and 

blade geometry, such as chord and twist distribution along the blade and the number of blades. In 

this work available performance maps from the F31/A31 test dataset were scaled to modern 

performance levels. Propeller source noise estimates are generated using NASA's CRPFAN 

[Whitfield 1990]. The predicted propeller noise sources are then passed to an aircraft noise 

prediction and propagation program, such as NASA's Aircraft Noise Prediction Program 

(ANOPP) [Zorumski 1982]. The aircraft noise prediction program will need to combine the 

propeller, engine, and aircraft noise sources and propagate them to compute noise levels at the 

three certification points. 

37
Figure 19. Open Rotor Modeling Methodology 

One of the challenges in capturing the fuel burn metrics for vehicles using counter 

rotating propellers is the power management of the engine, which is highly coupled to the 

propeller performance. The counter rotating propeller performance is usually characterized by a 

set of performance maps. These maps are needed to provide the loads to the counter rotating 

turbines that drives the propellers. The loading conditions and the variable pitch angles of the 

propellers along with propeller efficiency for all the flight conditions are needed to develop a 

reasonable operating schedule, e.g. power management, for the engine. How the engine is power 

managed through the flight envelope obviously impacts the fuel burn of the vehicle. Details of 

developing a set of power management rules for a counter rotating propeller propulsion system 

will be addressed in section 3.3.3.1.4 

The modeling of a gas turbine powering a counter rotating turbine propulsor is 

accomplished using a 0-D thermodynamic cycle analysis program, NPSS [NASA 2006]. Along 

with the thermodynamic performance, a flow path analysis for counter rotating propeller 

propulsion system is also needed to estimate the propulsion system weight. A potential candidate 

for this flow path analysis is be NASA's Weight Approximation for Turbine Engines (WATE++) 

Propeller/blade

geometry definition

Airfoil 

Analysis

cl, cd, cm as

fcn (αeff ,Mlocal)

Propeller performance

map (CT – CP table)

Range of αeff & Mlocalsweep

Generalized 

Propeller 

Performance 

Prediction

αeff & Mlocal

as fcn(r, psi)

Air Loads

Counter 

Rotating 

Propeller 

Noise 

Prediction

Source noise

Aircraft Noise 

Prediction 

Program

Engine Cycle, 

Flowpath, & 

Emissions Analysis

Mission 

& vehicle 

definition

Vehicle 

Synthesis & 

Sizing, Mission 

Analysis 

Engine performance deck, 

weight, dimensions, flowpath

Change 

vehicle 

design

Vehicle size,

fuel burn, 

emissions, etc.

Noise 

certification 

points

Airfoil

Analysis

Perform Noise 

& Fuel Burn 

Trades

NASA 

Fuel Burn, 

Emissions, & 

Noise

Goals

Met?

Balanced 

Design

Change engine design

Change propeller 

design

Yes

No

38
[Tong 2009] with augmentations to estimate propeller weight. This gas turbine model is also 

providing input into the emissions correlations that are modeled according to section 4.4.1. 

The engine modeling step is necessary in order to generate a suitable engine performance 

deck and propulsion system weight to perform vehicle sizing/synthesis and mission analysis to 

capture the fuel burn metric (for a given mission), which can be done using NASA's FLight 

Optimization System (FLOPS) [McCullers 2009]. The results of the mission analysis (i.e. 

vehicle size, geometric information, take-off and landing trajectories, etc.) are then fed into 

ANOPP. ANOPP also needs the engine state tables and engine mechanical design details from 

the combined NPSS/WATE++ engine model to predict the engine related noise sources. In total, 

ANOPP requires outputs from the propeller noise prediction code, engine state tables, engine 

flow path analysis, and vehicle synthesis/sizing and mission analysis to estimate the effective 

perceived noise level (EPNL) at the three certification locations, measured in EPNdB. 

Using this open rotor EDS analysis roadmap described above, the system analyst can now 

perform tradeoffs between the three critical system metrics (fuel burn, emissions, and noise) by 

perturbing either the counter rotating propeller/blade design variables, the engine design 

variables, or the vehicle design parameters. 

Major technical accomplishments include: 

• Updating the open rotor propeller map performance assumptions developed by 

Reference [Hendricks 2011] 

• Extending the engine multi-design point sizing algorithms to the five conditions 

used in EDS (sea level static, sea level hot day, rolling takeoff, cruise, and top of 

climb) 

• Developing power management logic in a manner that allows for future flexibility 

and optimization when integrated with noise assessment 

• Adding a predictive weight capability to assess open rotor weight 

• Accounting for aircraft design changes necessary to incorporate large diameter 

open rotor engines 

In each of the following sections, the EDS modifications are described and assumptions 

are noted. 

3.3.3.1.2 Propeller Modeling 

The work presented in [Hendricks 2011] used historical performance data from the GE 

un-ducted fan (UDF) [Hoff 1990] in order to estimate the counter-rotating propeller 

performance. 

One of the issues with using the GE historical data is its lack of completeness and 

continuity across the flight envelope. It is provided for cruise conditions and a rolling takeoff 

condition, but no other operating points. This makes it useful for obtaining back of the envelope 

calculations in order to estimate cruise efficiency or takeoff thrust; however, in a 

multidisciplinary, numerical M&S environment, such as EDS, it is necessary to obtain complete 

engine performance data for the entire vehicle flight envelope. The vehicle synthesis and sizing 

program (FLOPS [McCullers 2009]) in EDS requires a complete engine performance deck in 

order to evaluate climb and descent performance. Detailed engine data is also needed to evaluate 

vehicle noise trajectories and engine operating information for ANOPP [Zorumski 1982]. 

[Hendricks 2011] tried to overcome these data limitations by using surrogate modeling 

techniques to effectively interpolate the data between cruise and takeoff. The authors tried this 

approach but found it can lead to discontinuous and discrete engine thrust and fuel flow trends. 

39
The surrogate modeling approach also led to an inability to tradeoff performance using different 

pitch settings. Variable pitch settings are necessary for the power management scheme. 

Since EDS requires smooth, continuous maps, an alternate approach was undertaken to 

predict the propeller performance. The Georgia Tech research team conducted a more thorough 

literature search and discovered that NACA performed several full scale tests of counter-rotating 

propellers in the 1940’s [Biermann 1942]. While these performance tests were of straight-bladed, 

low speed propellers, they had the advantage that they contained complete performance data for 

a wide range of advance ratios and blade pitch settings. The old NACA maps were scaled until 

their trends matched the published performance of the historical F31/A31 set [Hoff 1990]. By 

scaling the diameter, the efficiency, and the thrust coefficient by fixed adders and scalars, the 

resulting performance maps were created and are shown in Figure 20. Figure 21 shows 

comparison to available F31/A31 data via the solid black and dashed lines. Good agreement is 

shown at the two test conditions for which data is available. 

Figure 20. EDS Counter Rotating Propeller Map 

Figure 21. EDS Counter-rotating propeller map comparison to F31/A31 data 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 1.0 2.0 3.0 4.0

Coef. of Thrust

Advance Ratio

10 Deg

15 Deg

20 Deg

25 Deg

30 Deg

35 Deg

40 Deg

45 Deg

50 Deg

55 Deg

60 Deg

0

0.5

1

1.5

2

2.5

3

0123456

Coef. of Power

Advance Ratio

10 Deg

15 Deg

20 Deg

25 Deg

30 Deg

35 Deg

40 Deg

45 Deg

50 Deg

55 Deg

60 Deg

0.00000

0.10000

0.20000

0.30000

0.40000

0.50000

0.60000

0.70000

0.80000

0.90000

1.00000

0.0 1.0 2.0 3.0 4.0 5.0

Eff vs. J 10 Deg

15 Deg

20 Deg

25 Deg

30 Deg

35 Deg

40 Deg

45 Deg

50 Deg

55 Deg

60 Deg

F31A31 M.2 40.1 deg

F7A7 M.72

40
3.3.3.1.3 Multi-design Point Approach 

The EDS environment uses a multi-design point (MDP) approach to size the engine. For 

a conventional turbofan the MDP process is thoroughly described in [Schutte 2012b], but it is 

summarized here for completeness. The engine’s aerodynamic design point is specified at cruise 

conditions. This is where the engine pressure ratios and efficiencies are set. There are also 

several off-design conditions where the engine sized at ADP is “flown” in order to satisfy other 

requirements. These include a top of climb condition which sets the fan over speed and thrust, 

the rolling takeoff condition which sets the maximum combustor temperature and sizes the 

cooling flows, the sea level static uninstalled condition which sets the rated thrust, and the seal 

level static hot day condition which ensures hot day performance can be met. The work in 

Reference [Hendricks 2011] served as a starting point because it contained the aerodynamic 

design point and rolling takeoff conditions. The three other points were added such that the final 

aerothermodynamic sizing logic is as shown in Table 25. 

Table 25. EDS Open Rotor MDP Setup 

Independent Variable Dependent Condition 

ADP Mass Flow Takeoff Thrust 

ADP Fuel-Air Ratio Top of Climb Thrust 

Top of Climb Fuel-Air Ratio TOC Core Mass Flow Ratio 

TOC/TO/SLS Blade Angle Max Corrected Prop Speed 

Propeller Power TOC Core Nozzle PR 

Prop Design RPM Design Advance Ratio 

Hot Day Fuel-Air Ratio Hot Day Thrust 

SLS Uninstalled FAR SLS Uninstalled Thrust 

Takeoff Fuel-Air Ratio Takeoff Max T4 

When NPSS, the core engine design and analysis component of EDS, sizes the engine, it 

uses physical sizing parameters such as mass flow, combustor fuel-to-air ratio, design propeller 

power, and component physical areas. In contrast the designer usually sizes the engine according 

to a set of mission requirements such as thrust and maximum combustor temperatures. In order 

to relate the physical sizing variables with the design requirements, the NPSS Newton-Raphson 

solver is used to vary all of the independent variables in Table 25 until the dependent conditions 

are met. While each independent often affects multiple dependent conditions, they are presented 

as pairs in order to discuss the philosophy behind the sizing process. The aerodynamic design 

point (ADP) mass flow sets the engine takeoff thrust and effectively sets the engine size. The 

ADP FAR and TOC FAR both set the TOC thrust and the ADP thrust. The ADP thrust it not 

directly specified; rather it is implicitly calculated through the TOC Core Mass Flow Ratio which 

is the ratio of mass flow through the engine core at TOC relative to the ADP sizing point. This 

value has an effect on the location of the minimum TSFC “bucket” or “bottom-of-loop” which 

affects the engine vehicle matching. The TOC thrust is set by the vehicle TOC rate of climb 

requirements and also sizes the propellers. For the off-design conditions, the blade angles, or 

blade pitch settings on the prop, are varied such that the propeller runs at a constant corrected tip 

speed. This assumption was made after reviewing data from [General Electric Company 1987] 

and keeping the assumption that maximum power in a conventional turbofan is often defined by 

100% corrected fan speed, relative to the aerodynamic design point. Propeller power obviously 

41
sets engine and propeller thrust, but the dependent condition is the TOC core nozzle pressure 

ratio. In conventional separate flow turbofans, the bypass ratio is often set by the extraction ratio 

(ratio of nozzle total pressures), or the velocity ratio (ratio of core to bypass jet velocities). 

Regardless of the exact method used to set the effective BPR, the idea is to maintain the 

appropriate energy balance between the core and bypass ratio to optimize propulsive efficiency. 

Since EDS is using a propeller map to calculate propeller thrust, there is no available information 

on the ambient pressure or velocity rise across the propeller. The propeller performance map 

implicitly sets these values; therefore, the core nozzle pressure ratio is the appropriate variable to 

control the thrust split between the core and propeller. The propeller design RPM is set to match 

a user specified advance ratio, and finally the combustor fuel-to-air ratios at the other off-design 

points to match specified thrust conditions. 

3.3.3.1.4 Power Management Logic 

The five sizing points for the engine are all at maximum power. Once the engine is sized, 

it is necessary to generate engine performance information at a wide variety of ambient flight 

conditions and power settings. While NPSS automatically performs the continuity and energy 

balances necessary to assess off-design performance, the modeler must specify how to control 

the free variables, which are essentially the parameters the operator would have either explicit or 

implicit control over during the course of flight. The burner fuel-to-air ratio (set by the throttle) 

controls engine core speed and power. The propeller blade pitch setting angle controls the power 

and torque extracted by the propeller. There are obviously many different ways to set these 

parameters, but the following control scheme has been implemented in EDS. 

The maximum core power setting at any flight condition is defined by 100% corrected 

low pressure compressor (LPC) speed and 100% corrected propeller speed. The combustor fuelto-air ratio and blade pitch settings are varied until these two speed requirements are satisfied. 

The propeller and LPC speeds are constrained by: 

• Maximum physical shaft speeds (mechanical constraint) 

• Minimum nozzle pressure ratio (assumed 1.05, necessary for core operability) 

• Maximum burner temperature (T4 – design choice) 

• Maximum and minimum blade pitch settings (15 to 60 degrees for the maps 

shown in Figure 20) 

For any given flight altitude and Mach number the above methodology is used to find the 

maximum engine power. A throttle hook of part-power thrust settings is then generated by 

varying the burner FAR until the specified part power thrust as a percentage of the maximum 

power thrust is achieved. During part power operation blade pitch setting is varied to maintain 

100% corrected propeller speed. The maximum power operating constraints also apply to part 

power conditions in addition to intrinsic limits on combustor operability and compressor surge 

margin constraints. 

It is worth noting that the assumption of a constant speed propeller was decided upon 

after trying several different control strategies. Obviously the blade pitch setting can be set to 

maximize thrust, propulsive efficiency, or minimize TSFC. These control schemes were 

evaluated, but some require optimization techniques to be embedded in the power management 

or pre-defined pitch setting schedules to be input. The constant speed assumption requires the 

least amount of additional run time or pre-defined data and also provides performance results 

that are in-line with the expected performance of more conventional systems, as shown in Figure 

24 and Figure 25. 

42
3.3.3.1.5 Weight Modeling 

EDS already uses the WATE++ [Tong 2009] code to predict the weight of conventional 

turbofan engine architectures. For the open rotor model, WATE++ is still used to predict the 

weight of the core engine components and the size of the core nacelle packaging; however, it 

must be augmented to predict the weight of the propeller system including the supporting 

subsystems and the pitch change mechanism. This was modeled using data available in [General 

Electric Company 1977] and recreated in Figure 22. The propeller weight is a function of the 

propeller disk loading, assumed to be 55 SHP/D^2 for this analysis, and the propeller diameter. 

The propeller diameter is sized by the assumed disk loading and the power requirements that 

result from the MDP sizing discussed previously. These weights are added to the uninstalled 

engine total. The gearbox and power turbine weights are estimated through standard WATE++ 

analysis elements. 

Figure 22. Propfan Weight Estimation [General Electric Company 1977] 

3.3.3.1.6 Aircraft Installation Effects 

Since the vehicle configuration being considered is a tube and wing design, the most 

logical place for the engine location is an aft-mounted fuselage installation. By examining data 

from [General Electric Company 1977, Fischer 1989], an installation factor of 1.6 on top of the 

bare engine weight including the prop fan was assumed within EDS. The Douglas Aircraft 

Company was able to design a pylon installation that resulted in little-to-no interference drag, so 

no additional drag other than pylon wetted area is assumed in EDS [Henne 1989]. In addition to 

43
the added engine weight, an additional fuselage structural weight penalty must also be 

accounted. Currently, FLOPS internal modeling is used to account for the effect of aft fuselage 

mounted engines. The present study also does not account for potential additional weight 

resulting from extra fuselage noise insulation material, which may represent on the order of one 

to two thousand pounds of weight on a single aisle aircraft. 

3.3.3.1.7 Verification of Expected Trends 

While it is difficult to fully validate the model due to the limitations and lack of available 

and complete public domain data, an attempt was undertaken to ensure that the model predicts 

benefits that are in line with what other research projects. In order to accomplish this goal, an 

open rotor engine of “modern design” was evaluated on a 737-800 type aircraft with all of the 

design modifications and features mentioned above. The results are compared against 

information GE has presented in the public domain in terms of engine efficiency gains and 

vehicle fuel burn benefits [Niskode 2010]. GE’s quoted benefits at the engine level are in terms 

of propulsive efficiency benefits relative to the current day CFM56. In order to have a fair 

comparison, the EDS model has assumed an overall pressure ratio (OPR) of 30, which is similar 

to the CFM56, with correspondingly similar turbomachinery component efficiencies and 

temperatures. The propeller design parameters are compared to the historical GE F7/A7 blade set 

at the ADP design condition as shown in Table 26. 

Table 26. EDS Prop Design Comparison 

Design Parameter F7/A7 EDS 

Front Rotor Diameter(ft) 11.67 11.33 

Advance Ratio 2.8 2.8 

CPQA 4.1 4.112 

CTQA 1.256 1.248 

Net Prop Efficiency 0.858 0.85 

Front Rotor Power (hp) 3767 3562 

Rear Rotor Power (hp) 3787 3562 

OPR N/A 29.7 

Figure 23 shows propulsive efficiency improvements of the open rotor relative to a 

CFM56-7B architecture. Since the EDS open rotor baseline has a similar OPR to the CFM56 and 

TSFC improvement is proportional to the product of the propulsive and thermal efficiencies of 

the engine, the EDS model should predict TSFC gains in line with the trends shown in Figure 23. 

The EDS open rotor model was used to generate thrust and TSFC information at Mach 0.8 at 

cruise altitude and Mach 0.25 at sea level. The results were then compared to the EDS model of a 

CFM56-7B and are shown in Figure 24 and Figure 25. 

In Figure 24 and Figure 25 the blue line represents the EDS representation of the CFM56, 

which has been previously calibrated. The red line represents the EDS performance of the open 

rotor using the propeller design parameters in Table 26 with a CFM56-like core. EDS predicts a 

44
23.8% reduction in TSFC at high speed cruise and predicts a 53% TSFC improvement at the 

takeoff condition. These results are encouraging since GE shows a 23% gain at cruise relative to 

the CFM56 and a 70% benefit at takeoff. The EDS model may be under predicting the takeoff 

benefit some, however, the EDS predicted TSFC is in line with the values calculated when GE 

designed the GE36 demonstrator [Hoff 1990]. While the EDS results do not fully validate the 

model, they do serve to show the benefits are in line with industry expectations. It should also be 

noted that no additional tuning of the model was performed in order to produce the results shown 

in In Figure 24 and Figure 25 

Figure 23. GE Representative Open Rotor Improvements [Niskode 2010] 

Figure 24. EDS Open Rotor High Speed Cruise Performance 

CFM56-7B

13% Benefit

Next Gen Narrowbody

Open Rotor 

Very High Bypass

Turbofan

0.7

0.75

0.8

0.85

0.9

0.95

1

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

Propulsive Efficiency

CFM56-7B

13% Benefit

Next Gen Narrowbody

Open Rotor 

Very High Bypass

Turbofan

0.7

0.75

0.8

0.85

0.9

0.95

1

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

Propulsive Efficiency

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

0.8

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

Fan Pressure Ratio

Propulsive Efficiency

CFM56-7B

Next Gen Narrowbody

Open Rotor 

Very High Bypass

Turbofan 50% Benefit

Takeoff (M = 0.25)

Cruise (M = 0.8)

45
Figure 25. EDS Open Rotor Takeoff Performance 

In order to baseline the vehicle performance benefits, the EDS 737-800 model was 

retrofitted with a tail mounted open rotor configuration and the resulting fuel burn benefit at the 

vehicle design range was assessed. The vehicle fuel burn relative to the baseline was 29.6%. This 

exceeds the benefits shown by GE, in Figure 26, but is acceptable for the purpose of establishing 

a baseline for technology assessments. 

Figure 26. Projected Open Rotor Fuel Burn Benefit [Niskode 2010] 

There may be additional installation impacts such as cabin noise insulation and it is also 

unknown what the baseline aircraft and mission rules are for the benefits shown in Figure 26. As 

another comparison, a recent NASA study estimated a 40% fuel burn benefit on an MD-90 type 

aircraft; albeit the reduction is relative to an early 1990’s MD-90 [Guynn 2011]. This shows that 

the benefit is highly dependent on the set of baseline technology assumptions. 

0

2

4

6

8

10

12

14

16

0 5 10 15 20 25 30 35

% Fue l Burn Be ne fit

CUM Margin, re: CH 4 (EPNdB)

Chap 5 (projected)

Modern Open Rotor 

Program Goal

GE36 (1980)

CFM56-5B (2010)

Leap-X ATF

Modern Open Rotor

(current estimate)

0

2

4

6

8

10

12

14

16

0 5 10 15 20 25 30 35

% Fue l Burn Be ne fit

CUM Margin, re: CH 4 (EPNdB)

Chap 5 (projected)

Modern Open Rotor 

Program Goal

GE36 (1980)

CFM56-5B (2010)

Leap-X ATF

Modern Open Rotor

(current estimate)

46
3.3.3.2 Noise Modeling Approach 

3.3.3.2.1 CRPFAN 

In order to model the fuel burn aspects of the open rotor, EDS required several involved, 

but not overly complex changes to its layout and computational modules. The biggest changes 

included the ability to power manage the propeller, integrate the counter-rotating propeller 

performance, and include the vehicle installation effects from a weight perspective. Noise 

prediction required more extensive EDS modifications. The GE developed and NASA funded 

Counter-Rotation PropFan Noise (CRPFAN) program was incorporated into the modeling loop 

for estimation of rotor noise [Whitfiled 1990]. The output of CRPFAN was incorporated into the 

ANOPP input file through the use of ANOPP’s Acoustic Data Module (ACD) which allows for 

the input of externally calculated source noise. This module introduces its own limitations and 

drawbacks that will be discussed in this section. 

Modeling of the open rotor engine noise comprises two major elements: source noise 

modeling with CRPFAN and incorporation of the engine noise into the ANOPP run using the 

ACD module. The use of CRPFAN requires a lengthy list of inputs including geometry, engine 

operating state, and performance values. Performance, state, and dimensional geometry values 

are gathered from several EDS modules while the blade sectional information is taken from the 

full scale F7/A7 blade set tested in the 1980s by NASA and GE. CRPFAN program outputs onethird octave band spectra of the steady loading and interaction tones produced by the two blade 

rows at the desired polar directivity angles. 

This propeller source noise estimation method has two substantial limitations. First, the 

semi-empirical model is validated for the F7/A7 blade set from the 1980s and may not provide a 

good estimation of noise produced by more modern blade designs that incorporate advanced 

features such as proplets. Indeed, current studies use the F31/A31 blade set as the historical 

baseline. The F31/A31 blade set is referred to as the historical baseline because it represents the 

best aerodynamic design from the 1980's era with noise levels lower than that of the F7/A7 [Van 

Zante 2013]. In order to implement CRPFAN with noise levels similar to the F31/A31 blade set, 

a parametric sensitivity study was performed in order to calibrate the CRPFAN inputs to 

replicate the source noise levels given by F31/A31 experimental data, provided by NASA. This 

process will be detailed in the following sections. Second, the model makes no estimation of the 

broadband noise component produced by the rotors. In order to overcome this, a broadband 

component is generated to mimic that observed in the F7/A7 tests performed by Hoff [Hoff 

1990]. The broadband component is assumed constant about the polar arc, and is added to the 

tonal noise calculated by CRPFAN. 

In order to incorporate the engine noise calculated by CRPFAN into the ANOPP run, the 

CRPFAN calculated open rotor source noise was input using the ANOPP ACD module. The 

ACD module requires input of noise data tables of one third octave band spectra for with 

variation in polar and azimuthal directivity angles. A table is required for a set of Mach numbers 

and power settings in a matrix form (for each power setting, a table is required for each Mach 

number). ANOPP then selects the appropriate table with the Mach number and power setting 

corresponding to that of each time step in the input flight path. Ideally, the tables would be 

constructed such that a unique Mach number and power setting would be present for each time 

step in the flight path to avoid interpolation error. However, as the takeoff trajectory tables are 

quite detailed, doing so routinely leads to an exceeded ACD input data limit. To avoid this, the 

time steps in the flight path are grouped intelligently and given a determined set of Mach 

47
numbers and power settings to reduce the number of data sets while minimizing the error in 

noise calculations. This logic has been automated and integrated within EDS so that it can be 

done in-line with the analysis process. 

3.3.3.2.2 Parametric Modeling Approach 

CRPFAN requires a set of input parameters in order to produce the source noise of a 

blade set. The original program is set to produce the source noise of the heritage F7/A7 blade set. 

In order to produce source noise of the more modern and quieter F31/A31 blade set a parametric 

study was performed. This involved altering the inputs to CRPFAN, which consist of geometric, 

aerodynamic, and performance parameters, to mimic experimental data given by NASA. It 

should be noted that the inputs to CRPFAN are quite involved and many are hard-coded into the 

input files for the F7/A7 blade set. Most are unknown for the F31/A31 blade set without 

performing experimental tests and having the physical blades. In order to model the open rotor 

parametrically, it becomes necessary to discover the physics and trends for both the CRPFAN 

program and the experimental data provided by NASA. 

The experimental data used for this study came from tests carried out in NASA Glenn's 9 

by 15 ft. Low Speed Wind Tunnel in 2010. The test rig used was configured with a 1/5th scale 

F31/A31 blade set consisting of 12 forward and 10 aft blades. The test rig was placed in 

proximity to a sideline microphone array to take noise measurements at various geometric angles 

and distances from the blades. The microphone array consisted of 18 microphones and was 

located 60 inches from the centerline of the engine [Van Zante 2011]. The microphone array was 

set up to take noise measurements at geometric angles ranging from 18 to 140 degrees relative to 

the engine where zero degrees is referenced toward the oncoming flow. The test consisted of 

taking noise measurements for various Mach numbers and angles of attack for power settings 

ranging from 12 to 100% power [Van Zante 2011]. The noise measurement data was then 

converted to full-scale and to both flight and static conditions. This was performed by 

application of an atmospheric attenuation model, removal of the background noise, scaling of the 

data, and correction of the spectral densities to account for spherical spreading. Also, the data 

was converted to 1/3rd octave band center frequencies to smooth out the peakedness of the data 

[Berton 2011]. An example of the noise spectrum for the F31/A31 blade set for 12%, 60%, and 

100% power at an observer angle of 90 degrees can be seen in Figure 27. 

48
Figure 27. Measured SPL versus Frequency for Mach Number of 0.2 at 12, 67.7, 100% Power and Geometric 

Angle of 90°. 

In order to better understand the inputs to CRPFAN and their relative influence on the 

predicted noise, a parametric trade study was performed. There were two main goals of this 

parametric study. The first goal was to determine the rotor geometry inputs that are the most 

influential in noise source generation and how these inputs affect the noise. This information will 

give insight to the influence of the geometry parameters on the noise source generation, which 

will be helpful in modeling the F31/A31 blade set. The second goal was to determine the most 

influential of the unknown inputs and what their relative effect is on the noise. This information 

will help to decide which of the unknown parameters is most important for defining the F31/A31 

blade set and will define the relationship between these parameters and the predicted noise, 

which will be useful when calibrating the inputs to match the predicted noise to the 

experimentally measured noise discussed previously. 

In an effort to accomplish the first goal, a screening study was performed to determine 

which of the known (geometry) parameters affected the source noise levels the most. This turned 

out to be the tip diameter of the fore rotor, the rotor spacing, the number of blades of the fore 

rotor, and the chord lengths of the rotors. To determine the sensitivity of these parameters, all 

other parameters were held constant and the parameter in question was varied over a predetermined range that encompassed values realistic to both the f7/A7 blade set as well as the 

F31/A31 blade set. An example of the number of blades for the fore rotor is given in Figure 28. 

This was done at fixed power setting, RPM, and observer angle. This provided insight into how 

to alter the most important geometric parameters, in tandem with performance parameters such 

as power setting and RPM, to achieve source noise results similar to the NASA data for the 

F31/A31 blade set. 

49
Figure 28. SPL versus Frequency for an Observer Angle of 90 Degrees and Variations in Number of Blades 

for the Fore Rotor 

A similar study was done for the unknown, or mostly aerodynamic, input parameters. 

This study was more crucial to determining the correct source noise for the F31/A31 blades, as 

the geometry and performance were more readily available than the aerodynamic characteristics. 

It was found, through an additional sensitivity study, that the circulation index, the tip vortex 

trajectory index, the lift coefficients of the aft rotor, and drag coefficients of the fore rotor were 

the significant unknown input parameters. To determine the sensitivity of the source noise levels 

to these parameters, a parametric study was done keeping all other parameters constant including 

power setting, RPM, and observer angle. An example of the unknown parameters is given for 

circulation index and can be seen in Figure 29. This provided insight into how to alter the most 

important aerodynamic parameters, in tandem with performance parameters such as power 

setting and RPM, to achieve source noise results similar to the NASA data for the F31/A31 blade 

set. 

Figure 29. SPL versus Frequency for an Observer Angle of 90 Degrees and Variations in Circulation Index 

50
After the parametric studies of both known and unknown parameters was completed, the 

input variables were varied over a range of values realistic to both the F7/A7 and F31/A31 blade 

sets through the use of a design of experiments. The results were analyzed and compared to the 

experimental F31/A31 data provided by NASA. Input parameters were then chosen and 

calibrated to best match the CRPFAN output source noise levels to the F31/A31 source noise 

levels. 

3.3.3.2.3 Verification of Expected Trends 

Once acceptable input parameters to CRPFAN were chosen, and the results mimicked the 

trends of the F31/A31 blade set, the results were verified against the NASA experimental data at 

wide range of power settings, Mach numbers, and RPM settings. This provided assurance that 

the trade study produced appropriate trends for the source noise not only at the design points, but 

at all points within the range of the data and possible configurations to be used within EDS. This 

verification was performed by holding constant the CRPFAN input parameters determined 

optimal through the parametric trade study and varying the power setting, Mach number, and 

RPM, diameter, and rotor blade angle inputs over the range required for EDS and the ACD table 

module. This was dictated by the geometry produced by other modules within EDS and the 

flightpath of the aircraft during departure and approach. An example of the CRPFAN SPL 

spectrum compared to the NASA experimental data prior to the parametric study is shown in 

Figure 30. The same comparison after the parametric study is shown in Figure 31. It should be 

noted that these comparisons were for the same power setting, Mach number, rotor diameter, 

observer angle, and RPM. The only difference is the calibration of the CRPFAN input 

parameters. 

Figure 30. Comparison of CRPFAN and NASA Experimental Data Source Noise for Observer Angle of 90° 

Pre-Parametric Study 

51
Figure 31. Comparison of CRPFAN and NASA Experimental Data Source Noise for Observer Angle of 90° 

Post-Parametric Study 

As can be observed in the figures, the SPL levels for the open rotor source noise prior to 

the parametric study differ greatly between the CRPFAN output, representative of F7/A7 blade 

set, and the experimental data, representative of the F31/A31 blade set. Indeed, the SPL levels 

are much higher for the CRPFAN output which is logical because the F7/A7 blade set represents 

an older and louder technology. The comparison from the post-parametric study is a great 

improvement in the difference in noise levels. The CRPFAN output is much closer to the SPL 

levels of the NASA experimental data, allowing it to be used as a representative of the F31/A31 

blade set. It should be noted that in both comparisons there are “gaps” in the noise spectrum at 

lower frequencies. This is a function of the limitations of CRPFAN. In order to overcome this 

limitation, the missing frequency levels were interpolated from the available data inside of EDS 

to produce complete ACD tables for ANOPP. This verification process allowed for confidence in 

the source noise levels modeled by CRPFAN to be closely representative of the F31/A31 blade 

set and suitable to integrate in EDS for complete noise analysis. 

3.3.3.2.4 CRPFAN-EDS Integration 

It is difficult to conduct a direct validation between EDS and publically available data; 

however, much can be done to baseline the model and check that the results being generated are 

within the realm of reasonableness. Making a direct comparison between EDS and real data is 

challenging in this specific application for several reasons. In terms of fuel burn, the EDS 

environment seeks to assess the performance benefits of a modern open rotor. In the past, GE 

performed a flight test of the open rotor using an F404 engine as the gas generator. The actual 

propulsor was essentially “bolted on” to the back of the fighter engine. While this type of test is 

important in assessing the feasibility of an open rotor, it does not provide the actual fuel burn 

benefit that could be gained using the system on a modern, high efficiency commercial engine 

52
core. Using an environment such as EDS allows the engineer to bring the F31/A31 blade set into 

the modern age by driving it with a LEAP-X type of engine core that will be seen on the next 

generation of commercial aircraft. Furthermore, since the propulsive efficiency of modern open 

rotor blade designs has only improved a point or two, the results will still be representative of the 

system benefit. 

Noise represents a bigger challenge due to the massive engineering leaps that have 

occurred since the 1980s in terms of noise analysis and engineering for noise. While previous 

generation open rotors were estimated to fall short of Stage IV noise regulations by as much as 7 

EPNdB, current designs are projected to exceed Stage IV margin by as much as 13-17 EPNdB 

[Croft 2012] [Van Zante 2014]. As a result it is difficult to use historical, empirical data while 

providing a modern representation of the noise capabilities of a next generation system. To 

overcome this, the EDS model benchmarking will be done in two parts. The EDS predicted fuel 

burn benefits will be performed using a modern engine core and will be compared to publically 

stated benefits on a next generation system. The EDS predicted noise benefits will be performed 

using the F31/A31 blade set and results will be compared to the NASA experimental data for 

F31/A31 as described previously. It is worth restating that this first iteration serves to check that 

all of the EDS linkages and prediction schemes are working as expected; following iterations 

will include more modern data input for noise prediction. 

The performance prediction methodology has been well outlined in previous work by the 

authors and will not be repeated here for the sake of brevity. However, several updates to the 

previous work have been made including the addition of approximately 1,200 lbm of noise 

insulation to the rear cabin of the aircraft using data from [Goldsmith 1981]. Also, the parametric 

propfan prediction model has been updated to include data from [Weisbrich 1982]. The authors 

believe the updated data is more representative of a modern configuration and the end result has 

been a small but significant increase in vehicle weight relative to the previous work. 

To baseline the fuel burn performance the following engine cycle, shown in Table 27, is 

used. Comparisons between the prop design on the EDS model and the F7/A7 engine data are 

also shown. Additionally, modifications to the aircraft include mounting the engines on the tail, 

redesigning the landing gear, shifting the wing, and resizing the horizontal and vertical tails. The 

OPR is not shown for the original F7/A7 design report because it is irrelevant for the purposes of 

comparison due to the fact that it was used on a military and not a commercial platform. The 

OPR selected for the EDS cycle is representative of the core engine pressure ratio, including the 

booster, of a LEAP-X or PW1000G technology level engine. 

Table 27. EDS Open Rotor Engine Cycle 

Cycle Parameter F7/A7 EDS 

Front Rotor Diameter(ft) 11.67 11.33 

Advance Ratio 2.8 2.8 

CPQA 4.1 4.112 

CTQA 1.256 1.248 

Net Prop Efficiency 0.858 0.85 

Front Rotor Power (hp) 3767 3562 

Rear Rotor Power (hp) 3787 3562 

OPR N/A 29.7 

53
Using the cycle assumptions in Table 27, the EDS model predicts a 23.8% reduction in 

vehicle fuel burn on a 737-800 aircraft. The reference point for comparison is a 737-800 with 

CFM56-7B27 engines. Publically available information presented by GE has shown an estimated 

26% reduction in fuel burn relative to the same platform [Niskode 2010]. The end result is a 

platform capable of predicting fuel burn to within a reasonable degree of accuracy. Assessing the 

baseline EDS model noise also shows a good match to public domain data. Comparisons are 

made between EDS, GE estimated noise levels from the original testing of the F7/A7, and NASA 

1998 generated noise results using the F31/A31 blade set. Results are shown in Table 28 [Croft 

2012, Van Zante 2014]. This noise analysis is for a narrow body aircraft. 

Table 28. EDS Open Rotor Noise Comparisons 

EDS 

(F31/A31) GE-Hoff (F7/A7) NASA (F31/A31)

Sideline (EPNdB) 90.17 96.8 92.8 

Cutback (EPNdB) 84.24 88.2 87.6 

Approach (EPNdB) 92.34 97.9 91.7 

Cumulative (EPNdB) 266.7 282.9 272.1 

Margin to Stg. IV 10.9 -7.1 5.7 

EDS predictions see a vast improvement over the heritage F7/A7 blade set and a slight 

improvement over the NASA 1998 F31/A31 improvement. While it is hard to draw direct 

comparison due to many changes in the underlying assumptions such as engine OPR and vehicle 

trajectories assumed, EDS does appear to perform well and follow the trends of the older 

F31/A31 blade set predictions. It should also be noted, that because of the rotor placement on the 

current EDS vehicle, noise shielding is also be implemented within EDS allowing for quieter 

certification noise levels. Current studies using more advanced blade sets predict the noise 

margin to Stage IV to be in the range of 15EPNdB – 17EPNdB [Van Zante 2014]. The EDS 

open rotor noise model is well on its way to being able to model the acoustics of the open rotor, 

but must rely on the F31/A31 data for calibration due to proprietary concerns of advanced blade 

designs. 

3.4 Aircraft Configurations 

3.4.1 Vehicle Sketch Pad 

Vehicle Sketch Pad (VSP) is a parametric airplane geometry modeling tool originally 

developed by NASA, but now open source. It enables airplanes to be modeled rapidly and 

efficiently for conceptual and preliminary design levels of analysis, without requiring a fully 

featured CAD program and a large time investment. All of the EDS models developed for ERA 

had a VSP model created for that geometry. Example wireframe and fully shaded VSP models 

are shown in Figure 32. 

54
Figure 32. Example VSP Wireframe and Shaded Model 

The geometry required to create a VSP model and the geometry required for an EDS 

model have a significant amount of overlap, but are not identical. The EDS model has simplified 

geometry for the fuselage and engine, only utilizing the length and diameter. The lifting surfaces 

are modeled with a reasonable amount of detail in EDS, but airfoils are still missing. 

The VSP models had to have the information missing from the EDS models 

approximated for all the configurations. Every configuration, except the HWB, had a baseline 

aircraft from which the fuselage and engine geometry could be approximated. The HWB 

geometry was a smoothed version of the EDS wing geometry, with additional sections added to 

the relatively coarse definition from FLOPS. The airfoils for all these models were simple 

NACA 4-series that matched the thickness to chord ratios of the EDS models, since they were 

intended primarily for visualization purposes and not used in any detailed analysis. 

3.4.2 Tube and Wing 

The standard configuration for transport aircraft since the B-47’s development in the late 

1940s has been the Tube and Wing (T&W). This configuration has a roughly cylindrical fuselage 

containing the passengers and/or cargo, an aft swept wing with pod mounted engines, and a 

horizontal and vertical tail attached to the end of the fuselage. The configuration has the 

advantage of being separable into its disparate parts for design and manufacturing, as well as 

making it easy to develop stretched or shrunk versions to accommodate more or fewer 

passengers, respectively. 

The Tube and Wing configuration was used as the baseline aircraft for all vehicle classes. 

Each vehicle class was modeled in EDS with the geometry displayed in Vehicle SketchPad 

(VSP) models. An example T&W vehicle is shown for the RTC of the LSA in Figure 33. Table 

29 through Table 34 contains data for the 1995 baseline vehicles modeled in EDS. 

55
Figure 33. Tube and Wing Configuration 

Table 29. Regional Jet 1995 Baseline 

Description RJ 1995 Baseline 

TOGW, lbs 85000 

Operating Empty Weight, lbs 47250 

Fuel Weight, lbs 19690 

Design Payload, lbs 18060 

Block Fuel, lbs 15579 

Passengers 86 

Design Range, nmi 1980.0 

Wing Area, sq ft 752.5 

Aspect Ratio 8.289 

Fuselage Length, ft 109.9 

Fuselage Diameter, ft 8.83 

T/W 0.34 

W/S, psf 113 

Table 30. Small Single Aisle 1995 Baseline 

Description SSA 1995 Baseline 

TOGW, lbs 155000 

Operating Empty Weight, lbs 83000 

Fuel Weight, lbs 45120 

Design Payload, lbs 26880 

Block Fuel, lbs 37415 

Passengers 128 

Design Range, nmi 3330 

Wing Area, sq ft 1409 

Aspect Ratio 9.74 

Fuselage Length, ft 105.6 

Fuselage Diameter, ft 13.2 

T/W 0.335 

W/S, psf 110 

56
Table 31. Large Single Aisle 1995 Baseline 

Description LSA 1995 Baseline 

TOGW, lbs 174857 

Operating Empty Weight, lbs 91289 

Fuel Weight, lbs 46398 

Design Payload, lbs 37170 

Block Fuel, lbs 38161 

Passengers 179 

Design Range, nmi 2960 

Wing Area, sq ft 1409 

Aspect Ratio 9.74 

Fuselage Length, ft 125 

Fuselage Diameter, ft 13.2 

T/W 0.31 

W/S, psf 124 

Table 32. Small Twin Aisle 1995 Baseline 

Description STA 1995 Baseline 

TOGW, lbs 413000 

Operating Empty Weight, lbs 198440 

Fuel Weight, lbs 159750 

Design Payload, lbs 54810 

Block Fuel, lbs 141988 

Passengers 261 

Design Range, nmi 5920 

Wing Area, sq ft 3200 

Aspect Ratio 8.09 

Fuselage Length, ft 176 

Fuselage Diameter, ft 17.7 

T/W 0.29 

W/S, psf 129 

57
Table 33. Large Twin Aisle 1995 Baseline 

Description LTA 1995 Baseline 

TOGW, lbs 657000 

Operating Empty Weight, lbs 317000 

Fuel Weight, lbs 275950 

Design Payload, lbs 64050 

Block Fuel, lbs 249180 

Passengers 305 

Design Range, nmi 7530 

Wing Area, sq ft 4927 

Aspect Ratio 8.81 

Fuselage Length, ft 206.5 

Fuselage Diameter, ft 20.2 

T/W 0.296 

W/S, psf 133 

Table 34. Very Large Aircraft 1995 Baseline 

Description VLA 1995 Baseline 

TOGW, lbs 876999 

Operating Empty Weight, lbs 403399 

Fuel Weight, lbs 386029 

Design Payload, lbs 87570 

Block Fuel, lbs 347168 

Passengers 417 

Design Range, nmi 7060 

Wing Area, sq ft 6203 

Aspect Ratio 7.61 

Fuselage Length, ft 225 

Fuselage Diameter, ft 26.6 

T/W 0.26 

W/S, psf 141 

Figure 34 through Figure 39 show the VSP models for the RTC tube and wings for the 

various class sizes. 

58
Figure 34. RJ RTC VSP Model 

Figure 35. SSA RTC VSP Model 

59
Figure 36. LSA RTC VSP Model 

Figure 37. STA RTC VSP Model 

60
Figure 38. LTA RTC VSP Model 

Figure 39. VLA RTC VSP Model 

3.4.3 Hybrid Wing Body 

The Hybrid Wing Body (HWB) is a configuration where the fuselage is smoothly 

integrated into the wing to provide additional lift, and the tail is eliminated. The aerodynamic 

performance of this configuration is superior to the T&W because of the increased lift, from the 

centerbody, and decreased drag, due to the lack of interference between the wing-fuselage and 

61
the lack of a tail. Another critical advantage of the HWB is the noise shielding provided by 

placing the engines on the aft end of the centerbody. 

There are some disadvantages to the HWB configuration as well. The integration of the 

payload into the blended centerbody requires the pressure vessel to be non-cylindrical, which is a 

significant structural challenge. The placement of the engines above the airplane makes 

maintenance more difficult. It is also challenging to design stretched or shortened variants of a 

given design relative to a tube and wing. Finally, the stability and control of an HWB is more 

challenging. The range of acceptable center of gravity locations tends to be narrower, because 

longitudinal control power is limited by the smaller moment arm of the elevons. 

The HWB configuration was applied to the STA, LTA and VLA vehicle classes. The 

LTA had both turbofan and open rotor engine variants developed in EDS. An example of the 

turbofan powered LTA vehicle is shown as a VSP model in Figure 40. 

Figure 40. Hybrid Wing Body Configuration 

3.4.3.1 Modeling References and EDS Baseline 

There is no in service HWB for passenger transport that can be used to calibrate the 

models and create the Reference Technology Collector EDS model for each vehicle class. It was 

therefore necessary to start by calibrating to notional vehicles from published data. Two 

configurations were modeled; one from the Boeing ERA report labeled the 0009A in [Bonet 

2011], and the second from the NASA OREIO study in [Pitera 2011]. 

The Boeing ERA report vehicle is a 2025 technology level vehicle capable of carrying 

224 passengers for 8000 nm. The technology added to the vehicle includes hybrid laminar flow 

control, riblets, composite structure, a geared turbofan, and an advanced APU. The configuration 

includes two podded engines, winglets, and canted vertical tails as seen in Figure 41. 

62
Figure 41. Boeing 0009A HWB Configuration [Bonet 2011] 

The mission, geometry, and technology assumptions were translated from [Bonet 2011] 

into an EDS model, with some of the fixed equipment weights hardcoded to reduce error. The 

technology assumptions were then adjusted to more closely match the final results. The final 

EDS model results are shown in Table 35. The overall weight distribution was closely matched 

with the largest variance in the approach speed, possibly resulting from a difference in the 

estimated maximum lift coefficient. 

Table 35. EDS Validation to Boeing ERA Report 

Description Boeing Report EDS Model 

TOGW, lbs 412,199 412,171 

Operating Empty Weight, lbs 229,935 229,306 

Fuel Weight, lbs 132,264 132,865 

Design Payload, lbs 50,000 50,000 

Block Fuel, lbs 121,288 120,878 

Design Range, nmi 8,000 8,000 

Wing Area, sq ft 8,048 8,048 

Aspect Ratio 5.62 5.62 

Cruise L/D 22.64 23.1 

Approach Speed, kts 131.38 127.5 

T/W 0.23 0.22 

W/S, psf 51.0 51.2 

The OREIO report vehicle is a 2025 technology level vehicle capable of carrying 100,000 

lbs of cargo for 6500 nm. The technology added to the vehicle includes hybrid laminar flow 

control, riblets, composite structure, an open rotor, and an advanced APU. The configuration 

includes three podded engines and canted vertical tails as seen in Figure 42. 

63
Figure 42. OREIO HWB Configuration [Pitera 2011] 

The mission, geometry, and technology assumptions were translated from [Pitera 2011] 

into an EDS model, with the open rotor engine deck generated as described in Section 3.3.3. The 

technology assumptions were then adjusted to more closely match the final results. The final 

EDS model results are shown in Table 36. The overall weight distribution was closely matched 

with the largest variance again in the approach speed. 

Table 36. EDS Validation to OREIO Study 

Description OREIO Report EDS Model 

TOGW, lbs 475,800 475,576 

Operating Empty Weight, lbs 229,935 229,306 

Fuel Weight, lbs 127,010 126,878 

Design Payload, lbs 100,000 100,000 

Design Range, nmi 6,500 6,500 

Wing Area, sq ft 8,010 8,010 

Aspect Ratio 5.646 5.646 

Cruise L/D 23.274 23.242 

Approach Speed, kts 131.38 127.5 

T/W 0.262 0.262 

W/S, psf 59.4 59.37 

The EDS model for both vehicles was able to adequately capture the most important 

characteristics. These were used as references and points of comparison for the final EDS HWB 

vehicles in the absence of better information. Neither vehicle is directly usable as an RTC 

reference. Therefore adjustments were made for each of the four EDS vehicles in three different 

size classes. 

3.4.3.2 HWB EDS Modeling 

Modifications to the EDS environment were necessary to model the HWB vehicles. The 

modifications were in the aerodynamic modeling (described in Section 3.4.3.3), and in the way 

the vehicles were automatically resized to maintain a fixed wing loading. 

A traditional T&W configuration in EDS has a fixed fuselage geometry based on the 

number of passengers, with the wing geometry dynamically scaling internal to FLOPS to 

64
maintain a fixed wing loading. It is possible to run FLOPS with fixed wing geometry and a 

floating wing loading, but this could easily result in an infeasible configuration, incapable of 

meeting performance requirements such as takeoff or climb. An HWB configuration, however, 

cannot be dynamically scaled inside of FLOPS because a large part of the reference wing area is 

contained in the centerbody, which must be sized for the payload and cannot be resized. 

The solution was to add an EDS solver loop to iterate externally to FLOPS to maintain a 

fixed wing loading. This necessitated the definition of a resizing strategy, which was to scale 

everything outboard of the centerbody while maintaining a constant aspect ratio for the entire 

vehicle. It would be possible to only scale the local chords of the outboard sections, but this 

would quickly result in the outboard sections having an unrealistically small chord from a 

structural standpoint. 

The resizing strategy also required surrogate models to be created for each vehicle class 

for each local chord of the outboard section and the total outboard semi-span as a function of 

total wing area. These simple surrogate models were created in Excel based on resizing studies 

using FLOPS to maintain constant vehicle aspect ratio. 

3.4.3.3 Aerodynamic Modeling 

The use of FLOPS as the sizing and synthesis tool in EDS limits the ability to accurately 

capture the unique aerodynamic characteristics of the HWB configuration, because the semiempirical aerodynamic model inside FLOPS is based on data from conventional tube and wing 

aircraft. The team therefore identified AVID APEX as an improved an analytical tool to integrate 

into the EDS environment. 

APEX was developed as part of a NASA contract specifically to model HWB 

configurations [AVID 2014] and has been validated against wind tunnel results in [Re 2005]. It 

integrates the vortex lattice aerodynamic analysis of VORLAX with a transonic airfoil code 

called TSFOIL and includes numerous other extensions and corrections. 

Even though APEX has been validated the team chose to build an APEX model of 0009A 

and compare the output to the limited aerodynamic results reported in [Bonet 2011]. The 

planform geometry for the 0009A was easily scaled from the report, but it was also necessary to 

include airfoil and twist distributions that were not available. The airfoil and twist distributions 

were therefore taken from [Re 2005], which should be representative of the Boeing vehicle. The 

geometry input to APEX is in the form of a VSP model. A simplified VSP model of the 0009A 

was created excluding the engine nacelles, pylons, and vertical tails. The geometry comparison 

between the modeled and Boeing vehicle is shown in Figure 43. 

 

Figure 43. Comparison of Actual to Modeled 0009A Geometry, right image from [Bonet 2011] 

65
There were only three points available in [Bonet 2011] to directly compare the APEX 

output. The model was able to closely capture the Boeing reported values. The direct comparison 

can be seen numerically in Table 37, and graphically in Figure 44. The settings used for this 

analysis were then used for the full aerodynamic analysis integrated into FLOPS. 

Figure 44. Comparison of APEX Aero to 0009A values from [Bonet 2011] 

Table 37. Comparison of APEX Aero to 0009A values from [Bonet 2011] 

Description Boeing Report APEX Model 

L/D at M=0.85, h=39k ft, CL=0.2067 22.64 22.69 

L/D at M=0.85, h=43k ft, CL=0.2119 22.38 22.87 

L/D at M=0.85, h=35k ft, CL=0.2412 23.92 23.42 

Direct integration of APEX into EDS was considered to be too expensive in 

computational time, and therefore a different strategy was used. A surrogate model of the lift and 

drag was generated for each vehicle class as a function of the geometry and technology factors 

that would vary. This surrogate was integrated into EDS and used to build a FLOPS external 

aero deck. A depiction of this integration process is shown in Figure 45. 

Figure 45. APEX Integration into EDS 

Calibrated 

APEX settings 

from 0009A

Generate 

surrogates for CD0

and CDi (offline)

Integrate 

surrogates 

into EDS

Surrogates used to 

generate external 

FLOPS aero deck

Function of flight condition, 

techs, and vehicle size

66
3.4.3.4 HWB Centerbody/Aft Body Weight 

3.4.3.4.1 Formulation and Motivation of HWB Centerbody 

The complex geometry and lack of data for innovative designs such as the HWB aircraft 

makes the optimization and design of such aircraft difficult. For this reason, finite element model 

(FEM) based analyses are used to provide enhanced capabilities for HWB sizing and weight 

estimation. 

This work focused on the centerbody and aft-body weight estimations. The centerbody of 

the HWB is shaped like a “home plate” in baseball as shown in Figure 46 [Nickol 2009]. The 

size of the centerbody is determined by similar requirements to a conventional aircraft including 

number of passengers, utility areas, aisles, and etc [Nickol 2009]. The aft body is defined as the 

area directly aft of the cabin as shown in Figure 47 [Nickol 2009]. 

The specified geometry of the HWB is then used to create a Vehicle Sketch Pad (VSP) 

surface model of the aircraft. Utilizing the structural model and specifying various inputs within 

MATLAB script, “HCD_Struct.m”, NASTRAN compatible bulk data files were generated. 

Some of the inputs included: primary structure definition, material properties of primary 

structural components, and parameter weights (landing gear, engine, systems, and etc.). The 

structural analysis, optimization, and weight calculation of the primary HWB structural 

components were then based on the Nastran finite element models for the centerbody, aft body, 

mid-section, and outboard wings. 

Data obtained from finite element analysis for five distinct, representative centerbody 

structural layouts was used to find the coefficients of a weight estimate equation in the form, 

ܹ௖௔௕௜௡ = ܭ௦ ∗ 0.316422(ܱܶܩܹ଴(.ଵ଺଺ହହଶ(ܵ௖௔௕௜௡)ଵ.଴଺ଵଵହ଼

FLOPS was then modified to include a new fuselage design subroutine for the HWB 

centerbody [Bradley 2004]. A fourth term for wing weight was also added to FLOPS to account 

for the aft-body weight. The standard wing weight estimation for the outboard wing section 

included three terms including W1, W2, and W3, relating to wing bending loads, control surfaces 

and shear material, and wing area, respectively. W3 also accounts for a variety of miscellaneous 

terms. 

67
Figure 46. HWB FLOPS Implementation Geometric Layout [Nickol 2009] 

Figure 47. Aft Body Weight Accounted for by Wing Weight W4 Term [Nickol 2009]

68
While the weight regression shown in equation 1 was based off of a few centerbody 

structural layouts, further goals are to examine whether this weight calculation will be 

representative of the centerbody structural layouts of the current HWB model. Also there is 

interest in how the weight estimation of the aft body compares to the FLOPS calculation. 

3.4.3.4.2 Inputs to Model 

To perform the finite element modeling in NASTRAN, compatible bulk data files need to 

be created. To do this, MATLAB script, “HCD_struct” was utilized, requiring five input files 

along with a VSP file. The VSP file specifically needed to be exported as a, “.dat” file to be read 

correctly in MATLAB. The VSP model used for this study’s specific geometry is shown again in 

Figure 48 and was based off of the geometry specified in FLOPS case 4. The five input files 

(Bwb_param_mat_PR_DISP, Bwb_param_lift, Bwb_param_aero, BWB_Struct_inputs, and 

BWB_ Param_Weights) did not differ much from the files provided. The datafiles provided were 

those used in the Boeing OREIO analysis, as will be discussed further in the validation section 

[Gern 2013]. 

Figure 48. HWB VSP Model 

The structural input file was modified to coordinate with the VSP structure and vehicle 

from FLOPS corresponding to the HWB with ITD technology package. A comparison of the 

data for the structural input file is shown in Table 38. It’s important to note that the same 

chordwise location assumptions of the front and rear spars for both mid-section and outboard 

were used in this study to correspond to previous studies similar to the OREIO. These specific 

chordwise spar locations were 12.5% and 62.5% for the front and rear, respectively. Along with 

this assumption, the front bulk head of the centerbody was again defined by the same standard 

cockpit size assumptions as previous studies.

69
Table 38. Structural Input File Comparison 

Original 

Inputs GT Inputs Units Variable Name Comment 

Centerbody 

1051.7 1104 In XLp Length of passenger cabin at centerline 

450.0 528 In Wf Width of passenger cabin 

656.4 528 In XLw Length of passenger cabin sidewall at outboard station 

or wing root 

96.0 96 In CPl Cockpit length, position of front bulkhead (fixed) 

30.0 30 In FSsb Front spar set back from leading edge 

3 3 - - Numofbays, number of centerbody bays 

3 3 - - Numofbaysaft, number of aftbody bays (must be 

identical to numofbays for now) 

Mid section is defined by centerbody and outboard wing parameters 

Outboard wing 

12.5 12.5 % Fspercent Front spar chord percentage 

62.5 62.5 % Fspercent Rear spar chord percentage 

480.0 552 In Wgbrky Spanwise location of wing break (connection between 

mid section and outboard) 

Aft body 

178.4 132 In Aftblen Length of aft body (from centerbody rear spar to center 

flap hingeline) 

450.0 528 In aftbwid Width of aft body at center flap hingeline 

70
Table 39. Parameter Weights Input File

Original 

Inputs GT Inputs Units Variable Name Comment 

Weights 

100000.0 64050.0 lbs Payload payload weight 

128045.0 120104 lbs Fuel fuel weight (Flops) 

23108.0 52370.0 lbs systems systems weight (Flops) 

Landing Gear Parameters 

19404 20180 lbs gear landing gear weight (Flops) 

100 136.9 in maingl main landing gear strut length 

75 87.3 in nosegl nose landing gear strut length 

0.2 0.2 - gearrat ratio of nose gear weight vs total gear weight 

0.6 0.6 - gearloc chordwise location of main gear on centerbody SOB 

Engine Parameters 

54222 22531 Lbs Engweight engine weight (Flops propulsion total) 

3 2 - Engnum number of engines 

126.6 126.6 In Rotordia rotor diameter for CONM2 (75% of 168.8in) 

48 48 In Rotclear rotor clearance from HWB upper skin 

77.7 134.1 In Nacdia nacelle diameter 

45 45 In Inplane distance from inlet plane to engine CG 

73.8 73.8 In Rplane distance from CG to mean rotor plane 

0.6 0.6 - engloc chordwise location of outboard engine on aftbody SOB

Vertical Tail Parameters 

2381 2381 Lbs tailweight vertical tail weight (Flops) 

80 80 In VT_off_y spanwise offset of VT from aftbody rear spar 

-80 -80 In VT_off_x chordwise offset of VT from aftbody rear spar 

35 35 Deg VT_q_swp VT quarter chord sweep 

222 222 In VT_span VT span (18.5ft OREIO report) 

190.7 190.7 In VT_rt_c VT root chord (1.13 * rotor diameter) 

81 81 In VT_tp_c VT tip chord (0.48 * rotor diameter) 

45 45 Deg VT_cant VT cant angle (zero means VT is vertical) was 32.4 

-16.9 -16.9 Deg VT_toe VT toe angle (zero is in direction of aircraft x-axis) was -

16.9

3.4.3.4.3 Incorporation into EDS 

All of the MATLAB plots created by HCD_struct are included in Figure 51. To focus on 

the resulting HWB structural representation, Figure 49 shows the resulting structural components 

for FEM modeling. This can be then compared to the original files in Figure 50 to further 

illustrate the differences between the structure of the OREIO [Gern 2013] and the model used. 

71
Figure 49. (Left) Primary Structure for FEM Modeling and (Right) Primary Structure with Overlay of 

Nastran Doublet Lattice Aerodynamic Paneling for Specified Geometry 

Figure 50. (Left) Primary Structure for FEM Modeling and (Right) Primary Structure with Overlay of 

Nastran Doublet Lattice Aerodynamic Paneling for Original Inputs 

72
Figure 51. MATLAB Figures Generated for the Specified Geometry

3.4.3.4.4 Validation of Model 

Upon receiving the HWB centerbody tool, the data files were run without any 

modification. The structure, as well as, the input files resembled the OREIO study [Gern 2013]. 

The landing gear weight, systems weight, fuel weight, and propulsion total weight, detailed in 

the parameter weight input file shown in the Table 39, matched those detailed in the OREIO 

weight table shown in Table 40. The output weights for the given vehicle also resembled the 

same weights as shown in Figure 52 from the same study [Gern 2013]. The centerbody weight 

estimate, through FEM based analysis, was 50,708 lbs; the aft body weight being 11,892 lbs and 

the total primary structural weight being 121,590lbs. The MATLAB plots that were generated 

73
are shown below in Figure 53. For these reasons, it is believed that the same study was passed to 

us and that the method used was the same as previous studies and we were able to then apply the 

similar method to our own study. 

Table 40. Boeing OREIO Weights Statement [Pitera 2005, Gern 2013] 

Figure 52. OREIO Weights Comparison for Different Analyses. [Gern 2013] 

74
Figure 53. MATLAB Figures Generated for the Original Files 

3.4.3.4.5 Comparison of Weights 

Centerbody Weight: The centerbody weight from the FEM-based analysis (65,493 lbs) is 

about 2.8% lighter than the FLOPs prediction value (67,382 lbs). 

Aft Body Weight: For HWB analyses in FLOPS, Term 4 of the wing weight breakdown 

corresponds to the weight of the aft body. The FEM based aft body weight prediction of 11,961 

lbs is about 5.3% lighter than the FLOPS aft body weight (12,635 lbs). 

The centerbody weight calculated from the FEM-based analysis, when compared to the 

FLOPs predicted value, is similar to the 2% difference shown in the ORIEO study [Gern 2013]. 

75
The 5.3% difference for the aft body of this study is a considerable improvement to that 

compared to the difference of roughly 24% in the ORIEO study; this is perhaps due to the inertia 

and thrust loads of the engines. The propulsion total weight in Table 40 shows a large difference 

between the OREIO study and our models. In general though, it’s observed that the aft-body 

structure has the higher degree of uncertainty for structural weight prediction when compared to 

the centerbody weight estimation. 

3.4.3.5 Development of HWB RTC 

One of the key requirements for the RTC development was maintaining the maximum 

amount of commonality in the assumptions and technology levels as possible between the HWB 

and the T&W baseline vehicles. The engine technology level and modeling parameters for the 

HWB vehicles were taken directly from the T&W engines. The mission and payload 

requirements were also directly translated from the T&W vehicles. The commonality was 

deemed necessary to make the comparisons between the configurations and the technology 

assessments as even as possible. 

The RTC EDS HWB vehicle geometry was developed based on the Boeing and OREIO 

vehicles with the centerbody geometry sized in FLOPS for the passenger load for the three 

vehicle classes. The reference vehicles were used in the selection of wing outboard geometry and 

The initial values of wing loading and thrust-to-weight ratio were taken from the Boeing 

0009A, but had to be modified. The wing loading was slightly increased to 52 psf to maintain 

commonality on the approach speed. The thrust-to-weight was increased from 0.22 to 0.27 for 

the STA and LTA, and up to 0.31 for the VLA. These increases were necessary to enable the 

vehicle to climb to a sufficiently high altitude. It is probable that the thrust lapse predicted for the 

EDS engine models is significantly worse than that predicted by Boeing. 

The final HWB RTC vehicle characteristics are shown in Table 41 for the STA, Table 42 

for the LTA, and Table 43 for the VLA. 

Table 41. Vehicle Characteristics of HWB STA RTC with a Geared Fan 

Description STA HWB GF RTC 

TOGW, lbs 367366 

Operating Empty Weight, lbs 192139 

Fuel Weight, lbs 120417 

Design Payload, lbs 54810 

Block Fuel, lbs 107953 

Passengers 261 

Design Range, nmi 5920 

Wing Area, sq ft 7070 

Aspect Ratio 7.95 

T/W 0.27 

W/S, psf 52 

76
Table 42. Vehicle Characteristics of HWB LTA RTC with a Geared Fan 

Description LTA HWB GF RTC 

TOGW, lbs 537229 

Operating Empty Weight, lbs 273149 

Fuel Weight, lbs 200030 

Design Payload, lbs 64050 

Block Fuel, lbs 181210 

Passengers 305 

Design Range, nmi 7530 

Wing Area, sq ft 10339 

Aspect Ratio 6.04 

T/W 0.27 

W/S, psf 52 

Table 43. Vehicle Characteristics of HWB VLA RTC with a Geared Fan 

Description VLA HWB GF RTC 

TOGW, lbs 805565 

Operating Empty Weight, lbs 435615 

Fuel Weight, lbs 282379 

Design Payload, lbs 87570 

Block Fuel, lbs 253681 

Passengers 417 

Design Range, nmi 7060 

Wing Area, sq ft 15489 

Aspect Ratio 7.08 

T/W 0.31 

W/S, psf 52 

3.4.4 Over Wing Nacelle 

The Over Wing Nacelle (OWN) configuration is a modification of the conventional tube 

and wing with the turbofans integrated into the wing structure. This engine integration provides 

some noise shielding of the jet exhaust. An additional modification from the T&W is that the tail 

is a T-tail configuration; the tail change is driven by the need to keep the horizontal tail out of the 

jet wash of the engine. 

The OWN model is based on the T&W SSA, which is the only vehicle class modeled as 

an OWN. The wing geometry is modified for the engine installation based on [Hahn 2010]. The 

VSP model for the SSA RTC of the OWN is shown in Figure 54, with the reference geometry 

from [Hahn 2010] shown in Figure 55. 

77
Figure 54. Over Wing Nacelle Configuration 

Figure 55. Over Wing Nacelle Reference Geometry from [Hahn 2010] 

3.4.5 Mid Fuselage Nacelle 

The Mid Fuselage Nacelle (MFN) aircraft refer to an unconventional aircraft 

configuration for which the engines are installed on the fuselage. This configuration also 

includes two decks in the fuselage. The engines are not necessarily installed at the rear fuselage 

as the conventional fuselage-mounted-engine aircraft, instead, they are installed close to the 

trailing edge of the wing or mid fuselage to provide noise shielding of the engine from the wing. 

Figure 56 shows an illustration of this concept. 

78
Figure 56. Concept of MFN Aircraft 

3.4.5.1 Baseline aircraft creation, design mission and configuration 

The EDS model of the baseline MFN aircraft is created based on the similar concept 

(2025-0027A) designed by Boeing in [Bonet 2011], which is referred to as the reference aircraft. 

An EDS aircraft model was created and calibrated/validated against this reference aircraft, which 

is referred as reference (EDS) model. This reference model is then modified to match the design 

mission, configuration/geometry, technology portfolio and performance of this research, 

resulting in the (EDS) baseline aircraft model. 

The design mission, configuration/geometry and performance data of this reference 

aircraft are from [Bonet 2011], and its Advanced Turbofan (ATF) engine 

configuration/geometry, cycle and performance data are from [Manglesdorf 2012]. The reason 

that the engine data of [Manglesdorf 2012] are used instead of those in [Bonet 2011] is because 

the ATF engines used in the two reports are similar but the former report has significant more 

details of the engine. 

79
Figure 57. NASA ERA Primary Mission Requirements (from [Manglesdorf 2012]) 

Figure 58. NASA ERA Reserve Mission Requirements (from [Manglesdorf 2012]) 

80
Table 44. MFN Vehicle and Mission Assumptions 

Variable Description NASA ERA MFN 

(LTA) 

Design Range, n.mi. 8000 7440 

Cruise Mach Number 0.85 0.84 

Number of Passengers 224 300 

3.4.5.2 Modeling and Design Considerations 

The configuration of the MFN aircraft is similar to that of conventional tube-wing aircraft 

with engines installed on the fuselage, except for two major differences. The first difference is 

that there are two decks in the passenger cabin/fuselage. This double deck fuselage configuration 

results in a shorter fuselage, which may in turn results in lighter weight and less aerodynamic 

drag. The second difference is that the engine inlet lip/face is very close to the trailing edge of 

the wing; actually it is above and a little bit upstream of the wing trailing edge. This engine-wing 

configuration is proposed with the expectation to have good shielding of the engine inlet noise. 

With those two main differences in mind, the design process for MFN is generally the 

same as for the conventional tube-wing configuration. During the design process, special models 

were made for weight and aerodynamic estimation of the double-deck fuselage; for aerodynamic 

estimation of the wing affected by close coupled engine; and for the noise shielding effect 

because of the engine-wing configuration. 

3.4.5.3 Modeling wing and double deck fuselage in FLOPS 

In FLOPS, for the conventional tube-wing configuration, the aerodynamics of the 

fuselage is captured by three main OML sizes, i.e. fuselage length, maximum fuselage width and 

maximum fuselage depth, while its weight needs an additional size, i.e. length of passenger 

compartment. Note here the fuselage weight estimation is for a fuselage with single deck 

passenger compartment. 

The double deck fuselage of MFN is equivalent to a longer single deck one of 

conventional tube-wing aircraft. It is found that FLOPS can also estimate both the aerodynamics 

and weight of a MFN fuselage by extrapolation usage of its fuselage weight estimation. That is, 

the nominal fuselage OML sizes will be given for aerodynamic estimation while a longer length 

of passenger compartment will be given for weight estimation. Since the number of passengers 

of MFN aircraft is 300 and that of the reference aircraft is 224, the fuselage and passenger 

compartment were scaled up for MFN aircraft from the reference aircraft. The double deck 

fuselage modeling results are shown in Table 45. As one can see, the length of passenger 

compartment is even greater than the fuselage length because of the extrapolation. 

 

81
Table 45. Double Deck Fuselage Modeling 

Variable Description [Bonet 2011] MFN 

(LTA) 

Fuselage Length, ft 154.8 171.5 

Maximum fuselage width, ft 17.5 21.1 

Maximum fuselage depth, ft 19.92 21.7 

Length of passenger compartment, ft 168.7 182.6 

Because of close coupling between the wing and engine, the lift and drag of the wing are 

expected to be affected. In FLOPS, those effects can be captured by three factors, i.e. airfoil 

technology parameter, factor to increase or decrease lift-independent drag coefficient, and factor 

to increase or decrease lift-dependent drag coefficient. Those factors are determined during the 

calibration process. 

3.4.5.4 Noise modeling 

The noise modeling for the MFN is very similar to that of a traditional configuration for a 

T&W aircraft. The airframe noise sources, such are leading edge noise, flap noise, horizontal tail 

noise, etc. The primary difference in in the modeling of the engine mounts, as it is in a different 

location than most large twin aisle aircraft. The more challenging part of the noise modeling for 

the MFN occurs with the engine noise sources. Since the engine is mounted above and in close 

proximity to the main wing, the engine noise will benefit from shielding effects from the 

shielding surface, the main wing. This was done through the use of the WING module in 

ANOPP. The WING module allows the user to input either a shielding or reflecting surface and 

alters the source noise propagation to ground the observers appropriately. The shielding surface 

was selected to be the main wing and defined for various engine sources. The shielding plane 

was defined differently for different engine source because the engine is large enough that the 

distance between the origins of the engine noise sources is not insignificant when accounting for 

shielding benefits. The engine noise sources that originate in the physical engine, such as fan 

inlet noise and combustor noise, were given a different shielding plane definition than the single 

stream jet noise sources, and the mixing jet noise source as well as the total jet noise source were 

given yet an altogether separate definition. This allowed for confidence that each noise source 

was receiving the appropriate amount of shielding benefit and an over/under compensation for 

shield would not occur. 

3.4.5.5 Engine and aircraft calibration 

As mentioned previously, the EDS model of the baseline MFN aircraft is modified from 

the reference aircraft model, which is calibrated/validated against the reference aircraft. The 

good quality of the calibration results can provide the confidence on the MFN aircraft model, and 

thus the calibration results of the reference model are provided. 

The ATF engine used on the reference aircraft is first calibrated/validated. As mentioned 

previously, the engine configuration, cycle and performance data are from [Manglesdorf 2012]. 

In Figure 59, the Lockheed Report ATF represents the ATF engine described in [Manglesdorf 

2012], and EDS ATF represents the ATF engine model created in EDS for the reference aircraft. 

82
As one can see, the engine calibration/validation results are very satisfactory in terms of cruise 

performance. 

Figure 59. Calibration of ATF Engine for MFN Reference Aircraft 

The reference aircraft model is then calibrated/validated against the reference aircraft 

described in [Bonet 2011]. During the calibration process, subsystem weights are matched; 

structural weights are matched by varying structural factors in FLOPS; and fuel burn is matched 

by varying drag factors in FLOPS with the AFT engines calibrated above. As one can see in 

Table 46, the aircraft calibration/validation results are very satisfactory for most of the 

differences being less than ±1%. 

Table 46. MFN Reference Aircraft Model Calibration/Validation Results 

Variable Description [Bonet 2011] EDS Output % Difference 

MTOGW (lb) 453,159 452,241 -0.20% 

OEW (lb) 242,354 242,271 -0.03% 

Wing Weight (lb) 63,665 63,278 -0.61% 

Propulsion System Weight (lb) 27,356 27,326 -0.11% 

Fuel Capacity at MTOGW (lb) 160,805 159,971 -0.52% 

Net Thrust (SLS, lb) 62,800 63,333 0.85% 

Fan Diameter (in) 109* 109.2 0.18% 

Bypass Ratio (Cruise) 13.7* 14.1 2.92% 

Fan Pressure Ratio (TOC) 1.58* 1.58 0.00% 

Overall Pressure Ratio (TOC) 65* 64.96 -0.06% 

Cruise TSFC 0.495** 0.495** -0.0% 

*2025 ATF data from Lockheed Report ([Manglesdorf 2012]) 

**Defined at calibrated throttle hook flight conditions

0.49

0.495

0.5

0.505

0.51

0.515

0.52

0.525

0.53

4000 5000 6000 7000 8000 9000 10000 11000 12000

TSFC, pph/lbf

Net Thrust, lbf

Cruise Performance

[40k ft, 0.85 Mach]

EDS ATF Lockheed Report ATF

83
3.4.6 Box Wing 

The box wing (BXW) aircraft refer to such an unconventional aircraft configuration that 

has two tandem wings connected at the wing tips with offsets or winglets, forming a box shape in 

the front or rear view. The forward wing is swept back and the aft wing forward, forming a 

diamond shape in a plan view; and the engines are installed under the aft wing; see Figure 60 for 

the illustration of this concept. 

Figure 60. Concept of Box Wing Aircraft 

3.4.6.1 Baseline aircraft creation, design mission and configuration 

The EDS model of the baseline BXW aircraft is created based on the similar concept 

designed by Lockheed Martin in [Manglesdorf 2012], which is referred as reference aircraft. An 

EDS aircraft model is created and validated against this reference aircraft, which is referred as 

intermediate/validation aircraft. This intermediate/validation aircraft is then modified to match 

the design mission, configuration/geometry, technology portfolio and performance of this 

research, resulting in the baseline aircraft. 

The design mission, configuration/geometry and performance data of this intermediate 

aircraft are from [Manglesdorf 2012], and its Ultra Fan 2025 engine configuration/geometry, 

cycle and performance data are also from [Manglesdorf 2012]. 

The mission requirements of BXW are developed based on those of the Boeing 777-200 

and the NASA ERA mission requirements. The resulting mission requirements of BXW are 

similar to the NASA ERA mission requirements, the primary and reserve missions of which are 

shown in Figure 57 and Figure 58, respectively, except for the major difference shown in Table 

47. 

Table 47. BXW Vehicle and Mission Assumptions 

Variable Description NASA ERA BXW 

(LTA) 

Design Range, n.mi. 8000 7440 

Cruise Mach Number 0.85 0.84 

Number of Passengers 224 300 

84
3.4.6.2 Modeling and Design Considerations 

This reference aircraft was the best result in terms of three measures of merit: fuel 

consumption, noise and emission, and those measures are used to evaluate and select 

configuration and technologies, as described in the Lockheed Martin report [Manglesdorf 2012]. 

For emission, the Lockheed Martin research team looked into more details including LTO NOX, 

particulate matter, hydrocarbon emissions, smoke, and anticipated fuel sulphur content. In this 

research, the emission measure of merit is focused on LTO NOx, cruise NOx and hydrocarbon 

emissions (represented by fuel consumption).

The configuration of the BXW aircraft is quite different from that of conventional tubewing aircraft with engines installed under the wing. It is a special kind of biplane or joined 

wings, both the front and aft wings producing positive lift, while the horizontal tail of the 

conventional tube-wing usually produces negative lift. Because the two wings are 

joined/supported at the tips by a vertical offset or wing let, it has the promise to significantly 

reduce the induced drag and moderately reduce the total structural weight of the two wings. 

Because of those aerodynamic and structural differences, special models/methods will be 

made for aerodynamic and weight estimation of the wings; and the design process of the 

conventional tube-wing configuration will be modified to accommodate those special 

models/methods. 

3.4.6.3 Box wing aircraft aerodynamic modeling 

Box wing is a special kind of biplane or joined wings with two wing tips being joined by 

a vertical winglet and both wings producing positive lift. In this closed wing system, the vertical 

winglets significantly reduce or even inhibit the formation of wingtip vortices, which are the 

major cause of the induced drag; in addition, there is no significant adverse wing interference 

because of no overlapping in the plan view and the offset between wings ([Wolkovitch 1986]). 

These features result in substantial reduction of induced drag with comparison to that of an 

equivalent monoplane wing of the same total lift and span ([Wolkovitch 1986] and [Prandtl 

1924]). 

For high speed (transonic) transport aircraft, since the induced drag is a large portion of 

the total drag, this configuration has the promise to substantially reduce the total drag at cruise, 

comparing to that of a conventional tube-wing configuration with the same total wing area and 

span ([Lange 1974] and [Salam 2012]). 

As a fast modeling method in FLOPS, a box wing airplane is represented by two FLOPS 

models (because lift of canard is ignored in FLOPS): 

• Front model: front wing + fuselage + winglets + engines; 

• Aft model: aft wing as a fly wing + engines. 

The (back-swept) front wing and the (forward-swept) aft wing have the same span, and 

the fly wing is modeled as a Blended-Wing-Body (BWB) airplane in FLOPS. The internal drag 

polars of the two models outputted by FLOPS are combined with correction to the induced drag 

as a function of sweep angles and offset. The resulting drag polar is used as the drag polar of the 

box wing aircraft. The following describes the process to combine the drag polars. The drag 

outputted by FLOPS includes the following components: 

85
() () CC DD lift _ dependent CD lift _ tindependen = +

( ) CD lift _ dependent Dp += CC Di

( ) CD lift _ tindependen DΔ += CC Dc

where CDp is the pressure drag, CDi is the induced drag, and CDΔ is the drag component as 

a function of altitude and Mach number, and CDc the compressive drag or wave drag. The total 

wing area of the box wing aircraft is the sum of the areas of the front and aft wings: 

AFT += SSS

where ST is the total wing reference area, SF is the front wing reference area, and SA the 

aft wing reference area. 

The lift and drag coefficients (drag polar) of the box wing aircraft can be calculated from 

those of the two aircraft models with correction for reference area, respectively. And after the 

correction for reference area, the resulting drag polar is deemed as that of an equivalent 

monoplane wing with the same span and total wing area. 

( ) ( )

T

A

AL FLOPS

T

F

L monoplane FL FLOPS S

S C

S

S C , = C , + ,

( ) ( )

T

A

ADx FLOPS

T

F

Dx monoplane FDx FLOPS S

S C

S

S C , = C , + ,

where the subscript F means the front wing, A the aft wing; and CDx can be any of CDp, 

CDi, CDΔ, or CDc. Since there is no significant adverse wing interference because of no overlapping 

in the plan view and the offset between wings, we assume that the lift coefficient of the box wing 

equals to that of the equivalent monoplane wing, i.e. 

,BWL = CC L,monoplane

For the drag components of the box wing and the equivalent monoplane wing, we assume 

that only the component of induced drag is different and needs to be adjusted/modified to get 

CDi,BW From [Buttazzo 2009], the relationship of minimum or theoretical induced drag between 

the box wing and equivalent monoplane wing is 

86
( )

( ) bh

bh

C

C

Di monoplane ltheoretica

Di BW ltheoretica

/81.204.1

/45.01

,

,

+

+ =

where h is the offset or the vertical distance at the tip of the two wings and b is the wing 

span. FLOPS actually outputs the minimum induced drag for a wing planform, therefore, 

From [Wolkovitch 1986], the main difference between an actual box wing and its 

theoretical one, which has the minimum induced drag, is their span efficiency factor, and the 

relationship based on data fitting is, 

( )

( ) °+°

Λ+Λ

+=

20tan29tan

tantan04.0 1 F A

ltheoretica

actual

e

e

where e is span efficiency factor and Λ is the sweep angle. 

Since 

Ae

C C L

Di

π

2

= and the lift coefficients of the actual and theoretical box wings are the 

same, we have, 

( ) actual

ltheoretica

Di BW ltheoretica

BWDi

e

e

C

C =

,

,

Combining the previous equations, the induced drag of a box wing can be estimated as 

BWDi CDi monoplane K

K C ,

2

1

, ×=

where 

bh

bh K

/81.204.1

/45.01

1 +

+ = , ( )

( ) °+°

Λ+Λ

+=

20tan29tan

tan04.0 tan

2 1 F A K

3.4.6.4 Estimation of the box wing weight 

FLOPS currently does not have the option to estimate the weight of a box wing, therefore 

it has to be done externally. [Howe 1996] suggests a general form of semi-empirical box wing 

weight estimation, 

( ) C Di ,monoplane C Di monoplane , ltheoretica =

87
9.0 3.0 5.0

41

1 33

21

cos 













 



 



 



 









 





+

+

Λ = λ τ

λ TOM D

W

V

S

bS NM CM

where C1 is a coefficient to be determined by data fitting, and the other symbols and units 

are listed in Table 48. 

Table 48. Symbols and Units of the Box Wing Weight Estimation Equation 

Symbol Note Unit 

Mw (Metal) wing mass/weight Kg 

b Span Meter 

S Wing Area Square meter 

Λ1/4 Quarter-chord sweep angle Degree 

λ Taper ratio 

MTOM Maximum takeoff mass Kg 

N Normal acceleration factor (2.5) 

VD Design diving speed Meter/second 

τ 

Average thickness to chord ratio 

(simple average of ratios at different 

spanwise locations)

[Jemitola 2013] gives C1=0.028 for each metal half (front or aft) wing of medium range 

box wing aircraft based on fitting the results of FEM structural analyses. For the box wing 

aircraft of this research, we assume it still can use the above general form of wing weight 

estimation equation; since its range is long range and thus its aspect ratio is different, C1 needs to 

be re-estimated. 

The data of passenger and cargo box wing aircraft from the Lockheed Martin report 

[Manglesdorf 2012] are used to estimate the coefficient C1 for long range box wing aircraft. For 

each of passenger and cargo box wing aircraft, a coefficient is estimated; the average of two 

values is used for metal wing of long range box wing aircraft; and finally, 

021.0 C1 =

 There are three notes. One note is that if composite material is used in the box wings, C1

needs to be further adjusted. The fact is considered during the coefficient estimation that the 

Lockheed Martin report uses composite material in the wings, resulting in 35% weight reduction 

from an otherwise metal wing. The last note is, for the design diving speed, we assume: 

88
D = 02.1 VV C

 where VC is the design cruise speed. 

3.4.6.5 Scaling of the box wing planform 

The scaling of the box wing planform is different from those of other aircraft types in this 

research. For box wing, the wing area and span are inputs and the aspect ratio is calculated; 

while for other aircraft types the area and aspect ratio are inputs and the span is calculated. 

Assuming the sweep angles of the box wing planform in the Lockheed Martin report 

[Manglesdorf 2012] have been optimized, we want to keep those values when the two wings are 

scaled according to the change of total wing area. This can be done by the following scaling 

procedure. 

A. Get the new total wing area, span and front wing area ratio (the ratio of the front wing 

area and total wing area, based on that of the Lockheed Martin report); 

B. Get the data of wing station location and chord length for the baseline wings; 

C. Calculate the new area of each wing according to the front wing area ratio; 

D. For each wing, calculate a temporary wing area based on the wing station locations 

and chord lengths of the baseline wing and the new span (match the span); 

E. For each wing, calculate the new chord lengths by scaling the baseline chord lengths 

with the ratio of the new wing area and the temporary wing area (match the area); 

F. For each wing, use the baseline wing station locations, new chord lengths and new 

span. 

3.4.6.6 Flowchart of box wing aerodynamic and weight estimation in 

EDS 

During the sizing/design process of the box wings, the convergence of the aerodynamic 

estimation and the convergence of weight estimation are coupled, since both the wing 

aerodynamics and wing weight estimations interact with aircraft total weight estimation. 

Therefore, the convergences of those two estimations need to be achieved at the same iteration 

step. The following flowchart in Figure 61 shows how this is achieved. 

89
Figure 61. Box Wing Aero and Weight Estimation FlowChart 

For Step 6 to make the customer aero deck for the box wing aircraft, the details are 

described in Section 3.4.6.3; and for Step 8 to estimate total wing weight, the details are 

described in Section 3.4.6.4. 

3.4.6.7 Box wing aerodynamic modeling in APEX 

One suggestion was to use APEX ([AVID 2014]) to model the aerodynamics of the box 

wing aircraft. APEX is a fast, medium-fidelity aerodynamic analysis tool based on a robust 

vortex-lattice theory, capable of analyses of transonic speed range and full aircraft configuration 

including wing, and fuselage. The main advantages of APEX include it can be fast, accurate and 

handle the full aircraft (although vertical tail, engine and its pylon are not modeled). If it could 

be shown to be appropriate for the box wing aircraft as well, it could provide a fast and accurate 

solution. 

Fixed span and SWSPLIT from DoE, SW from the solver (initial 

value from DoE)

Split SW into SWFW and SWAW (of the front and aft wings) based 

on SWSPLIT

Equivalent monoplane: scale span of template to fixed span, 

calculate wing area SW_tmp, scale CHD based on ratio of SW and 

SW_tmp, make FLOPS input file

Front & aft wings: scale spans of template to fixed span, calculate wing areas 

SWFW_tmp, SWAW_tmp scale CHD based on ratios of SWFW and SWFW_tmp, 

SWAW and SWAW_tmp, make FLOPS input files

Run FLOPS to get internal aero decks for the front and aft wings

Make the customer aero deck for the (equivalent) box 

wing aircraft based on the internal aero decks

Run FLOPS for the box wing aircraft

Estimate total wing weight of the box wing aircraft based on the box wing GW and geometry of the 

two wings, and set this estimation as the target for the solver to converge to by changing FRWI

1

2

3

4

5

6

7

8

90
Limited by the vortex-lattice theory that APEX based on, in addition to no vertical tail, 

engine or pylon, the closed wing system of the box wing aircraft has to be modeled as two 

separate parts in APEX, as shown in Figure 62 

 

Figure 62. Open Wing System for Modeling Box Wing in APEX 

A validation case has been executed for modeling box wing aircraft with APEX. The box 

wing aircraft described in the Lockheed Martin report [Manglesdorf 2012] was modeled in 

APEX Versions 1.1.3 and 1.5.0, at Mach 0.85, 0.75, 0.65, and altitude between 20,000 feet to 

50,000 feet. The aerodynamic results of APEX were compared to those in the Lockheed Martin 

report, and the following were observed: 

• APEX gives much greater induced drag and parasite drag Cdo, even though there 

are no vertical tail, engine and pylon; 

• APEX did not capture the effect of altitudes on drag polars, i.e. the drag polars 

almost do not change with altitude. 

Because of those discrepancies above, which are thought due to the limitation of its 

theory, APEX was not used for modeling the aerodynamics of box wing aircraft, instead, an 

approximation method using FLOPS was developed, as described in Section 3.4.6.3. 

3.4.6.8 Noise modeling 

The noise modeling for the box wing aircraft is separated into two parts, airframe noise 

source analysis and engine source noise analysis. The engine source noise analysis is done 

exactly as the T&W aircraft engine noise analysis (utilizing ANOPP) with the engine placement 

being designated to the aft of the aircraft. The airframe noise source analysis is done slightly 

different due the addition of the aft wing. Due to the fact that ANOPP cannot model a box wing 

configuration naturally, some creative manipulation was done to provide the most accurate and 

realistic airframe noise analysis as possible. The airframe sources that reside on the wing, such as 

leading edge noise, flap noise and slat noise, were modeled separately for the fore wing and 

source noise was evaluated at the fore wing location. The same was done for the aft wing noise 

sources with the source noise evaluation done at the aft wing location. These two separate noise 

groupings, fore wing sources and aft wing sources, were then propagated down to the observer 

location and summed together to provide a total airframe source noise analysis (with the addition 

of the vertical stabilizer noise). However, because of the limitations of ANOPP, there is a 

possibility of missing possible new noise sources produced by a boxed wing configuration. 

3.4.6.9 Engine and aircraft calibration 

As mentioned previously, the EDS model of the baseline BXW aircraft is modified from 

the reference aircraft model, which is calibrated/validated against the reference aircraft. The 

91
good quality of the calibration results can provide the confidence on the BXW aircraft model, 

and thus the calibration results of the reference model are provided. 

The Ultra Fan engine used on the reference aircraft is first calibrated/validated. As 

mentioned previously, the engine configuration, cycle and performance data are from 

[Manglesdorf 2012]. In Figure 63, the Rolls Royce – Ultra Fan represents the Ultra Fan engine 

described in [Manglesdorf 2012], and EDS – Ultra Fan represents the Ultra Fan engine model 

created in EDS for the reference aircraft. As one can see, the engine calibration/validation results 

are very satisfactory in terms of cruise performance. 

Figure 63. Calibration of Ultra Fan Engine for BXW Reference Aircraft 

The reference aircraft model is then calibrated/validated against the reference aircraft 

described in [Manglesdorf 2012]. During the calibration process, subsystem weights are 

matched; structural weights are matched by varying structural factors in FLOPS; and fuel burn is 

matched by varying drag factors in FLOPS with the Ultra Fan engines calibrated above. As one 

can see in Table 49, the aircraft calibration/validation results are very satisfactory for most of the 

differences being less than ±1%. 

0.46

0.465

0.47

0.475

0.48

0.485

0.49

0.495

0.5

0.505

0.51

6000 6500 7000 7500 8000 8500 9000 9500 10000 10500 11000

TSFC , pph/lbf

Net Thrust, lbf

EDS - Ultra Fan Rolls Royce - Ultra Fan

92
Table 49. BXW Reference Aircraft Model Calibration/Validation Results 

Variable Description [Manglesdorf 2012] EDS Output % Difference 

MTOGW (lb) 365,914 364,680 -0.34% 

OEW (lb) 191,381 191,737 0.19% 

Wing Weight (lb) 36,070 36,070 0.00% 

Propulsion System Weight (lb) 31,612 31,613 0.00% 

Mission Fuel Burn (lb) 122,460 122,768 0.25% 

Net Thrust (SLS, lb) 63,600 63,600 0.00% 

Nacelle Diameter (in) 184.0 184.0 0.00% 

Cruise L/D 21.5 21.7 0.93% 

Cruise TSFC 0.465 0.475 2.15% 

3.5 Vehicle Coefficients for Fleet Assessment 

Vehicle technology impacts were to be rolled up to the fleet level as part of this work by 

executing a fleet level environmental impact tool called AEDT. AEDT calculations are based on 

BADA vehicle definitions for high altitude and SAE Air1845 for calculations below 10,000ft. 

The generation of the necessary input xml files had been automated as part of the creation of 

EDS, but the sheer number of vehicles being considered in this case to cover all vehicle classes, 

year of introduction, and scenario combinations severely tested the robustness of the original 

algorithms. One of the main issues in properly defining the necessary coefficients for the AEDT 

input file is that in both BADA and SAE Air1845 the coefficients used often do not have a 

physical meaning. They are not things like Max Thrust or Overall Pressure Ratio, but rather 

thrust coefficient 1 and 2, which in some way represent variation of thrust with speed and 

altitude and must be extracted from multiple points in the engine deck as well as consideration of 

multiple missions and specific data at specific altitudes. Ensuring that the points in the engine 

deck selected for the calculations were available for all vehicle sizes and technology packages 

and that the conditions needed by AEDT had been considered in the EDS execution was 

extremely challenging and required several iterations. 

Most of the robustness issues were ironed out throughout the second year allowing the 

team to generate fleet level fuel burn results, however, additional issues were encountered when 

considering mission NOx due to the specific implementation of the Boeing Fuel Flow Method in 

AEDT which did not account for the potentially different behavior of modern combustors at 

altitude. Consideration of LTO NOx for year 2 also highlighted new robustness issues with the 

algorithms generating AEDT coefficients because these calculations explore a range of 

altitude/temperature/humidity conditions which had not been previously tested. The new 

algorithms developed have proven to be far more reliable in year 3 allowing for a nearly flawless 

link between the vehicle team and the fleet level assessments. 

93
4.0 Technology Modeling 

4.1 Technology Assessment Process 

Using EDS as the modeling and simulation environment, ASDL executed the technical 

approach depicted in Figure 64, which has three main elements: a parametric advanced vehicle 

modeling, a technology modeling and assessment element, and a vehicle performance evaluation 

element called metrics calculations. Modeling advanced configurations (left block of Figure 64) 

begins with a parametric geometry model with appropriate definition to predict the aerodynamic 

characteristics of the vehicle. This parametric geometry model will also drive the component 

weight sizing and estimation. Along with vehicle aerodynamic and weight prediction, a detailed 

and parametric propulsion sizing and performance model is also critical to modeling advanced 

concepts. The propulsion model must also be able to provide a propulsion system weight based 

on preliminary flowpath analysis. 

Once these subsystems have been synthesized based on an initial gross weight guess, the 

vehicle sizing is accomplished through a mission analysis, which is based on the mission 

definition either derived from the capability definitions or provided by the designer. The 

synthesis and sizing process is an iterative procedure for estimating vehicle gross weight by 

balancing fuel available and fuel required. 

Once the vehicle is synthesized and sized with the vehicle mass converged, the three 

main ERA metrics are computed (middle block of Figure 64). The fuel burn metric is computed 

by executing a performance analysis which has been developed by ASDL to also include a set of 

calculations to characterize the vehicle performance in formats consistent with FAA's Aviation 

Environmental Design Tool (AEDT) [Roof 2007]. This feature of the system design environment 

allows for direct connection to fleet level modeling for estimating system-wide metrics. The 

emissions metric for the landing and takeoff cycle as well as for cruise is computed using an 

emission model developed by ASDL under a previously NASA funded NRA. The noise metric, 

in terms of cumulative certification noise levels, is then estimated based on information from the 

vehicle sizing/performance and propulsion system analyses. In order for the ERA project to 

make informed decisions on the technology portfolio, the system design tool set must also be 

able to estimate vehicle cost, ranging from technology maturation to certification costs based on 

the information from vehicle modeling and technologies being considered. 

These computed metrics are the basis by which the potential ERA technologies are being 

evaluated to determine the best combinations that can achieve the ERA project goal of 

simultaneously meeting the fuel burn, noise, and emissions metrics. The upper-right block of 

Figure 64 shows a structured process to model and assess technologies, which begins with a list 

of candidate technologies from the ERA portfolio. First, the physical compatibility rules for this 

list of technologies must be established via a technology compatibility matrix (TCM). The 

purpose of the TCM is to define the relationships between the various technologies and 

configurations, whether they be compatible, incompatible, enablers or have direct interactions. 

Next, the impacts of each technologies modeled through the use of k-factors are organized via a 

technology impact matrix (TIM). The combination of the TCM and TIM produces the parametric 

ranges for the desired technology k-factors. Based on the number of k-factors and their 

established ranges, a design of experiment (DOE) is chosen. The DOE then dictate the specific 

input settings for each execution of advanced vehicle modeling and metrics calculation blocks of 

Figure 64. 

94
Figure 64. Technical Approach Process Chart 

The computed metrics are collected for each DOE simulation and utilized in a 

multivariate regression to generate surrogate models. These surrogate models represent the 

technology space to be explored and optimized to maximize their impact. These surrogate 

models are also the enablers to perform probabilistic analysis to establish the uncertainty in a 

concept achieving the ERA goals. 

4.2 Technology Compatibility Matrix (TCM) 

The portfolio of technologies to be simulated onboard the fleet of ERA vehicles contains 

many combinations of technologies that are incompatible. The Technology Compatibility Matrix 

(TCM), part of which is depicted in Figure 65, is used to prevent incompatible technologies from 

being simultaneously applied to a single vehicle simulation. Before generating an EDS input 

vector representing a vehicle with a unique package of technologies applied, the TCM is used to 

ensure that the technology package does not contain any incompatible technology pairs. 

Aero Prediction

Component 

Weight Sizing

Propulsion Sizing 

& Performance

Mission Analysis

No

Parametric 

Geometry 

Technology Space 

Exploration & 

Optimization

Probabilistic 

Analysis

Multivariate 

Regression

Surrogate Models

Fuel Burn

LTO & Cruise 

NOx Emissions

Noise

Performance Analysis

Emissions Prediction

Noise Prediction

Technology 

Portfolio 

Recommendation

Technology Infused Vehicles

Technology 

k-factor 

Parametric Ranges 

Design of 

Experiments

Technology 

Portfolio

Technology

Impact Matrix 

(TIM)

Technology 

Compatibility 

Matrix (TCM)

Technology Modeling & Assessment

Advanced Vehicle Modeling

Coefficient Generation 

for Fleet Analysis

Fuel 

Balance?

Yes

Metrics Calculations

EDS

95
Figure 65. Technology Compatibility Matrix (TCM) depiction showing the various relationships assumed 

present between ERA technology pairs. 

Within the TCM, numerical values are assigned to every possible technology pair. These 

values have been established through internal as well as external audits completed by subject 

matter experts. A matrix cell value of zero is used when the pair of technologies is compatible 

and the two technologies are not expected to interact with each other. If a technology pair is 

incompatible, a value of negative one is assigned to the corresponding cell within the TCM. If a 

particular technology requires an accompanying technology in order to be properly applied to a 

vehicle, a value of one is assigned to the cell corresponding to the pair of technologies. TCM 

cells corresponding to pairs of technologies that are expected to interact with each other contain 

a value of two. The expected interactions are not necessarily modeled, but the technology pairs 

with likely interactions are identified nonetheless. 

Whenever a discrete package of technologies is considered, the technology set is first 

evaluated using the TCM. If there are no incompatibilities present and no additional technologies 

are required according to the TCM, the EDS input vector is generated and the candidate vehicle 

is simulated. This process is also carried out when generating variable ranges for technology and 

design space explorations. For a given EDS input variable, all technologies that impact the 

variable are identified and a full factorial design is constructed to consider every possible subset 

of technologies that impact that particular variable. Each subset is then evaluated with the TCM 

and technology combinations are eliminated that contain any incompatibilities. The remaining 

(compatible) technology subsets are then used to determine the minimum and maximum possible 

input variable settings, properly bounding the input variable range within which technology 

space explorations are performed. 

4.3 Technology Impact Matrix (TIM) 

Any task related to the generation of EDS input vectors for candidate ERA vehicles with 

corresponding packages of applied technologies is performed using the Technology Impact 

Matrix (TIM). The various impacts of each technology contained within the ERA portfolio are 

96
contained within the TIM database, along with the TCM which identifies the relationships 

between all possible pairs of ERA technologies. The TIM database contains three point estimates 

of every technology impact for each of the ERA vehicles. The three points correspond to the 

minimum possible impact, the most likely impact, and the maximum possible impact of a given 

technology when applied to a given ERA vehicle. When generating EDS input vectors 

representing unique technology packages applied to a candidate vehicle, the most likely 

technology impacts are typically applied to the vehicle for a single point deterministic EDS 

simulation. When generating input variable ranges for an explorative design of experiments 

whose resultant data will be used for surrogate model training, the minimum and maximum 

possible impacts of each technology are used. This accounts for the largest possible variability in 

the assumed technology impacts, determining the maximum input variable ranges required for 

any possible probabilistic assessment. This allows for probabilistic assessments to be performed, 

employing the resultant surrogate models and avoiding extrapolation of the Artificial Neural 

Network (ANN) mathematical representations of the EDS modeling and simulation environment. 

An example of the three point technology impact estimates for a given technology when applied 

across various ERA vehicles is shown in Figure 66, which displays the impacts of the 

compressor intercooler technology when applied to the engines on board the twin aisle ERA 

vehicles. 

Figure 66. Example of the three point estimates of technology impacts for various ERA vehicles. 

4.3.1 Vehicle - Input vector generation 

The primary function of the Technology Impact Matrix is to document and propagate 

technology impacts to desired vehicles in the form of EDS input vectors. An input vector is a 

combination of values for EDS variables that fully defines a vehicle. In order to establish an 

input vector there are multiple steps. The user first selects the package of technologies that will 

be applied to appropriate EDS variables. The corresponding impacts of the technology package 

are defined by their particular function on the aircraft, and have been quantified through 

literature search and by subject matter experts. Nominal, or most likely, impacts of each 

technology are inputted into the TIM for each vehicle class. When multiple technologies affect a 

single EDS input variable, technology combination rules are used to determine the resultant input 

variable setting for the selected technology package. These rules will be discussed in detail 

further in the text. With the technology impacts identified and mapped to EDS variables, an EDS 

input vector can be generated for a desired vehicle and selected technology package. The vector 

is entered into EDS and the design case is launched through its corresponding modules. 

However, appropriate baselines for each vehicle class must first be defined. 

97
4.3.1.1 Baseline definition 

There are two distinct baselines that are established – the “1995 baseline” and the 

Reference Technology Collector (RTC) – for proper use of the TIM and interpretation of vehicle 

design explorations. The 1995 baseline for each vehicle is representative of an aircraft of the 

same seat class that is in service today, and has been calibrated based on publically available 

data. For each ERA vehicle, the technology level is then updated to a consistent level across all 

vehicle classes, and the resultant RTC is used as the datum from which all performance 

improvements are measured. 

From the baseline vehicles, Reference Technology Collectors are established as the 

current state-of-the-art for three engine types – the Advanced Direct Drive (ADD), the Geared 

Fan (GF), and Open Rotor (OR). The update from a vehicle’s baseline to the RTC corresponding 

to a particular advanced engine architecture is performed through the TIM, applying the correct 

advanced cycle and vehicle RTC updates. RTC level technologies are also applied, resulting in 

the definition of a current starting point from which vehicle technology explorations can begin. 

The steps of the process by which technology packages are applied to the RTC vehicles, 

compatibility is verified using the TCM, and the resultant EDS input vector is generated is 

described in the following sections. 

4.3.1.2 Tech combo rules 

Each technology within the ERA portfolio contains a set of impacts that affect EDS input 

variables based on their expected function(s) on the engine or aircraft. As an example, some 

technologies are implemented to increase the efficiency of a particular ERA vehicle system, 

while others are used to reduce weight or influence other performance parameters. Many 

technologies also have accompanying negative impacts which also must be accounted. As such, 

the TIM employs multiple technology combination rules in order to properly apply the various 

types of technology impacts to the baseline definition. It is important to remember that, in the 

context of the technology combination rules, the baseline refers to the 1995 baseline, not the 

RTC’s. There are a total of eight different variable categories used in the TIM – absolute, 

discrete, delta, scalar, noise, formula, switch, and frame. 

Technologies whose impact is represented as an absolute variable are implemented as a 

simple replacement of baseline values. It is important to note that multiple technologies whose 

impacts are applied to the same variable are absolute are not applied in the same technology 

package; this is ensured via incompatibility in the TCM. A typical example of the use of absolute 

variables is how technologies such as ceramic matrix composites (CMC’s) or polymer matrix 

composites (PMC’s) affect material density. Applying advanced materials to change the density 

generally has one of two benefits within the engine: to reduce engine weight or to allow for 

higher maximum temperatures. Similar in technology combination rule to absolute impacts are 

discrete variable impacts. 

Discrete variables are used for technologies that affect variables that are discrete in 

nature; logic in the TIM ensures that the value of a discrete input variable remains a whole 

number. If a technology impact is discrete, it too is applied as a replacement to the baseline and 

cannot be applied in the same technology package as another technology with a discrete impact 

on a given EDS variable. For example, the RTC geared turbofan cycle dictates the main fan, just 

before the low pressure compressor (LPC), has 18 blades; however, with the implementation of 

the Advanced GTF cycle, the blade count is lowered to 16, replacing the RTC values and 

98
effectively reducing the weight of the engine. Not all technology impacts manifest as a 

replacement to a baseline value, delta and scalar variables act as an additive impact and a percent 

change respectively to the baseline value. 

Technology impacts combined using the delta variable combination rule are summed and 

added to the baseline instead of replacing it. 

௜݇෍+ ଴ݔ = ௔௧௘௟ௗݔ

௜

For each rule, x0 is the baseline variable value and ki are the individual technology 

impacts. This combination rule is often used for technologies that improve engine component 

efficiencies. As an example, in the case of the Advanced ITD GTF cycle, the high pressure 

turbine (HPT) efficiency is affected by two technologies, T95: Advanced ITD GTF cycle and 

T67: Advanced engine components. These account for a 0.25% and a 0.1% increase in HPT 

efficiency respectively; thus a 0.35% increase in the total HPT efficiency from the baseline 

value. An engine efficiency increase may allow for a decrease in total fuel burn. 

The scalar technology combination rule dictates that technology impacts be represented 

as a percent increase or decrease from the baseline value, as calculated below. 

௜݇෍1+൬଴ ݔ = ௥௔௟௔௖௦ݔ

௜

൰

Typically, the scalar rule is applied to technologies that variables like airframe drag. For 

example, if T3.2: Damage Arresting Stitched Composites and T86: 60 Degree Winglets were 

applied to the LTA, kT3.2 is -0.1 and kT86 is 0.017. This would result in an 8.3% net increase in 

the parasitic drag of the wing. In this case, there is a possible reduction in weight due to the 

composite technology applied to the wing, but an increase in drag. The winglets are an example 

of a method by which the drag may be mitigated. A study would be required to quantify whether 

or not the drag reduction from the winglet is beneficial due to the added weight imposed. This 

could quickly be done by creating two EDS input vectors, one including the winglets and one 

excluding the winglets. The TIM is very effective at producing input vectors for trade studies 

quickly. While scalar technology combinations apply to many variables, technology impacts 

concerning noise variables are combined in a distinct manner. 

Impacts on noise variables are calculated using a diminishing summation because sources 

of noise are not simply additive. Thus, the six greatest technological impacts are weighted and 

added to the baseline value as shown in the following equation for the same noise source, 

 

ݔ௜௢௡௦௘ =	ݔ ଴+) 	 ݇ଵ + 0.75݇ଶ + 0.5݇ଷ + 0.33݇ସ + 0.16݇ହ + 0.08݇଺)

The impacts, k, are in order from largest to smallest. So, k1 represents the largest impact 

on the particular EDS input variable, k2 the second largest, and so forth. The ANOPP module 

within EDS predicts the noise based on several factors outputted from aircraft and engine sizing. 

The baseline values for noise variables in each vehicles respective TIM are zero, thus an increase 

or decrease in noise is simply accounted as an offset to what is predicted in ANOPP. The 

99
remaining three variable types—formula, switch, and frame—do not have technology impact 

combination rules, as they serve a different purpose. 

Formula variables are not calculated by the addition of technologies; rather, by some 

equation relating the variable to other input variables. There are only two such EDS input 

variables, aircraft gross weight (GW) and wing area (SW): 

 

൬) ݏ݊݃݅݊݁ܧ݂݋#) ∗ ݎ݈݊݅݊݃ܵܿܽܽݑܶ = ܹܩ

ݐݏݑݎhܶ	ܵܮܵ

ܹܴܶ ൰

ܹܵ =

ܹܩ

ܹܴܵ

where SLS Thrust is Standard Sea-Level Thrust, TWR is thrust to weight ratio, and WSR 

is the wing loading. The tuning scalar is present to account for airframe variation. That is, for 

conventional TW models, the tuning scalar is 1.05, increasing the gross weight calculation by 

5% in order to provide the vehicle simulation with 5% excess weight for fuel; while, for the LTA 

Hybrid Wing Body (HWB) the tuning scalar is 0.92, reducing the gross weight calculation by 

roughly 8.3%. These are used to ensure the calculations are representative of data for similar 

aircraft in literature. The formula variables are used to set the initial values for the iterative 

vehicle sizing loop of EDS. There are multiple technologies that require more than input variable 

changes in order to be properly represented within EDS. 

Switch variables are used to activate certain logic within EDS to simulate technologies 

that require more than just input variable adjustments to adequately model. For example, 

intercooling requires additional components and linkports that would otherwise be absent in the 

engine. The switch activates the architectural changes to the engine necessary to model the 

intercooler technology. With this switch activated, the technology T22.1: Compressor Intercooler 

impacts on EDS input variables are able to be propagated to the model. Finally, in addition to all 

of the aforementioned variable types, frame variables define all other EDS input variables 

required to model an aircraft. 

Frame variables establish values for input variables that are not impacted by any 

technologies. These variables simply required to fully define the vehicle in EDS; they are not 

variables that are explored with technology infusion. 

4.3.2 DoE Generation for Surrogate Training– Procedure 

The first steps required for the design space exploration are performed using the 

Technology Impact Matrix tool. A comprehensive Design of Experiments (DoE) with sufficient 

edge points and space filling interior points is necessary to establish accurate surrogate models 

for desired responses. The baseline values for all vehicles must be confirmed and technology 

impacts need to be updated with the most current data from literature and subject matter experts. 

Specific vehicle design space exploration DoE’s, also referred to as surrogate DoE’s, are then 

generated. The steps are as follows: first, variable ranges must be determined to account for the 

variability assumed present in all compatible technology combinations that could possibly 

impact each variable. This is done by applying the entire portfolio of ERA technologies to obtain 

the absolute maxima and minima for each EDS input variable. Then, using the ranges, edge 

100
points or upper and lower limits to each of the variables are generated. This is to ensure that the 

extremes of the design space are adequately represented such that if ambitious technology 

packages are applied, the responses can be accurately modeled via surrogate equations. Space 

filling points, which are randomly generated values for each variable between the ranges, are 

established to ensure that there is an adequate sample size within the boundaries of the design 

space to accurately predict responses for technology packages within the ranges. The final cases 

needed in the surrogate DoE’s were random technology packages. These are required for their 

use as validation points during the surrogate equation generation. The space filling and edge 

points do not necessarily indicate a feasible technology package, thus it is important to test a 

significant portion of input vectors that were feasible technology combinations. Using the 

random technology packages, the validity of the surrogate equation is determined. 

4.3.2.1 Variable Range Determination for an exploration 

Once the minimum and maximum variable impacts of each technology are established in 

the tool, the absolute minimum and maximum for each variable for the exploration DoE is 

established, thus determining the range. For each variable, a full factorial design is constructed, 

considering all subsets of technologies impacting that variable. All incompatible combinations 

are eliminated, and the remaining compatible combinations’ minimum possible impacts are 

applied. The minimum possible input variable value is determined by the compatible technology 

combination that produces it. The same procedure is conducted to determine the maximum of the 

range using the maximum possible impact. With the surrogate DoE ranges determined, the 

surrogate DoE was generated. 

4.3.2.2 Generation of actual DoE for exploration 

DoE generators implement the ranges for each of the design variables to determine edge 

points and interior points within the design space. To establish edge points or DoE vectors that 

are comprised of random combinations of minimum and maximum values for each input 

variable, the generator calculates random samples for the input variables where each variable 

now only has two-levels, minimum and maximum. These edge points are imperative when fitting 

surrogates, because if they were not part of the equation, competitive designs that push approach 

the limit of the design variables will not be encompassed within the surrogate equation. 

In EDS, there are many variables to consider when defining a vehicle, 281 for TW 

models. To conduct a full factorial DoE on the edge points of the design space, approximately 

2281 or 3.89 x 1084 points would be required; however through sensitivity studies, it has been 

shown that if edge points comprise 20% of the cases for the exploration DoE, the surrogate 

models predict well near the edges. The space filling designs are randomly sampled with uniform 

preference placed throughout the continuous design and technology space considered for the 

surrogate exploration. For a given vehicle, an exploration DoE is comprised of roughly 15,000 

cases or input vectors, each representing a distinct aircraft. Further sensitivity studies were 

conducted during the summer of 2014 suggesting that increasing the number of cases in the 

surrogate DoE from 15,000 to 60,000 did not increase the accuracy of the surrogates. The edge 

point cases account for 20% of the input vectors and the random interior point cases account for 

roughly 66.6% of input vectors. The remaining 13.3% or 2,000 cases are comprised of feasible 

random technology packages. When training the surrogate models using the ANN’s in BRAINN, 

75% of the cases are used for training, 25% for validation and 0% for testing. 

101
4.3.2.3 Random tech package generation 

There is an automated functionality designed within the TIM tool that allows the user to 

generate random technology packages. With the use if the TIM and the previously outlined 

technology combination rules, the random technology packages are converted into DoE input 

vectors. Randomly sampled technology combinations are generated, represented by ones and 

zeros corresponding to ERA technologies either being, “on” or “off”, and corresponding EDS 

input vectors are produced for each. The tool ensures that a given technology package does not 

have any incompatible technologies applied via consultation of the Technology Combination 

Matrix (TCM). If an incompatibility is present, logic in the tool discerns whether either of the 

two incompatible technologies is an RTC technology. If one is an RTC technology, the other is 

removed; however, if neither are RTC technologies, one is randomly selected and removed. 

After this, incompatibility audit begins again and the same actions are taken if one is found. If 

not, the vector is selected for use as a random technology package. The tool then converts each 

random technology package vector into a corresponding EDS input vector. It used a given 

vehicle’s technology impact matrix and the technology combination rules to generate the vector 

of input variable settings, considering the technologies applied. 2,000 random, compatible, 

technology packages were generated and the resultant input vectors were appended to the end of 

the explorative DoE. The DoE, now containing 15,000 unique parametric vehicle design samples 

that lie throughout the design and technology space considered, is prepared to run in EDS in 

order to produce data with which surrogate models can be trained and evaluated. 

4.4 Advanced Combustor Modeling 

The goal of the NOx emissions prediction task is to estimate NOx emissions indices (EI) 

at the landing/takeoff (LTO) cycle conditions (and thereby estimate the regulated parameter 

Dp/F00) for all ERA candidate engine cycles. Two models have been developed; one 

representing current combustion technology levels (e.g. Talon II, approximately 30% below the 

CAEP/6 limits) herein called the “standard” or “baseline” combustors, and one representing 

advanced low emissions combustor technology capable of meeting the N+2 goals for the 2020 

timeframe (70% below the CAEP/6 limits), herein called the “advanced” combustor. 

The emissions are predicted by employing a chemical reactor network which runs using 

Chemkin TM. The network architecture has been developed to represent the baseline and 

advanced combustors assuming a rich-quench-lean (RQL) type of combustor. The network 

models the primary or rich combustion zone as a group of perfectly-stirred reactors (PSR) and 

the secondary or quench combustion zone as another group of PSRs. The lean and dilution 

combustion zones are each represented by single plug-flow reactors (PFR). 

The operating parameters for each of the reactors in the network are determined by a “1-

D flow model” which runs as part of the NPSS engine cycle. The 1-D flow model takes the 

combustor entrance pressure, temperature, airflow, and fuel flow (P3, T3, W3, and Wfuel, 

respectively) at each engine power setting and computes the reactor parameters according to a set 

of pre-defined rules. The model is “tuned” ahead of time to reflect either the standard or the 

advanced combustor. The details of the modeling process are discussed in the following sections. 

The ChemkinTM reactor network model takes approximately two minutes to run the four 

power settings for the LTO cycle for any given engine. As a result, the model cannot be run inline with the EDS model to predict the emissions for the several thousand ERA candidate engine 

cycles. Instead, the tuned models for both the standard and advanced combustors are first run 

102
throughout the anticipated ERA design space (in terms of P3, T3, W3, and Wfuel) and a neuralnet regression model is developed for each combustor. The neural-net equations are then 

programmed into EDS and are used for the ERA data generation. 

4.4.1 Emissions Prediction Methodology 

The ASDL approach to NOx emissions prediction uses analytical reactor-based models to 

bridge the gap between empirical and high fidelity approaches, in order to provide the capability 

to perform design trades at the conceptual level and to quickly predict emissions as the engine 

cycle and architecture are parametrically varied. The general organization and flow of the NOx 

emissions prediction methodology is illustrated in Figure 67 below. 

Figure 67. NOx Emissions Prediction Model 

The semi-analytical model consists of three main elements: The Object-Oriented 1-D 

Flow Analysis Model, the Chemical Reactor Network (CRN) Model, and the Emissions 

Regression Model. The 1-D flow model and the CRN are wrapped and run within the 

ModelCenterTM environment to generate the emissions predictions for a given set of inputs. The 

model must first be tuned, and then the tuned model is run through a design-of-experiments 

(DOE) table to generate the data required for training and verifying the neural-net regression 

model. The general steps required for model development are summarized below: 

• Model tuning 

o Establish target values of EINOx vs. Wfuel for a number of different engine 

cycles 

o Targets are established based upon existing engine data from the ICAO data bank 

o Calibrate the model to the target EINOx values for each engine cycle 

o Engine cycle model provides thrust and P3, T3, W3 vs. fuel flow inputs 

o Use Filtered Monte Carlo process to determine suitable values for model tuning 

parameters 

• Regression model development 

103
o Run a DOE on the calibrated models over a range of engine cycles, holding the 

tuning parameters fixed, to generate emissions data over the projected ERA 

engine design space 

o Fit a neural net regression model to the DOE data 

o Incorporate the neural net regression model into EDS 

o The combustor 1-D flow model is required as an assembly in EDS to provide the 

necessary inputs 

The ASDL method for NOx emissions prediction has its origins in the University 

Research Engineering Technology Institute (URETI) on Aeropropulsion & Power Technology 

(UAPT) during 2005-2007. This early research performed a proof-of-concept investigation of 

conventional single annular combustor architecture. This research was followed by NASA NRA 

NNX07AO08A during 2007 –2010, which extended the methodology to modern low-emissions 

combustor architectures. 

In 2013, the methodology was applied to an RQL combustor architecture for the ERA. 

The main difference in this work relative to the previous work is that the quench zone model, 

which was originally represented by a single partially-stirred reactor (PaSR), was replaced by a 

group of PSRs to decrease the model execution time. The execution time was reduced from 

about 10 minutes per LTO cycle to about 2 minutes per LTO cycle. However, the neural-net 

regression model was difficult to fit, and the results were unsatisfactory. For 2014, many of the 

model features modified in order to improve the model performance. This report focuses on 

those model improvements and the final results for 2014. 

4.4.2 Description of Combustor Technology Levels 

For the purposes of this study, the baseline or standard combustor was taken to be the 

Talon II emissions level. The Talon II engines in the ICAO data base are summarized in Table 

50. 

Table 50. Talon II Engines in ICAO Data Base 

Engine OPR Rated FN (kN) Dp/F00

PW4164 30.1 284.68 42.7 

PW4168 31.84 302.5 46.0 

PW4168A 31.84 302.5 46.0 

PW4X58 28.41 258.0 39.9 

PW6122A 25.7 98.31 32.8 

PW6124A 28 105.87 39.4 

PW4164-1D 31.3 286.9 44.9 

PW4168-1D 33.1 305.1 48.5 

PW4168A-1D 33.1 305.1 48.5 

PW4170 33.8 311.4 49.9 

The Dp/F00 values for these engines are almost linearly increasing with engine overall 

pressure ratio (OPR), and on average are 28.17% below the CAEP/6 limit, as shown in Figure 

68. 

104
Figure 68. Talon II Emissions Relative to CAEP/6 

For tuning purposes, additional engine parameters are required beyond the data available 

in the ICAO data base. These parameters are estimated by using calibrated EDS engine models 

run at the LTO conditions. Due to resource limitations, only two target engines could be 

calibrated in EDS. These calibrations were performed during 2013. The PW6124 and the 

PW4168 engines were selected, as representative of the 150-passenger and 300-passenger thrust 

classes, respectively. The emissions model was tuned to match the emissions levels of these two 

engines, which established the technology level for the standard or baseline combustor. A “sizing 

rule” is used to ensure that engines with about the same bypass ratio (BPR) will fall 

approximately along the same Dp/F00-vs.-OPR line regardless of the thrust class or airflow size. 

Engines with higher BPR will show better emissions due to the engine cycle effects on the 

Dp/F00-vs.-OPR characteristic. 

Because the model is not a true predictive model and must be tuned to a target emissions 

level, the advanced combustors were assumed to meet the ERA goals (70% below CAEP/6) for 

the same two target engines (the PW6124 and PW4168). The emissions model, including the 

“sizing rule”, was tuned to match these emissions levels, which established the technology level 

for the advanced combustor. 

4.4.3 ModelCenterTM Environment 

As mentioned previously, two of the model elements, the 1-D flow model, the CRN, are 

wrapped and run within the ModelCenterTM environment. These two model elements are 

described in detail in the following sections. 

Chemical Reactor Network (CRN) Model 

105
A Chemical Reactor Network (CRN) was implemented to calculate chemical 

compositions at each zone of the combustor. Each combustor zone was modeled using a 

combination of idealized simple reactors such as a Perfectly Stirred Reactor (PSR) and a Plug 

Flow Reactor (PRF), as shown in Figure 69. 

Figure 69. Chemical Reaction Network (CRN) of Rich, Quick-Quench, Lean (RQL) Combustor (Screenshot 

of CHEMKIN GUI) 

The primary zone is modeled as a combination of pre-mixed combustion and diffusion 

(droplet) burning. At higher temperatures any droplets are assumed to evaporate and so the 

combustion is treated as completely pre-mixed. At lower temperatures, larger droplets are 

assumed to persist so that a portion of the combustion is treated as diffusion burning. 

A group of nine PSRs are used to represent combustion in the primary zone. Even though 

the overall fuel-to-air ratio of the primary zone of RQL combustor is in a fuel rich condition, the 

non-uniform mixing of fuel and air results in local regions operating near the stoichiometric 

condition. These near-stoichiometric regions contribute to the NOx emissions. The nine PSRs 

represent the thermochemical characteristics of the overall primary zone by statistically 

distributing the mass flow and fuel-to-air ratio with a Gaussian distribution based on Heywood’s 

mixing parameter ܵ = ఙ

థ where σ is the standard deviation and ߶ is the mean equivalence ratio. 

The empirical unmixedness curve of Sturgess et al, shown in Figure 70, is used to find ܵ as a 

function of the primary zone equivalence ratio. 

106
Figure 70. Experimental Results of Unmixedness Parameter Collected by Sturgess et al. 

A uniform residence time is assigned to each PSR in the primary zone. This was an 

improvement from the 2013 model, in which the reactor volume was distributed in a way that 

resulted in different residence times in each reactor due to different temperature and density. As 

a result, the reacting flow spent less time in the near-stoichiometric branch and more time in the 

far-stoichiometric branches, diminishing the desired unmixedness effect. 

In prior years, the 30-step Kollrack chemical mechanism was used to model Jet-A 

combustion. However the Kollrack mechanism only accounts for thermal NOx and does not 

account for prompt NOx and N2O NOx pathways which are known to be important for low 

emissions combustors. Also the Kollrack mechanism has no pressure corrections on any reaction 

steps and so may not respond properly for higher OPR engine cycles. For these reasons, in 2014 

the model was updated to use the GRI-MECH 3.0 mechanism. The GRI 3.0 mechanism 

comprises 53 species and 325 equations. It contains detailed NOx reactions, but it does not 

contain the Jet-A mechanism (it is intended for propane, C3H8). To model Jet-A, a two-stage fuel 

pyrolysis taken from Kollrack mechanism was added to the GRI 3.0 mechanism: 

ܥଵଶܪଶଷ + ܱଶ → 5ܥଶܪସ + ܥଶܪଷ + ܱଶ

ܱ + ସܪଶܥ6 → ܪܱ + ଶଷܪଵଶܥ

107
One of the nine PSRs is assigned to the droplet burning model, as illustrated in Figure 71. 

The droplet model accounts for unevaporated fuel droplets. The Sauter mean diameter (SMD) of 

the droplets is estimated, and the time required for the droplets to evaporate is compared to the 

droplet residence time. 

Figure 71. Droplet Model 

Any unevaporated droplets are assigned to the first PSR and are burned assuming a 

stoichiometric equivalence ratio to simulate diffusion burning. The remaining mixture is 

distributed among the remaining PSRs using the Sturgess unmixedness curve. 

The Quench zone, where a large volume of air flow is injected and mixed with the 

products of the primary zone, is represented by a group of ten PSRs. Non-uniform fuel-to-air 

ratio over the quench field is modeled by an asymmetric beta distribution rather than a 

symmetric Gaussian distribution as in the primary zone. As in the primary zone, a uniform 

residence time is used for the group of PSRs representing the quench zone. 

The lean zone and the dilution zone are represented by PFRs because these regions are 

where the flow is relatively calm with lower turbulence. The final concentrations of NO and NO2

at the combustor exit are used to compute the EINOx. 

4.4.4 1-D Flow Model 

The W3, P3, T3, and Wfuel entering the combustor are specified for each LTO operating 

condition. Take-off power is considered to be the design point. Each of the combustion zones 

performs the following calculations on-design: 

• The equivalence ratio φ and the residence time τ are specified 

• Given φ, the local air flow ݉ሶ and flow fraction frac are calculated 

• The fuel-air mixture is burned at the specified φ to find the local temperature and density 

• Finally the effective volume of the zone is found by ܸ = ௠ሶ ఛ

ఘ

Off-design, each of the combustion zones performs the following calculations: 

• Given the flow fraction frac, calculate the local air flow ݉ሶ and equivalence ratio φ

108
• The fuel-air mixture is burned at the computed φ to find the local temperature and density 

• Given the effective volume, find the residence time by ߬ = ఘ௏

௠ሶ

After these calculations are performed, the inputs required for the Chemkin TM model are 

computed. The rich zone is divided up between the nine PSRs according to the droplet model 

and Sturgess unmixedness curve as described above. The quench zone is divided up between the 

ten PSRs according to the beta distribution as described above. The output parameters are written 

to a file for ModelCenter TM to pass to ChemkinTM. 

• The 1-D Flow Model comprises several files written in NPSS. The major files are 

described briefly below: 

• Total_Combustor.int – the main element, which models the combustion in the rich, 

quench, and lean zones. 

• Total_Combustor.mdl – the model file, which initializes the parameters required by 

Total_Combustor.int 

• Chemkin_Rich.fnc – Applies the Sturgess unmixedness curve and distributes the fuel and 

air for the rich zone 

• Droplet_Model.fnc – performs the droplet size and evaporation calculations required for 

the rich zone 

• Volumes.fnc – computes the effective volumes of each combustion zone 

• Chemkin_Inputs.view – creates the ChemkinTM input file from the 1-D flow model 

outputs 

4.4.5 Model Tuning Process 

As mentioned previously, the model is calibrated to target Dp/F00-vs.-OPR lines. 

Previously, the targets did not account for BPR and intercooling effects on the Dp/F00

characteristic. While EINOx responds to (P3, T3, FAR), Dp/F00 also depends on FN and SFC. 

Consider the definition of Dp/F00: 

௣ܦ

଴଴ܨ

௜ݔܱܰܫܧ ∗ ௜ݐ ∗ ௜,௙ݓ෍ =

ை்ܨ

௜ݔܱܰܫܧ ∗ ௜ܥܨܵ ෍~

௜

Thus for the same EINOx-vs.-Wfuel characteristic, improving BPR improves Dp/F00 for 

a given OPR. Also, intercooling lowers the T3 out of proportion to the P3 and therefore lowers 

EINOx for a given OPR. To better account for these effects in 2014, the model tuning process 

was divided into three parts: 

• Calibrate the model tuning parameters at 30 OPR 

• Develop and calibrate a “sizing rule” to account for increasing OPR while holding BPR 

at a relatively constant value and with no intercooling 

• Verify that the effects of higher BPR and intercooling look reasonable 

These steps take into account the fact that the Dp/F00-vs.-OPR curve is actually 

dependent on the additional variables BPR and intercooling. It is assumed that the N+2 goal of 

70% below CAEP/6 was intended to apply to engines of similar BPR and OPR as current 

engines. Advanced engines with higher BPR and/or intercooling will show further improvement 

109
due to the engine cycle, independent of the combustor technology level. The model is therefore 

first tuned to establish the technology level of the combustor for current OPR and BPR levels. 

The first step in the tuning process sets the combustor technology level by matching the 

target Dp/F00-vs.-OPR for the PW6124 and PW4168 engines. The second step extends these 

results to higher OPR engines by means of a “sizing rule” to be explained later. Since no specific 

targets can be set for higher BPR or intercooled engines, the third step only evaluates the results 

at these conditions to ensure that they appear reasonable. These steps are described in more detail 

in the following sections. 

4.4.5.1 Calibration at 30 OPR 

This step refers to the tuning of the model to match target EINOx levels using the 

PW6124 and PW4168 engine cycle data (at approximately 30 OPR) and thereby to establish the 

combustion technology levels. Two models are created, one for the current (standard or baseline) 

combustor technology (CAEP/6 – 30%) and one for the advanced technology (CAEP/6 – 70%). 

4.4.5.2 Pareto analysis of the calibration factors 

The model comprises several parameters which may be used to match the target EINOx 

values at each of the LTO cycle power settings. The 14 calibration parameters available are 

shown in Table 51. 

Table 51. Combustor Technology Calibration Factors 

Calibration Parameter Definition 

dPdiffP Pressure drop 

EqRatioPZ, EqRatioSZ, 

EqRatioDZ 

Equivalence ratio of each zone. Lean zone has the same EqRatio with the 

secondary (quench) zone.

ResTimePZ, ResTimeSZ, 

ResTimeLZ, ResTimeDZ Residence time of each zone 

k_DropTime Fraction of residence time of droplet PSR branch to ResTimePZ 

k_evap Fraction of volume allowed droplet to evaporate before burning process 

EffPZ Efficiency of primary zone 

k_mixedness Tuning parameter (multiplier) to adjust Sturgess unmixedness curve 

Alpha, Beta Shape parameter of beta distribution for the quench zone (SZ) 

To reduce the number of variables required for tuning, a sensitivity study was performed 

by perturbing each of the calibration parameters at the PW 6124 A cycle for three cases: high 

power (take-off), low power (idle), and elevated pressure (2X pressure). The normalized effect of 

the parameters to the output NOx emission is presented in Figure 72. 

110
Figure 72. Sensitivity Study Results for Model Tuning Parameters 

Six parameters were screened out and set to nominal values in accordance with the 

sensitivity analysis result. The model was calibrated with the remaining eight driving parameters, 

which are the design equivalence ratios and residence times in the three combustion zones, 

k_DropTime, and the shape parameters of the beta distribution for the quench zone. These 

parameters thus define the combustor technology level for the baseline and advanced 

combustors. 

Among the eight calibration parameters, five parameters (design equivalence ratios and 

design residence times in combustion zones) were set to be constant since they apply only to the 

design or takeoff power setting, while the other three parameters (k_DropTime, Alpha, and Beta) 

were allowed to vary with power setting. k_DropTime determines the flow residence time of the 

droplet branch and Alpha and Beta determine the shape of beta distribution in Quench Zone. 

111
The calibration processes started from generating a set of sample points of calibration 

parameters among the pre-determined design space for each of baseline and advanced 

combustors. Regression equations of EINOx as a function of the eight calibration parameters 

were created from a DOE and a Genetic Algorithm was used to find solutions to satisfy the target 

EINOx values listed in the ICAO databank. The GA was run in a “nested” fashion; first the fixed 

parameters were determined and then the power-varying parameters were determined. 

While the above approach was successful for the baseline combustor, it failed in the case 

of the advanced combustor. Some of resultant sets of solutions were not realistic and the others 

leaded high errors. For the advanced combustor, Neural Net equations were created to generate a 

prediction profiler in the JMP environment. Next, a Filtered Monte Carlo process was used to 

investigate each parameter in the prediction profiler and the points whose gradient was too stiff 

or too flat were removed. The remaining points were refined through optimization using the 

desirability function in the JMP environment. Then the output was evaluated from the model 

runs with refined input values. 

In both cases, the parameter k_DropTime was modeled as a function of primary zone 

equivalence ratio, which varies with engine power setting: 

(௓௉߶)݂ = ܶ݅݉݁݌݋ݎܦ_݇

Also, for the quench zone parameters Alpha and Beta, it was found that the data 

correlated best when the ratio of the standard deviation to the mean of the beta distribution was 

modeled as a function of the equivalence ratio of Quench Zone. Then given Beta, Alpha may be 

calculated from the algebraic relationship. 

ߪ

(௓ௌ(߶݂ = ߤ

,ߚ൬ ݂=ߙ

ߪ

൰ ߤ

The final tuning parameter values are summarized in Table 45. Keep in mind that the 

equivalence ratios and residences times specified in the Table apply to the Takeoff (design) 

condition only; off-design these values are computed as described in the 1-D Flow Model 

discussion. 

112
Table 52. Results for Model Calibration at 30 OPR 

 Combustor Type 

Calib. Parameter Baseline Advanced 

dPdiffqP 0.054 0.054 

EqRatioPZ 1.64635389 1.43 

EqRatioSZ 0.68732829 0.64 

EqRatioDZ 0.4 0.4 

ResTimePZ 0.00031948 0.0002 

ResTimeSZ 0.00114741 0.002 

ResTimeLZ 0.00101548 0.0013 

ResTimeDZ 0.0005 0.0005 

k_evap 0.23 0.23 

EffPZ 0.94 0.94 

k_Mixedness 1 1 

k_DropTime Function of ln(EqRatioPZ) Function of EqRatioPZ 

Beta 7 7 

SigMu Function of EqRatioSZ Function of EqRatioSZ 

4.4.5.3 Calibration at Higher OPR – The “Sizing Rule” 

To scale the reference (30 OPR) engine to other engines, a scaling parameter was 

modeled. Because the model calculates the effective volume of the reaction zone based on the 

input residence time, the scaling parameter is applied to the input residence time so that it is 

manageable during the calibration process. 

One important assumption is that the scaling parameter is applicable to all reaction zones 

in one engine. This assumption is possible due to independent effects of the primary zone 

residence time and the secondary zone residence time, as shown in the calibration parameter 

sensitivity study. That is, the EINOx at high power is highly sensitive to the secondary zone 

residence time while the primary zone residence time has negligible effect. However at low 

power, the primary zone residence time is the dominant parameter while the secondary zone 

shows negligible effect. Therefore, one scaling parameter can be used for both zones for NOx 

prediction even though the resultant total volume may be over- or under- predicted. 

The scaling parameter, which is the ratio of residence time of target engine to the 

residence time of reference engine, was modeled as a function of cycle parameters of target 

engines (pressure, temperature, flowrate and fuel-to-air ratio) at the combustor inlet. The 

residence time scaling relationship is defined in the equation below: 

߬

߬௥௘௙ =ܽቆ ܲ

ܲ௥௘௙ቇ

௕

ቆ ܶ

ܶ௥௘௙ቇ

௖

ቆ ܹଷ

ܹଷ௥௘௙

ቇ

ௗ

ቆ FAR

FAR௥௘௙ቇ

௘

݁݌ݔ ൭݂ ቆ

1

ܶ − 1ܶ௥௘௙ቇ൱

This equation results from the volume scaling following the derivation given in 

Mattingly. The exponents a, b, c, d, e, and f are to be found by regression analysis to match the 

113
calibration engines to the desired Dp/F00-vs-OPR characteristic. The target engine cycles were 

selected from the previous year’s ERA design space. To control for the effects of the engine 

cycle on the Dp/F00-vs-OPR line, the cycles were filtered to hold BPR about constant at a 

relatively low value (approx. 10-11) with no intercooling. A number of engine cycles meeting 

these requirements were selected randomly over the range from 30-60 OPR. Engines from both 

the 150-passenger and 300-passenger size classes were selected, but because of the restricted 

BPR range all of the calibration engines were ADD architecture. 

The calibration results are shown in Figure 73. The upper line is the CAEP/6 limit, while 

the middle line represents the baseline target of CAEP/6 -30%. 

Figure 73. Calibration Result of Target Engines 

It may be seen that the sizing rule achieves good agreement with the targets for the 

baseline combustor. The advanced combustor follows a parallel slope to the baseline combustor, 

because it uses the same sizing rule. The results indicate that meet the N+2 EINOx goal at 30 

OPR, but advanced engine cycle technology such as higher BPR and/or intercooling may be 

necessary to meet the N+2 EINOx goal at higher OPR. 

Because the calibration targeted Dp/F00 only, the model was tested to confirm that the 

model reflected the general trend of nitrogen oxides emissions in sub-power modes. To do that, 

the LTO cycle of sixty-seven engines from the previous year’s ERA study were selected with 

similar levels of bypass ratio and low intercooler flow. Run results of those engines showed the 

expected physical trends, as shown in Figure 74. 

114
Figure 74. EINOx Predictions for Calibrated High OPR Engines 

As mentioned above, the same sizing rule was applied to the advanced combustor 

because the same rule gave reasonable prediction of Dp/F00 for the advanced combustor 

engines. However, advanced technology may change the relationship between the 

thermodynamic cycle and the effective size (or effective residence time). Therefore, further 

research on the sizing principle for the advanced combustor will improve fidelity of prediction 

model. 

4.4.5.4 Advanced Cycles – The “Intercooler Rule” 

As discussed previously, many of the advanced high-OPR engine cycles require an 

intercooler. For these engines the emissions will be improved relative to the ERA target not 

because of the combustor technology but merely due to the engine operation. To account for this 

behavior, the combustor technology level has been separated from the cycle effects by means of 

the previous two calibration steps. However the intercooler results in a takeoff T3 

disproportionately lower than the design P3, which causes a difficulty in applying the previously 

defined sizing rule. The design T3 should be determined as if there were no intercooler, and the 

combustor sized for that condition. 

It is required to estimate a fictitious design T3 given the T3 at takeoff power to correctly 

size the combustor. The relationship between T3 and P3 is shown in Figure 75. A number of 

engine cycles generated in EDS were tested by turning on and off the intercooler flow. The “X” 

in the figures indicates when the intercooler is off, and the diamonds indicate when the 

intercooler is working. The solid black line in the figure is the regressed power series of samples 

without intercooler flow. The expected design T3 was calculated from the regressed relationship 

and applied to the sizing rule. 

115
Figure 75. Relationship between T3 and P3 with Intercooler Effect 

Figure 76 shows the results of complete model with target engines and additional 

validation engines after the intercooler logic was integrated to the model. Upper line is the 

CAEP/6 limit, middle line is CAEP/6 -30% limit, and the bottom line indicates CAEP/6 -75% 

for reference. The validation engines have wide range of bypass ratio and intercooler flow. 

116
Figure 76. Results of Complete Model 

It may be seen that the validation points follow the same general trends as the calibration 

points, but have lower emissions due to higher BPR and/or intercooling. Several of the engines 

do meet the N+2 EINOx goal at high OPR. 

4.4.5.5 Generation of Neural Net Regression Model for EDS 

A DOE table was established to generate Neural Net surrogate models for both baseline 

and advanced combustors. Input variables required are P3, T3, W3, and Wfuel, for each power 

setting (TO, CO, AP, and ID) for a number of sample engines. These input variables cannot be 

selected independently. Instead, alternative variables which are independent of engine flow size 

and vary more predictably were selected to create the DOE table. These alternative variables for 

the design (takeoff) condition are as follows: 

ܱܴܲ =

ܲଷ

ܲௌ்஽

117
−1ߛ = ௬௣௢௟݁

ߛ

݈݊(ܲଷ⁄ ) ܲௌ்஽

݈݊(ܶଷ⁄ ) ܶௌ்஽

ܹ௖ = ܹଷඥܶଷ

ܲଷ

= ܴܣܨ

ܹ௙௨௘௟

ܹଷ

The alternative variables for part power operation are as follows: 

݇௉య,௜ = ܲଷ,௜

ܲଷ,்ை

݇௘೛೚೗೤,௜ = ݁௣௢௟௬,௜

݁௣௢௟௬,்ை

݇ௐ೎,௜ = ܹ௖,௜

ܹ௖,்ை

݇ி஺ோ,௜ = ܣܨܴ௜

ை்ܴܣܨ

Suitable ranges for the alternative values were determined by examination of data from 

available EDS runs from the previous year’s study. The final selected ranges are shown Table 

53. 

118
Table 53. Ranges for DOE Variables 

Parameter Minimum Maximum 

OPR 25 70 

epoly 0.93 1.0 

Wc 2 12 

FAR 0.02 0.035 

 0.93 0.82 ࡸ࡯,૜ࡼ࢑

 1.0 0.95 ࡸ࡯,࢟࢒࢕࢖ࢋ࢑

 1.01 1.0 ࡸ࡯,ࢉࢃ࢑

 0.95 0.9 ࡸ࡯,ࡾ࡭ࡲ࢑

 0.47 0.3 ࡼ࡭,૜ࡼ࢑

 0.99 0.85 ࡼ࡭,࢟࢒࢕࢖ࢋ࢑

 1.1 1.0 ࡼ࡭,ࢉࢃ࢑

 0.75 0.3 ࡼ࡭,ࡾ࡭ࡲ࢑

 0.22 0.13 ࡰࡵ,૜ࡼ࢑

 0.95 0.75 ࡰࡵ,࢟࢒࢕࢖ࢋ࢑

 1.2 1.05 ࡰࡵ,ࢉࢃ࢑

 0.6 0.3 ࡰࡵ,ࡾ࡭ࡲ࢑

An additional variable was provided for the intercooler delta_T3, both on- and off-design, 

and was allowed to vary from 50 to 250 with a bias as a function of the P3. The T3 computed 

using the epoly from the above table was then reduced by the amount of delta_T3. 

From these ranges for the design (takeoff) variables, the DOE table consists of three 

categories of samples: Corner points and Optimal Latin Hypercube (OLH) points for model 

training, and random points for model validation. For the part power variables, uniformly 

distributed random values were selected. 

A total 1440 points were generated per each power mode for both baseline and advanced 

combustors. These points were run in the ModelCenterTM environment, taking approximately one 

week on three computers to generate the results. The results were then regressed to make the 

Neural Net equations. It was not possible to build one Neural Net equation suitable for all power 

settings; instead a separate NN equation was created for each power setting, and for each 

combustor type (baseline and advanced). 

The NN equations were a function of the 21 variables summarized in Table 54 

119
Table 54. EINOx Neural-Net Regression Independent Variables 

Variable Definition 

P3_des Design compressor outlet / burner inlet pressure

T3_des Design compressor outlet / burner inlet temperature

W3_des Design air flow rate into the burner from the 

compressor 

Wfuel_des Design flow rate of fuel injected into the burner from 

the fuel injector 

P3_offdes Calculated pressure of air leaving compressor and 

entering burner (from flow model)

T3_offdes Calculated temperature of air leaving compressor and 

entering burner (from flow model)

W3_offdes Calculated flow rate of air leaving compressor and 

entering burner (from flow model)

Wfuel_offdes Calculated flow rate of fuel injected intot he burner 

from the fuel injector (from flow model)

L_Comb Length of the combustor (as sized by the 1-D flow 

model) 

EqRatioPZ Equivalence ratio of the primary zone (computed from 

the flow model) 

EqRatioSZ Equivalence ratio of the secondary zone (computed 

from the flow model) 

EqratioLZ Equivalence ratio of the lean zone (computed from the 

flow model) 

EqRatioDZ Equivalence ratio of the dilution zone (computed from 

the flow model) 

ResTimePZ Residence Time of the primary zone (as determined 

from calibration of the model) 

ResTimeSZ Residence Time of the secondary zone (as determined 

from calibration of the model) 

ResTimeLZ Residence Time of the lean zone (as determined from 

calibration of the model) 

ResTimeDZ Residence Time of the dilutiony zone (as determined 

from calibration of the model) 

TempPZ Temperature of the primary zone after burning

T4 Temperature of the air (plus small amount of leftover 

fuel) leaving the combustor 

SMD Droplet size as computed from the droplet model 

within the flow model 

Unmixedness Unmixedness of the fuel within the combustor before 

burning occurs 

The regression statistics are summarized in Table 55. 

120
Table 55. EINOx Regression Model Statistics 

Combustor Power Hidden 

Nodes 

MFE, 

mean

MFE, 

stdev

MRE, 

mean

MRE, 

stdev

R2

, 

training 

R2, 

validation

Baseline TO 21 2.7E-06 .007 .0006 .011 1 1

 CL 15 4.6E-10 .008 .0002 .023 1 1

 AP 21 7.4E-08 .132 .025 .36 .9998 .9982

 ID 15 .00016 .097 .0024 .19 .9995 .9479

Advanced TO 25 .0008 .147 .0029 .24 .9998 .9996

 CL 18 4.2E-05 .007 .002 .04 1 1

 AP 18 .0015 .25 .023 .52 .9988 .9947

 ID 19 2.2E-05 .075 .0047 .11 .9995 .9990

The Model Fit Error (MFE) represents the distribution of the residuals of the data used to 

fit the model. It describes the accuracy with which the model was fit to the training data. The 

Model Representation Error (MRE) represents the distribution of the residuals of the validation 

data, additional data which was not used to fit the model. It describes the ability of the model to 

accurately predict the response within the ranges of the DOE variables. As a general rule, the 

mean errors for the MFE and MRE should be near zero, and the standard deviations should be 

less than one. 

It may be seen that, due to the nonlinearity of the model, a relatively large number of 

hidden layers were required. No overfit was detected. The Approach conditions generally gave 

the worst fits, although all the fits were deemed to be excellent. 

The NN equations were coded into the EDS model as described in the following section. 

4.4.6 Incorporation of Emissions Model into EDS 

All of the NPSS code for the 1-D flow model was identical to that used in the 

ModelCenterTM environment described above. The only difference was that the EI.case file in 

EDS was modified to create two combustor assemblies, one for the baseline combustor and one 

for the advanced combustor. The tuning parameter inputs for each combustor were specified 

when the Total_Combustor element was instantiated in each assembly. Then, instead of writing 

the 1-D flow outputs to a ChemkinTM input file, the Neural Net equations were evaluated and the 

EINOx values were computed for each power setting. Finally, the Dp/F00 parameter was 

computed for each combustor. 

As a test of this implementation, several test cases were run with EDS. These results are 

shown in Figure 77. 

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Figure 77. EDS Implementation Test Results 

These test cases were run for the RTC engines, for which the BPR ranged from 13.9 to 

18.8. It may be seen that the baseline combustors fall below target line due to the higher BPR 

than that assumed for the calibration. This is expected. Also the advanced combustors fall along 

the target line, again due to the higher BPR. Thus the NOx model was verified to be correctly 

implemented in EDS. 

5.0 Surrogate Model Generation 

5.1 Surrogate Modeling Methodology 

Surrogate models are a mathematical representation of a complex analytical tool or group 

of analytical tools that are limited to a specific problem. The replacement of that analytical tool 

with a virtually instantaneous equation enables rapid optimization, sensitivity analysis and 

visualization of design tradeoffs to make better decisions. The EDS environment has over one 

hundred independent inputs with over 120 modeled technologies, and without speeding up the 

evaluation process it would be virtually impossible to evaluate them. The cost to using surrogate 

models is that the analytical tool must be run many times up front to generate the data from 

which the surrogate model is created. 

122
The overall surrogate modeling process is shown in Figure 78. After examining the 

Technology Compatibility Matrix and the Technology Impact Matrix it is possible to assess the 

maximum and minimum limits expected for each of the variables. The set of runs that are 

evaluated before creating the surrogate model is coming from a set of techniques known as 

Design of Experiments (DOE). The DOE used for this project is a Latin hypercube that captures 

the variability throughout the design space. The surrogate models are then created and evaluated 

using a standard set of criteria that ensures the full model is adequately captured by the 

surrogate. 

Figure 78. Surrogate Modeling Process 

There are many different methods to create surrogate models and many types of surrogate 

models that can be created. A Multi-Layer Perceptron (MLP) feedforward Artificial Neural 

Network (ANN) with a single hidden layer was used in this project. The ANN is a powerful and 

flexible surrogate modeling method that can handle non-linear and large scale problems, and 

ASDL has extensive experience and knowledge in their creation and application. 

Neural Nets work by mapping a set of input variables to a set of responses through a set 

of filters, called the hidden layer. There can be more than one hidden layer, but only one is 

necessary for the overwhelming majority of problems. The hidden layer consists of hidden 

nodes, analogous to neurons in a biological neuron. An example diagram illustrating the 

connections between the layers is shown in Figure 79 

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Figure 79. Neural Network Conceptual Diagram 

As a conceptual example, the input layer is like a person’s five senses, receiving data 

from the outside world. The hidden layer is like the brain, processing this data. And the output 

layers would be a response to stimuli, like talking or typing. As the figure shows, each of the 

inputs has an influence on the hidden nodes and each of the hidden nodes has an influence on the 

responses. The correct number of hidden nodes depends very much on the problem; the number 

of hidden nodes is iteratively determined for each response. 

A Neural Network, like all other forms of regression, fits an equation through a given set 

of data. The mathematical form of the Neural Net equation is described below. The data being 

regressed is called the training set for a Neural Network. The training algorithm is the 

optimization method used to determine the coefficients for the network that minimize the 

training error. 

The mathematics of a single hidden layer Neural Network will now be described. In a 

biological system the neurons are either sending or not sending a signal, based on the inputs they 

receive from other neurons. They therefore have a step function for their activation function- on 

or off. For a numerical regression the use of a step function is problematic because it is not 

continuous and differentiable. Most Neural Networks use something called the logistic function. 

The logistic sigmoid function has the effect of “squishing” the inputs to it such that it always 

outputs a value between 0 and 1. 

( ) z e

zS − + =

1

1

This logistic function is used to calculate the value for each of the hidden nodes for both 

the function approximation and classification problems. The value of the hidden node is taken by 

applying the logistic function to a linear term related to the input variables. 

( )



 





+= =

N

i

j j XbaSH iij

1

Where: aj is the intercept term for the jth hidden node 

bij is the coefficient for the ith design variable 

Xi is the value of the ith design variable 

Hj is the value of the jth hidden node 

and N is the number of input variables 

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The calculation of the final response will depend on the problem type. For function 

approximation, the value of the response is found through a linear term that depends on the value 

of the hidden nodes. 

( ) =

+=

NH

j

kk HfeR jjk

1

Where: ek is the intercept term for the kth response 

fjk is the coefficient for the jth hidden node and kth response 

Hj is the value of the jth hidden node (defined above) 

and NH is the number of hidden nodes 

The different steps described above can be combined to create the following unified form 

of the Neural Net equation for function approximation. 

 ( ) = 



 



 +−  







































+

+=

=

H

N

i iijj

N

j Xba

kk jk

e

feR

1 1 1

1

Where: aj is the intercept term for the jth hidden node 

bij is the coefficient for the ith design variable 

Xi is the value of the ith design variable 

N is the number of input variables 

ek is the intercept term for the kth response 

fjk is the coefficient for the jth hidden node and kth response 

and NH is the number of hidden nodes 

The four coefficients (a, b, e and f in the above) are modified to fit the training set as well 

as possible, minimizing the sum square error or mean square error of the training set, depending 

on the training algorithm selected. 

5.2 Surrogate Modeling Process Description 

Using the methodology described in the previous section a set of 45 surrogates were 

created for 8 vehicle and engine combinations which are the LSA, LSA OWN, LTA, LTA MFN, 

LTA HWB, LTA BXW using the geared fan engine cycle and the LSA and LTA HWB using the 

open rotor engine cycle. The neural network surrogates were trained on thousands of EDS runs 

for each vehicle and engine combination. All surrogate models were fit as function of 

independent technology and cycle parameters, as well as TOGW. To improve the accuracy of 

some neural networks including emissions and noise surrogates, nested models were used which 

were fit as function of additional relevant EDS output data. The 45 response metrics created for 

each of the 8 vehicle and engine combinations are listed in Table 56. The names in this table 

appear in abbreviated form as used within EDS. A description of the full metric names and 

associated units is provided in Appendix A. 

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Due to differences in architecture the EDS models for the open rotor vehicles described 

several responses with different names than the geared fan vehicles. For example in the geared 

fan architecture the fan diameter is tracked by the response metric, diamFan; but in the open 

rotor architecture this response is replaced by Prop Diam. These responses are numbered 40 to 

43 in Table 56 

Table 56. 45 Response Metric Names for ERA Surrogates 

Mission Responses Airframe 

Responses Engine Responses 

1. desBlockFuel 11. desTOGW 17. SLS_UI_OPR 27. EI_Standard_AP 37. ICAO_TO_Tt3 

2. Cruise_SFC_Min 12. Fuse_Weight 18. T3max 28. EI_Standard_CO 38. ICAO_TO_Wfuel 

3. Approach 13. OEW 19. ADP_BPR 29. EI_Standard_ID 39. LPC_diam 

4. Cutback 14. VT_Weight 20. HPC_diam 30. EI_Standard_TO 40. Nacelle_Diam/Dnac 

5. Sideline 15. Wing_Weight 21. HPCLastStageWc 31. ICAO_AP_Tt3 41. enginePodLength/Xnac 

6. TOAll_TOFL 16. HT_Weight 22. HPCLastStgBladeHeight 32. ICAO_AP_Wfuel 42. engPodWt/WENG 

7. TOOEO_TOFL 11. desTOGW 23. EI_NOx_AP 33. ICAO_CO_Tt3 43. diamFan/Prop Diam 

8. AbortALL_TOFL 12. Fuse_Weight 24. EI_NOx_CO 34. ICAO_CO_Wfuel 44. ICAO_ID_Equiv 

9. AbortOEO_TOFL 25. EI_NOx_ID 35. ICAO_ID_Tt3 45. ICAO_AP_Equiv 

10. desFAR_LDGFL 26. EI_NOx_TO 36. ICAO_ID_Wfuel 

5.3 Surrogate Model Results 

Over 350 surrogates were created in total for all 8 vehicle combinations. Each surrogate 

was evaluated by standard goodness of fit checks as described in the surrogate methodology 

section to ensure high fit quality and prediction accuracy. These goodness of fit checks include 

Model Fit Error, Model Representation Error, Actual by Predicted fit line, Residual error plot, 

and R-squared values. All surrogates are within a 3% error tolerance, excluding NOx and TOFL 

fits. NOx surrogates and TOFL fits are all within a 5% error tolerance. The fit statistics for each 

surrogate have been tabulated for all 8 vehicle and engine combinations in Appendix B. 

Once the best goodness of fit criteria was achieved the surrogate training process was 

completed and the neural networks were saved in equation form. These equations were imported 

into the ERA Dashboard which uses the surrogate models to quickly perform technology 

package evaluations, design space exploration, probabilistic assessments, response tradeoffs, 

etc... The surrogate predictions in the dashboard were also validated against local runs in (EDS) 

for specific tech packages to ensure accuracy of surrogate models on feasible solutions found by 

the dashboard. 

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6.0 Probabilistic Analysis 

6.1 Literature Review of Probabilistic Assessment Methods 

and Technology Impacts 

6.1.1 Introduction and Background 

Probabilistic methods take on many different meanings in a variety of disciplines from 

combinatorial mathematics to actuarial sciences. For our purposes in engineering analysis we 

refer to probabilistic methods as non-deterministic assessment techniques which allow for the 

inclusion of uncertainty in engineering design. There is a large body of literature for nondeterministic approaches in the context of assessing future technologies, concepts, systems, for 

which the impact on performance is uncertain. This review will survey various aspects of this 

literature and note how these areas have contributed to probabilistic methods in engineering. 

Probabilistic methods have aided designers to progress beyond point designs and 

deterministic answers. They have facilitated uncertainty quantification and the creation of robust 

designs. Ryan and Townsend [Ryan 1993] discuss the benefits of probabilistic methods in 

aerospace applications and how they have contributed to the success of key programs. The 

authors describe how uncertainty in design requirements and environmental condition 

necessitates a level of safety in design, and while this used to be mitigated using a deterministic 

safety factor, probability is a better approach for measuring this uncertainty as previously 

demonstrated in space applications including space shuttle main engine design. They also 

advocate the use of probabilistic and reliability methods which model individual parameter 

uncertainty with probability distributions. Their work promotes probabilistic engineering 

approaches even if they are not highly accurate because they require engineers to know more 

about the problem giving them an idea about the risk involved in a design and the design 

parameter sensitivities; which they claim will ultimately yield a more robust design. 

Probabilistic methods are employed using a number of different approaches which can be 

broadly categorized as analytical and numerical. Analytical approaches are further discussed in 

the next section. A basic numerical approach in non-deterministic assessment techniques was 

reported as early as 1968 [Timson 1968]. This approach is summarized in five basic steps: 

1. Determine design equations relating properties at lower levels to measures of 

system level performance 

2. Obtain subjective probabilities for subsystem properties 

3. Use Monte Carlo to sample from these probabilities 

4. Generate statistical measures for resulting systems performance distribution 

5. Compare statistical measures across time periods for an indication of progress 

This methodology bears archival value not only due to its early origin but also because 

this basic outline for probabilistic methods has not significantly changed in over 50 years. Much 

of the literature which applies probabilistic assessment techniques either knowingly, or 

unknowingly, employs this basic outline. There are variations on this approach but the basic 

structure has been used extensively. 

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6.1.2 Analytical Approaches 

Analytical approaches for probabilistic assessment exist in the literature and were 

extensively used in the 1990’s time frame. They are not as popular as numerical approaches but 

they created the framework for many of the techniques employed today. Due to their 

foundational nature it is relevant to mention some of these methods briefly. The underlying 

theory of these approaches is based upon a mathematical representation but the evaluation of the 

method or associated functions may be simplified by using computer simulations. This is not to 

be confused with numerical approaches which are also evaluated using computer simulations but 

are not necessarily based upon explicit mathematical theorems and are sampling based in nature. 

6.1.2.1 Most Probable Point Analysis 

Most Probable Point (MPP) method is a probabilistic analysis method for calculating the 

probability of failure given a constraint in an uncertain multi-dimensional space. The technique 

was applied heavily and early on by the structural reliability community [Elseifi 2001, Khalessi 

1993, Lin 1993]. This method explores a joint probabilistic space by evaluating limit state 

functions in an effort to identify the failure region for a particular system. The MPP approach 

identifies the failure boundary in the joint probabilistic space while minimizing the number of 

limit state function evaluations as explained by [Du 2005]. This approach lost popularity due to 

the higher dimensionality of recent applications and the proportionally increasing cost of 

function evaluations with the number of random variables [Du 2005]. In the aerospace 

community the technique did not see extensive use for technology evaluation and 

implementation but it did establish how to analyze functions and constraints in a joint 

probabilistic space defined by multiple random variables. Typical examples of the method exist 

in the context of aerospace structures and systems analysis [Shi 2001, Green 2002, Lyle 2003]. 

6.1.2.2 Fast Probability Integration 

The Fast Probability Integration technique is a probability analysis code [SwRI 1995] 

which is based on MPP analysis and attempts to solve the same analytical equations which 

represent the limit state function in a joint probabilistic space. The FPI code claims to have 

efficient techniques which eliminate the need for large amounts of expensive function 

evaluations. It also links itself directly to the analysis code removing the need for surrogate 

models which were at that time expensive to create and limited in the number of variables they 

could include [Mavris AIAA 1997-5508]. FPI has been used to assess individual technologies 

and their impact on system reliability [Boyce 1989, Journal of Propulsion] as well as to examine 

the effect of uncertain technological impacts from a holistic air traffic system perspective 

[Mavris AIAA 2000-5612]. The process outlined in the latter work describing how to handle 

uncertainty as a result of technology insertion with probability distributions is particularly 

relevant and still used in many applications. However advancements in computing capabilities 

significantly reduced the computational effort required to use more accurate techniques such as 

Monte Carlo simulations and surrogate models. 

6.1.2.3 Other Analytical Approaches 

Many other techniques exist for handling uncertainty probabilistically which are based 

upon mathematical formulations. These include Polynomial Chaos expansions which have been 

128
used to address limitations in uncertainty propagation in reliability optimization [Kim 2006] and 

aerodynamic analyses [Yamazaki 2013]. The polynomial chaos expansions serve as efficient 

approximations of the model outputs to improve computational time and accuracy. 

The Fourier Amplitude Sensitivity Test (FAST) is a variance based method which can 

probabilistically determine sensitivities to individual parameter uncertainties of a function. It 

models multi-dimensional systems as waves and frequencies, then applies a Fourier transform to 

determine the sensitivities of parameters [Cukier 1978]. Within the aerospace community it has 

been used in structural analysis [Adelman 1986] and the impact prediction of decaying objects in 

space [Ronse 2014]. 

These analytical methods are very popular for probabilistically handling uncertainty of 

various kinds but have not been extensively applied to handling uncertainty for technology 

modeling in the aerospace community. Numerical approaches are far more popular for this 

purpose and will be the focus of the next section. 

6.1.3 Numerical Approaches 

Numerical methods and non-deterministic approaches are the most popular ways to 

conduct probabilistic assessments in the literature. This is facilitated in part by advanced 

computing capabilities which make simulations and sampling very cheap and efficient. In 

addition modern software packages have simplified the use and implementation of these 

methods. They have also made the ensuing analysis much easier through a variety of graphical 

and visual interfaces. Even with these notable improvements, the basic process used in these 

numerical probabilistic assessments has not departed significantly from the method outlined by 

Timson [Timson 1968] many years ago. 

The first step in the process calls for determining design equations relating properties at 

lower levels to measures of system level performance. Basically to form a relationship which can 

describe a model output by influencing one or more model inputs. These equations can be based 

on basic laws of science or they can be more complex metamodels. Surrogates of this nature are 

commonly used in probabilistic analysis and come in many forms. These include polynomial 

response surfaces, kriging [Rumpfkeil 2011], support vector machines [Missoum 2009], and 

artificial neural networks which have been described in detail earlier in Section 5.1. These 

surrogate models are a key enabler for numerical approaches of probabilistic assessment. 

6.1.3.1 Response Surfaces 

Response surfaces are powerful functions which can quickly mimic a model output which 

is difficult or time-consuming to obtain directly. They can be particularly useful in performing 

sensitivity analyses, design space exploration, sensitivity analyses, probabilistic assessments, and 

system optimization which can require thousands of model evaluations. They are formed by a 

curve fit which minimizes the error through a certain number of model evaluations which been 

run in advance. The response surface is typically of second order but can be formed with higher 

order terms to capture interaction terms and non-linearity. The predictive nature of the response 

surface surrogate is always verified with test points of the model to ensure high accuracy. Further 

reading on the fundamentals of response surfaces can be found in various texts [Myers 2002, 

Khuri 2006, Box 1987]. Response surfaces have been used in the probabilistic assessment of 

large structures [Shah 1998], space landing systems [Slade 2006], and many other technology 

based applications [Mercer 2006, Taylor 2002, Lin 2001]. They have also been used to assess 

129
aircraft technology impacts [Soban 2013] and forecast uncertainty in aircraft technologies [Kirby 

2000, Raczynski 2006]. 

6.1.3.2 Subjective Probabilty 

Once a relationship has been formed between design inputs and system level outputs, the 

next step in Timson’s approach is to obtain subjective probabilities for subsystem properties. As 

is the case with many phrases discussed in the literature, Subjective Probability or subjectivity in 

probabilistics can have different meanings. These differences can lead to misinterpretation of 

ideas so it is important to understand what is referred to by subjectivity. Subjectivity can describe 

a user input in a modeling process, or decisions made by an analyst to achieve a certain solution, 

or even the translation of qualitative statements into quantitative values. In all of these areas 

subjectivity undoubtedly plays a role, and that role has been both praised and criticized in the 

past. For this reason, to advantageously use subjectivity in probabilistics, analysts should be 

aware of its usefulness and drawbacks to best apply it to their needs. Several examples of how 

subjectivity has been discussed and applied in the literature are also presented. 

It is important to recognize the role of bias in subjective probability which can arise from 

different sources including subject matter experts and also from analysts themselves. This can be 

a result of prior experiences, environmental factors, and the training received by the analysts as 

well. Bias can affect the way in which subjective probabilities are formed. Subjective 

probabilities are very important in probabilistic assessment because they inform the distributions 

of the input random variables. When these distributions are modeled accurately the results of the 

probabilistic assessment are credible and meaningful. However if poor assumptions are made 

regarding the probability distributions of random variables or the relationships between them, the 

results of the probabilistic assessment can be misleading or false. One such example of bias in 

training is the tendency in many engineering applications to assume normality of input 

distributions due its familiarity and ease of implementation. The normal distribution is easily 

understood and widely taught to practitioners such that it has become a default choice in many 

applications [Shah 1998, Dubos 2008, Dell'Elce 2014] even though multiple statistical tests exist 

to check for the appropriateness of the normal distribution including the Anderson-Darling test, 

D'Agostino's K-squared test, the Jarque-Bera test, etc. Conrow [Conrow 2010a] offers a detailed 

critique of the work published by Dubos, Saleh, and Braun [Dubos 2008] in which he states that 

normal distributions should not have been assumed for random variables and provides his own 

analysis which employs the Anderson-Darling and Kolmogorov-Smirnov [Kolmogorov 1933, 

Smirnov 1948] statistical tests to define distribution type. 

Once the distribution types have been defined accurately, it is also important to correctly 

represent the relationships between random variates. Another example of bias in training and due 

to environmental factors is the common assumption that random variables in a probabilistic 

assessments are independent, even though this can be a poor assumption with consequences on 

probabilistic assessment results. In many applications [Prandini 1999, McCormick 2000, Zhang 

2012] dependence between random variables is not modeled because it is simply ignored, a 

strong bias towards methodological simplicity is preferred, there is high difficulty in 

characterizing the underlying dependence structure, or there is an expectation of low strength in 

random variable dependence. However these factors are not credible as many techniques exist to 

measure statistical correlation including the well-known Pearson correlation coefficient, 

Kendall’s tau parameter, Spearman’s correlation parameter, etc. Techniques also exist to easily 

include random variable dependence in probabilistic assessments, particularly in the context of 

130
future aircraft technology impacts. Zaidi, Jimenez, and Mavris [Zaidi 2015] suggest a copula 

based method for better representing the relationships between random variables in probabilistic 

assessments using subjective input from subject matter experts. 

Conrow [Conrow 2010b] described and evaluated an earlier subjective probability scales 

example from 1977 in which qualitative phrases were assigned quantitative probability values 

from 23 respondents. For example the phrase “probably not” was assigned a probability value of 

the event occurring ranging from approximately 0% to 45% from the different respondents and 

the phrase “highly likely” was assigned values for the event occurring from approximately 50% 

to 95%. Conrow provides a graphic in his paper from the original authors which summarizes 

their work which tested 16 different qualitative phrases and how the subjects interpreted their 

probability values. The assessment from the original authors has been used in the past in broad 

applications including defense and industry in order to convert phrases into useful statistics; 

however Conrow cites several issues with this type of work and specifically with this example. 

He points to misworded probability statements, incorrect probability bounds, and distortions in 

the graphical grid used to summarize the results by the original authors. Conrow states that 

because of these deficiencies, descriptive statistics gathered from this example are not reliable. 

As a result he conducted a new survey using the same 16 qualitative probability statements and 

gathers results from 141 participant engineers and project managers. In the results of his survey 

he finds problems with misinterpretation from participants and also numerous outliers in the 

quantitative values provided by the respondents. In light of these results he states from his own 

text [Conrow 2003] that “estimative probability tables and scales should be viewed as more of a 

“last resort” than first choice and not used in a risk analysis unless they are the only available 

means to evaluate probability”. He explains this position because definitions for probability 

statements are not unique to different people, there is a high degree of uncertainty associated 

with most statements making the range of variability too high, people are often not familiar with 

the systems they are subjectively analyzing or how it performs, etc. He concludes his work by 

stating that probability scales based on subjective probability statements leave too much room 

for response error and in cases where such input is required he recommends using “precisely 

worded probability-related scales based upon maturity, resources, etc.” 

Ananthasayanam and Sarkar [Ananthasayanam 2005] point out that while today’s 

engineering activities often require preciseness and performance even under uncertain 

conditions, it is the subjectivity of the user which helps to propel the engineering process through 

its numerous design stages until a solution is found. They believe this subjectivity of the user is 

useful and necessary to today’s engineering problems and discuss its usefulness in estimation 

theory and two aerospace modeling applications. Again it is important to note that these authors 

are referring to the subjectivity provided from a user in a modeling process rather than the 

subjectivity involved in quantification tasks evaluated by Conrow [Conrow 2003] earlier. They 

describe the estimation theory process where choice of the appropriate algorithm to analyze the 

data in the presence of the expected noise and uncertainty is up to the analyst. The optimization 

algorithm itself contains a great deal of subjectivity including initial step size, starting points of 

the routine, update criteria, the convergence criteria and the overall guidance of the algorithm are 

all subjectively decided on by the user. The authors conclude by reiterating that the subjective 

decisions made by the user provide simplicity, mathematical and numerical traceability, and 

guides the analysis to a successful conclusion. 

131
6.1.3.3 Probabilty Distribution Encoding 

After recognizing the role of subjectivity in probabilistic assessments, one is better 

prepared to complete Timson’s second step of obtaining subjective probabilities for subsystem 

properties. These probabilities are often obtained by encoding distributions in a variety of ways, 

including surveys, interviews, and engineering judgment. It can be said that the obtaining of 

subjective probabilities and the encoding of distributions is the most important step in Timson’s 

outline and of any probabilistic assessment. The validity of the subjectively encoded 

distributions dramatically governs the integrity of the probabilistic assessment results. For this 

reason, significant attention has been given to examining many different works and key 

examples which demonstrate this critical step. This is to underscore the importance of correctly 

obtaining these subjective inputs and thereby lending credibility to the outcome of the 

probabilistic assessment. 

All probabilistic assessments require the use of probability distributions, the question is 

how to obtain them. Ideally probability distributions would be based on analysis of real data 

using statistical tests to ensure good representation by a particular type of distribution. This 

process and the value of sampling from valid empirical distributions is described by Conrow 

[Conrow 2003]. However in many applications real world data is not available, or difficult to 

obtain, such as with future technology impacts. Since simply assuming normal distributions is 

not appropriate, then numerical analyses must have some way of encoding distributions. In these 

situations the literature turns to subject matter experts who provide various forms of information 

to subjectively inform probability distributions for random variables. This process of encoding 

distributions and prior examples of how subject matter experts have encoded parameters for 

different types of distributions is the focus of this section. Subjective probabilities can also be 

obtained by other means such as various forms of interviews and surveys which are discussed in 

this section as well. 

Spetzler C. S., Stael von Holstein [Spetzler 1975] wrote a seminal paper on probability 

encoding summarizing the practice of the Decision Analysis Group at Stanford Research 

Institute. They state that Probability encoding is “the process of extracting and quantifying 

individual judgment about uncertain quantities” and is a major part of decision analysis. They 

discuss three main phases of this process, the Deterministic phase, the Probabilistic phase, and 

the Informal phase. The Deterministic phase establishes variable definitions and the formal 

model, the Probabilistic phase assigns probability distributions to relevant uncertainties, and the 

informal phase is a judgment process where the value of the information is assessed and 

compared to the cost of obtaining it in order to reduce uncertainty of important variables. The 

authors provide classifications of variable types such as decision variables which can be 

controlled by the decision maker and state variables which are outside of their control. They 

discuss how uncertainty of a variable can be encoded directly or the underlying phenomena can 

be modeled and the uncertainty of associated variables can be assessed. They provide principles 

for encoding uncertain quantities and how to best elicit information from subjects in order to 

enable subject cooperation and obtain meaningful data such as employing a sensitivity analysis 

to determine important uncertainties and using exact definitions and meaningful scales. They 

review the importance of Modes of Judgment which is the intuitive process by which people 

assess uncertainty using cues which are not completely reliable or valid, but still produce 

reasonable answers while yielding systematic biases. There are three main points to understand 

from this section of the paper, that people are not aware of the cues they use, it is difficult to 

control cues used by subjects, and people can be made aware of their biases and trained to 

132
control them. The authors continue to discuss sources of bias such as motivational and cognitive 

biases and other encoding phenomena such as subject adjustment and anchoring. The authors 

also provide a discussion on encoding methodology and define the basic types including fixed 

value P-method, fixed probability V-method, or the neither fixed PV method, and how to encode 

each type such as using a probability wheel. They also provide a description of the interview 

process with the subject and explain the basic steps which should be taken including motivating, 

structuring, conditioning, encoding, and verifying. The authors have intentionally given general 

guidelines for probability encoding and state that each problem will have its own application of 

these principles. They believe their work is unique because of several distinguishing principles; 

they believe the pre-encoding steps are critical and more time consuming than the encoding step, 

they recommend only ordinal judgments for probability assignments, find the probability wheel 

as an effective encoding technique for most subjects (interestingly this does not agree with later 

work by Abbas et al [Abbas 2008] and Brooks and O’Leary [Brooks 1983]) , recommend more 

than one technique be used in order to check for consistency. 

Keefer and Bodily [Keefer 1983] compare the accuracy of approximations representing 

continuous distributions or estimating their parameters, with emphasis on those requiring just 

three points from the underlying CDF for use in Program Evaluation and Review Technique 

(PERT) and related techniques. They make comparisons based on how well the mean and 

variance of different methods are approximated. They note large differences in accuracy among 

approximation methods including a set of beta distributions. They also consider the triangular 

distribution in which they state that it “generally provides an imperfect representation of the 

actual density function, but is simple and convenient to use. Its use is often based on the premise 

that very little information is available about the actual distribution anyway – e.g. only subjective 

estimates of “high,” “low,” and “most likely values.” They examine the accuracy of four 

triangular-based formulas to approximate mean and variance, providing an indication of the 

appropriateness of the triangular distributions over and assortment of other distributions. They 

use an assortment of 78 beta distributions, investigating it thoroughly because of the many 

shapes it can take, using alpha less than beta shape parameters, where each can take integer 

values ranging from 2 to 60. While they find that the beta did not perform well in general it is 

worth noting that Johnson [Johnson 1997] was able to obtain good results using the beta 

distribution when limiting the shapes that that it can assume. Triangular distribution 

approximations were found to perform poorly as well, particularly with respect to other 

candidate methods. They recommend a new three-point approximation method which is an 

extension of Pearson and Turkey [Pearson 1965] noting that it is generally more accurate by a 

wide margin and has the advantage of being median based which is useful in consistency 

checking but does require percentage fractiles which may be difficult to determine. 

Brooks and O’Leary [Brooks 1983] performed a comparison of encoding techniques and 

surveyed professionals whose focus was the evaluation of uncertainty. Their study compared 

four encoding methods; Bisection, Fractile, Cumulative, and Probability Wheel and surveyed 

participants who were brokers speculating in the stock market. The authors desired this 

procedure because they did not want to assume or rely on understanding of statistics from the 

judges while trying to accurately representing their opinions. In the Bisection method the subject 

specifies lowest and highest possible values and the analyst suggests values for comparison 

beginning with the limits. The Fractile is similar to the bisection but the subject provides the 

values directly and begins with the median value. In the Cumulative method the subject is asked 

to provide a probability of falling above or below specified values. The Probability Wheel 

133
involves participating in a lottery where the participant wins if the value exceeds a specified 

threshold, or with a wheel with pointer falling in a wedge. The size of the wedge is varied until 

the participant is indifferent to the two choices. A Friedman Test [Coyover 1908] was used by 

the authors because the responses were rankings. The authors report that no statistical 

significance was found in the difference across accuracy rankings but there was among 

preference ranks. The Probability Wheel was found to be the least desirable, which tends to 

agree with the results found by Abbas et al [Abbas 2008] in the comparison of fixed probability 

versus fixed value encoding techniques. The authors also generated average distributions for 

each subject and used the Kolmogorov-Smirnov statistical test (similar to Conrow’s work 

[Conrow 2010a]) to determine if there were any outlier distributions among the participants. The 

authors also noted that subjects often asked the analyst to repeat statements multiple times, 

revealing the importance of the analyst as a resource in eliciting the data. 

Batson and Love [Batson 1988] discuss early techniques for including risk analysis in 

terms of uncertainty quantification of new technologies in aircraft conceptual design studies. The 

authors define technology assessment as “an unbiased, systematic approach to estimating 

technology benefit, development cost, and schedule and to establishing the uncertainty 

associated with those estimates”. They state that classical approaches included defining a 

baseline and technology improved alternatives with various technology mixes to provide a 

measure of technology benefit. However cost and schedule estimates were deterministic and 

often limited to Gantt chart with dollar amounts associated with each activity. If uncertainty was 

done at all, it was unsophisticated, qualitative, and an afterthought. According to the authors, risk 

analysis is the “identification, quantification, reduction of the uncertainty in a system. Its 

philosophy holds that uncertainty about new technologies is best quantified by eliciting judgment 

from subject matter experts and synthesizing uncertainty through mathematical models to assess 

system-level impact. The authors used an analytical model called Airlift Fleet Cost-Effectiveness 

Uncertainty estimator (AFCUE) in their work, which is a set of equations that represents the 

sequence of calculations in the conceptual design process. It does not replace the actual process 

but rather provides a very simplified representation to capture only significant factors and 

sensitivities. They used subjective probability encoding in order to perform uncertainty 

quantification, and used a four-part questionnaire for which responses are used to establish a beta 

distribution. They used this to generate distributions for four design input variables in the 

calculation of range that could be associated with technology levels. For known payload levels 

they generated range distributions to create range uncertainty analysis, and a 99% confidence 

interval by connecting min and max points across the range distributions to form a tolerance 

band. The different technology demonstrators were ranked according to life cycle cost including 

uncertainty bands. The authors feel that their approach is innovative because they have extended 

a cost uncertainty analysis to include performance and effectiveness. It would be easier to 

interpret the value of this method if it could include a correlation to TRL or another modernly 

known technology maturation scale. 

Williams [Williams 1992] offers a discussion on how to estimate the proper distribution 

parameters and which distributions to select when modeling project networks under uncertainty. 

He acknowledges there are many forms of uncertainty to consider including temporal, financial, 

technical, and their interactions but focuses on temporal uncertainty and specifically those 

related to activity-duration which is particularly relevant to technology modeling. He indicates 

that five parameters must implicitly or explicitly be stated to define an activity-duration 

distribution; position, spread, skew, minimum, and maximum. He places the most importance on 

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position because it is a usually a common parameter such as mean, mode, median or target value, 

which experts are familiar with. He does not advise obtaining this from other sources because to 

do so “is to lose your best source of estimates.” The author states that several criteria should be 

kept in mind when estimating parameters. The parameters and assumptions must be easily 

understood by the estimator, such as mode and ‘optimistic value’ where as ‘certainty of 

minimum’ is less so. Parameters must be easily estimated because even if they are easily 

understood, difficult estimation can compromise the quality of estimates. Easily calculated 

percentiles are important because it helps to show the estimator the implications of choosing a 

certain parameter. Applicability of lower and upper limits to the distribution should be kept in 

mind and other particular considerations must be considered such as a priori assumptions, 

availability of historical data, etc. He also recommends the triangular distribution due to its 

simplicity but acknowledges the usefulness of the gamma and beta distribution while noting that 

they are not as transparent to the project planner. He concludes with some rules and 

recommendations including, “the parameters and distributions used must be meaningful to the 

project planner; thus the problems of selecting a distribution and estimating its parameters are 

psychological and practical rather than mathematical”. This is a key point to understand and 

account for regarding the encoding of probability distributions in probabilistic assessments. 

Johnson [Johnson 1997] investigated a method for using a triangular distributions as a 

substitute for beta distributions in risk analysis. He states that while the beta distribution is 

suitable for uncertainty studies because it can take a wide array of shapes over a finite interval, 

its functional form is complex and its parameters are harder to estimate. He proposes the 

triangular distribution as a suitable proxy for beta because triangular is much simpler in 

functional form and easier to understand. He states the differences between the two are rarely 

significant and proposes a method to estimate triangular distribution parameters from two 

extreme percentiles and a median. The author admits limitations of the triangular distribution in 

that it cannot reasonably approximate a U-shaped, J-shaped, or uniform distribution, which can 

be accomplished using a beta distribution. For this reason he limits his tries to focus on 

investigating beta distributions which are unimodal, positively skewed, and satisfy both shape 

parameters greater than or equal to one. He demonstrates that his method for estimating 

triangular distribution parameters using a linear combination of two extreme fractiles and the 

median actually produces very similar distributions as a beta whose shape parameters are both 

greater than or equal to two (bell shaped on both sides). He also states that when comparing the 

error of estimating mean and variance, his procedure performs better than or comparable to 

previous work such as that of Keefer and Bodily [Keefer 1983] It is worth noting that when 

comparing probabilistic distributions Conrow [Conrow 2010a] suggests examinations such as the 

Anderson-Darling and Kolmogorov-Smirnov statistical tests would yield greater certainty in 

estimation error and a proper measure of the difference between two distributions. 

Ward [Ward 1998] presents a paper on the High Speed Research Program Technology 

Tracking and Assessment Process used to measure technology development progress and 

quantify technology uncertainties and propagate them at the technology level. It has two phases, 

an audit phase where subjective probability distributions (beta distributions) are constructed 

using expert opinion from the Technology Management Team (TMT), and a metrics integration 

phase where uncertainties in the vehicle level metrics are quantified by processing random 

samples of technology level metrics through an aircraft sizing code using the subjective 

probability distributions developed in technology audit phase. However, Ward points out that the 

process requires the assessment of inherently subjective probability distributions with a high 

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degree of fidelity, and since a significant part of the effort of the process are allocated to this task 

“the process has shown itself to be more useful in a tracking and risk assessment role than as a 

decision tool.” This indicates that development of probability distributions is difficult and for the 

most part resource intensive. A sensitivity analysis is employed to show the effect of a 

technology metrics forecasted range on the vehicle metric’s values however the sensitivity 

analysis does not account for interactions between technologies. This is critical to any 

technology portfolio analysis because some may not be useable while others are may/or may not 

be useable to their full potential. Ward notes that the probability distributions generated are “but 

one possible realization of an infinite number of distributions.” He points out that non-parametric 

methods can be used to estimate confidence bands on the empirical CDF, and notes that the size 

of the confidence band is inversely proportional to N, the number of samples. Tracking 

technology and vehicle distributions illustrates a changing level of uncertainty and risk. He says 

that one can reasonably expect uncertainty to decrease as the program progresses as measured, 

for example, by TRL. The author concludes by stating that the process provides management 

with insight into technology development and helps point to where risk mitigation strategies will 

be of greatest value. 

Kirby and Mavris [Kirby 1999] perform work on handling technology uncertainty in 

preliminary aircraft design stages due to incomplete knowledge regarding the impact of future 

technologies. While their methodology is very involved they did show how technology impact 

uncertainty can be captured using Weibull distributions. The Weibull distribution is chosen 

because it “is a family of distributions that can assume the properties of other distributions such 

as an exponential, normal, or Rayleigh.” They subjectively encoded Weibull distributions for 

technologies at given TRL levels and adjusted the Weibull distribution shape parameters to 

follow a range of applicable values in the probabilistic assessment. The sensitivity of the 

response metrics to the Weibull shape parameters and the future technology impacts, was then 

evaluated via a Monte Carlo simulation. 

Abbas et al [Abbas 2008] present the results of an experiment comparing two methods 

for encoding probability distributions of continuous variables; the first gets values of a variable 

through comparison with a fixed probability wheel and the second through comparisons with 

fixed values of the variable. Their decision analysis study was conducted using human 

participants as judges who were asked binary questions based on different decision analysis 

methods described above. The results of the experiment suggested a slight superiority of fixed 

value over fixed probability which the authors attribute to the fact that people in general having 

more experience in their daily lives with making judgments similar to Fixed Value types of 

decisions. They mention the difficulty with fixed probability comparisons is that it requires 

judges to estimate the level at which an event would reach a certain probability which is not as 

familiar as estimating the likelihood that an event will exceed some predetermined threshold. 

Results from the experiments were compared by fitting the results into Beta distributions; 

however the authors do not claim that Beta distributions are superior to others for this analysis, 

only that they have been used in prior Bayesian analyses. They conclude by stating that the 

results were nearly indistinguishable for the two encoding methods but there were systematic 

differences including the fixed variable method had higher variances which is good because the 

authors believe subjective probabilities are often too narrow reflecting overconfidence of the 

judges. 

Hendricks et. al. [Hendricks 2009] discuss a systems engineering approach for strategic 

planning of the air transportation system in which technology infusion is also considered. 

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Technology-based solutions are essential to the entire system and their impacts must be assessed 

in an uncertain future context. The uncertainty they considered stemmed from the risk of the 

technology in terms the improvement it attempts to make in system performance and from the 

estimated completion date of a technology based on its maturity. The authors suggest using the 

beta distribution to model these uncertainties while concurrently employing a weighting scheme 

based on the TRL of each technology. By varying the beta parameter they are able to model the 

impact and uncertainty associated with a technology based on information available about the 

technology program. 

Jimenez et. al. [Jimenez 2011] present a comparison of four different probabilistic 

assessments performed on the NASA Environmentally Responsible Aviation (ERA) problem for 

future aircraft technology section. Each assessment is performed using a different approach to 

subjectively encode technology impact distributions based on the information available from 

subject matter experts. The four main types of input distributions evaluated are the uniform 

distribution, the Weibull distribution, the mode-centered Weibull distribution, and the triangular 

distribution. Each probabilistic assessment approach reflects varying levels of information 

availability and encoding effort which ultimately drives the uncertainty in the response metrics in 

the output of the probabilistic assessments. The level of uncertainty on the main outputs, 

cumulative aircraft noise and fuel burn reduction, is depicted well with data clusters for the 

different distribution types and other varying assumptions in each probabilistic assessment. The 

authors conclude by stating that while general trends can be extracted about the ERA concepts 

and technologies from all four probabilistic assessments, they favor the mode-centered Weibull 

input distribution as having the best balance of data requirements and acceptability of underlying 

assumptions. 

6.1.3.4 Probability Distribution Sampling 

After establishing subjective probabilities and accurately representing random variables, 

the next step in Timson’s method [Timson 1968] is to use a Monte Carlo simulation to sample 

from the probability distributions defined in the previous step. Monte Carlo simulations are 

indeed powerful tools used in a multitude of disciplines to quickly generate samples from 

probability distributions in a pseudorandom fashion. For readers interested in the theory behind 

random number generators and the methods with which they are created, see Park and Miller 

[Park 1988]. These samples are used to evaluate objective functions to generate statistics which 

provide useful information about the responses of interest. There are many different variations of 

Monte Carlo simulations including Markov chain Monte Carlo [Metropolis 1953], Direct 

simulation Monte Carlo [Bird 1994, Sun 2009], and others whose evolution has been discussed 

by Richey [Richey 2010]. They are found in a plethora of applications in the literature [Penzo 

1963, Cunningham 1979, Igarashi 1994, Moss 2005] and specifically for technology modeling 

purposes [Rooney 2004, Hutchings 2008, Akram 2011]. Monte Carlo simulations are particularly 

useful in technology modeling applications because they help analysts to quantify the impacts of 

many different forms of technological uncertainties which are beyond their control, yet must be 

accounted for. 

6.1.3.5 Other Sampling Techniques 

There are also other ways to sample from probability distributions such as Gibb’s 

sampling [Geman 1984], Rubin Importance-Sampling [Rubin 1987], Sobol’s sequences, and 

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other techniques which are surveyed in detail by [Gelfand 1990]. Sobol’s sequences for 

uncertainty modeling are particularly useful in sampling and random number generation due to 

the distribution of the resulting sequences. The basic theory attempts to create an efficient and 

quick summation of a function to approximate the integral of the function [Sobol 1976]. It can be 

used to form quasi-random uniform partitions in a given interval. Quasi-random partitioning 

results in less randomly scattered points than traditional Monte Carlo methods such that there is 

more uniform dispersement of the samples. This is because quasi-random sampling generates a 

set of points that are “maximally self-avoiding” [Sambridge 2002]. Sambridge and Mosegaard 

[Sambridge 2002] also provide a helpful graphical depiction of the difference between 

pseudorandom and quasi-random sequences. The technique has been applied in sampling for 

design optimization [Mehmani 2012] and improving the development of continuous response 

surfaces [Paul-Dubois-Taine 2013]. 

6.1.3.6 Design for Robustness 

The final steps in Timson’s method [Timson 1968] calls for the development of statistical 

measures for resulting systems performance distributions and the comparison of those statistical 

measures across time periods for an indication of progress. These steps instruct how to handle 

the output data once the Monte Carlo simulation has been run and resulting probability 

distributions are obtained. Many different statistical measures exist which are commonly known 

including mean, variance, and higher order moments of distributions. These can be used to assess 

progress for any set of inputs that may be changing over time. 

The conventional robust design methodology seeks to find settings of design variables 

which minimizes the response variation to noise variables which cannot be controlled [Phadke 

1995]. When this philosophy is applied to probabilistic assessments, it seeks to answer a specific 

question of how to reduce the variability in the output subject to the uncertain input distributions. 

This has particular relevance to technology modeling because as technologies mature more 

information about their impacts becomes available. This information would naturally change the 

input distributions, and in principle reduce their variance and increase their mean about the 

expected value. Over time these changes in input distributions would have a corresponding 

change on the response distributions where variance decreases and mean is centered closer to 

target values. 

Design of robustness with respect to technology modeling can also be conducted with a 

focus on specific responses such as direct operating cost or return on investment. Technologies 

can be infused in designs to make a particular concepts more insensitive to noise variables in a 

probabilistic assessment, such as fuel price. DeLaurentis [DeLaurentis 2000] applied a form of 

stochastic optimization in order to account for uncertainties in conceptual aircraft design and to 

identify a robust design in the face of those uncertainties. His methodology is formulated to 

handle uncertainties with mathematical models, operational environments, response 

measurement, and input requirements. It divides parameters into design variables and uncertain 

variables, and requires the user to assign pdf distributions to the uncertain variables based on 

historical data or expert opinion. Through a combined design of experiments a cdf of the 

response values is created by sampling the pdf distributions in each case repeatedly. The cdf is 

then discretized into n points, where n is the desired number of probability levels for each 

objective, and the discretized points of the cdf are used to generate the response surfaces which 

provide a functional relationship between the design variables and the cdf. He suggests not using 

fewer than 5 points when discretizing a cdf in order to regress it and form an appropriately 

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accurate representation of the cdf. This type of approach implies a relatively fast execution time 

is needed, at least in the preliminary run of the design of experiments which may be 

computationally expensive depending on the number of variables required. The methodology is 

applied to the design of a supersonic transport and an optimization procedure is executed where 

the probability of a specific economic response ($/RPM) is maximized as a function of design 

variables and uncertain variables, being less than a particular threshold value. 

6.1.3.7 Stochastic Optimization 

Stochastic optimization encompasses a general class of optimization models 

characterized by the generation and implementation of random variables. There are two general 

types of such models. One pertains to optimization algorithms where the search of preferred 

solutions is governed by stochastic processes. Genetic algorithms, simulated annealing, and ant 

colony optimization are some of the most well-known and are well documented in optimization 

textbooks [Vanderplaats 2001, Rao, 2009, Onwubolu 2004]. These optimization techniques are 

well suited for discrete objective topologies or highly nonlinear multi-modal objective functions 

where numerous local optima are reasonably expected. It is worth noting that, whereas these 

search algorithms are themselves stochastic, the objective functions upon which they are 

typically applied are deterministic and invariant. Optimization techniques tailored to stochastic 

objective functions comprise the other type of stochastic models, and are the set with which we 

are specifically concerned here. 

Recall that Timson’s method [Timson 1968] calls for the development of statistical 

measures for resulting systems performance distributions and the comparison of those statistical 

measures across time periods for an indication of progress. As this approach only provides a 

basic outline, it can be reasonably asked how else progress in statistical measures can be tracked 

besides over time periods. One such manner would be with iterative optimization problems 

where input distribution settings are improved with each evaluation. Stochastic optimization 

problems with stochastic objective functions can demonstrate improvement in statistical 

performance measures with each iteration. 

Sues et al [Sues 1996] presented a unique stochastic optimization methodology for 

aeropropulsion components and demonstrated it on the design of an axial compressor. Their 

methodology involves three basic parts including response surface development for the objective 

function and each constraint, stochastic optimization using the response surfaces, and refinement 

of the response surface. The refinement of the response surface is about the optimum point for 

the objective function and about the Most Probable Point for the failure constraints. The authors 

state they do not discuss the FORM approach, however the handling of failure constraints using 

FORM and Most Probable Point method is discussed in an earlier section of this survey. They 

incorporate 3 types of variables in their methodology; deterministic design variables, random 

design variables and simple random variables whose mean values do not change during 

optimization. From an optimization perspective these simple random variables with fixed mean 

values are used to perform deterministic optimization. The methodology differentiates between 

single occurrence random variables (occur once during the component lifecycle) and operational 

random variables (vary periodically throughout the lifecycle) and use a two level Monte Carlo 

simulation to handle each type. The optimization scheme models single occurrence random 

variables in the upper level ensuring the reliability constraints are met, and models operational 

random variables in the lower level which maximize the expected value of the objective function 

during normal operating conditions. The authors claim that their methodology is different from 

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previous work because their objective function is performance based, that being to minimize cost 

of the compressor blades which is a function of the weight and efficiency of the design. It 

incorporates a statistical expectation for the efficiency of the design which is a function of two 

twist parameters in the blade design and a blade thickness variable which were assigned 

lognormal distributions to account for uncertainties in manufacturing. A number of other 

parameters including material modulus, yield strength, and rotor speeds were also incorporated 

as simple random variables with fixed mean values. The authors compared the results of their 

baseline case, the deterministic optimization, the stochastic optimization, and deterministic 

optimization with safety factors and concluded that the stochastic optimization yielded the best 

results because the stress and deflection constraints were reliability based ensuring a moderate 

overall cost but good performance under extreme rotor speed conditions. They admit that the 

safety factors in the fourth case were chosen arbitrarily resulting in a conservative solution, but 

are confident that the third case using stochastic optimization handles constraints well. The 

constraints are reliability based and formulated as very high probabilities which ensure that the 

actual stress does not rise above the max stress for each analysis case, yielding a design with 

good balance between performance and reliability. 

6.1.3.8 Technology Modeling 

This section reviews a number of different aerospace technology applications which 

borrow from various probabilistic methods discussed above. These works apply different forms 

of probabilistic methods to handle future technologies in a non-deterministic fashion. These 

applications are not presented in a particular order but are each found to be noteworthy uses of 

probabilistic methods in the context of future aircraft technologies. 

In order to design aircraft for the future which are competitive and capable, engineers 

must consider the impact of developing technologies which will eventually become part of the 

final design. The literature contains textual references for how to conduct these technology 

modeling exercises including Porter et. al. [Porter 1980] and Twiss [Twiss 1992]. Since these 

maturing technologies are beyond the control of most conceptual designers, uncertainty and its 

impact on the final design must be considered. Probabilistic assessment techniques and the 

methods described earlier in this section provide the tools to accurately judge these uncertain 

technology impacts and evaluate how they can be integrated into future aircraft. 

There are many considerations to make when forecasting the effect of a technology on a 

future aircraft including its holistic impacts, what benefits and degradations are involved with it, 

how it will interact with other technologies under consideration, the timeframe it is likely to 

mature in, its maintainability, associated costs, etc. Obviously not all of these questions can be 

immediately answered for complex systems like future aircraft, but the literature contains notable 

works which have addressed some of them. 

Kirby [Kirby 2001] proposes the Technology Identification, Evaluation, and Selection 

methodology which contains many useful techniques for technology modeling. This 

methodology formalizes a normative and explorative approach to design space exploration of the 

vehicle and technology space. It incorporates two key techniques for evaluating a portfolio of 

technologies and making selections between them including the Technology Impact Matrix 

(TIM) and the Technology Compatibility Matrix (TCM). The TIM contains the predicted impact 

values of each technology if they were matured to full-scale application (TRL = 9). The TCM 

allows a designer to quickly remove technology combinations which are “not physically 

realizable” from consideration. In this methodology Weibull distributions were used to 

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probabilistically represent the uncertainty associated with a technological impact given its 

current TRL level. The TIM and TCM can be used to create many different future technology 

vehicles with varying levels of technological uncertainty, which can be probabilistically 

evaluated to determine the best suite of technologies for a particular set of requirements. 

Landry and Archer [Landry 2014] present a high-level state model of the national 

airspace system (NAS) which can account for uncertainty within the NAS due to incorporation 

of various technologies and concepts. The authors perform a rigorous mapping of technologies to 

sources of uncertainty. They utilize multiple probability distributions including Erlang, Gamma, 

and Bernoulli distributions to model the source of uncertainty with each technology. They 

indicate the nominal distribution type and parameters for probabilistically modeling the 

technological uncertainty, noting the sources of uncertainty and the expected effects. They use 

various settings of the input distributions to reflect different assumptions and run simulations to 

evaluate the effects of changing these inputs. They hope to identify emergent effects and provide 

guidance for prioritizing the development and integration of specific technologies into the NAS. 

Mavris and Bandte [Mavris 1995] point to a need for a method to identify and assess key 

technologies while ensuring feasibility and viability, considering all disciplines and lifecycle 

phases as advocated by advanced product design philosophies. Technology risk assessments are 

also needed to avoid optimistic or pessimistic biases. They propose a methodology that accounts 

for benefits and risks of technologies replacing point design solutions for ones featuring 

variability. The core of the proposed method is a Robust Design approach that uses RSM as an 

enabling technique. RSM is used to conduct analysis of variance in a screening test to pick most 

significant contributors to performance and economic metrics, and to conduct Monte Carlo 

simulations on probabilistic variables. All economic noise variables identified were given 

triangular distributions based on historical data which was used to determine ranges and mode, 

with the exception of an engine technology complexity factor that was uniformly varied. 

Economic target value is compared against statistics of the resulting distribution. They found that 

the baseline is not good enough to meet targets so technology infusion is used to achieve the 

required weight reduction. These technology improvements are selected by the designer and 

have significant impact on the feasibility of the design. 

Weisbin et. al. [Weisbin 2004] propose a methodology for technology selection for new 

initiatives which can support selection and decision making of R&D tasks. Their methodology is 

demonstrated on Mars exploration rover projects. Technology options are addressed in the 

methodology by capturing uncertainties in the capabilities using probability distributions on 

performance attributes. The authors developed a technology costing procedure which includes 

uncertainty and an interview based peer review of the technology cost estimate. Their selection 

process is based on evaluations of risk, cost, and performance. Projects are selected in this trade 

space assuming a given total budget. Their selection process also identifies the main drivers of 

the result and is flexible enough to adapt many other applications. 

Suh et. al. [Suh 2009] propose a methodology for estimating the impact of technology 

infusion in complex systems including changes to the original system, cost of technology 

infusion, potential market impact, and an estimation of net present value of the infused 

technology. Their approach focuses on the creation of Design structure Matrix (DSM) [Eppinger 

1994] which is a matrix representation of the original product that includes the product 

components and the connections between them. Their 10 step methodology is applied to a 

complex printing system modeled by an 84 element DSM. A delta DSM is created describing the 

changes between the original and technology infused product. Value and cost of the existing 

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product is calculated by attribute based theories and then the value of the technology infused 

product is found. Uncertainty in demand and cost is incorporated using probability distributions 

on annual demand and machine population cost savings to determine the change in net present 

value of the technology infused system by Monte Carlo simulation. Their work also includes a 

notable literature survey and gap analysis for technology infusion problems. 

Mavris et al [Mavris 1997] present a probabilistic approach that extends traditional 

design methods to account for disciplinary, economic, and technological uncertainty. The 

probabilistic approach is facilitated by Fast Probability Integration (FPI). They use FPI to 

determine the cumulative probability distributions of the design space, bounded by economic 

objectives and performance constraints. The impact of technology readiness and associated risks 

was also studied but not presented in this work. They placed ranges on geometric and mission 

design parameters to expand the original point design to a design space which is explored in two 

steps; first identify feasible designs and then find economically viable designs to determine the 

most robust solutions which exist in the design space. The authors used FLOPS and ALCCA as 

part of six step methodology to assess the technical feasibility and economic viability of future 

aircraft concepts including design space exploration and technology infusion. They also used “k” 

factors to perform technology infusion which actually modify technical metrics like SFC or L/D 

in order to simulate the discontinuity in benefits or penalties associated with a new technology. 

They conclude by stating the importance of including economic uncertainty in design because 

80% of the design space in the conventional configuration was economically viable, which is a 

flawed result because uncertainty exists in economic parameters and not considering them leads 

to false viability. 

6.2 Overview of Methodology 

The approach here undertaken to conduct the probabilistic analysis follows the 

fundamental numerical approach for non-deterministic assessment [Timson 1968] as described 

above in Section 6.1.1. This approach is summarized in five basic steps: 

1. Determine design equations relating properties at lower levels to measures of 

system level performance 

2. Obtain subjective probabilities for subsystem properties 

3. Use Monte Carlo to sample from these probabilities 

4. Generate statistical measures for resulting systems performance distribution 

5. Compare statistical measures across time periods for an indication of progress 

For this application the design equations in step 1 relate technology impact factors (kfactors) to vehicle level environmental measures of performance. The underlying modeling and 

simulation capability is the EDS, described in Section 3.1. Significant runtime reductions are 

required for the implementation of the probabilistic analysis, as well as the multi-objective 

optimization analysis discussed later in Section 7.2. Accordingly, surrogate models were 

generated for a variety of intermediate vehicle environmental metrics as a function of technology 

impact factors and select design variables, as described in detail in Section 5.0 Surrogate Model 

Generation, and as reported in Appendix A: EDS Surrogates Response Names, Descriptions, and 

Units and Appendix B: EDS Vehicle Surrogate Fit Statistics. 

The development of surrogate models requires that a training data set be first procured, in 

this case as a space-filling design of experiments. All experimental designs, including spacefilling designs, require that the bounding minimum and maximum values of all model inputs, 

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namely model regression variables, be established a-priori. The identification of such value 

bounds is in itself predicated on the probability distributions defined for model inputs according 

subjective encoding in step 2 of the numerical approach method here adopted. Accordingly, the 

order of Step 1 and Step 2 were reversed in this effort, first conducting subjective encoding for 

all technology impacts across all technologies, and then leveraging on resulting distributions to 

determine minimum and maximum values for all technology impacts. 

Subjective encoding was conducted with the elicitation of a minimum, most likely, and 

maximum values, found from previous experience and anecdotal evidence to be very intuitive for 

technologists, subject matter experts, and researchers involved in this effort. In the case of the 

latter, three-point estimates offered leveraged on an extensive and thorough review of published 

literature on each technology that is documented in the Technology Report - Jeff discussed in 

Section 4.5. Whenever multiple inputs were procured for a given technology impact estimate 

harmonization of values was attained with discussions and consensus-building activities. The 

minimum, most likely, and maximum impact values for each technology impact were used to 

define a triangular distribution on the basis of the minimum, mode, and maximum values 

respectively. Because it is possible for different technologies to contribute to the same 

technology impact factor it is necessary to consider how said impact contributions are aggregated 

to then establish the minimum and maximum values of the resulting aggregate. Technology 

combinations are governed by the Technology Compatibility Matrix (Section 4.2), and the 

specific aggregation rules for different technology impact factors are described in detail in 

Section 4.3.1.2. Aggregate minimum and maximum values for all technology impact factors are 

thus determined by exhaustively considering limiting technology combinations. This process is 

presented in detail in Section 4.3.2. 

The development of surrogate models and encoding of probability distributions for the 

probabilistic technology impact matrix are fundamental components of the implementation of the 

remainder of the probabilistic analysis. The sampling of inputs pursuant of distributions, 

evaluation of the surrogates, and analysis of the resulting sample of environmental metrics, are 

all integrated in a software implementation of the method. 

An initial implementation was conducted in MATLAB where native functions and 

capabilities are leveraged for efficient random variable sampling, array passing and array 

evaluation of embedded surrogate model functions, and statistical analysis and visualization of 

results. This implementation was developed for four fixed technology combinations, namely an 

ERA core technology set, and Best Fuel Burn, Best Noise, and Best NOX technology 

combinations identified from the ERA core technology set plus additional technologies fitting 

the N+2 maturation goals. This work is reported in [Jimenez 2011]. Technology assumptions and 

modeling, vehicle modeling, and technology combinations of interested have since been updated 

significantly, although the underlying methodology and implementation remain relevant for the 

probabilistic technology assessment domain. 

6.3 Implementation of Flexible and Extensible Probabilisitic 

Analysis and Representative Results 

While computationally efficient, a shortcoming of the above mentioned effort is that only 

four technology combinations were identified for analysis, and the implementation of the method 

did not feature extensibility to any allowable technology combination in the full combinatorial 

set. In response to the desire for flexibility and extensibility a second implementation was 

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developed in the statistical discovery and visualization software JMP. This implementation 

leverages on native JMP capabilities for efficient probabilistic sampling, surrogate model 

evaluation, and visualization and analysis of resulting samples, while facilitating the design of an 

interactive dashboard for analysis and results presentation. In this second implementation it is 

possible to select any subset of technologies as applied to any of the technology collectors, and 

having resolved potential technology incompatibility conflicts, readily obtain a combined sample 

of vehicle-level environmental metrics of interest for which descriptive statistics are 

automatically displayed. The probabilistic analysis interface is shown in Figure 80 where circular 

callouts have been placed with numbered referencing to indicate the different features of the 

dashboard explained next. 

(1) Technology Collectors – In the leftmost area of the dashboard the different 

technology collectors are displayed in a vertical array, each presented with a 3-D 

isometric view generated in VSP. The vehicle selected by the user is highlighted 

whereas all other options are shown in faded greyscale. 

(2) Technologies – To the right of the technology collectors is an array of rectangular 

objects, or buttons, each corresponding to a technology of the ERA portfolio under 

consideration. Technologies are color coded: reference technology collector 

technologies – light blue; engine fuel reduction – dark blue; engine noise – green; 

airframe aerodynamic – tan; airframe noise – orange; airframe weight and 

subsystems – red; engine emissions – grey blue. The user can select/deselect any 

technology by clicking on the corresponding button. The dashboard checks for 

possible incompatibilities between technologies, and for the applicability of 

technologies onto technology collectors, and displays the buttons of viable 

technologies accordingly. 

(3) Technology Combinations Display and Controls – To the right of the array of 

technology buttons, on the top region, is the display of technology combinations in 

the ERA metric space, also referred to as the objective space in the context of multiobjective optimization analysis addressed in Section 7.2. The technology 

combinations displayed include the set of Pareto optimal solutions, namely those 

comprising the edge of the tradeoff in the objective space (see Section 7.2), and a 

subset of non-optimal solutions obtained by progressively removing technologies 

from Pareto optimal solutions. The buttons above the display control the display 

type, which includes options such as 2D or 3D, inclusion of only Pareto optimal 

solutions or additional solutions, color-coding of solutions, and scaling of the axes 

and display within the axes. The buttons immediately above and to the left of the 

display allow the user to select the ERA metrics to be used for each of the axes of the 

data plot. ERA goal values for the corresponding metrics are shown as green dotted 

lines. The user can select a technology combination by clicking on any of the 

solutions displayed which is immediately reflected by updating the technology 

selections in the array to the left. Conversely, technology combinations based on the 

selections to the left are shown on the technology combinations display. 

(4) Vehicle 3D Geometric Wire Model – Below the technology combinations display is 

a 3D geometric wire model of the vehicle where the wing, engines, and tail surfaces 

optimally resized as a result of the inclusion of technology is shown in red and over 

144
the original geometry. Controls to the right allow for toggling of views of the 3D 

model. 

(5) Probabilistic Analysis Controls – The right portion of the dashboard pertains to the 

controls and displays for probabilistic analysis. At the top of this region of the 

dashboard is the button to execute the probabilistic analysis for the selected 

technology collector and combination of technologies. Here the user can select the 

sample size, i.e. the number of simulations, set by default to 500. There is also an 

option for the user to review and edit the probabilistic distributions for the 

technology impacts which are the inputs of the surrogate models that will be 

evaluated with the sample of inputs to yield a sample of ERA metrics outputs. 

(6) Selected Technologies and Vehicle – This small portion of the display clearly 

indicates the technology collector and the list of technologies selected. 

(7) Technology Impact Factor Statistics – This display lists all technology impact factors 

that will be used in the probabilistic analysis given the technologies selected. The 

minimum, mean, and maximum value of the probabilistic distribution of each 

technology impact factor is shown along with the factor units. 

(8) Histograms for Technology Impact Factor Samples – When the probabilistic analysis 

is run samples for all technology impact factors, inputs to the surrogate models for 

ERA metrics, are generated. Histograms of the samples are automatically displayed 

along with quantiles and other statistical features. 

(9) Probabilistic Sample of ERA Metrics – When evaluated with the sample inputs, the 

surrogate models yield a sample of ERA metric values. These are displayed in either 

a 2D format (where the two metrics can be selected), or in a 3D scatterplot. For the 

2D display quantile density contours are generated with a kernel density estimator 

and displayed with color coding to easily indicate the regions of greater or lesser 

sample density, and thus of greater or lesser probability. Similarly cumulative 

probability ellipsoids can be displayed for the 3D display. 

145
Figure 80. Probabilistic analysis interface dashboard in ERA systems analysis calculator 

146
7.0 Results 

7.1 RTC/ITD Data 

Table 57. RJ Tube and Wing Geometry 

Description EDS Base RTC – ADD RTC - GF ITD - GF 

Fuselage Length, ft 109.90 109.90 109.90 109.90 

Fuselage Diameter, ft 8.83 8.83 8.83 8.83 

Wing Area, sq ft 752.5 666.4 658.4 674.9 

Wing Aspect Ratio 8.29 10.00 10.00 11.00 

Wing Sweep, deg 26.99 26.99 26.99 26.99 

Horizontal Tail Area, sq ft 173.13 131.35 129.01 127.65 

Horizontal Tail Aspect Ratio 4.59 4.59 4.59 4.59 

Horizontal Tail Sweep, deg 29.49 29.49 29.49 29.49 

Vertical Tail Area, sq ft 103.07 103.06 103.06 88.63 

Vertical Tail Aspect Ratio 1.11 1.11 1.11 1.11 

Vertical Tail Sweep, deg 43.15 43.15 43.15 43.15 

Nacelle Diameter, in 61.10 65.30 68.70 61.90 

Engine Nacelle Length, ft 7.33 6.99 7.27 6.54 

Main Gear Length, in 47.23 47.23 47.23 47.23 

Nose Gear Length, in 46.88 46.88 46.88 46.88 

Table 58. RJ Tube and Wing Vehicle Characteristics 

Description EDS Base RTC – ADD RTC - GF ITD – GF 

TOGW, lbs 85,000 75,272 74,374 68,611 

Operating Empty Weight, lbs 47,250 43,144 42,704 39,360 

Fuel Weight, lbs 19,690 14,068 13,610 11,190 

Design Payload, lbs 18,060 18,060 18,060 18,060 

Block Fuel, lbs 15,579 11,113 10,834 8,984 

Design Range, nmi 1,980 1,980 1,980 1,980 

Wing Area, sq ft 752.5 666.4 658.4 674.9 

Aspect Ratio 8.29 10.00 10.00 11.00 

Cruise L/D 16.10 17.14 16.95 19.32 

Approach Speed, kts 126.7 129.8 130.0 124.7 

T/W 0.338 0.338 0.338 0.338 

W/S, psf 113.0 113.0 113.0 101.7 

CB/AP/SL Noise, EPNdB 85.6/93.6/98.2 75.9/88.7/86.7 72.7/86.5/83.4 70.2/82.9/81.0 

Cumulative Noise, EPNdB 277.5 251.3 242.6 234.0 

 

147
Table 59. RJ Tube and Wing Propulsion System Characteristics 

Description EDS Base RTC – ADD RTC - GF ITD - GF 

SLS Thrust, lbs 15,118 12,708 12,544 11,597 

SLS Airflow, pps 452.1 486.8 577.3 586.3 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 12,923 10,863 10,723 9,913 

Cruise TSFC, lb/lbf·hr 0.659 0.539 0.530 0.485 

Total Engine Weight, lbs 4,207.0 3,334.4 3,190.8 2,415.8 

Engine Nacelle Length, ft 7.33 6.99 7.27 6.54 

Fan / Nacelle Diameter, in 50.0/61.1 51.4/65.3 54.9/68.7 54.9/61.9 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.63/17.0/27.5 1.55/22.0/46.7 1.45/16.0/45.6 1.35/17.5/50.3 

BPRD (M0.80/35K) 5.09 10.96 13.87 18.39 

OPR SLS Uninstalled 23.08 37.39 32.01 36.14 

T4max (M0.25/0K) deg R 3,170 3,550 3,550 3,550 

Fan Tip Mach No. 1.59 1.26 1.16 1.08 

LPC / HPC / HPT / LPT Stages 0/10/2/4 4/10/2/7 3/8/2/3 3/7/2/3 

Table 60. SSA Tube and Wing Geometry 

Description EDS Base RTC – ADD RTC - GF RTC - OR ITD - GF 

Fuselage Length, ft 105.6 105.6 105.6 105.6 105.6 

Fuselage Diameter, ft 12.75 12.75 12.75 12.75 12.75 

Wing Area, sq ft 1,409 1,285 1,271 1,298 1,260 

Wing Aspect Ratio 9.74 10.0 10.0 11.0 11.0 

Wing Sweep, deg 25.72 25.72 25.72 25.72 25.72 

Horizontal Tail Area, sq ft 359.3 309.0 303.8 298.9 285.9 

Horizontal Tail Aspect Ratio 6.27 6.27 6.27 6.27 6.27 

Horizontal Tail Sweep, deg 29.91 29.91 29.91 29.91 29.91 

Vertical Tail Area, sq ft 277.8 245.3 241.2 277.9 214.7 

Vertical Tail Aspect Ratio 1.92 1.92 1.92 1.92 1.92 

Vertical Tail Sweep, deg 35.0 35.0 35.0 35.0 35.0 

Nacelle Diameter, in 82.6 89.1 92.4 NA 84.6 

Engine Nacelle Length, ft 9.33 9.39 9.43 7.66 8.63 

Main Gear Length, in 71.4 77.9 81.2 71.4 73.4 

Nose Gear Length, in 44.6 49.1 51.4 44.2 46.0 

 

148
Table 61. SSA Tube and Wing Vehicle Characteristics 

Description EDS Base RTC – ADD RTC - GF RTC - OR ITD - GF 

TOGW, lbs 155,000 141,395 139,816 128,474 124,722 

Operating Empty Weight, lbs 83,000 77,289 76,718 76,142 70,070 

Fuel Weight, lbs 45,120 37,225 36,217 25,452 27,772 

Design Payload, lbs 26,880 26,880 26,880 26,880 26,880 

Block Fuel, lbs 37,415 30,940 30,229 20,906 23,126 

Design Range, nmi 3,330 3,330 3,330 3,330 3,330 

Wing Area, sq ft 1,409 1,285 1,271 1,298 1,260 

Aspect Ratio 9.74 10.0 10.0 11.0 11.0 

Cruise L/D 19.57 19.13 19.04 21.92 21.81 

Approach Speed, kts 130.0 132.3 132.5 130.1 128.7 

T/W 0.335 0.335 0.335 0.342 0.335 

W/S, psf 110.0 110.0 110.0 99.0 99.0 

CB/AP/SL Noise, EPNdB 85.9/93.8/97.0 80.5/89.1/87.6 78.8/88.6/84.6 79.1/91.4/84.9 75.2/84.9/81.0 

Cumulative Noise, EPNdB 276.6 257.2 252.0 255.4 241.1 

Table 62. SSA Tube and Wing Propulsion System Characteristics 

Description EDS Base RTC – ADD RTC - GF RTC - OR ITD - GF 

SLS Thrust, lbs 27,300 24,934 24,655 23,164 21,979 

SLS Airflow, pps 795.7 916.0 1,066.3 61.38 1,096.7 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 22,783 20,809 20,576 21,529 18,343 

Cruise TSFC, lb/lbf·hr 0.615 0.535 0.527 0.420 0.484 

Total Engine Weight, lbs 5,897.2 5,496.1 5,316.3 1,677.5 4,033.1 

Engine Nacelle Length, ft 9.33 9.40 9.43 7.66 8.63 

Fan / Nacelle Diameter, in 62.6/82.6 69.1/89.1 72.4/92.4 130.7/NA 74.6/84.6 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.69/9.37/30.1 1.55/22.0/46.7 1.45/16.0/45.6 NA/3.35/54.4 1.35/17.5/50.3 

BPRD (M0.80/35K) 5.11 10.09 12.29 NA 18.93 

OPR SLS Uninstalled 28.6 40.9 36.7 53.4 36.8 

T4max (M0.25/0K) deg R 3,300 3,550 3,550 3,550 3,550 

Fan Tip Mach No. 1.40 1.27 1.16 NA 1.08 

LPC / HPC / HPT / LPT Stages 4/9/1/4 4/10/2/7 2/8/2/3 6/5/1/1 3/7/2/3 

 

149
Table 63. LSA Tube and Wing Geometry 

Description EDS Base RTC – ADD RTC - GF RTC - OR ITD - GF 

Fuselage Length, ft 124.75 124.75 124.75 124.75 124.75 

Fuselage Diameter, ft 12.75 12.75 12.75 12.75 12.75 

Wing Area, sq ft 1,408.5 1,299.0 1,286.0 1,340.7 1,294.3 

Wing Aspect Ratio 9.74 10.0 10.0 11.0 11.0 

Wing Sweep, deg 25.72 25.72 25.72 25.72 25.72 

Horizontal Tail Area, sq ft 359.20 314.00 309.29 313.92 297.78 

Horizontal Tail Aspect Ratio 6.27 6.27 6.27 6.27 6.27 

Horizontal Tail Sweep, deg 29.91 29.91 29.91 29.91 29.91 

Vertical Tail Area, sq ft 277.75 249.25 245.51 277.85 223.61 

Vertical Tail Aspect Ratio 1.92 1.92 1.92 1.92 1.92 

Vertical Tail Sweep, deg 35.00 35.00 35.00 35.00 35.00 

Nacelle Diameter, in 82.6 89.3 92.7 NA 85.3 

Engine Nacelle Length, ft 9.33 9.44 9.47 7.96 8.71 

Main Gear Length, in 71.38 78.08 81.48 71.38 74.08 

Nose Gear Length, in 44.56 49.25 51.63 44.24 46.45 

Table 64. LSA Tube and Wing Vehicle Characteristics 

Description EDS Base RTC – ADD RTC - GF RTC - OR ITD - GF 

TOGW, lbs 174,857 161,265 159,649 149,757 144,579 

Operating Empty Weight, lbs 91,289 85,619 85,022 85,519 78,280 

Fuel Weight, lbs 46,398 38,476 37,457 27,068 29,129 

Design Payload, lbs 37,170 37,170 37,170 37,170 37,170 

Block Fuel, lbs 38,161 31,697 30,991 21,974 24,074 

Design Range, nmi 2,960 2,960 2,960 2,960 2,960 

Wing Area, sq ft 1,408.5 1,299.0 1,286.0 1,340.7 1,294.3 

Aspect Ratio 9.74 10.00 10.00 11.00 11.00 

Cruise L/D 19.15 19.02 18.90 21.56 21.48 

Approach Speed, kts 140.3 142.4 142.7 139.3 137.9 

T/W 0.312 0.312 0.312 0.342 0.312 

W/S, psf 124.15 124.15 124.15 111.70 111.70 

CB/AP/SL Noise, EPNdB 88.3/95.0/98.7 81.5/90.3/88.8 79.2/89.8/87.0 79.3/92.3/88.2 76.1/86.0/82.8 

Cumulative Noise, EPNdB 281.98 260.59 255.99 259.9 244.86 

 

150
Table 65. LSA Tube and Wing Propulsion System Characteristics 

Description EDS Base RTC – ADD RTC - GF RTC - OR ITD - GF 

SLS Thrust, lbs 27,300 25,137 24,869 25,665 22,418 

SLS Airflow, pps 795.7 923.1 1,075.6 67.54 1,118.7 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 22,783 20,977 20,753 23,854 18,707 

Cruise TSFC, lb/lbf·hr 0.615 0.536 0.527 0.420 0.483 

Total Engine Weight, lbs 5,897.1 5,568.5 5,364.2 1,881.4 4,109.0 

Engine Nacelle Length, ft 9.33 9.44 9.47 7.96 8.71 

Fan / Nacelle Diameter, in 62.6/82.6 69.3/89.3 72.7/92.7 137.6/NA 75.3/85.3 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 

1.69/9.37/30.

1

1.55/22.0/46.

7

1.45/16.0/45.

6 NA/3.33/54.4 1.35/17.5/50.3

BPRD (M0.80/35K) 5.11 10.03 12.32 NA 18.99 

OPR SLS Uninstalled 28.61 40.94 36.66 53.30 36.74 

T4max (M0.25/0K) deg R 3,300 3,550 3,550 3,550 3,550 

Fan Tip Mach No. 1.40 1.26 1.16 NA 1.08 

LPC / HPC / HPT / LPT Stages 4/9/1/4 4/10/2/7 2/8/2/3 6/5/1/1 3/7/2/3 

Table 66. LSA Over-the-Wing Nacelle Geometry 

Description RTC – ADD RTC – GF ITD - GF 

Fuselage Length, ft 124.75 124.75 124.75 

Fuselage Diameter, ft 12.75 12.75 12.75 

Wing Area, sq ft 1,311.0 1,296.7 1,286.9 

Wing Aspect Ratio 10.00 10.00 11.00 

Wing Sweep, deg 25.72 25.72 25.72 

Horizontal Tail Area, sq ft 318.37 313.18 295.22 

Horizontal Tail Aspect Ratio 6.27 6.27 6.27 

Horizontal Tail Sweep, deg 29.91 29.91 29.91 

Vertical Tail Area, sq ft 279.85 279.85 240.67 

Vertical Tail Aspect Ratio 1.92 1.92 1.92 

Vertical Tail Sweep, deg 35.0 35.0 35.0 

Nacelle Diameter, in 89.7 93.0 85.1 

Engine Nacelle Length, ft 9.48 9.51 8.68 

Main Gear Length, in 71.38 71.38 71.38 

Nose Gear Length, in 44.24 44.24 44.24 

 

151
Table 67. LSA Over-the-Wing Vehicle Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

TOGW, lbs 162,758 160,984 143,749 

Operating Empty Weight, lbs 85,779 85,027 78,055 

Fuel Weight, lbs 39,810 38,787 28,524 

Design Payload, lbs 37,170 37,170 37,170 

Block Fuel, lbs 32,846 32,138 23,567 

Design Range, nmi 2,960 2,960 2,960 

Wing Area, sq ft 1,311.0 1,296.7 1,286.9 

Aspect Ratio 10.00 10.00 11.00 

Cruise L/D 18.71 18.59 21.11 

Approach Speed, kts 142.0 142.2 138.2 

T/W 0.312 0.312 0.312 

W/S, psf 124.15 124.15 111.70 

CB/AP/SL Noise, EPNdB 79.7/88.1/87.6 77.2/88.1/85.6 73.2/84.9/81.9 

Cumulative Noise, EPNdB 255.37 250.93 240.00 

Table 68. LSA Over-the-Wing Propulsion System Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

SLS Thrust, lbs 25,374 25,075 22,265 

SLS Airflow, pps 931.8 1,084.5 1,111.1 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 21,175 20,925 18,580 

Cruise TSFC, lb/lbf·hr 0.536 0.527 0.483 

Total Engine Weight, lbs 5,618.8 5,405.8 4,083.7 

Engine Nacelle Length, ft 9.48 9.51 8.68 

Fan / Nacelle Diameter, in 69.7/89.7 73.0/93.0 75.1/85.1 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.55/22.0/46.7 1.45/16.0/45.6 1.35/17.5/50.3 

BPRD (M0.80/35K) 10.04 12.33 18.98 

OPR SLS Uninstalled 40.93 36.65 36.74 

T4max (M0.25/0K) deg R 3,550 3,550 3,550 

Fan Tip Mach No. 1.27 1.16 1.08 

LPC / HPC / HPT / LPT Stages 4/10/2/7 2/8/2/3 3/7/2/3 

 

152
Table 69. STA Tube and Wing Geometry 

Description EDS Base RTC – ADD RTC - GF ITD - GF 

Fuselage Length, ft 176.0 176.0 176.0 176.0 

Fuselage Diameter, ft 17.08 17.08 17.08 17.08 

Wing Area, sq ft 3,200 2,765 2,754 2,658 

Wing Aspect Ratio 8.09 10.00 10.00 11.00 

Wing Sweep, deg 30.73 30.73 30.73 30.73 

Horizontal Tail Area, sq ft 843.3 609.3 605.9 547.7 

Horizontal Tail Aspect Ratio 4.51 4.51 4.51 4.51 

Horizontal Tail Sweep, deg 32.53 32.53 32.53 32.53 

Vertical Tail Area, sq ft 433.5 387.1 384.9 329.2 

Vertical Tail Aspect Ratio 1.82 1.82 1.82 1.82 

Vertical Tail Sweep, deg 40.38 40.38 40.38 40.38 

Nacelle Diameter, in 129.5 132.9 138.8 121.0 

Engine Nacelle Length, ft 13.86 11.09 12.03 11.13 

Main Gear Length, in 130.0 133.4 139.3 121.5 

Nose Gear Length, in 90.75 93.13 97.26 84.80 

Table 70. STA Tube and Wing Vehicle Characteristics 

Description EDS Base RTC – ADD RTC - GF ITD – GF 

TOGW, lbs 413,000 356,892 355,545 308,821 

Operating Empty Weight, lbs 198,440 173,563 173,891 156,639 

Fuel Weight, lbs 159,750 128,519 126,843 973,71.8 

Design Payload, lbs 54,810 54,810 54,810 54,810 

Block Fuel, lbs 141,989 114,799 113,603 86,918 

Design Range, nmi 5,920 5,920 5,920 5,920 

Wing Area, sq ft 3,200 2,765 2,754 2,658 

Aspect Ratio 8.09 10.00 10.00 11.00 

Cruise L/D 19.07 18.04 17.79 20.22 

Approach Speed, kts 147.3 151.1 151.3 148.6 

T/W 0.291 0.291 0.291 0.291 

W/S, psf 129.1 129.1 129.1 116.2 

CB/AP/SL Noise, EPNdB 93.1/96.1/99.3 86.6/93.3/90.4 83.5/92.3/87.6 80.2/89.7/85.1 

Cumulative Noise, EPNdB 288.4 270.2 263.4 255.0 

 

153
Table 71. STA Tube and Wing Propulsion System Characteristics 

Description EDS Base RTC – ADD RTC - GF ITD - GF 

SLS Thrust, lbs 61,267 51,856 51,649 44,871 

SLS Airflow, pps 1,765 1,879 2,238 2,211 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 50,219 41,781 41,614 36,153 

Cruise TSFC, lb/lbf·hr 0.603 0.522 0.514 0.486 

Total Engine Weight, lbs 13,451 8,355 8,453 6,654 

Engine Nacelle Length, ft 13.86 11.09 12.03 11.13 

Fan / Nacelle Diameter, in 94.5/129.5 97.9/132.9 103.8/138.8 103.5/121.0 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.64/12.6/30.3 1.50/23.0/42.6 1.40/16.0/42.9 1.35/20.0/54.7 

BPRD (M0.80/35K) 5.39 12.48 15.54 19.96 

OPR SLS Uninstalled 31.86 42.04 39.99 43.18 

T4max (M0.25/0K) deg R 3,425 3,550 3,550 3,550 

Fan Tip Mach No. 1.28 1.17 1.06 1.06 

LPC / HPC / HPT / LPT Stages 4/14/2/5 3/10/2/8 4/8/2/4 4/8/2/4 

Table 72. STA Hybrid Wing-Body Geometry 

Description RTC – ADD RTC – GF ITD - GF 

Fuselage Length, ft 106.9 106.9 106.9 

Fuselage Diameter, ft 30.2 30.2 30.2 

Wing Area, sq ft 7,064 7,070 5,875 

Wing Aspect Ratio 7.95 7.95 8.48 

Wing Sweep, deg 36 36 36 

Horizontal Tail Area, sq ft NA NA NA 

Horizontal Tail Aspect Ratio NA NA NA 

Horizontal Tail Sweep, deg NA NA NA 

Vertical Tail Area, sq ft 118.8 118.8 118.8 

Vertical Tail Aspect Ratio 1.95 1.95 1.95 

Vertical Tail Sweep, deg 39.4 39.4 39.4 

Nacelle Diameter, in 130.6 137.1 116.6 

Engine Nacelle Length, ft 11.87 12.76 11.37 

Main Gear Length, in 108 108 108 

Nose Gear Length, in 75.35 75.35 75.35 

 

154
Table 73. STA Hybrid Wing-Body Vehicle Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

TOGW, lbs 367,036 367,366 305,517 

Operating Empty Weight, lbs 191,565 192,139 164,752 

Fuel Weight, lbs 120,661 120,417 85,955 

Design Payload, lbs 54,810 54,810 54,810 

Block Fuel, lbs 107,684 107,954 76,616 

Design Range, nmi 5,920 5,920 5,920 

Wing Area, sq ft 7,064 7,070 5,875 

Aspect Ratio 7.95 7.95 8.48 

Cruise L/D 20.46 20.02 22.55 

Approach Speed, kts 140.8 140.7 146.5 

T/W 0.27 0.27 0.27 

W/S, psf 51.96 51.96 52.01 

CB/AP/SL Noise, EPNdB 82.7/82.5/92.2 77.1/82.5/88.7 74.0/82.0/86.4 

Cumulative Noise, EPNdB 257.3 248.3 242.4 

Table 74. STA Hybrid Wing-Body Propulsion System Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

SLS Thrust, lbs 49,641 49,596 41,211 

SLS Airflow, pps 1,792 2,158 2,028 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 39,994 39,958 33,202 

Cruise TSFC, lb/lbf·hr 0.527 0.516 0.486 

Total Engine Weight, lbs 8,651.60 8,856.20 6,489.30 

Engine Nacelle Length, ft 11.87 12.76 11.37 

Fan / Nacelle Diameter, in 95.6/130.6 102.1/137.1 99.1/116.6 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.50/23.0/42.6 1.40/16.0/42.9 1.35/20.0/54.7 

BPRD (M0.80/35K) 12.06 15.3 19.82 

OPR SLS Uninstalled 42.56 39.62 43.56 

T4max (M0.25/0K) deg R 3,550 3,550 3,550 

Fan Tip Mach No. 1.17 1.06 1.06 

LPC / HPC / HPT / LPT Stages 3/10/2/7 4/8/2/4 4/8/2/4 

 

155
Table 75. LTA Tube and Wing Geometry 

Description EDS Base RTC – ADD RTC - GF ITD - GF 

Fuselage Length, ft 206.5 206.5 206.5 206.5 

Fuselage Diameter, ft 20.50 20.50 20.50 20.50 

Wing Area, sq ft 4,927 4,206 4,150 3,819 

Wing Aspect Ratio 8.81 10.0 10.0 11.0 

Wing Sweep, deg 30.94 30.94 30.94 30.94 

Horizontal Tail Area, sq ft 1,119 828.4 812.1 683.5 

Horizontal Tail Aspect Ratio 4.62 4.62 4.62 4.62 

Horizontal Tail Sweep, deg 34.82 34.82 34.82 34.82 

Vertical Tail Area, sq ft 528.0 443.6 434.8 346.2 

Vertical Tail Aspect Ratio 1.84 1.84 1.84 1.84 

Vertical Tail Sweep, deg 40.03 40.03 40.03 40.03 

Nacelle Diameter, in 158.0 158.5 166.2 144.6 

Engine Nacelle Length, ft 16.4 15.0 16.1 14.3 

Main Gear Length, in 136.9 137.4 145.1 123.5 

Nose Gear Length, in 87.3 87.6 93.0 77.9 

Table 76. LTA Tube and Wing Vehicle Characteristics 

Description EDS Base RTC – ADD RTC - GF ITD – GF 

TOGW, lbs 657,000 560,769 553,405 458,306 

Operating Empty Weight, lbs 317,000 272,714 271,414 234,489 

Fuel Weight, lbs 275,950 224,006 217,941 159,767 

Design Payload, lbs 64,050 64,050 64,050 64,050 

Block Fuel, lbs 249,180 202,271 197,358 144,280 

Design Range, nmi 7,530 7,530 7,530 7,530 

Wing Area, sq ft 4,927 4,206 4,150 3,819 

Aspect Ratio 8.81 10.0 10.0 11.0 

Cruise L/D 18.75 19.19 19.28 21.68 

Approach Speed, kts 139.0 142.1 142.6 140.8 

T/W 0.296 0.296 0.296 0.296 

W/S, psf 133.3 133.3 133.3 120.0 

CB/AP/SL Noise, EPNdB 92.7/96.8/97.7 88.9/94.5/92.7 86.3/93.2/90.5 82.8/91.0/87.4 

Cumulative Noise, EPNdB 287.2 276.1 270.0 261.3 

 

156
Table 77. LTA Tube and Wing Propulsion System Characteristics 

Description EDS Base RTC – ADD RTC - GF ITD - GF 

SLS Thrust, lbs 97,300 83,192 82,113 67,844 

SLS Airflow, pps 3,135 2,999 3,570 3,338 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 78,400 67,033 66,163 54,665 

Cruise TSFC, lb/lbf·hr 0.552 0.531 0.519 0.492 

Total Engine Weight, lbs 21,164 15,239 15,109 10,782 

Engine Nacelle Length, ft 16.4 15.0 16.1 14.3 

Fan / Nacelle Diameter, in 123.0/158.0 123.5/158.5 131.2/166.2 127.1/144.6 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.58/20.0/39.2 1.50/23.0/42.6 1.40/16.0/42.9 1.35/20.0/54.7 

BPRD (M0.80/35K) 8.80 12.44 15.88 20.42 

OPR SLS Uninstalled 40.53 42.33 39.35 43.32 

T4max (M0.25/0K) deg R 3,450 3,550 3,550 3,550 

Fan Tip Mach No. 1.26 1.17 1.06 1.06 

LPC / HPC / HPT / LPT Stages 3/10/2/6 3/10/2/7 4/8/2/4 4/8/2/4 

Table 78. LTA MFN Geometry 

Description RTC – ADD RTC – GF ITD - GF 

Fuselage Length, ft 171.5 171.5 171.5 

Fuselage Diameter, ft 21.41 21.41 21.41 

Wing Area, sq ft 4,067 4,008 3,709 

Wing Aspect Ratio 10.0 10.0 11.0 

Wing Sweep, deg 33.0 33.0 33.0 

Horizontal Tail Area, sq ft 948.5 928.1 787.7 

Horizontal Tail Aspect Ratio 4.10 4.10 4.10 

Horizontal Tail Sweep, deg 35.0 35.0 35.0 

Vertical Tail Area, sq ft 528.0 528.0 454.1 

Vertical Tail Aspect Ratio 1.42 1.42 1.42 

Vertical Tail Sweep, deg 46.0 46.0 46.0 

Nacelle Diameter, in 154.3 161.6 140.2 

Engine Nacelle Length, ft 14.57 15.58 13.85 

Main Gear Length, in 136.9 136.9 136.9 

Nose Gear Length, in 87.3 87.3 87.3 

 

157
Table 79. LTA MFN Vehicle Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

TOGW, lbs 553,092 545,129 453,994 

Operating Empty Weight, lbs 270,904 268,910 233,203 

Fuel Weight, lbs 218,138 212,169 156,741 

Design Payload, lbs 64,050 64,050 64,050 

Block Fuel, lbs 197,014 192,161 141,604 

Design Range, nmi 7,530 7,530 7,530 

Wing Area, sq ft 4,066 4,008 3,709 

Aspect Ratio 10.0 10.0 11.0 

Cruise L/D 19.67 19.45 21.85 

Approach Speed, kts 144.1 144.6 142.6 

T/W 0.280 0.280 0.280 

W/S, psf 136.0 136.0 122.4 

CB/AP/SL Noise, EPNdB 86.7/88.7/89.2 84.6/88.9/86.4 80.9/86.8/83.8 

Cumulative Noise, EPNdB 264.6 259.9 251.6 

Table 80. LTA MFN Propulsion System Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

SLS Thrust, lbs 77,597 76,459 63,313 

SLS Airflow, pps 2,798 3,324 3,115 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 62,524 61,607 51,015 

Cruise TSFC, lb/lbf·hr 0.533 0.520 0.493 

Total Engine Weight, lbs 14,136 13,997 10,044 

Engine Nacelle Length, ft 14.57 15.58 13.85 

Fan / Nacelle Diameter, in 119.3/154.3 126.6/161.6 122.7/140.2 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.50/23.0/42.6 1.40/16.0/42.9 1.35/20.0/54.7 

BPRD (M0.80/35K) 12.38 15.80 20.34 

OPR SLS Uninstalled 42.36 39.39 43.35 

T4max (M0.25/0K) deg R 3,550 3,550 3,550 

Fan Tip Mach No. 1.17 1.06 1.06 

LPC / HPC / HPT / LPT Stages 3/10/2/7 4/8/2/4 4/8/2/4 

 

158
Table 81. LTA Box Wing Geometry 

Description RTC – ADD RTC – GF ITD - GF 

Fuselage Length, ft 220.0 220.0 220.0 

Fuselage Diameter, ft 20.50 20.50 20.50 

Wing Area, sq ft 6,317 6,276 5,066 

Wing Aspect Ratio 6.33 6.37 7.90 

Wing Sweep, deg 44.0 44.0 44.0 

Horizontal Tail Area, sq ft NA NA NA 

Horizontal Tail Aspect Ratio NA NA NA 

Horizontal Tail Sweep, deg NA NA NA 

Vertical Tail Area, sq ft 528.0 528.0 528.0 

Vertical Tail Aspect Ratio 1.50 1.50 1.50 

Vertical Tail Sweep, deg 47.0 47.0 47.0 

Nacelle Diameter, in 166.1 174.8 152.3 

Engine Nacelle Length, ft 15.89 17.06 15.05 

Main Gear Length, in 136.9 136.9 136.9 

Nose Gear Length, in 87.25 87.25 87.25 

Table 82. LTA Box Wing Vehicle Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

TOGW, lbs 613,688 608,444 511,373 

Operating Empty Weight, lbs 294,179 293,854 271,925 

Fuel Weight, lbs 255,459 250,540 175,398 

Design Payload, lbs 64,050 64,050 64,050 

Block Fuel, lbs 230,858 227,161 158,296 

Design Range, nmi 7,530 7,530 7,530 

Wing Area, sq ft 6,317 6,276 5,066 

Aspect Ratio 6.33 6.37 7.90 

Cruise L/D 19.23 19.01 22.15 

Approach Speed, kts 119.3 119.5 128.6 

T/W 0.308 0.309 0.296 

W/S, psf 97.1 96.9 100.9 

CB/AP/SL Noise, EPNdB 88.0/92.8/94.0 85.0/91.6/91.3 83.7/90.8/88.1 

Cumulative Noise, EPNdB 274.8 267.8 262.5 

 

159
Table 83. LTA Box Wing Propulsion System Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

SLS Thrust, lbs 93,800 93,254 76,302 

SLS Airflow, pps 3,381 4,053 3,758 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 75,581 75,141 61,481 

Cruise TSFC, lb/lbf·hr 0.529 0.516 0.487 

Total Engine Weight, lbs 17,419 17,354 12,147 

Engine Nacelle Length, ft 15.89 17.06 15.05 

Fan / Nacelle Diameter, in 131.1/166.1 139.8/174.8 134.8/152.3 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.50/23.0/42.6 1.40/16.0/42.9 1.35/20.0/54.7 

BPRD (M0.80/35K) 12.50 15.99 20.82 

OPR SLS Uninstalled 42.30 39.29 43.21 

T4max (M0.25/0K) deg R 3,550 3,550 3,550 

Fan Tip Mach No. 1.17 1.06 1.06 

LPC / HPC / HPT / LPT Stages 3/10/2/7 4/8/2/4 4/8/2/4 

Table 84. LTA Hybrid Wing-Body Geometry 

Description RTC – ADD RTC - GF RTC – OR* ITD - GF 

Fuselage Length, ft 120.9 120.9 120.9 120.9 

Fuselage Diameter, ft 32.85 32.85 32.85 32.85 

Wing Area, sq ft 10,391 10,339 8,140 8,100 

Wing Aspect Ratio 6.04 6.04 6.05 6.05 

Wing Sweep, deg 36.0 36.0 36.0 36.0 

Horizontal Tail Area, sq ft NA NA NA NA 

Horizontal Tail Aspect Ratio NA NA NA NA 

Horizontal Tail Sweep, deg NA NA NA NA 

Vertical Tail Area, sq ft 140.0 140.0 140.0 140.0 

Vertical Tail Aspect Ratio 1.95 1.95 1.95 1.95 

Vertical Tail Sweep, deg 39.4 39.4 39.4 39.4 

Nacelle Diameter, in 150.4 158.4 NA 134.1 

Engine Nacelle Length, ft 14.14 15.22 8.69 13.25 

Main Gear Length, in 136.9 136.9 136.9 136.9 

Nose Gear Length, in 87.3 87.3 87.3 87.3 

*HWB OR has cruise Mach # = 0.8, all other vehicles have cruise Mach # of 0.84 

 

160
Table 85. LTA Hybrid Wing-Body Vehicle Characteristics 

Description RTC – ADD RTC - GF RTC – OR* ITD - GF 

TOGW, lbs 540,008 537,230 423,221 421,039 

Operating Empty Weight, lbs 273,774 273,149 231,460 223,613 

Fuel Weight, lbs 202,183 200,030 127,711 133,377 

Design Payload, lbs 64,050 64,050 64,050 64,050 

Block Fuel, lbs 182,476 181,211 114,395 120,104 

Design Range, nmi 7,530 7,530 7,530 7,530 

Wing Area, sq ft 10,391 10,339 8,140 8,100 

Aspect Ratio 6.04 6.04 6.05 6.05 

Cruise L/D 21.74 21.34 26.04 23.74 

Approach Speed, kts 133.8 133.8 141.8 140.6 

T/W 0.270 0.270 0.270 0.270 

W/S, psf 52.0 52.0 52.0 52.0 

CB/AP/SL Noise, EPNdB 83.8/83.4/93.3 78.0/83.4/91.3 80.1/83.7/93.1 74.6/82.7/87.4 

Cumulative Noise, EPNdB 260.5 252.7 256.9 244.8 

*HWB OR has cruise Mach # = 0.8, all other vehicles have cruise Mach # of 0.84 

Table 86. LTA Hybrid Wing-Body Propulsion System Characteristics 

Description RTC – ADD RTC - GF RTC – OR* ITD - GF 

SLS Thrust, lbs 72,600 72,600 39,240 57,116 

SLS Airflow, pps 2,618 3,156 96.7 2,810 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 58,500 58,500 32,514 46,023 

Cruise TSFC, lb/lbf·hr 0.534 0.522 0.418 0.495 

Total Engine Weight, lbs 13,154 13,247 2,493 9,076 

Engine Nacelle Length, ft 14.14 15.22 8.69 13.25 

Fan / Nacelle Diameter, in 115.4/150.4 123.4/158.4 160.9/NA 116.6/134.1 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.50/23.0/42.6 1.40/16.0/42.9 NA/3.1/49.3 1.35/20.0/54.7 

BPRD (M0.80/35K) 12.32 15.74 NA 20.21 

OPR SLS Uninstalled 42.39 39.42 48.7 43.40 

T4max (M0.25/0K) deg R 3,550 3,550 3,550 3,550 

Fan Tip Mach No. 1.17 1.06 0.00 1.06 

LPC / HPC / HPT / LPT Stages 3/10/2/7 4/8/2/4 4/4/1/1 4/8/2/4 

*HWB OR has cruise Mach # = 0.8, all other vehicles have cruise Mach # of 0.84 

 

161
Table 87. VLA Tube and Wing Geometry 

Description EDS Base RTC – ADD RTC - GF ITD - GF 

Fuselage Length, ft 225.2 225.2 225.2 225.2 

Fuselage Diameter, ft 23.95 23.95 23.95 23.95 

Wing Area, sq ft 6,203 5,424 5,359 5,076 

Wing Aspect Ratio 7.61 10.0 10.0 11.0 

Wing Sweep, deg 39.7 39.7 39.7 39.7 

Horizontal Tail Area, sq ft 1,494 1,066 1,047 920 

Horizontal Tail Aspect Ratio 3.60 3.60 3.60 3.60 

Horizontal Tail Sweep, deg 36.6 36.6 36.6 36.6 

Vertical Tail Area, sq ft 855.2 801.7 787.3 654.6 

Vertical Tail Aspect Ratio 1.26 1.26 1.26 1.26 

Vertical Tail Sweep, deg 44.7 44.7 44.7 44.7 

Nacelle Diameter, in 129.4 136.1 141.4 122.7 

Engine Nacelle Length, ft 14.78 12.63 13.59 12.45 

Main Gear Length, in 115.5 122.2 127.5 108.8 

Nose Gear Length, in 86.3 91.0 94.7 81.6 

Table 88. VLA Tube and Wing Vehicle Characteristics 

Description EDS Base RTC – ADD RTC - GF ITD – GF 

TOGW, lbs 876,999 766,919 757,687 645,889 

Operating Empty Weight, lbs 403,400 368,169 365,639 328,515 

Fuel Weight, lbs 386,030 311,180 304,478 229,804 

Design Payload, lbs 87,570 87,570 87,570 87,570 

Block Fuel, lbs 347,168 280,754 275,158 207,205 

Design Range, nmi 7,060 7,060 7,060 7,060 

Wing Area, sq ft 6,203 5,424 5,359 5,076 

Aspect Ratio 7.61 10.0 10.0 11.0 

Cruise L/D 18.96 18.13 17.94 20.06 

Approach Speed, kts 156.9 161.9 162.3 160.4 

T/W 0.259 0.297 0.297 0.297 

W/S, psf 141.4 141.4 141.4 127.2 

CB/AP/SL Noise, EPNdB 99.3/104.6/100.3 87.8/95.1/93.5 85.1/94.5/90.9 82.3/91.8/88.5 

Cumulative Noise, EPNdB 304.3 276.4 270.5 262.6 

 

162
Table 89. VLA Tube and Wing Propulsion System Characteristics 

Description EDS Base RTC – ADD RTC - GF ITD - GF 

SLS Thrust, lbs 62,000 56,964 56,249 47,696 

SLS Airflow, pps 1,791 2,037 2,393 2,320 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 52,038 45,897 45,321 38,430 

Cruise TSFC, lb/lbf·hr 0.578 0.484 0.473 0.453 

Total Engine Weight, lbs 11,965 8,515 8,170 6,331 

Engine Nacelle Length, ft 14.78 12.63 13.59 12.45 

Fan / Nacelle Diameter, in 94.4/129.4 101.1/136.1 106.4/141.4 105.2/122.7 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.76/8.81/28.1 1.50/23.0/42.6 1.40/16.0/42.9 1.35/20.0/54.7 

BPRD (M0.80/35K) 4.89 12.37 14.99 19.87 

OPR SLS Uninstalled 29.18 42.76 41.51 43.96 

T4max (M0.25/0K) deg R 3,150 3,550 3,550 3,550 

Fan Tip Mach No. 1.69 1.17 1.06 1.06 

LPC / HPC / HPT / LPT Stages 4/11/2/4 3/10/2/7 4/8/2/4 4/8/2/4 

Table 90. VLA Hybrid Wing-Body Geometry 

Description RTC – ADD RTC – GF ITD - GF 

Fuselage Length, ft 143.1 143.1 143.1 

Fuselage Diameter, ft 37.06 37.06 37.06 

Wing Area, sq ft 15,916 15,489 12,264 

Wing Aspect Ratio 7.05 7.08 7.44 

Wing Sweep, deg 36.0 36.0 36.0 

Horizontal Tail Area, sq ft NA NA NA 

Horizontal Tail Aspect Ratio NA NA NA 

Horizontal Tail Sweep, deg NA NA NA 

Vertical Tail Area, sq ft 210.0 210.0 210.0 

Vertical Tail Aspect Ratio 1.95 1.95 1.95 

Vertical Tail Sweep, deg 39.4 39.4 39.4 

Nacelle Diameter, in 158.6 167.0 141.0 

Engine Nacelle Length, ft 16.74 17.76 15.39 

Main Gear Length, in 115.5 115.5 115.5 

Nose Gear Length, in 86.33 86.33 86.33 

 

163
Table 91. VLA Hybrid Wing-Body Vehicle Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

TOGW, lbs 827,476 805,565 637,784 

Operating Empty Weight, lbs 443,190 435,616 348,546 

Fuel Weight, lbs 296,716 282,380 201,668 

Design Payload, lbs 87,570 87,570 87,570 

Block Fuel, lbs 266,155 253,682 180,699 

Design Range, nmi 7,060 7,060 7,060 

Wing Area, sq ft 15,916 15,489 12,264 

Aspect Ratio 7.05 7.08 7.44 

Cruise L/D 22.27 22.00 23.34 

Approach Speed, kts 137.3 138.2 143.6 

T/W 0.310 0.310 0.310 

W/S, psf 51.99 52.01 52.00 

CB/AP/SL Noise, EPNdB 78.5/81.0/96.1 73.1/80.9/93.3 70.2/80.5/90.1 

Cumulative Noise, EPNdB 255.6 247.3 240.8 

Table 92. VLA Hybrid Wing-Body Propulsion System Characteristics 

Description RTC – ADD RTC – GF ITD - GF 

SLS Thrust, lbs 85,440 83,189 65,884 

SLS Airflow, pps 3,049 3,614 3,201 

Takeoff Thrust 

(M0.25/0K, Hot), lbs 68,845 67,031 53,087 

Cruise TSFC, lb/lbf·hr 0.486 0.473 0.456 

Total Engine Weight, lbs 17,595 17,177 10,888 

Engine Nacelle Length, ft 16.74 17.76 15.39 

Fan / Nacelle Diameter, in 123.6/158.6 132.0/167.0 123.5/141.0 

FPRD / HPCPRD / OPRD 

(M0.80/35K) 1.50/23.0/42.6 1.40/16.0/42.9 1.35/20.0/54.7 

BPRD (M0.80/35K) 12.07 15.43 19.40 

OPR SLS Uninstalled 42.55 39.30 43.87 

T4max (M0.25/0K) deg R 3,550 3,550 3,550 

Fan Tip Mach No. 1.17 1.06 1.06 

LPC / HPC / HPT / LPT Stages 3/10/2/7 4/8/2/4 4/8/2/4 

7.2 Multi-Objective Genetic Algorithm for N+2 Technology 

Pareto Frontier 

7.2.1 Motivation for Multi-Objective Optimiztion 

Resource limitations required the informed down-selection of an airframe and propulsion 

technology portfolio to meet environmental goals. This problem is not unique to aviation 

164
environmental impact or to aeronautics in general and features some common traits. A research 

and development portfolio is typically associated with a set of aggressive performance 

requirements pursuant of overarching policy that frame and motivate the development of 

enabling technologies. Albeit methodological diversity most approaches to the composition of 

such a portfolio incorporate basic steps for the identification of a pool of candidate technologies, 

the comparative assessment of technology combinations (each embodying a unique candidate 

portfolio), and ultimately the down-selection of the portfolio. In this context questions that 

typically emerge include: 

• What are the most favorable combinations of technologies relative to the goals in 

question? 

• To what extent does the technology portfolio satisfy the goals? What is the best that 

can be achieved? 

• What technologies (or combinations thereof) are critical in reaching prescribed goals? 

There are also a number of noteworthy challenges and practical considerations. The size 

of the problem, and thus the magnitude of the effort, generally scales with the number of 

candidate technologies to be evaluated as well as with the number and nature of measures of 

performance with which they ought to be evaluated. The set of possible technology combinations 

grows very quickly with the number of candidate technologies, often resulting in a prohibitively 

large combinatorial set even for a modestly sized pool of candidates. This issue is further 

compounded by the resource expense of evaluating distinct technology combinations in terms of 

relevant measures of performance. Although an exhaustive examination of the combinatorial set 

is desired (or even ideal) it is often unrealizable in practice. A related issue pertains to the 

combinatorial logic required to observe technology compatibilities; it reduces the size of the total 

set of alternatives but typically not enough to make exhaustive exploration feasible, and instead 

adds to the complexity of the problem setup. 

Another consideration is the plurality of goals. The comparative assessment of a pool of 

candidate solutions on the basis of a single measure of performance is, for the most part, trivial. 

However, a multiplicity of (conflicting) goals implies the need to address tradeoffs inherent 

across unique technology combinations. While solutions at the corners of the trade space may be 

readily disregarded for performing well relative to one goal and poorly in all others, solutions 

with a more balanced valuation profile are typically of particular interest. Adequate 

characterization of the technology trade space is therefore crucial in informing and supporting 

portfolio selection. 

In lieu of the above considerations technology portfolio composition may be abstracted 

and generalized as a multi-objective optimization sub-problem followed by a multi-attribute 

decision making sub-problem. This is consistent with work by Deb (2002) who notes that the 

former yields the set of tradeoff optimal solutions (also referred to as non-dominated or Paretooptimal), and the latter yields a single choice from the optimal set. We subscribe to this view and 

argue that a multi-objective genetic algorithm is ideally suited to address many of the traits and 

considerations specific to aircraft technology portfolio composition discussed above, and offer 

the following observations: 

First, the combinatorial set associated with the pool of candidate technologies comprises 

a discrete solution space. The treatment of discrete solution sets is inherent in the formulation of 

genetic algorithms, particularly with regards to the encoding and manipulations of solutions via a 

genetic bit string. More specifically, by assigning each technology in the candidate pool to a 

single bit, the bit string efficiently encompasses the entire technology pool and defines distinct 

165
combinations on the basis of what technologies are included (1) or not (0). The evolutionary 

logic of the genetic algorithm yields favorable solutions while avoiding (in most practical cases) 

the need to span the entire combinatorial space, thus allowing for resource savings in solution 

evaluation that may be significant. It may be reasonably expected that objective functions are 

highly sensitive to the addition or removal of a single technology from a given combination, and 

that a myriad of potentially steep local optima may exist. Genetic algorithms include stochastic 

components that allow solutions to evolve out of local optima without discarding them, unless 

more favorable ones are found. Ultimately, the optimization yields a set of non-dominated 

discrete solutions that encompass fundamental tradeoffs across the multiple objectives and that 

can be studied to gain insight about the technology trade space. 

7.2.2 Formulation of the Technology Portfolio Problem 

We formulate the technology portfolio selection problem as follows. Consider a set τ of n

technologies Ti. 

߬ = ሼܶଵ, ܶଶ 	...,ܶ௡ሽ

A technology combination is defined by the vector 

ሿ௡ ݐ,...,ଶݐ ,ଵݐሾ = ݐ

Where 

ݐ ൜ = ௜1, 				if ܶ௜ is included

0,	if ܶ௜ is not included (3) 

 

To distinguish between distinct technology combinations we use superscript notation so 

௜ݐ that

௝

 refers to the inclusion of the i

th technology in the jth combination. 

The validity of concurrently including any two technologies of the set τ in a given 

technology combination is encoded on a pair-wise basis via the compatibility operator χ, defined 

as 

߯൫ܶ௜, ܶ௝൯=߯௜௝ = ൜1,	 if ܶ௜ is compatible with ܶ௝

0,	if ܶ௜ is not compatible with ܶ௝

 (4) 

Based on this definition a technology is always compatible with itself, so that 

߯௜௜ = 1 

The technology compatibility matrix Χ with elements χij is defined for a given technology 

set τ. Χ is square (n×n), symmetric, and identity diagonal. 

For the simplest case where there are no technology incompatibilities and all χij=1 there 

are 2n

 possible technology combinations t

i

. We define this 2n set of all possible instances of the 

vector t as the domain of the alternative space and denote it as A. We denote the set of internally 

compatible technology combinations as 

166
 ܣ ⊇ ఞܣ

Objective functions are evaluated for technology combinations, which we express as 

 ܯ ... 1,2, = ݉ ,(ݐ)௠݂

For the type of application considered in this paper objective functions will typically 

constitute some measure of system performance produced via modeling capabilities. 

Technologies are modeled via correction factors applied to the underlying model relations 

resulting in system performance changes. Accordingly, it may be necessary to create appropriate 

mapping functions so that the vector t may be used directly as an argument of the objective 

functions. 

As a generalization we assume that the objective function f

k

 takes as an argument the 

technology attribute vector k. An appropriate mapping function ݂Ԧ must be defined such that 

݇ → ݐ	:Ԧ݂

So that the objective function f with argument t may be expressed as 

ቁ)ݐ)Ԧ݂ቀ௞ ݂) = ݐ)݂

We address the issue of compatibility for technology combinations by including an 

arbitrarily large penalty factor into the objective function for incompatible combinations. The 

compatibility of a given technology combination t

l

 may be determined by estimating the 

parameter 

௜ݐ ∶ ݆ ݅,	∀		 ௝௜߯ෑ߶௟ =௝ݐ∧1= ௟

௟ = 1

Because the product is inclusive of all the pairwise compatibility operators corresponding 

to the technologies included in the combination t

l

, the result is always zero whenever there is at 

least one incompatibility. A penalty factor may therefore be simply defined as 

ߨ = ௟ቊΛ if ߶௟ = 0

1 if ߶௟ = 1

Here Λ is a constant that yields an arbitrarily large penalty to the objective function. 

Some caution should be exercised in selecting a value for Λ, particularly with regards to whether 

the optimization is a minimization or maximization of the objective function, as well as the 

expected range of objective function values for feasible solutions. The penalty factor may then 

be incorporated within the objective function as follows 

௟ߨ = (ݐ)ሚ݂

݂௞ ቀ݂Ԧ(ݐ(ቁ

This approach is comparable to the creation of a pseudo-objective function for 

constrained problems where a penalty function is appended to the original objective function to 

account for the constraint (see for instance Vanderplaats, 2001, Ch. 2.5 and Ch. 5). Use of 

167
penalty functions is much simpler and easier to visualize whenever the objective and penalty 

functions are continuous and well behaved. For the class of problems we address in this work the 

discrete nature and of the technology set and added combinatorial complexity resulting from 

incompatibilities are not conducive to a straightforward or visual examination of the 

compatibility constraint, though the principle and effect achieved via the penalty factor is 

ultimately the same. 

Accordingly the classical optimization problem may be formulated as 

Min / Max ݂(ݐ(,

Subject to ߶(Χ, ݐ < (0

 ܣ∋ݐ

Or, if the pseudo-objective function is to be explicitly stated in the formulation, then 

Min / Max ݂ሚ(ݐ(,

ܣ∋ݐ to Subject

This is, of course, a generalized setup for an optimization problem; the choice of a 

specific algorithm or optimization approach should be adequately reflected by revising as needed 

the above mathematical formulation. 

The literature broadly outlines two basic approaches with regards to optimization and 

selection which we consider here for the formulation of the technology portfolio problem. The 

first approach is based on the solution of the multi-objective optimization problem where all 

objectives are equally important. The result is a tradeoff-optimal set of feasible solutions from 

which a subset or a single preferred solution must be ultimately identified. Because all solutions 

in that set are equally optimal selection cannot be conducted solely on the basis of objectives 

employed in the optimization. Posterior information on the relative preference of objectives is 

thus collected and applied to realize the selection. In fact, Deb (2002) notes that “each trade-off 

solution corresponds to a specific order of importance of the objectives.” The second approach 

solves a single objective optimization where the relative preference of multiple objectives is fully 

known a-priori and used to construct the objective function as a weighted objective aggregate. 

We find that the first approach is preferable because it produces a set of tradeoff-optimal 

solutions from which insight can obtained, and because it allows for the comparative assessment 

of choices resulting from different selection techniques and objective preference weightings. 

Multi-objective optimization (first approach) is generally realized with respect the 

dominance of individual solutions t

j

 from the set A (or some relevant subset thereof). The 

concept of dominance is well documented in the literature, and we adopt it to this problem as 

follows. 

For a set of performance objectives 

168
݂ሚ

ܯ , ... 1,2, = ݉		 ;(ݐ)௠

a technology combination t

i

 strongly dominates t

j

 if t

i

 is better than t

j

 in all M 

performance objectives, or 

ሚ݂௝ ↔ ݐ ≻ ௜ݐ

௜ݐ൫௠

൯≺݂ሚ

௠൫ݐ௝൯	∀	݉

Similarly, t

i

 weakly dominates t

j

 if t

i

 is no worse than t

j

 in all M performance objectives 

and t

i

 is better than t

j

 in at least one objective, or 

ሚ݂௝ ↔ ݐ ≽ ௜ݐ

௜ݐ൫௠

൯≼݂ሚ

௠൫ݐ௝൯	∀	݅	 ∧ ∃	݈:	݂ሚ

௜ݐ൫௟

൯≺݂ሚ

௟൫ݐ௝൯

The set of technology combinations that are not dominated by any other technology 

combination comprises the non-dominated set. For any subset of solutions B from the total set A

of technology combinations, there is a non-dominated set of solutions B’ such that 

ܣ ⊃ ܤ ⊃ ′ܤ

In this context the general purpose of an optimization routine is to identify the nondominated set A’ of the total set of technology combinations A. In practice, A’ may not be fully 

identified and instead it is approximated as the non-dominated subset B’ (Zitzler, 2003). An 

optimization approach evident in evolutionary algorithms is to recursively generate sets of 

solutions B, identify non-dominated solutions B’, and retain them across iterations so as to 

approximate (B’) or explicitly identify the set of non-dominated solutions A’. (For simplicity we 

refer only to A’ from here on.) 

The preference-based selection from A’ may be conducted according to any number of 

multi-attribute selection techniques. In general these techniques apply some function that 

combines performance objective values and corresponding objective weights into a single 

aggregate metric, effectively reducing the problem to a ranking of solutions according to this 

weighted aggregate. One of the simplest and most commonly used options is the weighted sum 

of scaled values, as shown below where there asterisk denotes the normalizing function value. 

௜ݐ൫ܨ

௠ݓ෍=൯

௜ݐ൫௠݂

൯

∗(ݐ)௠݂

ெ

௠ୀଵ

Although the literature offers much diversity in multi-attribute decision making 

techniques (e.g. Sen, 1998) it also cautions to recognize and understand the inherent 

assumptions, limitations and shortcomings of each before committing them to use (e.g. Roman, 

2004). 

7.2.3 Implementation of the Multi-Objective Genetic Algorithm for 

the Technology Portfolio Problem 

The technology portfolio problem is conducted for the 300-passenger class (Large Twin 

Aisle) tube-and-wing concept. We implement Deb’s Non-dominated Sorting Genetic Algorithm 

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II (NSGA-II) (Deb, 2002) which incorporates improvements in computational efficiency, elitism, 

and diversity preservation over its original formulation. Non-dominated sorting featured in 

NSGA was originally proposed by Goldberg (1989) and is found in many variants of multiobjective genetic algorithms. This approach assigns a rank of 1 to all non-dominated solutions in 

a set, removes them, and recursively assigns subsequent rank values to non-dominated solutions 

of the remaining set. 

We select the NSGA-II for several reasons. First, it has been shown to be more efficient 

than other competing approaches. Second, it produces more uniformly spread non-dominated 

sets relative to competing approaches. This is achieved by calculating the crowding distance to 

assess the density of solutions surrounding a particular solution. Rather than sorting solutions 

solely on dominance rank the NSGA-II combines rank and crowding distance into a crowdedcomparison operator for sorting. The crowded comparison operator ≺௡ is defined as: 

݅ ≺௡ ݆	↔ (݅௥௔௡௞ < ݆௥௔௡௞) ∨ ൫(݅௥௔௡௞ = ݆௥௔௡௞) 	∧	 (݅ௗ௜௦௧௔௡௖௘ < ݆ௗ௜௦௧௔௡௖௘)൯

For continuous decision and objective spaces this feature yields some uniformity across 

solutions on the non-dominated set and avoids potential clustering that typically occurs otherwise 

with genetic algorithms. This in turn allows for a more directed management of the population 

size so that the Pareto hyper-surface may be more readily discerned at a more modest 

computational burden. A comparable effect is produced for the technology portfolio problem 

noting that natural clustering of solutions is more likely and reasonably expected for discrete 

decision and objective spaces. For a reasonably sized population all top-ranked solutions will be 

preserved by NSGA-II regardless of their natural clustering, as will those of subsequent ranks 

that offer the most uniform spreading of solutions. This feature is certainly desirable if one hopes 

to understand the technology trade-space by examining and characterizing the non-dominated set 

with respect to performance goals. Lastly, due to the popularity and broad acceptance of the 

NSGA-II algorithm, many commercial software products and optimization tools feature prepackaged implementations along with several options for customization. 

We implement the NSGA-II in MATLAB® via its gamultiobj solver. The genotype of 

each technology combination is encoded with a 91 bit string corresponding to each of the 91 

technologies in the candidate pool. The inclusion of individual technologies is therefore handled 

explicitly by the corresponding binary bit. Accordingly, we set the population size at 1,500 

following a heuristic factor of ~15 relative to the number of independent decision variables. 

It is common practice to randomly seed the initial population to avoid the introduction of 

design biases. This approach, while easy to implement, may yield unfeasible solutions for the 

initial population. Although this rarely presents a problem for the approximation of the nondominated set, we intentionally seed the initial population with randomly generated solutions 

that satisfy compatibility. 

Preliminary test runs showed that, as expected, a fraction of the new solutions created 

with the crossover operator are incompatible. However we noticed that on average the two-point 

crossover option resulted in a smaller fraction of incompatible combinations. Although the issue 

of compatibility is addressed via a penalty function, we find that a larger fraction of feasible 

designs is certainly preferable. Accordingly, we adopt the two-point cross-over for which the 

default 0.8 crossover fraction was deemed adequate. In a similar fashion we investigated the 

effect of the mutation scheme and observed that it did not produce noteworthy effects except for 

170
very high mutation rates uncharacteristic of most typical applications. A uniform mutation 

scheme with a 0.01 rate was thus adopted for simplicity. 

The stopping criterion is the convergence of the spread of the non-dominated set. The 

spread is a scalar measure of the distribution of points along the Pareto front estimated as a 

function of the Euclidean distance between consecutive (neighboring) solutions along the Pareto 

front and the average value of these distances. It assumes values ranging between 0 for evenly 

distributed solutions and 1 for unevenly distributed (clustered) solutions. Spread convergence is 

achieved when the change in spread over the last N iterations is less than a tolerance value ε. 

Default values N=100 and ε=10-4 were preserved. 

7.2.4 Characterization of the Technology Tradespace 

7.2.4.1 Visualization and Format of Results 

We set the measures of performance defining N+2 environmental goals in as objective 

functions. It is worth noting again that these metrics are defined as improvements relative to a 

prescribed reference. In the case of fuel burn the reference is the mission fuel burn for the 

reference vehicle, whereas the limits for noise and NOX are regulatory. The noise limit is defined 

as a function of takeoff gross weight whereas the goal for NOX is defined as a function of the 

overall pressure ratio of the engine(s). 

The resulting non-dominated set of technology combinations is visually depicted in 

Figure 81 as a 3-D plot with each axis corresponding to one of the N+2 measures of 

performance, and each data point representing a distinct technology combination. Bivariate plots 

are also shown. We note a number of readily identifiable features of the Pareto surface and apply 

differentiation with greyscale and data point markers to further facilitate their discernment. 

171
Figure 81. Pareto frontier of non-dominated technology combinations – data point greyscale and markers for 

feature differentiation purposes 

7.2.4.2 Corners and Tradeoffs of the Technology Trade Space 

The corners of the trade space are the regions of the Pareto frontier featuring solutions 

whose performance are among the best for one objective and among the worse for all others. 

They are relevant because they comprise the extremes of the Pareto frontier, and although they 

do not offer suitable solutions in most practical applications of multi-objective optimization, they 

offer a good basis upon which overarching objective tradeoffs are characterized. 

Assuming that all three environmental performance objectives are in conflict, at least 

with respect to the technology pool under consideration, it is reasonable to expect that there are 

three corresponding corners. The 3-D data plot in Figure 81 indeed shows solutions populating 

the noise margin corner (solutions with the best noise margin, and the lowest reductions of NOX

and fuel burn). However, there is not a fuel burn or a NOX reduction corner, but rather solutions 

featuring high reductions in fuel burn and NOX concurrently, and poor noise margin. This can be 

attributed to the existence of solutions with high reduction of NOX and fuel burn and their 

dominance over those with good performance in only one of two these objectives. Technology 

combinations comprising the fuel burn reduction and NOX reduction corners indeed exist but are 

not preserved by the NSGA-II algorithm on account of being dominated. It is thus possible to 

172
characterize the Pareto frontier in terms of a noise margin corner, a joint NOX and fuel burn 

reduction corner, and the tradeoff between the two. In turn, understanding each of these features 

with regards to technologies, their effects, and the compatibility between them is most 

meaningful. 

Technology combinations in the noise margin corner are found to contain all airframe 

and engine noise technologies with only a couple of exceptions. Also present are combustor 

technologies for NOX, which indicates that inclusion of these two technologies is not sufficient 

to guarantee the highest NOX reduction values; additional synergistic technologies impacting the 

engine cycle may be needed. It is worth noting that most of the airframe weight reduction 

technologies conducive to low fuel burn are absent, as are most engine technologies for fuel 

consumption reduction. These technology trends are expected, as the greatest noise margin 

values are obtained with quiet aircraft with a higher noise limit driven by a higher takeoff gross 

weight. 

Solutions in the fuel burn and NOX reduction corner feature an aggressive use of drag 

reduction technologies. Technologies in all solutions of this corner include higher aspect ratio 

wings enabled by structural technologies and spanwise load control, and adaptive wing control. 

Discrete roughness elements are also observed for all solutions in this corner, and are found to be 

exclusive to solutions featuring fuel burn reductions above ~35%. All the airframe weight 

reduction technologies are also featured in this corner, and advanced materials and structures are 

exclusive to this region of the Pareto frontier. As expected many engine technologies for specific 

fuel consumption are also featured in this corner, often times exclusively, including advanced 

engine component materials. Although both combustor technologies for NOX are pervasive in 

this region we note that favorable NOX reduction values result from their synergistic combination 

with engine technologies that allow for higher temperatures prior to the combustor inlet and/or 

that result in higher overall pressure ratio (OPR). This effect is explained in detail in following 

subsections. The more moderate engine noise technologies are also observed in this region and 

produce some clustering patterns discussed later. More aggressive engine noise technologies as 

well as most airframe noise technologies incur in fuel burn penalties and are not observed here. 

The tradeoff between these two corners is realized by non-dominated combinations for 

which there is some inclusion /exclusion of compatible technologies relative to those governing 

each corner as described above. The degradations in noise margin allowing for greater reductions 

in fuel burn and NOX can be readily appreciated in the 3-D plot of Figure 81. The plot for fuel 

burn reduction vs. noise margin reveals that that tradeoff between these two objectives is 

approximately linear across most values of NOX reduction. The tradeoff between NOX reduction 

and noise margin is somewhat richer in features. For values of noise margin between 22 and 30 

dB NOX reduction is almost invariant at 87% approx. In this region fuel burn reduction values 

are high, ranging between 45% and 55%. There is a smooth round tradeoff for noise margin 

values between 30 and 36 dB, where NOX reduction ranges between ~87% and 83%. In this 

region the range of fuel burn reduction values broadens significantly and extends from 20 to 

60%. For values of noise margin between 36 and 37 dB NOX reduction drops sharply and almost 

linearly to approximately 69%. In this region the range of fuel burn reduction values collapses to 

from 20-60% to 15%. 

7.2.4.3 NOX Reduction - Compressor intercooler 

One of the most obvious features of the Pareto frontier is the distinction of two major 

regions, one achieving significantly higher percent reduction in NOX (round markers) over the 

173
second one (triangular markers). Examination of the data reveals that higher NOX reduction is 

attributed to the inclusion of the compressor intercooler, a technology that relaxes the 

compressor temperature limit and enables an increase in pressure ratio for higher fuel efficiency. 

This reduction in compressor airflow temperature propagates downstream and results in a lower 

temperature at the combustor inlet stage (T3), effectively reducing NOX which is positively 

correlated with T3. In addition, the increase in compressor pressure ratio results in a higher value 

of OPR which in turn increases (relaxes) the CAEP/6 NOX emissions limit value. The 

combination of lower NOX amounts and a higher limiting value produces notably greater percent 

reductions. 

7.2.4.4 NOX Reduction through Active Control Compressor 

Technologies 

Another noteworthy feature of the Pareto surface is the clustering of solutions into long 

parallel bands in the region of lower LTO NOX reduction values. These solutions are shown in 

Figure 81 with triangular data points, and cluster differentiation through greyscale contrast. Data 

reveals that two engine technologies, namely active compressor clearance control and active 

compressor flow control, govern the emergence of these clusters. Both technologies increase the 

efficiency of the compressor which results in a reduction of combustor inlet temperature (T3) for 

a given pressure ratio (or alternatively allow for greater pressure increases at more modest 

temperature increases). Lower temperatures are ultimately manifested as lower NOX emissions, 

or conversely as greater percent NOX reductions. The four possible ways in which the two 

technologies can be combined yield the four corresponding clustering bands along the LTO NOX

reduction axis. Absence of both technologies leads to the lower LTO NOX reduction values, 

whereas higher reduction values are achieved with the inclusion of both technologies. The latter 

also features two distinct regions that span the lower and upper half of fuel burn reduction 

values. Data results reveal that this additional segmentation and clustering stems from two 

mutually exclusive subsets of technologies for fuel burn reduction, one clearly less beneficial 

than the other with regards to that objective. Mutually exclusive subsets result from the pairwise 

compatibility rules defined for the complete pool of candidate technologies. 

7.2.4.5 Noise Margin Improvements through Combinations of Engine 

Noise Technologies 

A third noteworthy feature of the optimal set is the series of clusters forming a striated 

pattern on the high fuel burn reduction, high emissions reduction, and low noise margin corner. 

These clusters are most readily noted via greyscale differentiation in the top left corner of the 

plot for Fuel Burn Percent Reduction vs. Noise Margin in Figure 81. Inspection of these 

solutions reveal that discretization along the noise margin objective is produced by specific 

combinations of eight engine noise technologies. Six of them are passive such as noisecancelling liners; two are active technologies featuring flow injection. From left to right clusters 

offer increasing noise margin as more engine noise technologies are added. The first cluster to 

the left does not include any of the eight engine noise technologies. Growing combinations of up 

to five passive technologies result in the subsequent seven solution clusters, all noted with 

greyscale differentiation. For the first four clusters to the left we note relatively consistent fuel 

burn reduction values ranging between 50% and 55% and consistent NOX reduction at 87%. This 

174
behavior is attributed to a fairly consistent set of fuel burn technologies in these clusters. For the 

next four data point clusters identified there is continuing increase in achieved noise margin 

along with a growing range of values fuel burn reduction, particularly for the eight cluster which 

spans between 30% and 60%, that can be attributed to a richer variety of fuel reduction 

technologies. In general each of these eight clusters encompass a two dimensional Pareto frontier 

with NOX reduction approximately fixed at ~87% and a fixed subset of passive engine noise 

technologies where tradeoffs between noise margin and fuel burn reduction occur on the basis of 

what fuel burn technologies are included. 

Beyond the eight identified cluster the trend of increasing noise margin and broader value 

ranges for fuel burn is observed to continue, albeit at the expense of small reductions in NOX. In 

this central tradeoff region of the solution set the presence of numerous technologies governing 

performance on all objectives makes it impossible to discern distinct clusters for noise margin 

improvements. However we are able to broadly identify the progressive inclusion of active 

engine noise technologies beyond this point. Active technologies carry a fuel burn penalty that 

contributes, along with the inclusion and exclusion of many other technologies, to the increasing 

range of observed fuel burn reduction values. 

7.2.4.6 Number of Technologies 

It is pertinent to examine the number of technologies present in solutions of the Pareto 

frontier to determine any relevant correlation between the number of technologies and observed 

levels of performance. A 3-D plot of the Pareto frontier is shown in Figure 82 where darker 

shades of the greyscale denote an increasing number of technologies, ranging from 21 to 50. This 

number of technologies includes distinct applications of the same technology on different vehicle 

components such as advanced materials for different airframe elements. A consistent trend is 

observed with the number of technologies increasing from the noise corner of the trade space 

towards a region close to (but not exactly at) the fuel burn and NOX reduction corner. This 

observation suggests noise margin in isolation is achievable with fewer technologies, whereas 

concurrent good performance of fuel burn and NOX reduction is achievable albeit requiring a 

significant number of technological improvements. This trend is preserved across the two main 

regions of the Pareto frontier distinguished by the presence of the compressor intercooler. The 

technology combinations with the most technologies generally tend to favor engine fuel burn 

technologies, lightweight structures technologies, and airframe aerodynamic technologies that 

have few penalties. 

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Figure 82. Pareto frontier of technology combinations indicating number of technologies present in each 

solution 

7.2.4.7 Gap to N+2 Environmental Performance Goals 

It is also pertinent to examine how solutions in the Pareto frontier approach the N+2 

performance goals. The concurrent achievement of these goals is of particular relevance and 

defines a point in the objective space. The distance between solutions in the Pareto frontier and 

the N+2 goal point characterizes the remaining gap, if any, and more importantly provides a tool 

for the selection of a technology combination on the basis of minimizing this gap. This approach 

is well documented in the literature as the Euclidean distance to the ideal objective vector (see 

for instance Sen, 1998, p 26). 

We estimate the normalized Euclidean distance to the N+2 goal point for all Pareto 

frontier solutions and color them accordingly in greyscale to identify the regions with the 

smallest gap. The 3-D plot is shown in Figure 83 Left. Closer proximity is denoted by darker 

greyscale, with the closest solutions in black. It is worth noting that the region of the Pareto 

frontier closest to the N+2 goal point is not that with the greatest number of technologies as can 

be noted by comparison with Figure 82, and where the compressor intercooler is not applied. 

This observation can be explained in part by the fact that there are many solutions 

exceeding the fuel burn reduction and/or the NOX reduction goal. The calculation of the 

Euclidean distance in the objective space does not assign preference or utility to the relative 

176
location of a point of interest with respect to the reference (goal point). A larger margin above 

goal values contributes to a greater Euclidean distance. If selection is conducted on solely on the 

basis of this Euclidean distance then solutions closer to the goal are preferred over those farther 

away, including those that exceed a subset of goals. This approach is appropriate if indeed 

exceeding performance goal values presents a declining utility. 

We correct this effect by defining the ideal objective vector in the classical formulation, 

namely as the maximum observed value along each individual objective, and then estimating the 

Euclidean distance to that point. The ideal objective vector is presented in Table 93 alongside 

N+2 goals. The 3-D plot of the Pareto frontier colored by Euclidean distance to the ideal 

objective vectors is shown in Figure 83 Right. As can be expected, comparison with Figure 82 

indicates good correlation between proximity to the ideal objective vector and the number of 

technologies. 

Figure 83. Pareto frontier of technology combinations with greyscale indicating Euclidean distance to N+2 

goal vector (Left) and ideal objective vector (Right) 

Table 93. Comparison of N+2 goals and ideal objective vector 

Technology Benefits N+2 Goals Ideal Objective Vector 

Noise 

(cum. Below Stage 4) -42 dB -37 dB 

LTO NOx Emissions 

(below CAEP/6) -75% -87% 

Aircraft Fuel / Energy Consumption 

(rel. to 2005 best in class) -50% -60% 

7.2.4.8 Identification of Enabling Technologies 

The Euclidean distance to a suitable reference vector in the objective space allows for the 

selection of a single technology combination. However, a more robust approach is to examine a 

top-ranking subset of solutions to identify the most frequently occurring technologies. 

Technologies identified in this manner comprise a portfolio that is common within the prescribed 

favorable region of the Pareto frontier and can provide some robustness against small 

uncertainties within this region. To do so we identify the top 30 technologies, or 2% of the 

population size, based on proximity to the N+2 goals and the ideal objective vector respectively, 

177
and identify technologies present in at least 20 of the 30 combinations. Performance statistics for 

each set of 30 solutions are summarized in Table 94. Comparison of the two technology sets 

confirms that solutions near the ideal objective feature a more aggressive use of drag and weight 

reduction technologies that yield notably higher fuel burn and NOX improvements, albeit at more 

modest noise margin. Engine technologies emerge as a common factor conducive to favorable 

tradeoff-optimal performance independent of the reference vector of choice. 

Table 94. Performance statistics for top 30 (2%) solutions based on proximity to N+2 Goal and ideal objective 

Statistic Fuel Burn 

Percent Reduction 

NOX 

Percent Reduction Noise Margin 

N+2 

Mean 48.4 76.3 33.165 

Max 51.53 76.9 33.68 

Min 45.53 75.65 32.56 

Ideal Vector 

Mean 56.86 84.6 30.94 

Max 58.69 85.4 31.64 

Min 55.16 83.9 30.33 

7.2.5 Comparison of Vehicle Concepts’ Technology Tradespace 

with Pareto-Optimal Sets 

7.2.5.1 Formulation and Implemenation of the Comparative 

Assessment 

The identification and analysis of the Pareto-optimal set of solutions for the 300-

passenger Large Twin Aisle tube and wing concept was repeated for several other advanced 

vehicle concepts so as to conduct a comparative assessment. Said comparison subscribes to the 

view that an examination of the Pareto-optimal set reveals very insightful trends on the inherent 

performance tradeoffs between environmental objectives, and quantitatively bounds the 

capabilities of vehicle concept and the technology portfolio. By comparing entire Pareto-optimal 

sets rather than single design points the analysis explicitly accounts for the potentially realizable 

capabilities of the entire technology portfolio as applied to different vehicle concepts, offering a 

broader and more informed perspective. In turn the Pareto-optimal set of any vehicle concept can 

be examined in detail as documented in Section 7.2.4 for the Large Twin Aisle tube and wing 

concept. 

7.2.5.2 Vehicle Concepts 

Eight different vehicles are used in this study as technology collectors to contribute 

toward achieving the environmental performance goals. The conventional tube and wing vehicles 

are a basis of comparison for the other vehicles and are modeled based on what is flown today. 

The large twin aisle tube and wing is based off of similar parameters of the Boeing 777 and GE 

90 engines. The large single aisle tube and wing is a smaller scale of this aircraft having a 150 

passenger capacity. These two vehicles, the LTA and LSA, serve as a means to examine the 

scalability effects at the technology level and how they propagate in the generation of a Pareto 

optimal set. The other six vehicles are vehicles with variations in engine placement, replacement 

of the traditional geared turbofan engine with an open rotor engine, as well as vehicles with 

178
exotic configurations. The conventional tube and wing vehicles including both the large twin 

aisle and large single aisle are configured with a different engine placement, fuselage mounted 

and over the wing nacelle concepts respectively. These engine placements are implemented in 

order to shield fan and jet noise sources. As well as changing the engine placement, an open 

rotor engine is a further alteration to the vehicle combinations, specifically applied to the large 

single aisle and the large twin aisle hybrid wing body. Exchanging the gear turbofan engine with 

an open rotor engine allows for an increase in propulsive efficiency thus cutting fuel 

consumption. One downfall of the open rotor engine is an increase of the overall noise of the 

aircraft, for this reason this engine is studied on the hybrid wing body in order to help mitigate 

the noise increase. The hybrid wing body configuration is also meant as a favorable 

aerodynamics and structural benefits due to good span loading. The inclusion of this 

configuration was inspired by the increasing studies of the hybrid wing body in recent years; 

although these studies gave insight to potential benefits of this aircraft, the configuration of this 

study was not a replica of work from these studies. Another exotic vehicle configuration used as 

a technology collector in this study is the box wing vehicle, this aircraft would offer low induced 

drag and alleged structural superiority, allowing the aircraft to achieve lower fuel consumption 

(Caja, 2012). Lockheed Martin as well as others have patents out for the study of this particular 

vehicle concept motivating the inclusion of this vehicle configuration in this study. 

Figure 84. Vehicle concepts for Pareto-Optimal set comparison 

7.2.5.3 Basis for Comparative Assessment of Pareto-Optimal Sets 

A number of Pareto-optimal set characteristics are identified as the basis for the 

comparative assessment. The span of the Pareto set in the objective space offers a measure of the 

variability in performance along each environmental impact objective attributed to the 

technologies in the portfolio as applied to a vehicle concept. The location of the corners of the 

Pareto set in the objective space provide another basis for comparison in terms of extreme 

solutions providing the best achievable performance along any one objective. It helps compare 

concepts in terms of what is the best that can be achieved along each objective individually. The 

span of the Pareto set and location of the corners collectively inform whether solutions are able 

to meet or exceed individual N+2 goals. 

179
The ‘central’ region of a Pareto set where optimal solutions offer a more balanced 

compromise relative to all objectives is typically of greater interest. In this application it is 

especially relevant because N+2 goals are defined to be met concurrently. These considerations 

are carried as a basis for comparison by means of the normalized Euclidean distance to the N+2 

goal point in the objective space. This distance reflects how close any solution is to meeting all 

N+2 goals. 

Examination of the Pareto surface also includes a characterization of clusters and features 

and ensuing analysis to identify underlying technology effects. In general clusters arise due to 

the discrete nature of the allowable combinatorial space for technologies manifested in the 

continuous objective space. In addition, technology incompatibilities yield larger discontinuities 

as mutually exclusive subsets of technologies are included or excluded from solutions as if they 

were a single option. The clusters are identified first by use of visual inspection and then verified 

by the use of hierarchical clustering with distance-based criteria such as average and centroid. By 

identifying technologies or technology combinations that cause the clusters or features of the 

Pareto frontier we know what is required to achieve the values of objectives that are represented 

in those features. 

The numbers of technologies are evaluated for each combination within the Pareto set. 

The points where the greatest and least amounts of technologies are taken note of and then any 

visible trends are identified and examined against other features of the Pareto surface. The 

amount of technologies within a set can be considered less attractive than a smaller subset of 

technologies due to the increase in cost. Although this is not a direct correlation it is still an 

important gauge of which set of technologies should be investigated more to invest in. 

7.2.5.4 Comparative Assessment for Vehicle Concepts 

A superposition of Pareto sets for all vehicle concepts is shown in Figure 85. In general, 

the variability observed for any objective, or tradeoffs between objectives, within a Pareto set 

can be exclusively attributed to technology effects. On the other hand the difference observed 

between Pareto sets along any objective can be exclusively attributed to vehicle concept 

configuration. Pareto set span values are summarized in Table 95. 

It is readily evident that, in general, variability in noise margin capabilities attributed to 

vehicle concept configuration are significantly greater than those attributable to technology 

impacts. Vehicle concepts featuring engine noise shielding, namely the HWB LTA, MFN LTA, 

and OWN LSA, have “flatter” Pareto frontiers along the noise margin axis and are located on 

higher noise margin values. This indicates that vehicle concept noise shielding features have a 

dominant effect on noise margin and yield the most benefits. It further suggests that noise 

shielding effects overwhelm, and therefore diminish, the sensitivity to technology effects which 

are typically of a much more moderate order of magnitude. This trend is less accentuated for the 

OWN LSA, suggesting that these effects and configuration benefits don’t scale down favorably. 

This may be explained in part by the fact that noise margin is reference to takeoff gross weight, 

and that for LTA vehicle the greater gross weight relaxes the noise limit and allows for greater 

margins. Effects of a greater engine noise source for LTA vehicles may not be important given 

the shielding effects. For aircraft without noise shielding features Pareto frontiers are less flat 

and more curved along the noise margin axis, suggesting a greater sensitivity to noise technology 

effects relative to vehicles with noise shielding. They are also located at lower noise margin 

values. The most extreme case is clearly the LSA UDF where there is no shielding, and the 

intense noise source of the UDF engine governs the relatively poor noise performance of the 

180
concept. The LTA HWB UDF concept presents an interesting compromise, showing noise 

margin values comparable to the LTA, LSA, and LTA BW. 

For the fuel burn reduction and NOx reduction objectives significant variability can be 

attributed to the engine cycle/architecture, namely GTF or UDF. For concepts with the same 

engine variability is attributed to technology effects and airframe concept. These trends are most 

easily visualized as a superposition of the convex hull of all Pareto frontiers along the two 

objectives in question, as shown in Figure 86. It is readily evident that concepts with UDF 

feature distinctively lower values of NOX % reduction relative to concepts with the GF, although 

the upper bound of fuel burn reduction values are comparable for the two engine concepts. For 

concepts with the GF or UDF, separately, the effect of vehicle concept is most readily observed 

as modest differences in the range of values for these two objectives. For concepts with the GF 

engine tt can be noted that all vehicles have roughly the same bounds and span of values for NOx 

reduction and fuel burn reduction. We note however that the LTA HWB features the greatest fuel 

burn reduction maxima and, more notably, greatest minima, altogether suggesting the best fuel 

burn values on average. The LSA concepts consistently show lesser fuel burn reduction maxima. 

Figure 85. Superposition of Pareto sets for vehicle concepts in the objective space 

181
Figure 86. Superposition of vehicle concept Pareto set convex hull projections in NOX percent reduction vs 

fuel burn percent reduction 

For all vehicles key technologies were identified as responsible for some of the most 

prominent clustering features observed in the Pareto frontier. Pareto set ranges and clustering 

technologies for all vehicle concepts are presented in Table 95. Corresponding 3D isometric 

scatter plots of Pareto frontiers for all vehicle concepts, with data points grey-scale coded as 

noted in Table 95, are shown in Figure 87 for concepts with the GF, and in Figure 88 for 

concepts with the UDF. It is obvious that there are similar technologies that govern the main 

features/clusters of each vehicle, including the compressor intercooler, blade tone control, fan 

vertical acoustic splitter, and the electro mechanical flight control actuators. The compressor 

intercooler, as stated previously, impacts the NOX percent reduction by effectively lowering the 

temperature at the combustor inlet (T3). The blade tone control technology was also previously 

mentioned in the study of the noise corner of the large twin aisle. This technology reduces the 

vortices that propagated downstream which cause tonal noise as a result of pressure perturbations 

on stators and duct acoustic modes, thus improving the noise margin values. 

Table 95. Pareto set ranges and clustering technologies for vehicle concepts 

Vehicle FB Range NOx Range Noise Margin 

Range

Cluster Technologies 

LTA 15-53% 81-90% 27-41dB Black = Compressor Intercooler OFF 

Blade Tone Control via Trailing Edge Blowing ON 

Light Gray = Compressor Intercooler ON 

Blade Tone Control via Trailing Edge Blowing ON 

Med. Gray = Compressor Intercooler ON 

Blade Tone Control via Trailing Edge Blowing OFF

LTA-MFN 19-53% 82-90% 39-48dB Black = Compressor Intercooler ON

182
Light Gray = Compressor Intercooler OFF

LTA-BW 21-45% 79-90% 32-38dB Black = Compressor Intercooler OFF 

Blade Tone Control via Trailing Edge Blowing OFF 

Fan Vertical Acoustic Splitter ON 

Light Gray = Compressor Intercooler OFF 

Blade Tone Control via Trailing Edge Blowing ON 

Fan Vertical Acoustic Splitter OFF 

Mid gray = Compressor Intercooler ON 

Blade Tone Control via Trailing Edge Blowing ON 

Fan Vertical Acoustic Splitter OFF 

Dark Gray = Compressor Intercooler ON 

Blade Tone Control via Trailing Edge Blowing OFF 

Fan Vertical Acoustic Splitter OFF

LTA-HWB 33-58% 80-90% 45-51dB Black = Compressor Intercooler ON 

Light Gray = Compressor Intercooler OFF

LSA 20-49% 80-89% 33-39dB Black = Compressor Intercooler OFF 

Blade Tone Control via Trailing Edge Blowing OFF 

Fan Vertical Acoustic Splitter ON 

Light Gray = Compressor Intercooler OFF 

Blade Tone Control via Trailing Edge Blowing ON 

Fan Vertical Acoustic Splitter OFF 

Mid gray = Compressor Intercooler ON 

Blade Tone Control via Trailing Edge Blowing ON 

Fan Vertical Acoustic Splitter OFF 

Dark Gray = Compressor Intercooler ON 

Blade Tone Control via Trailing Edge Blowing OFF 

Fan Vertical Acoustic Splitter OFF

LSA-OWN 29-50% 83-89% 37-43dB Black = Compressor Intercooler OFF 

Blade Tone Control via Trailing Edge Blowing ON 

Fan Vertical Acoustic Splitter OFF 

Light Gray = Compressor Intercooler OFF 

Blade Tone Control via Trailing Edge Blowing OFF 

Fan Vertical Acoustic Splitter OFF 

Mid Gray = Compressor Intercooler ON 

Blade Tone Control via Trailing Edge Blowing ON 

Fan Vertical Acoustic Splitter OFF 

Dark Gray = Compressor Intercooler ON 

Blade Tone Control via Trailing Edge Blowing OFF 

Fan Vertical Acoustic Splitter OFF

LSA-UDF 36-54% 72-75% 14-19dB Black = Electro Mechanical OFF 

Light Gray = Electro Mechanical ON

183
LTA-HWB-UDF 44-60% 70-76% 33-39dB Black = DRE for HLFC – Wing OFF 

Mid Gray = DRE for HLFC ON 

HLFC Suction – Wing OFF 

Light Gray = All other points

184
Figure 87. Pareto set clustering associated with identified influencing technologies - GF 

185
Figure 88. Pareto set clustering associated with identified influencing technologies - UDF 

The number of technologies present in each of the points of the Pareto front is also 

examined. Corresponding 3D isometric scatter plots of Pareto frontiers for all vehicle concepts, 

with data points grey-scale coded to indicate a smaller (darker data points) or larger (lighter data 

points) number of technologies are shown in Figure 89 for concepts with the GF, and in Figure 

90 for concepts with the UDF. Comparative assessment of all Pareto fronts indicate the same 

general trend for all vehicles where there is an increase in number of technologies with proximity 

to fuel burn and NOX corner. 

186
Figure 89. Trends in the number of technologies per solution in Pareto optimal sets - GF 

187
Figure 90. Trends in the number of technologies per solution in Pareto optimal sets - UDF 

Broad impact critical technologies are identified as those that appear in all solutions of 

the Pareto set for all vehicle concepts. There is a total of sixteen technologies meeting this 

criterion. They are presented in Table 96, where they are organized by technology impact area. 

Results suggest the importance of engine fuel burn and noise technologies areas. 

Table 96. Broad impact critical technologies appearing in all Pareto-optimal solutions for all vehicle concepts 

Vehicle Set Impact Area Amt. of Techs Representative Technologies

All 8 Vehicles 

Engine Fuel Burn 5 

Advanced TBC Coatings - HPT Blade

Advanced TBC Coatings - LPT Blade

Polymer Matrix Composites (PMC) - Nacelles

PMC Fan Blade with Metal Leading Edge

Highly Loaded LP Turbine

Engine Noise 2 Combustor Noise Plug Liner

Active Pylons Shaping/Blowing

Engine Emission Tech 1 RQL Combustor 

2010 Baseline Techs 2 Zero Splice Inlet 

Excrescence Reduction

Other 2 Blisk 

Ti-Al - LPT Aft Blades 

GTF Only 

Engine Fuel Burn 3 

Polymer Matrix Composites (PMC) - Fan Case

Polymer Matrix Composites (PMC) - Fan 

Stator 

Polymer Matrix Composites (PMC) - Bypass 

Duct 

Engine Noise 3 

Aft Cowl Liners 

Compound Rotor Sweep for UHB Fan (GTF)

Variable Area Nozzle - GTF

2010 Baseline Techs 1 Fixed Geometry Core Chevrons

UDF Only Engine Fuel Burn 2 Active Turbine Clearance ControlActive Compressor Clearance Control

Engine Noise 1 Lip Liner 

188
7.3 Technology Portfolio Performance Comparison to ERA 

Goals 

Figure 91. LSA T&W Technology Portfolio Performance 

189
Figure 92. LSA OWN Technology Portfolio Performance 

190
Figure 93. LSA OR Technology Portfolio Performance 

191
Figure 94. LTA T&W Technology Portfolio Performance 

192
Figure 95. LTA MFN Technology Portfolio Performance 

193
Figure 96. LTA BW Technology Portfolio Performance 

194
Figure 97. LTA HWB Technology Portfolio Performance 

195
Figure 98. LTA HWB OR Technology Portfolio Performance 

196
7.4 Comparison of ERA Technologies to Goal 

Figure 99. ERA Comparison to Fuel Burn Goal 

197
Figure 100. ERA Comparison to Noise Margin Goal 

198
Figure 101. ERA Comparison to Emissions Goal 

199
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206
9.0 Appendices 

9.1 Appendix A: EDS Surrogates Response Names, 

Descriptions, and Units 

1. desBlockFuel – Mission Block Fuel (lbs) 

2. Cruise_SFC_Min – Specific Fuel Consumption at Cruise (lbm/(hr-lbf)) 

3. Approach – Noise Level (dB) 

4. Cutback – Noise Level (dB) 

5. Sideline – Noise Level (dB) 

6. TOAll_TOFL – All Engines Operating Takeoff Field Length (feet) 

7. TOOEO_TOFL – One Engine Operating Takeoff Field Length (feet) 

8. AbortALL_TOFL – Abort All Engines Operating Takeoff Field Length (feet) 

9. AbortOEO_TOFL – Abort One Engine Operating Takeoff Field Length (feet) 

10. desFAR_LDGFL – Design Landing Field Length (feet) 

11. desTOGW – TakeOff Gross Weight (lbs) 

12. Fuse_Weight – Fuselage Weight (lbs) 

13. OEW – Operating Empty Weight (lbs) 

14. VT_Weight – Vertical Tail Weight (lbs) 

15. Wing_Weight – Wing Weight (lbs) 

16. HT_Weight – Horizontal Tail Weight (lbs) 

17. SLS_UI_OPR – Overall Pressure Ratio at Sea Level Static 

18. T3max – Maximum Compressor Exit Temperature (R) 

19. ADP_BPR – Fan Bypass Ratio at Aerodynamic Design Point 

20. HPC_diam – High Pressure Compressor Diameter (in) 

21. HPCLastStageWc – Compressor Exit Corrected Airflow (lbm/sec) 

22. HPCLastStgBladeHeight – Compressor Last Stage Blade Height (in) 

23. EI_NOx_AP – Approach Advanced Combustor Emissions (grams of NOx/kN of Thrust) 

24. EI_NOx_CO – Cutout Advanced Combustor Emissions (grams of NOx/kN of Thrust) 

25. EI_NOx_ID – Idle Advanced Combustor Emissions (grams of NOx/kN of Thrust) 

26. EI_NOx_TO – Takeoff Advanced Combustor Emissions (grams of NOx/kN of Thrust) 

27. EI_Standard_AP – Approach Standard Combustor Emissions (grams of NOx/kN of 

Thrust) 

28. EI_Standard_CO – Cutout Standard Combustor Emissions (grams of NOx/kN of Thrust) 

29. EI_Standard_ID– Idle Standard Combustor Emissions (grams of NOx/kN of Thrust) 

30. EI_Standard_TO – Takeoff Standard Combustor Emissions (grams of NOx/kN of Thrust) 

31. ICAO_AP_Tt3 – Approach Total Temperature Compressor Exit (R) 

32. ICAO_AP_Wfuel – Approach Fuel Flow (lbs) 

33. ICAO_CO_Tt3 – Cutout Total Temperature Compressor Exit (R) 

34. ICAO_CO_Wfuel – Cutout Fuel Flow (lbs) 

35. ICAO_ID_Tt3 – IdleTotal Temperature Compressor Exit (R) 

36. ICAO_ID_Wfuel – Idle Fuel Flow (lbs) 

37. ICAO_TO_Tt3 – Takeoff Total Temperature Compressor Exit (R) 

38. ICAO_TO_Wfuel – Takeoff Fuel Flow (lbs) 

39. LPC_diam – Low Pressure Compressor Diameter (in) 

40. Nacelle_Diam/Dnac – Nacelle Diameter (in) 

207
41. enginePodLength/Xnac – Nacelle Length (in) 

42. engPodWt/WENG – Engine Weight (lbs) 

43. diamFan/Prop Diam – Engine Fan Diameter (in) 

44. ICAO_ID_Equiv – Combustor Equivalence Ratio at Idle Power Setting 

45. ICAO_AP_Equiv – Combustor Equivalence Ratio at Approach Power Setting 

9.2 Appendix B: EDS Vehicle Surrogate Fit Statistics 

9.2.1 LSA Vehicle Fit Statistics 

Surrogate Name MFE Mean 

Absolute Value 

MFE 

Standard 

Deviation

MRE Mean 

Absolute Value 

MRE 

Standard 

Deviation

AbortALL_TOFL 0.0039 0.5021 0.0050 0.4803 

AbortOEO_TOFL 0.0035 0.6063 0.0662 0.5798 

ADP_BPR 0.0125 0.9964 0.5573 1.1981 

Approach 0.0007 0.1200 0.0345 0.1179 

Cruise_SFC_Min 0.0005 0.3158 0.1872 0.3550 

Cutback 0.0004 0.2259 0.0976 0.2143 

desBlockFuel 0.0017 0.4146 0.1600 0.3930 

desFAR_LDGFL 0.0001 0.0690 0.0142 0.0756 

desTOGW 0.0017 0.2788 0.0989 0.2955 

diamFan 0.0005 0.1805 0.0675 0.1661 

EI_NOx_AP 0.0000 0.0361 0.0045 0.0640 

EI_NOx_CO 0.0009 0.1011 0.0053 0.1337 

EI_NOx_ID_Lower 0.0000 0.0132 0.0021 0.0169 

EI_NOx_TO 0.0021 0.1042 0.0154 0.1581 

EI_Standard_AP 0.0003 0.2696 0.0113 0.3338 

EI_Standard_CO 0.0012 0.3689 0.0455 0.3470 

EI_Standard_ID_Lower 0.0002 0.0283 0.0018 0.0618 

EI_Standard_TO 0.0053 0.4426 0.0481 0.5815 

enginePodLength 0.0015 0.8031 0.1933 0.9658 

engPodWt 0.0066 0.7299 0.2351 0.7348 

Fuse_Weight 0.0000 0.0024 0.0000 0.0024 

HPC_diam 0.0039 0.5659 0.1289 0.5850 

HPCLastStageWc 0.0062 0.8302 0.1027 0.8413 

HPCLastStgBladeHeight 0.0062 0.7743 0.0916 0.7945 

HT_Weight 0.0003 0.0732 0.0065 0.0751 

ICAO_AP_Equiv 0.0001 0.3430 0.0329 0.3470 

ICAO_AP_Tt3 0.0001 0.1714 0.0572 0.1621 

ICAO_AP_Wfuel 0.0096 0.8275 0.0670 0.8427 

ICAO_CO_Tt3 0.0032 0.2244 0.0081 0.2034 

ICAO_CO_Wfuel 0.0034 0.7566 0.3267 0.7832 

208
ICAO_ID_Equiv 0.0044 0.5205 0.1784 0.6224 

ICAO_ID_Tt3 0.0146 0.2142 0.0290 0.2028 

ICAO_ID_Wfuel 0.0000 0.0014 0.0002 0.0013 

ICAO_TO_Tt3 0.0002 0.2581 0.0375 0.2483 

ICAO_TO_Wfuel 0.0174 0.8641 0.2587 0.8288 

LPC_diam 0.0070 0.4697 0.2229 0.5383 

Nacelle_Diam 0.0008 0.1698 0.0498 0.1587 

OEW 0.0003 0.1465 0.0454 0.1296 

Sideline 0.0072 0.2270 0.0891 0.2203 

SLS_UI_OPR 0.0033 0.2425 0.1456 0.2749 

T3max 0.0005 0.2962 0.0459 0.3201 

TOAll_TOFL 0.0033 0.5291 0.0550 0.5432 

TOOEO_TOFL 0.0061 0.7702 0.2924 0.9496 

VT_Weight 0.0040 0.0544 0.0138 0.0561 

Wing_Weight 0.0002 0.2065 0.0904 0.1575 

209
9.2.2 LSA OWN Vehicle Fit Statistics 

Surrogate Name MFE Mean 

Absolute Value 

MFE 

Standard 

Deviation

MRE Mean 

Absolute Value 

MRE 

Standard 

Deviation

AbortALL_TOFL 0.0008 0.2422 -0.1723 0.4205 

AbortOEO_TOFL 0.0010 0.2518 -0.0066 0.3515 

ADP_BPR 0.0086 1.0002 -0.3500 0.9809 

Approach 0.0001 0.0897 0.0013 0.0820 

Cruise_SFC_Min 0.0000 0.0018 0.0004 0.0017 

Cutback 0.0004 0.1961 -0.0280 0.1555 

desBlockFuel 0.0010 0.3189 0.1762 0.3406 

desFAR_LDGFL 0.0001 0.0701 -0.0382 0.0724 

desTOGW 0.0008 0.2820 0.0681 0.2836 

diamFan 0.0003 0.1834 -0.0436 0.1583 

EI_NOx_AP 0.0011 0.0375 0.0029 0.0638 

EI_NOx_CO 0.0000 0.1429 0.0089 0.1218 

EI_NOx_ID_Lower -0.0010 0.0103 -0.0030 0.0176 

EI_NOx_TO -0.0002 0.1369 0.0222 0.1431 

EI_Standard_AP -0.0018 0.2075 0.0107 0.3005 

EI_Standard_CO 0.0194 0.3954 -0.0113 0.4307 

EI_Standard_ID_Lower 0.0000 0.0245 0.0068 0.0404 

EI_Standard_TO 0.0140 0.3272 0.0276 0.2933 

enginePodLength 0.0032 0.5512 0.1845 0.5707 

engPodWt 0.0039 0.6894 -0.1859 0.7734 

Fuse_Weight 0.0000 0.0023 0.0007 0.0024 

HPC_diam 0.0025 0.5356 0.2401 0.5525 

HPCLastStageWc 0.0205 0.9236 0.2423 0.9183 

HPCLastStgBladeHeight 0.0056 0.7580 0.2518 0.7909 

HT_Weight 0.0011 0.0290 0.0049 0.0280 

ICAO_AP_Equiv 0.0018 0.3647 0.0112 0.3670 

ICAO_AP_Tt3 0.0000 0.1166 0.0427 0.1301 

ICAO_AP_Wfuel 0.0020 0.7215 0.1551 0.6939 

ICAO_CO_Tt3 0.0004 0.1956 0.0148 0.1612 

ICAO_CO_Wfuel 0.0026 0.5853 0.1392 0.6243 

ICAO_ID_Equiv -0.0029 0.5087 0.0154 0.5361 

ICAO_ID_Tt3 0.0003 0.2067 -0.0281 0.1948 

ICAO_ID_Wfuel 0.0000 0.0014 0.0000 0.0013 

ICAO_TO_Tt3 0.0035 0.2369 0.0323 0.2253 

ICAO_TO_Wfuel 0.0032 0.6077 0.1291 0.6630 

LPC_diam 0.0032 0.5722 0.1170 0.5408 

Nacelle_Diam 0.0003 0.1512 -0.0715 0.1368 

OEW -0.0001 0.1357 -0.0730 0.1479 

Sideline 0.0005 0.2103 -0.0173 0.1675 

210
SLS_UI_OPR 0.0004 0.2078 0.1550 0.2405 

T3max 0.0007 0.2735 0.0335 0.2825 

TOAll_TOFL 0.0001 0.2148 0.0805 0.2223 

TOOEO_TOFL 0.0018 0.4961 -0.0325 0.6508 

VT_Weight 0.0001 0.0332 -0.0016 0.0336 

Wing_Weight -0.0002 0.0434 0.0246 0.0421 

 

211
9.2.4 LTA Vehicle Fit Statistics 

Surrogate Name MFE Mean 

Absolute Value 

MFE 

Standard 

Deviation

MRE Mean 

Absolute Value 

MRE 

Standard 

Deviation

AbortALL_TOFL 0.0018 0.4733 -0.0017 0.7474 

AbortOEO_TOFL -0.0020 0.6664 -0.0855 0.4888 

ADP_BPR 0.0072 0.6449 -0.2170 0.6574 

Approach -0.0003 0.1941 -0.0245 0.2334 

Cruise_SFC_Min -0.0043 0.2728 -0.0060 0.3443 

Cutback 0.0000 0.1680 -0.0506 0.1510 

desBlockFuel 0.0017 0.2271 0.0091 0.2768 

desFAR_LDGFL 0.0003 0.0709 -0.0010 0.0861 

desTOGW 0.0016 0.3400 -0.0113 0.5202 

diamFan 0.0003 0.1827 0.0005 0.2039 

EI_NOx_AP -0.0105 0.0847 -0.0178 0.1460 

EI_NOx_CO -0.0001 0.1145 -0.0318 0.1332 

EI_NOx_ID_Lower 0.0000 0.0081 -0.0012 0.0239 

EI_NOx_TO 0.0000 0.1968 -0.0053 0.3086 

EI_Standard_AP 0.0000 0.2820 -0.0214 0.3691 

EI_Standard_CO -0.0002 0.2028 0.0115 0.3753 

EI_Standard_ID_Lower 0.0004 0.0280 -0.0012 0.0841 

EI_Standard_TO 0.0021 0.7861 0.0246 1.4084 

enginePodLength 0.0028 0.5179 -0.0164 0.5985 

engPodWt 0.0083 0.7006 0.0283 0.6921 

Fuse_Weight 0.0000 0.0032 0.0001 0.0035 

HPC_diam 0.0197 0.4070 -0.0137 0.5386 

HPCLastStageWc 0.0051 0.7806 0.3272 0.8166 

HPCLastStgBladeHeight 0.0011 0.4340 -0.0005 0.5857 

HT_Weight 0.0004 0.0180 0.0001 0.0174 

ICAO_AP_Equiv 0.0000 0.0034 -0.0003 0.0030 

ICAO_AP_Tt3 0.0005 0.1454 0.0424 0.1491 

ICAO_AP_Wfuel 0.0013 0.5416 -0.0112 0.7381 

ICAO_CO_Tt3 0.0082 0.1942 -0.0557 0.2115 

ICAO_CO_Wfuel 0.0015 0.4544 -0.0112 0.5955 

ICAO_ID_Equiv 0.0000 0.0023 0.0004 0.0025 

ICAO_ID_Tt3 0.0003 0.1554 -0.0026 0.1632 

ICAO_ID_Wfuel 0.0000 0.0029 0.0001 0.0041 

ICAO_TO_Tt3 0.0008 0.2176 -0.0078 0.3486 

ICAO_TO_Wfuel 0.0105 0.4570 0.0123 0.6304 

LPC_diam -0.0002 0.3550 0.0135 0.4704 

Nacelle_Diam 0.0003 0.1574 -0.0018 0.1750 

OEW 0.0004 0.1299 0.0003 0.1566 

Sideline 0.0003 0.1785 -0.0667 0.2003 

212
SLS_UI_OPR 0.0003 0.2177 0.0001 0.2556 

T3max 0.0008 0.2867 0.0112 0.3380 

TOAll_TOFL 0.0032 0.5623 -0.0187 0.5562 

TOOEO_TOFL 0.0141 1.1926 -0.2561 1.0912 

VT_Weight 0.0000 0.0450 -0.0005 0.0552 

Wing_Weight 0.0037 0.0800 0.0042 0.1010 

 

213
9.2.6 LTA MFN Vehicle Fit Statistics 

Surrogate Name MFE Mean 

Absolute Value 

MFE 

Standard 

Deviation

MRE Mean 

Absolute Value 

MRE 

Standard 

Deviation

AbortALL_TOFL 0.0002 0.2245 -0.0509 0.2180 

AbortOEO_TOFL 0.0002 0.2245 0.0509 0.2180 

ADP_BPR 0.0016 0.1254 0.0385 0.1266 

Approach 0.0004 0.0741 0.0096 0.0752 

Cruise_SFC_Min 0.0005 0.2311 0.1913 0.2782 

Cutback 0.0008 0.2095 0.0955 0.1948 

desBlockFuel 0.0001 0.1614 0.0338 0.1735 

desFAR_LDGFL 0.0000 0.0930 0.0674 0.1247 

desTOGW 0.0008 0.3118 0.1861 0.3795 

diamFan 0.0000 0.2422 0.0983 0.1947 

EI_NOx_AP 0.0000 0.1155 0.0102 0.1303 

EI_NOx_CO 0.0002 0.1274 0.0237 0.1249 

EI_NOx_ID_Lower 0.0000 0.0137 0.0007 0.0130 

EI_NOx_TO 0.0002 0.1993 0.0457 0.2119 

EI_Standard_AP 0.0008 0.3022 0.0068 0.3976 

EI_Standard_CO 0.0058 0.5305 0.1699 0.5065 

EI_Standard_ID_Lower 0.0004 0.0360 0.0018 0.0440 

EI_Standard_TO 0.0654 0.9952 0.0511 1.0463 

enginePodLength 0.0055 0.5356 0.0633 0.5325 

engPodWt 0.0083 0.7482 0.0047 0.7489 

Fuse_Weight 0.0000 0.0055 0.0000 0.0006 

HPC_diam 0.0045 0.5780 0.1185 0.5568 

HPCLastStageWc 0.0353 0.8323 0.3571 0.8585 

HPCLastStgBladeHeight 0.0000 0.0033 0.0013 0.0035 

HT_Weight 0.0009 0.0382 0.0139 0.0380 

ICAO_AP_Equiv 0.0000 0.3657 0.1721 0.3679 

ICAO_AP_Tt3 0.0011 0.1426 0.0143 0.1416 

ICAO_AP_Wfuel 0.0038 0.5110 0.3720 0.6275 

ICAO_CO_Tt3 0.0001 0.2852 0.1149 0.3164 

ICAO_CO_Wfuel 0.0015 0.3863 0.0926 0.3796 

ICAO_ID_Equiv 0.0004 0.5292 0.1385 0.5101 

ICAO_ID_Tt3 0.0002 0.1809 0.0095 0.1545 

ICAO_ID_Wfuel 0.0000 0.0030 0.0015 0.0033 

ICAO_TO_Tt3 0.0013 0.2894 0.0621 0.2817 

ICAO_TO_Wfuel 0.0000 0.0173 0.0049 0.0165 

LPC_diam 0.0022 0.4978 0.2985 0.4885 

Nacelle_Diam 0.0003 0.1742 0.0524 0.1241 

OEW 0.0002 0.1465 0.0553 0.1462 

Sideline 0.0007 0.1601 0.0135 0.1373 

214
SLS_UI_OPR 0.0002 0.2048 0.1074 0.1976 

T3max 0.0007 0.2446 0.0470 0.2212 

TOAll_TOFL 0.0009 0.2955 0.0143 0.2664 

TOOEO_TOFL 0.0008 0.2765 0.0069 0.2883 

VT_Weight 0.0000 0.0075 0.0004 0.0070 

Wing_Weight 0.0008 0.0537 0.0076 0.0386 

 

215
9.2.8 LTA Box Wing Vehicle Fit Statistics 

Surrogate Name MFE Mean 

Absolute Value 

MFE 

Standard 

Deviation

MRE Mean 

Absolute Value 

MRE 

Standard 

Deviation

AbortALL_TOFL 0.0012 0.1404 0.0237 0.1757 

AbortOEO_TOFL 0.0004 0.1430 0.0276 0.1824 

ADP_BPR 0.0054 0.7662 0.1977 0.6837 

Approach 0.0009 0.0919 0.0055 0.0826 

Cruise_SFC_Min 0.0005 0.2020 0.1482 0.2261 

Cutback 0.0006 0.1733 0.0334 0.1550 

desBlockFuel 0.0008 0.1317 0.0106 0.1363 

desFAR_LDGFL 0.0019 0.0625 0.0187 0.0659 

desTOGW 0.0015 0.2545 0.0323 0.2576 

diamFan 0.0002 0.2148 0.0373 0.2143 

EI_NOx_AP 0.0000 0.0237 0.0002 0.0211 

EI_NOx_CO 0.0001 0.0965 0.0276 0.1137 

EI_NOx_ID_Lower 0.0000 0.0099 0.0011 0.0103 

EI_NOx_TO 0.0004 0.1752 0.0355 0.2454 

EI_Standard_AP 0.0006 0.2625 0.0533 0.4021 

EI_Standard_CO 0.0052 0.4016 0.1440 0.4960 

EI_Standard_ID_Lower 0.0000 0.0401 0.0069 0.0454 

EI_Standard_TO 0.0018 0.4818 0.0411 0.6993 

enginePodLength 0.0080 0.8844 0.1105 0.9318 

engPodWt 0.0057 0.8479 0.1361 0.8661 

Fuse_Weight 0.0000 0.0005 0.0001 0.0005 

HPC_diam 0.0022 0.4462 0.1857 0.4641 

HPCLastStageWc 0.0066 0.8362 0.3448 0.8733 

HPCLastStgBladeHeight 0.0001 0.5002 0.2480 0.5292 

HT_Weight N/A N/A N/A N/A 

ICAO_AP_Equiv 0.0000 0.0037 0.0012 0.0035 

ICAO_AP_Tt3 0.0003 0.1616 0.0070 0.1312 

ICAO_AP_Wfuel 0.0168 0.6157 0.2911 0.6744 

ICAO_CO_Tt3 0.0005 0.2364 0.0941 0.2445 

ICAO_CO_Wfuel 0.0018 0.5221 0.0525 0.5452 

ICAO_ID_Equiv 0.0000 0.0029 0.0007 0.0028 

ICAO_ID_Tt3 0.0007 0.1906 0.0544 0.1700 

ICAO_ID_Wfuel 0.0001 0.0036 0.0013 0.0037 

ICAO_TO_Tt3 0.0009 0.2973 0.0475 0.2672 

ICAO_TO_Wfuel 0.0005 0.5245 0.2049 0.5673 

LPC_diam 0.0033 0.3937 0.1062 0.3766 

Nacelle_Diam 0.0013 0.1946 0.0018 0.1852 

OEW 0.0012 0.1272 0.0365 0.1092 

Sideline 0.0004 0.1970 0.0241 0.1799 

216
SLS_UI_OPR 0.0072 0.2385 0.1653 0.2568 

T3max 0.0020 0.3187 0.0665 0.2835 

TOAll_TOFL 0.0003 0.1611 0.0047 0.2105 

TOOEO_TOFL 0.0004 0.1875 0.0192 0.2310 

VT_Weight 0.0000 0.0068 0.0003 0.0062 

Wing_Weight 0.0000 0.0136 0.0012 0.0109 

 

217
9.2.10 LTA HWB Vehicle Fit Statistics 

Surrogate Name MFE Mean 

Absolute Value 

MFE 

Standard 

Deviation

MRE Mean 

Absolute Value 

MRE 

Standard 

Deviation

AbortALL_TOFL 0.0023 0.4251 0.1429 0.6336 

AbortOEO_TOFL 0.0024 0.4813 0.1633 0.6370 

ADP_BPR 0.0047 0.6824 0.3029 0.6797 

Approach-FixedValue(82.9dB) 0.5700 0.1793 N/A N/A 

Cruise_SFC_Min 0.0005 0.2162 0.2138 0.2611 

Cutback 0.0016 0.1355 0.0384 0.1417 

desBlockFuel 0.0007 0.1377 0.0898 0.1640 

desFAR_LDGFL 0.0000 0.0747 0.0033 0.0768 

desTOGW 0.0011 0.3317 0.1747 0.4662 

diamFan 0.0007 0.2578 0.0806 0.1979 

EI_NOx_AP 0.0011 0.0428 0.0077 0.1648 

EI_NOx_CO 0.0006 0.1327 0.0699 0.1253 

EI_NOx_ID_Lower 0.0002 0.0115 0.0019 0.0109 

EI_NOx_TO 0.0025 0.2244 0.0084 0.2461 

EI_Standard_AP 0.0047 0.2969 0.0442 0.4482 

EI_Standard_CO 0.0006 0.4200 0.2273 0.3948 

EI_Standard_ID_Lower 0.0000 0.0330 0.0118 0.0397 

EI_Standard_TO 0.0028 0.7138 0.0042 0.7677 

enginePodLength 0.0001 0.5556 0.1103 0.5753 

engPodWt 0.0012 0.9696 0.1535 0.8765 

Fuse_Weight 0.0000 0.0028 0.0000 0.0018 

HPC_diam 0.0025 0.5903 0.1175 0.6442 

HPCLastStageWc 0.0057 0.8626 0.4918 0.9862 

HPCLastStgBladeHeight 0.0030 0.4817 0.0808 0.4675 

HT_Weight N/A N/A N/A N/A 

ICAO_AP_Equiv 0.0002 0.3707 0.0702 0.3743 

ICAO_AP_Tt3 0.0000 0.1302 0.0059 0.1067 

ICAO_AP_Wfuel 0.0073 0.5170 0.1771 0.5478 

ICAO_CO_Tt3 0.0104 0.2597 0.1545 0.2643 

ICAO_CO_Wfuel 0.0009 0.3891 0.0278 0.3733 

ICAO_ID_Equiv 0.0002 0.4681 0.0846 0.4810 

ICAO_ID_Tt3 0.0006 0.1859 0.0304 0.1539 

ICAO_ID_Wfuel 0.0000 0.0025 0.0013 0.0027 

ICAO_TO_Tt3 0.0025 0.2758 0.0636 0.2756 

ICAO_TO_Wfuel 0.0015 0.3698 0.1435 0.3761 

LPC_diam 0.0027 0.5281 0.2807 0.5061 

Nacelle_Diam 0.0095 0.1690 0.0219 0.1195 

OEW 0.0000 0.1060 0.0324 0.1053 

Sideline 0.0015 0.1899 0.1012 0.2005 

218
SLS_UI_OPR 0.0004 0.2422 0.2086 0.2816 

T3max 0.0006 0.2329 0.0255 0.2215 

TOAll_TOFL 0.0020 0.4520 0.0897 0.5403 

TOOEO_TOFL 0.0110 0.4097 0.0832 0.5141 

VT_Weight N/A N/A N/A N/A 

Wing_Weight 0.0000 0.0251 0.0030 0.0249 

219
9.2.11 LSA UDF (Open Rotor) Vehicle Fit Statistics 

Surrogate Name MFE Mean 

Absolute Value 

MFE 

Standard 

Deviation

MRE Mean 

Absolute Value 

MRE 

Standard 

Deviation

AbortALL_TOFL 0.0033 0.1387 -0.0586 0.2474 

AbortOEO_TOFL -0.0003 0.1456 -0.0629 0.2701 

Approach -0.0007 0.2489 -0.0867 0.2535 

Cruise_SFC_Min 0.0034 0.1251 -0.0115 0.1342 

Cutback -0.0003 0.1424 0.0395 0.1566 

desBlockFuel 0.0015 0.3436 0.0620 0.3425 

desFAR_LDGFL 0.0001 0.0723 -0.0747 0.1501 

desTOGW -0.0002 0.2061 0.0278 0.2630 

EI_NOx_AP_lower -0.0001 0.0115 -0.0013 0.0142 

EI_NOx_CO 0.0000 0.1047 0.0263 0.1659 

EI_NOx_ID 0.0000 0.0087 -0.0002 0.0079 

EI_NOx_TO 0.0008 0.0768 0.0567 0.1773 

EI_Standard_AP_lower 0.0000 0.0105 -0.0002 0.0132 

EI_Standard_CO -0.0009 0.1158 -0.0817 0.1668 

EI_Standard_ID -0.0001 0.0083 -0.0096 0.0142 

EI_Standard_TO -0.0002 0.0852 0.1008 0.1993 

Fuse_Weight 0.0000 0.0018 -0.0162 0.0183 

HPC_diam 0.0013 0.3604 0.1240 0.4116 

HPCLastStageWc 0.0041 0.4799 0.2590 0.5359 

HPCLastStgBladeHeight 0.0097 0.5979 0.0521 0.7588 

HT_Weight -0.0003 0.0879 -0.0151 0.1539 

ICAO_AP_Equiv 0.0089 0.3471 0.2448 0.5310 

ICAO_AP_Tt3 0.0001 0.0713 0.0925 0.1301 

ICAO_AP_Wfuel -0.0088 0.4992 -0.0013 0.6078 

ICAO_CO_Tt3 0.0004 0.0529 0.0470 0.0828 

ICAO_CO_Wfuel -0.0161 0.3535 -0.0340 0.3358 

ICAO_ID_Equiv -0.0008 0.3934 0.1784 0.5017 

ICAO_ID_Tt3 -0.0012 0.1143 0.0749 0.1254 

ICAO_ID_Wfuel 0.0053 0.6466 -0.0036 0.6909 

ICAO_TO_Tt3 -0.0011 0.0583 0.0012 0.0810 

ICAO_TO_Wfuel 0.0010 0.3800 0.0215 0.3816 

LPC_diam 0.0010 0.3102 -0.0379 0.3481 

OEW 0.0002 0.1230 -0.2116 0.2643 

Sideline -0.0004 0.1325 -0.0033 0.1270 

SLS_UI_OPR 0.0002 0.1122 0.0635 0.1720 

T3max 0.0000 0.0663 0.0111 0.0583 

TOAll_TOFL 0.0006 0.1611 -0.0152 0.2702 

TOOEO_TOFL 0.0003 0.1459 -0.0123 0.2563 

VT_Weight -0.0005 0.0307 -0.0036 0.0321 

220
Wing_Weight -0.0002 0.0586 -0.0286 0.0818 

WENG 0.0088 0.6824 0.0352 0.7752 

UDF_Prop_Diam 0.0001 0.1044 0.0307 0.1047 

Xnac 0.0170 0.6333 0.1130 0.6990 

Dnac 0.0001 0.1192 0.0025 0.1176 

9.2.12 LTA 

221
9.2.13 HWB UDF (Open Rotor) Vehicle Fit Statistics 

Surrogate Name MFE Mean 

Absolute Value 

MFE 

Standard 

Deviation

MRE Mean 

Absolute Value 

MRE 

Standard 

Deviation

AbortALL_TOFL 0.0002 0.1520 0.0138 0.2150 

AbortOEO_TOFL 0.0021 0.1691 0.0134 0.2215 

Approach-FixedValue(84.8dB) 1.2250 0.5224 N/A N/A 

Cruise_SFC_Min 0.0001 0.1094 0.0034 0.1117 

Cutback 0.0004 0.1802 0.0076 0.2146 

desBlockFuel 0.0007 0.1354 0.0114 0.1341 

desFAR_LDGFL 0.0001 0.0720 0.0041 0.0747 

desTOGW 0.0007 0.2306 0.0619 0.2797 

EI_NOx_AP_lower 0.0000 0.0608 0.0000 0.0680 

EI_NOx_CO 0.0040 0.5174 0.0280 0.6106 

EI_NOx_ID 0.0008 0.1381 0.0324 0.1675 

EI_NOx_TO 0.0007 0.2263 0.0445 0.2348 

EI_Standard_AP_lower 0.0000 0.0684 0.0065 0.0790 

EI_Standard_CO 0.0048 0.5030 0.0379 0.5729 

EI_Standard_ID 0.0005 0.1235 0.0219 0.1392 

EI_Standard_TO 0.0002 0.1901 0.0581 0.2410 

Fuse_Weight 0.0000 0.0011 0.0000 0.0009 

HPC_diam 0.0100 0.3977 0.1040 0.4482 

HPCLastStageWc 0.0066 0.7028 0.1248 0.7483 

HPCLastStgBladeHeight 0.0015 0.3437 0.0266 0.3541 

HT_Weight N/A N/A N/A N/A 

ICAO_AP_Equiv 0.0003 0.1184 0.0145 0.1789 

ICAO_AP_Tt3 0.0002 0.0484 0.0113 0.0855 

ICAO_AP_Wfuel 0.0000 0.0051 0.0016 0.0094 

ICAO_CO_Tt3 0.0000 0.0509 0.0073 0.0689 

ICAO_CO_Wfuel 0.0003 0.0130 0.0034 0.0191 

ICAO_ID_Equiv 0.0008 0.1963 0.0543 0.2408 

ICAO_ID_Tt3 0.0001 0.0647 0.0154 0.0770 

ICAO_ID_Wfuel 0.0000 0.0019 0.0004 0.0028 

ICAO_TO_Tt3 0.0000 0.0713 0.0038 0.0853 

ICAO_TO_Wfuel 0.0000 0.0121 0.0021 0.0170 

LPC_diam 0.0051 0.3298 0.0351 0.3422 

OEW 0.0003 0.1147 0.0110 0.0987 

Sideline 0.0014 0.1424 0.0049 0.1971 

SLS_UI_OPR 0.0001 0.3270 0.0146 0.4663 

T3max 0.0010 0.1520 0.0065 0.1877 

TOAll_TOFL 0.0002 0.1965 0.0252 0.2452 

TOOEO_TOFL 0.0002 0.1849 0.0216 0.2309 

VT_Weight N/A N/A N/A N/A 

222
Wing_Weight 0.0000 0.0308 0.0031 0.0291 

WENG 0.0043 0.6629 0.0309 0.7074 

UDF_Prop_Diam 0.0021 0.2607 0.0151 0.2728 

Xnac 0.0110 0.8683 0.1427 1.0590 

Dnac 0.0009 0.2602 0.0066 0.2767 

223
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Application of Deterministic and Probabilistic System Design Methods and 

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