NASA/CR–2015-218704/Volume II

Subsonic Ultra Green Aircraft Research:

Phase II – Volume II – Hybrid Electric Design 

Exploration 

Marty K. Bradley and Christopher K. Droney 

Boeing Research and Technology, Huntington Beach, California 

 

April 2015
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NASA/CR–2015-218704/Volume II

Subsonic Ultra Green Aircraft Research:

Phase II – Volume II Hybrid Electric Design 

Exploration 

Marty K. Bradley and Christopher K. Droney 

Boeing Research and Technology, Huntington Beach, California 

National Aeronautics and 

Space Administration

Langley Research Center Prepared for Langley Research Center

Hampton, Virginia 23681-2199 under Contract NNL08AA16B

April 2015
Available from: 

NASA STI Program / Mail Stop 148 

NASA Langley Research Center 

Hampton, VA 23681-2199 

Fax: 757-864-6500 

The use of trademarks or names of manufacturers in this report is for accu rate reporting and does not constitute an 

official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics and 

Space Administration. 
NASA Contract NNL08AA16B – NNL11AA00T – Subsonic Ultra Green Aircraft Research – Phase II

Volume II – Hybrid Electric Design Exploration

Abstract

This Final Report summarizes the hybrid electric concept design, analysis, technology, and 

modeling work accomplished by the Boeing Subsonic Ultra Green Aircraft Research (SUGAR) 

team. The time period of the work was February 2012 through June 2014. This includes work 

that was part of Task 2.2 (Hybrid Electric Concept Studies and Exploration) and Task 3.3 (Hybrid 

Electric Modeling Environment). The team consisted of Boeing Research and Technology, 

Boeing Commercial Airplanes, General Electric, and Georgia Tech. 

A variety of performance and sizing tasks were conducted including looking at hybrid electric 

versions of a conventional tube-and-wing aircraft and a hybrid wing body. Trends and results 

were similar to the high wing Truss Braced Wing (TBW) SUGAR Volt aircraft. The SUGAR Volt 

was updated based on results from the TBW work in Task 2.1 (documented in a separate 

report) and new engine performance models from team member GE. 

Energy cost and acoustic analyses were conducted and technology roadmaps were updated for 

hybrid electric and battery technology. NOx emissions were provided by GE for landing and 

takeoff (LTO) and cruise.

Georgia Tech developed detailed NPSS models for hybrid electric components and tested the

modeling environment with an integrated analysis of superconducting and nonsuperconducting hybrid electric engines.

The hybrid electric SUGAR Volt was shown to produce significant additional emissions and fuel 

burn reductions beyond those levels achieved by the conventionally powered SUGAR High. The 

SUGAR Volt was able to meet the NASA goals for fuel burn. Total energy utilization was not 

decreased but reduced energy cost can be achieved for some cost scenarios. The team was not 

able to identify a technology development path to meet the noise goals established by NASA.

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Volume II – Hybrid Electric Design Exploration

Acknowledgements

This project and report reflect the combined efforts of the SUGAR team. The team members are 

Boeing Research and Technology, Boeing Commercial Airplanes, GE Aviation, and the Georgia 

Institute of Technology. The coordinated effort of this team has produced this final report. In 

addition to the two primary authors from Boeing, a partial list of writers that contributed to this 

report includes Ronen Elkoby from Boeing, Jeff Hamel from GE, and Chris Perullo from Georgia 

Tech.

Funding for this effort is provided by the NASA Fundamental Aeronautics Program, through the 

Subsonic Fixed Wing organization. The contract number is NNL08AA16B task order 

NNL11AA00T. 

The team would like to thank Erik Olson of the NASA Langley Research Center for his guidance 

as the NASA Contracting Officer Technical Representative (COTR), and the task technical 

advisors (TA’s): Gerry Brown and Jim Felder. Gerry Brown and Jim Felder were important 

subject matter experts who contributed significantly to the team’s efforts.

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Volume II – Hybrid Electric Design Exploration

Table of Contents

Abstract............................................................................................................................................ i

Acknowledgements..........................................................................................................................ii

Table of Contents............................................................................................................................iii

List of Tables and Figures................................................................................................................. v

Tables........................................................................................................................................... v

Figures.........................................................................................................................................vi

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

2.0 Hybrid Electric Vehicle Concept Study................................................................................. 4

2.1 Configuration Basis, Design Trades, and Evolution.......................................................... 4

2.1.1 Hybrid Electric propulsion for a HWB and Conventional Tube and Wing ................ 6

2.1.2 Propulsion System Development.............................................................................. 8

2.2 Configuration Description .............................................................................................. 18

2.2.1 Integration .............................................................................................................. 19

2.2.2 Battery Pods............................................................................................................ 21

2.2.3 Propulsion ............................................................................................................... 22

2.3 Configuration Analysis and Final Performance.............................................................. 24

2.3.1 Aerodynamics ......................................................................................................... 24

2.3.2 Mass Properties...................................................................................................... 25

2.3.3 Performance and Sizing .......................................................................................... 25

2.3.4 Acoustic Analysis..................................................................................................... 38

2.3.5 hFan+2: Emissions................................................................................................... 56

2.3.6 Energy Cost Analysis............................................................................................... 56

2.4 Technology Plans and Roadmaps................................................................................... 63

2.4.1 Hybrid Electric Engine............................................................................................. 63

2.4.2 High Performance Batteries.................................................................................... 68

3.0 Hybrid Electric Propulsion Modeling Environment & Sizing Studies................................. 72

3.1 Hybrid Electric Modeling Environment.......................................................................... 72

3.1.1 Electric Model ......................................................................................................... 73

3.1.2 Fuel Cell Model ....................................................................................................... 88

3.1.3 Battery Model ....................................................................................................... 105

3.1.4 Cryocooler Model ................................................................................................. 113

3.1.5 Generating High Speed Propeller Performance Map........................................... 136

3.2 Hybrid Electric Sizing Studies....................................................................................... 171

3.2.1 Modeling Methodology ........................................................................................ 171

3.2.2 Gas Turbine Modeling Considerations.................................................................. 172

3.2.3 Verification of the NPSS Hybrid Electric Model to GE Public Domain Data ......... 176

3.2.4 Hybrid Electric Engine Sizing Studies.................................................................... 178

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4.0 Summary .......................................................................................................................... 187

4.1 Performance and Sizing Analysis.................................................................................. 187

4.2 Emissions Analysis........................................................................................................ 187

4.3 Noise analysis............................................................................................................... 187

4.4 Energy cost analysis ..................................................................................................... 187

4.5 Technology Plans and Roadmaps................................................................................. 188

4.6 Hybrid Electric Propulsion Modeling Environment...................................................... 188

4.7 Conclusions................................................................................................................... 188

4.8 Recommendations ....................................................................................................... 188

References .................................................................................................................................. 189

Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets......................... A

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List of Tables and Figures

Tables

Table 2.1 – hFan+ Key Characteristics .......................................................................................... 14

Table 2.2 – hFan+ LTO NOx Emissions.......................................................................................... 14

Table 2.3 – 765-095 Revision History ........................................................................................... 19

Table 2.4 – Principal Characteristics............................................................................................. 21

Table 2.5 - hFan+2 Key Characteristics......................................................................................... 24

Table 2.6 – Battery Aero Characteristics ...................................................................................... 24

Table 2.7 – 765-096-RA Group Weight Statement....................................................................... 25

Table 2.8 – 765-096 Rev A Mission Performance Comparison .................................................... 30

Table 2.9 – 765-096 Rev A Core Shutdown Trade ........................................................................ 31

Table 2.10 – Flight Performance Parameters for Noise Analysis ................................................. 40

Table 2.11 – Main Gear & Nose Gear Noise Prediction Input Parameters .................................. 45

Table 2.12 – Slat Noise Prediction Input Parameters................................................................... 46

Table 2.13 – Flap Noise Prediction Input Parameters at Approach ............................................. 48

Table 2.14 – Flap Noise Prediction Input Parameters at Cutback ................................................ 48

Table 2.15 – Flap Noise Prediction Input Parameters at Sideline ................................................ 48

Table 2.16 – GE Noise Modeling Assumptions for the SUGAR HE Volt Engine ............................ 49

Table 2.17 – Parametric Noise Reduction & Stage 4 CUM Margins for SUGAR High................... 54

Table 2.18 – Parametric Noise Reduction & Stage 4 CUM Margins for SUGAR Volt ................... 54

Table 2.19 – Potential Airframe Noise Reduction Technology..................................................... 55

Table 2.20 – Potential Engine Noise Reduction Technology ........................................................ 55

Table 2.21 – 750 Balanced and Core Shutdown NOx Emissions .................................................. 56

Table 2.22 – Cost Assumptions..................................................................................................... 58

Table 2.23 – Cost Results for Nominal Cost Assumptions............................................................ 58

Table 2.24 – Cost Results for More Optimistic Battery Assumptions........................................... 60

Table 2.25 – Cost Results for Most Optimistic Battery Assumptions........................................... 61

Table 3.1 – NPSS HTS Motor Validation........................................................................................ 80

Table 3.2 – Parameter Estimates.................................................................................................. 82

Table 3.3 – Validation data used for pressure variation tests...................................................... 99

Table 3.4 – Validation inputs for composition variation tests.................................................... 100

Table 3.5 – Inputs used for improved matching of composition tests....................................... 101

Table 3.6 – Inputs for temperature variation validation ............................................................ 103

Table 3.7 – Trend line parameters for YSZ resistances............................................................... 105

Table 3.8 – Necessary values from discharge curve................................................................... 108

Table 3.9 – Validation Inputs...................................................................................................... 111

Table 3.10 – Comparison of integration results ......................................................................... 112

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Table 3.11 – Sizing test inputs and results.................................................................................. 113

Table 3.12 – Thermodynamic Cycle Summary............................................................................ 114

Table 3.13 – NPSS Model Independents and Dependents......................................................... 119

Table 3.14 – Comparison NPSS inputs to Source Inputs First Validation ................................... 122

Table 3.15 – Comparison NPSS inputs to Source Inputs Second Validation .............................. 124

Table 3.16 – NPSS Off Design Dependent and Independent Setup ........................................... 126

Table 3.17 – F7A7 Design and Operational Variable Values....................................................... 138

Table 3.18 – Hamilton – Standard Performance Maps Design Variable Ranges........................ 153

Table 3.19 – Hamilton – Standard Performance Maps Selected for F7A7 Performance 

Estimation ........................................................................................................... 157

Table 3.20 – Conventional Power Code Definitions................................................................... 173

Table 3.21 – Conventional NPSS Power Management Scheme ................................................. 173

Table 3.22 – Electric Power Code Definitions............................................................................. 174

Table 3.23 – Georgia Tech hFan Calibration Point ..................................................................... 176

Table 3.24 – NPSS Hybrid Electric Weight Assumptions............................................................. 178

Table 3.25 – Core Sensitivity to Sizing With Motor (60 OPR, 2500R T41, FPR 1.45).................. 182

Table 3.26 – All Fuel Penalties for Smaller Core......................................................................... 185

Figures

Figure 1.1 – SUGAR Phase II Tasks and Groupings ......................................................................... 2

Figure 1.2 – SUGAR Phase II Team Structure.................................................................................. 3

Figure 2.1 – Hybrid Electric Task Flow ............................................................................................ 5

Figure 2.2 – SUGAR Hybrid Electric Concepts................................................................................. 6

Figure 2.3 – Hybrid Electric Tube and Wing Sizing Results............................................................. 7

Figure 2.4 – Hybrid Electric Aircraft Thrust Requirements ............................................................. 8

Figure 2.5 – Booster Stall Margin with Electric Fan Power Augmentation .................................... 9

Figure 2.6 – Fan Speed with Electric Power Augmentation ......................................................... 10

Figure 2.7 – Specific Fuel Consumption with Electric Power Augmentation ............................... 11

Figure 2.8 – SUGAR Volt Aircraft Sensitivities .............................................................................. 12

Figure 2.9 – Hybrid Electric hFan+ Architecture........................................................................... 13

Figure 2.10 – Hybrid Electric Architecture with Conventional Electric Technology..................... 15

Figure 2.11 – Conventional 8,000HP Electric System Efficiency Map .......................................... 16

Figure 2.12 – Hybrid Electric Architecture with Superconducting Electric Technology............... 17

Figure 2.13 - Sample Trajectories for Continuous Descent and Descent Regeneration .............. 17

Figure 2.14 – SUGAR Volt (765-096-RevA) General Arrangement............................................... 20

Figure 2.15 – 750 Balanced Thrust Requirements........................................................................ 23

Figure 2.16 – 750 Core Shutdown Thrust Requirements ............................................................. 23

Figure 2.17 - 765-096-RA 1380 HP sizing Plot............................................................................... 28

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Figure 2.18 - 765-096-RA 1750 HP sizing Plot............................................................................... 28

Figure 2.19 - 765-096-RA 7150 HP sizing Plot............................................................................... 29

Figure 2.20 - Economic Mission Fuel and Energy Reduction........................................................ 32

Figure 2.21 - Payload v. Range Chart for Hybrid Electric Aircraft................................................. 33

Figure 2.22 - Fuel Burn per Segment for Hybrid Electric Aircraft................................................. 34

Figure 2.23 - Energy Use per Segment for Hybrid Airplanes........................................................ 34

Figure 2.24 - Specific Range (Energy) per Segment for Hybrid Airplanes .................................... 35

Figure 2.25 - Fuel Burn v. Range for Hybrid Aircraft..................................................................... 36

Figure 2.26 – Energy v. Range for Hybrid Aircraft ........................................................................ 37

Figure 2.27 – Battery Weight v. Range for Hybrid Aircraft........................................................... 37

Figure 2.28 – Modes of operation for hybrid airplanes................................................................ 38

Figure 2.29 – Fleet Fuel and Energy Reduction as compared to NASA Goal................................ 38

Figure 2.30 – Noise Levels & Stage 4 Margins for the SUGAR High & Volt .................................. 39

Figure 2.31 – FAR 36 Noise Profile................................................................................................ 40

Figure 2.32 – Overall Noise Analysis Process................................................................................ 41

Figure 2.33 – SUGAR Geometry at Approach ............................................................................... 42

Figure 2.34 – CFD++ Solution Convergence.................................................................................. 43

Figure 2.35 – CFD++ Computed Mach Cuts.................................................................................. 43

Figure 2.36 – Post-Processing of CFD++ Data for Computing Sectional Lift Coefficient.............. 44

Figure 2.37 – Airframe Noise Analysis Process............................................................................. 45

Figure 2.38 – SUGAR Slat System Deployment for Approach/Cutback/Sideline ......................... 46

Figure 2.39 – SUGAR Slat System 3-Element Definition for Noise Analysis................................. 46

Figure 2.40 – SUGAR Flap System Deployment at Approach ....................................................... 47

Figure 2.41 – SUGAR Flap System Deployment at Cutback/Sideline ........................................... 47

Figure 2.42 – SUGAR Flap System Definition for Noise Analysis.................................................. 47

Figure 2.43 – GE Noise-Power-Distance (NPD) Predictions for Hardwall Engine......................... 49

Figure 2.44 – Process for Derivation of SUGAR HE Treated Engine Noise ................................... 50

Figure 2.45 – PNLT Contribution at AP from All Subcomponents for SUGAR Volt....................... 51

Figure 2.46 – PNLT Contribution at CB from All Subcomponents for SUGAR Volt....................... 52

Figure 2.47 – PNLT Contribution at SL from All Subcomponents for SUGAR Volt........................ 52

Figure 2.48 – Comparison of Total Engine and Airframe Noise for the SUGAR Volt.................... 53

Figure 2.49 – Energy + Battery Cost Results for Nominal Assumptions....................................... 59

Figure 2.50 – Total Cost Results for Nominal Assumptions.......................................................... 59

Figure 2.51 – Energy + Battery Cost Results for More Optimistic Battery Assumptions ............. 60

Figure 2.52 – Total Cost Results for More Optimistic Battery Assumptions................................ 61

Figure 2.53 – Energy + Battery Cost Results for Most Optimistic Battery Assumptions.............. 62

Figure 2.54 – Total Cost Results for Most Optimistic Battery Assumptions................................. 62

Figure 2.55 – Hybrid Electric Engine Technologies*..................................................................... 67

Figure 2.56 – High Performance Battery Technology Roadmap .................................................. 71

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Figure 3.1 – Half Wave Rectifier ................................................................................................... 76

Figure 3.2 – Solver layout diagram ............................................................................................... 78

Figure 3.3 – Motor Loss Buildup ................................................................................................... 83

Figure 3.4 – SRM Torque-Speed Characteristic ............................................................................ 84

Figure 3.5 – Motor Off-Design Operation..................................................................................... 88

Figure 3.6 – Diagram of SOFC ....................................................................................................... 91

Figure 3.7 – SOFC Architectures ................................................................................................... 92

Figure 3.8 – Formation of the polarization curve......................................................................... 93

Figure 3.9 – Comparison of model pressure test output with output from (24)......................... 99

Figure 3.10 – Comparison of model composition test outputs with results from (24).............. 101

Figure 3.11 – Model output from improved composition matching with results from (24)...... 101

Figure 3.12 – Initial comparison of model output with experimental data from (23)............... 104

Figure 3.13 – Comparison of improved matching output with experimental data (23)............ 104

Figure 3.14 – Arrhenius plot of YSZ resistances.......................................................................... 105

Figure 3.15 – Example battery discharge curve.......................................................................... 108

Figure 3.16 – Data flow for Sizing algorithm............................................................................... 111

Figure 3.17 – Battery performance model validation plots........................................................ 112

Figure 3.18 – Power Capacity vs Specific Mass Cryocooler Survey ............................................ 115

Figure 3.19 – Power vs Mass for Cryocooler Types.................................................................... 115

Figure 3.20 – Power Capacity vs Percent Carnot for Cryocooler Types..................................... 116

Figure 3.21 – Closed Circuit Reverse Turbo Brayton Cycle......................................................... 117

Figure 3.22 – Reverse Turbo Brayton Diagram (from Creare Contract #NAS5-31281).............. 118

Figure 3.23 – NPSS Block Schematic of Reverse Turbo Brayton Cycle ....................................... 119

Figure 3.24 – setTotalSP workaround using Secant Solver ........................................................ 121

Figure 3.25 – NPSS Model Comparison to Actual Data First Validation..................................... 122

Figure 3.26 – First Validation H-S Diagram................................................................................. 123

Figure 3.27 – NPSS Model Comparison to Actual Data Second Validation ................................ 125

Figure 3.28 – Second Validation H-S Diagram ............................................................................ 125

Figure 3.29 – Rejection Temperature ......................................................................................... 127

Figure 3.30 – Heat Rejection Load.............................................................................................. 127

Figure 3.31 – HPC Power............................................................................................................. 128

Figure 3.32 – HPT Power............................................................................................................. 128

Figure 3.33 – FS_2 Temperature................................................................................................. 129

Figure 3.34 – FS_2 Pressure........................................................................................................ 129

Figure 3.35 – FS_2 Massflow ...................................................................................................... 129

Figure 3.36 – FS_3 Temperature................................................................................................. 130

Figure 3.37 – FS_3 Pressure........................................................................................................ 130

Figure 3.38 – FS_3 Massflow ...................................................................................................... 130

Figure 3.39 – FS_5 Temperature................................................................................................. 131

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Figure 3.40 – FS_5 Pressure........................................................................................................ 131

Figure 3.41 – FS_5 Massflow ...................................................................................................... 131

Figure 3.42 – FS_6 Temperature................................................................................................. 132

Figure 3.43 – FS_6 Pressure........................................................................................................ 132

Figure 3.44 – FS_6 Massflow ...................................................................................................... 132

Figure 3.45 – FS_7 Temperature................................................................................................. 133

Figure 3.46 – FS_7 Pressure........................................................................................................ 133

Figure 3.47 – FS_7 Massflow ...................................................................................................... 133

Figure 3.48 – RC_4 Temperature................................................................................................ 134

Figure 3.49 – RC_4 Pressure ....................................................................................................... 134

Figure 3.50 – RC_4 Massflow...................................................................................................... 134

Figure 3.51 – HX_1 at HEX .......................................................................................................... 135

Figure 3.52 – CX_1 at CEX ........................................................................................................... 135

Figure 3.53 – CX_3 Heat Absorption........................................................................................... 135

Figure 3.54 – HX_1 Across HEX ................................................................................................... 135

Figure 3.55 – CX_1 Across CEX.................................................................................................... 136

Figure 3.56 – Turbine Pressure Ratio.......................................................................................... 136

Figure 3.57 – Compressor Pressure Ratio................................................................................... 136

Figure 3.58 – F7A7 CPQA vs. Advance Ratio Map......................................................................... 138

Figure 3.59 – F7A7 Efficiency vs. CPQA / J3 Map........................................................................... 139

Figure 3.60 – F7A7 CPQA vs. Advance Ratio for 2 ≤ J ≤ 3.............................................................. 140

Figure 3.61 – F7A7 Efficiency vs. CPQA / J3 for 2 ≤ J ≤ 3 ............................................................... 140

Figure 3.62 – Constant Speed Propeller Operation.................................................................... 142

Figure 3.63 – NACA Method Correction for Blade Number and Geometry (39)........................ 143

Figure 3.64 – NACA Method Compressibility Correction (39).................................................... 144

Figure 3.65 – NACA Method Counter-Rotation Correction (39)................................................. 145

Figure 3.66 – SR-1 Peak Efficiency Points based on Pitch Angle ................................................ 147

Figure 3.67 – SR-1 Peak Efficiency Points based on Advance Ratio ........................................... 147

Figure 3.68 – Repopulated Compressibility Correction Chart based on SR-1 Data.................... 148

Figure 3.69 – Blade Geometry Correction based on SR-1 Data.................................................. 149

Figure 3.70 – Validation Test Efficiency vs. Advance Ratio for the Adjusted NACA Method 

at Mach 0.2 ......................................................................................................... 150

Figure 3.71 – Validation Test Efficiency Error for the Adjusted NACA Method at M=0.2.......... 150

Figure 3.72 – Validation Test Efficiency vs. Advance Ratio for the Adjusted NACA Method 

at M=0.6.............................................................................................................. 151

Figure 3.73 – Validation Test Efficiency Percent Error for the Adjusted NACA Method at 

M=0.6.................................................................................................................. 151

Figure 3.74 – Validation Test Efficiency vs. Advance Ratio for the Adjusted NACA Method 

at M=0.75............................................................................................................ 152

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Figure 3.75 – Validation Test Efficiency Percent Error for the Adjusted NACA Method at 

M=0.75................................................................................................................ 152

Figure 3.76 – Sample Hamilton – Standard Performance Map (40)........................................... 154

Figure 3.77 – Hamilton – Standard General Compressibility Correction (40)............................ 155

Figure 3.78 – Hamilton – Standard Mach Number Adjustment for Blade Camber (40) ............ 156

Figure 3.79 – Interpolated CPQA vs. Advance Ratio Map for F7A7.............................................. 158

Figure 3.80 – Interpolated Efficiency vs. Advance Ratio Map for F7A7 ..................................... 158

Figure 3.81 – Output Generation Steps for the Validation Test................................................. 159

Figure 3.82 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 55.7° ........................... 160

Figure 3.83 – Validation Test Efficiency Percent Error at a Pitch Angle of 55.7°........................ 160

Figure 3.84 – CPQA vs. J Validation Test at a Pitch Angle of 55.7° ............................................... 161

Figure 3.85 – Validation Test CPQA Percent Error at a Pitch Angle of 55.7° ................................ 161

Figure 3.86 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 56.9° ........................... 162

Figure 3.87 – Validation Test Efficiency Percent Error at a Pitch Angle of 56.9°........................ 162

Figure 3.88 – CPQA vs. Advance Ratio Validation Test at a Pitch Angle of 56.9° ......................... 163

Figure 3.89 – Validation Test CPQA Percent Error at a Pitch Angle of 56.9° ................................ 163

Figure 3.90 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 58.5° ........................... 164

Figure 3.91 – Validation Test Efficiency Percent Error at a Pitch Angle of 58.5°........................ 165

Figure 3.92 – CPQA vs. Advance Ratio Validation Test at a Pitch Angle of 58.5° ......................... 165

Figure 3.93 – Validation Test CPQA Percent Error at a Pitch Angle of 58.5° ................................ 166

Figure 3.94 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 60.2° ........................... 166

Figure 3.95 – Validation Test Efficiency Percent Error at a Pitch Angle of 60.2°........................ 167

Figure 3.96 – CPQA vs. Advance Ratio Validation Test at a Pitch Angle of 60.2° ......................... 167

Figure 3.97 – Validation Test CPQA Percent Error at a Pitch Angle of 60.2° ................................ 168

Figure 3.98 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 61.2° ........................... 169

Figure 3.99 – Validation Test Efficiency Percent Error at a Pitch Angle of 61.2°........................ 169

Figure 3.100 – CPQA vs. Advance Ratio Validation Test at a Pitch Angle of 61.2° ....................... 170

Figure 3.101 – Validation Test CPQA Percent Efficiency at a Pitch Angle of 61.2°....................... 170

Figure 3.102 – Hybrid Electric Engine Architecture (53)............................................................. 172

Figure 3.103 – GE hFan Powerhook............................................................................................ 176

Figure 3.104 – Georgia Tech Hybrid Electric Trends................................................................... 177

Figure 3.105 – Georgia Tech Average Performance................................................................... 177

Figure 3.106 – Motor Sized Non-Cryogenic Fan Sizing Breakdown............................................ 179

Figure 3.107 – Cryogenic Cycle Trades....................................................................................... 180

Figure 3.108 – Cryogenic System Sizing...................................................................................... 181

Figure 3.109 – FPR Electric and Sizing Trades............................................................................. 182

Figure 3.110 – OPR / T41 Trade Space........................................................................................ 183

Figure 3.111 – OPR/T41 Electric and Sizing Trades .................................................................... 184

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1.0 Introduction

In 2009-2010, Boeing conducted the Subsonic Ultra Green Aircraft Research (SUGAR) study for 

NASA. In this study, Boeing identified and analyzed advanced concepts and technologies for 

aircraft that would fly in the 2030-2035 timeframe. Large possible improvements in fuel burn, 

emissions, and noise were identified and roadmaps developed for key technologies. Specific 

recommendations were made in the Boeing Phase I Final Report(1): 

• Conduct additional design and analysis of hybrid electric gas turbine propulsion

• Conduct a comprehensive study of high aspect ratio truss braced wings

• Consider additional noise technologies (partially addressed as part of hybrid electric 

tasks) 

• Conduct a follow-on study to consider the synergistic benefits of methane and/or 

hydrogen fuel

Considering the recommendations of Boeing and the other contractor teams, as well as 

program objectives, NASA developed these Research Objectives for Phase II: 

• Experimental and Higher-Fidelity Exploration of Key Technologies to investigate the 

prioritized technology challenges identified in Phase I and begin moving toward the 

realization of the proposed vehicle concept(s) capabilities that would enable an entry 

into service (EIS) in the 2030-2035 timeframe, market permitting

• N+3 Advanced Vehicle Concept Study to further explore, refine, and otherwise update 

the preferred N+3 advanced vehicle and component concept(s) identified and 

developed during Phase I

• N+4 Advanced Vehicle Concept Study to leverage the substantial investment of Phase I

and study the effect of additional technology development time beyond that assumed in 

Phase I

Boeing structured the SUGAR Phase II program to address the recommendations from Phase I

as well as the research objectives provided by NASA. Three major tasks, corresponding to the 

Research Objectives, are included in this Phase II effort. Subtasks are organized and grouped by 

technical area (Figure 1.1). 

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Figure 1.1 – SUGAR Phase II Tasks and Groupings

The SUGAR team from Phase I consisted of Boeing Research and Technology, Boeing 

Commercial Airplanes, General Electric, and Georgia Tech. In Phase II (depicted in Figure 1.2), 

the team was expanded to include Virginia Tech, NextGen Aeronautics, and Microcraft to 

enhance the technical capability and to build hardware to support the testing of the trussbraced wing tasks. 

SUGAR Phase II

N+4 Advanced Concept Study

Truss Braced Wing

SUGAR Phase I

Year 1 Year 2 Year 3

Truss Braced Wing Concept Refinement & Higher Fidelity Exploration (Task 2.1)

Truss Braced Wing Experimental Validation (Task 3.1)

Advanced Aerodynamic Technology Experiment (Task 3.2)

N+4 Concept Study (Task 1.0)

Task 1 Task 2 Task 3

Hybrid Electric Propulsion

Hybrid Electric Concept Refinement & Higher Fidelity Exploration (Task 2.2)

Hybrid Electric Sim. (Task 3.3)

Management

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Figure 1.2 – SUGAR Phase II Team Structure

This is the Final Report for Tasks 2.2 and 3.3 only. Task 1 is fully documented in the Task 1 final 

report (2) and Truss Braced Wing Tasks 2.1, 3.1, and 3.2 are fully documented in a separate 

Final Report (Volume I – Truss Braced Wing Design Exploration) and Test Report (Volume III – 

Truss Braced Wing Aeroelastic Test Report). In 2012, additional resources were allocated to 

Task 3.3 to allow more integrated hybrid electric modeling by Georgia Tech and those results 

are documented in the report. 

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Volume II – Hybrid Electric Design Exploration

2.0 Hybrid Electric Vehicle Concept Study

As part of SUGAR Phase II, hybrid electric vehicles were analyzed. Due to the iterative nature of 

airplane development, trades are performed at various stages of development. For clarity, this 

document breaks the configuration development into three sections.

Section 2.1 – Configuration Basis, Design Trades, and Evolution, focuses on the trades and logic 

that lead to the air vehicle’s current embodiment. All data contained within is intended to 

document the mid-iteration trades and should not be correlated to the final configuration 

performance unless specifically noted.

Section 2.2 – Configuration Description, details the final configuration in detail. This section 

represents the vehicle as analyzed for vehicle performance.

Section 2.3 – Configuration Analysis and Final Performance contains the analysis results and 

performance data for the vehicle described in the previous section.

2.1 Configuration Basis, Design Trades, and Evolution

One recommendation from Phase I was to perform additional design and analysis of hybrid

electric gas turbine propulsion. To address this, in Phase II the team was funded to perform the 

following design and analysis tasks: 

• Refine the hybrid gas turbine/ battery powered (hFan) propulsion system concept

arrived at in Phase I.

• Investigate conceptual layouts of the hybrid architecture. 

• Generate propulsion data for the revised engine concept(s) across the mission flight 

envelope, gas generator power settings and battery-to-gas generator power splits.

• Update its integration, resizing the configuration and updating the performance 

assessment. 

• Conduct a trade study on total energy cost by parametrically varying fuel cost, battery 

performance, battery life, battery cost, and electricity cost. 

• Estimate the noise during takeoff and approach with combined electric-turbine 

operation. 

• Integrate the hybrid gas turbine/ battery powered propulsion system on the Refined 

SUGAR (conventional low-wing tube-and-wing configuration) and SUGAR Ray (Hybrid 

Wing body), resize those configurations and assess their fuel burn, emissions, noise and 

takeoff performance compared to NASA’s Subsonic Fixed Wing N+3 goals. 

• Update the technology roadmaps from Phase I.

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This work has been organized into the tasks and task flow shown in Figure 2.1. The various new 

configuration nicknames are shown in Figure 2.2, with the Task 2.2 configurations highlighted. 

Figure 2.1 – Hybrid Electric Task Flow

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Volume II – Hybrid Electric Design Exploration

Figure 2.2 – SUGAR Hybrid Electric Concepts

2.1.1 Hybrid Electric propulsion for a HWB and Conventional Tube and Wing

At the end of Phase I, since the SUGAR Volt was based on the high span truss-braced SUGAR 

High configuration, there was a question as to whether the benefits of hybrid electric 

propulsion would apply equally well to conventional tube-and-wing and Hybrid Wing Body 

configurations. At the beginning of Task 2.2, a sizing study was performed to investigate this 

question. Results showed nearly identical performance improvements. Additional information 

on these results is shown below.

Please note: the information contained within this section is not directly comparable to the 

results shown in Section 2.3. These results are produced with a different set of engine data, 

vehicle analysis methods, and performance tools and are more comparable to SUGAR Phase I 

results.

2.1.1.1 Hybrid Electric HWB “SUGAR Sting Ray”

The HE increment for the HE HWB was very similar to original SUGAR Volt. At the 900 nm 

average mission range, adding hybrid electric propulsion reduces fuel burn by:

• 25%-46% compared to original SUGAR Ray

• 57%-70% compared to SUGAR Free Baseline

HWB

“SUGAR Ray”

Baseline

“SUGAR Free”

Reference

“Refined SUGAR”

TBW High L/D

“SUGAR High”

“SUGAR 

Volt ”

N N+3 N+4

Conventional 

Tube & Wing

Unconstrained*

Conventional 

Tube & Wing

High Span 

Truss Braced 

Tube & Wing

HWB

“Super

Refined SUGAR”

N+3 

Hybrid Electric

“SUGAR Sting 

Ray”

“SUGAR 

Electric Eel”

N+4 Reference 

“Super

Refined SUGAR”

Advanced N+4 

Technology

“SUGAR 

Freeze”

* Optimum span

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Similar to the original SUGAR Volt from Phase I, the SUGAR Ray-HE (SUGAR Sting Ray) had a fuel 

burn advantage at ranges less than ~2,800 nm. 

2.1.1.2 Hybrid Electric Tube & Wing “SUGAR Electric Eel”

The HE increment for the HE tube and wing configuration was very similar to original SUGAR 

Volt. The “Super Refined SUGAR” configuration, with the gFan+ engine and without wing span 

constraints, was used as a baseline. At the 900 nm average mission range, adding hybrid 

electric propulsion reduces fuel burn by:

• 33%-55% compared to conventional gFan+ propulsion

• 65%-79% compared to SUGAR Free Baseline

The SUGAR Electric Eel had a wingspan of approximately 162 ft. and a cruise L/D of 

approximately 24.5. It is likely that a span constrained version (118 ft.) would not have as 

significant a benefit for HE propulsion, as the heavy electrical components would carry a larger 

performance penalty due to the lower vehicle lift-to-drag ratio.

Similar to the original SUGAR Volt from Phase I, the Super Refined SUGAR-HE (SUGAR Electric 

Eel) had a fuel burn advantage at ranges less than 2,800-2,900 nm (Figure 2.3).

Figure 2.3 – Hybrid Electric Tube and Wing Sizing Results

0

5,000

10,000

15,000

20,000

25,000

30,000

0 500 1000 1500 2000 2500 3000 3500

Range, NM

Fuel Burn, lbs

0

10,000

20,000

30,000

40,000

50,000

60,000

Battery Weight (lbs)

Battery Weight 

for 10,000 HP 

Jet Fuel Only

Jet + 5,000 HP Electric

Jet+ 10,000 HP Electric

Average Mission Range

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2.1.2 Propulsion System Development

This study is focused on refining the hybrid gas turbine/electric powered propulsion system 

architecture developed in Phase I. The objectives are to decrease fuel burn, noise, and 

emissions while evolving a viable design. The hFan engine proposed in Phase I of the program 

was modeled as a mixed-flow hybrid electric turbofan and post processed to create a separateflow representation of the propulsion system performance dataset based on program time 

constraints and funding. 

For increased fidelity and functionality, the mixed-flow NPSS model used in Phase I was 

reworked for use in Phase II. The mixer was removed and a core nozzle was added to create a 

separate-flow NPSS model. The improved model maintains separate core and bypass streams. 

The separate-flow turbofan model enables accurate modeling of thrust bookkeeping and turbo 

machinery operating lines. In addition, the separate-flow model allows both variable fan and 

core nozzles. The variable area nozzles provide stability margin on the fan and booster when 

electric power is applied to the Low Pressure (LP) spool. 

2.1.2.1 Hybrid Electric Aircraft Thrust Requirements

Boeing provided thrust requirements representative of the Hybrid Electric (HE) aircraft concept, 

termed the SUGAR Volt, for use in hybrid electric propulsion system studies. These 

requirements, along with the Boeing aircraft are shown in Figure 2.4. GE has provided scaling 

rules to resize the engine as the aircraft evolves, as well as for use with other single aisle

aircraft.

Figure 2.4 – Hybrid Electric Aircraft Thrust Requirements

SUGAR Volt

TOGW: 170,000 lbf

Top of Climb Thrust per engine: 3,500 lbf

Takeoff BET required per engine: 17,500 lbf

Takeoff SLST: 20,100 lbf Boeing Concept Artwork

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2.1.2.2 Initial Hybrid Electric Turbo Machinery Modeling (hFan+) 

Utilizing the separate-flow turbofan NPSS model, preliminary power sweeps have been 

executed to analyze the effects of adding electric power to the LP spool. Initial runs modeled 

electric fan power augmentation of up to 6,000HP in increments of 1,000HP. Power 

augmentation sweeps have been completed at takeoff and cruise flight conditions. 

As electric power is added to the LP spool the booster stall margin drops toward the minimum 

acceptable level. The variable core nozzle closes to alleviate the booster operating line, but its 

authority is not sufficient to maintain the required booster stall margin. Thus, as electric power 

is added, variable bleed valve (VBV) doors open to direct flow from the booster into the fan 

stream. The VBV modulates to the minimum acceptable booster stall margin, as shown below 

in Figure 2.5. An innovative VBV design, capable of accepting large fractions of the booster 

airflow will be required for this architecture. The ability to efficiently mix the bleed flow with 

the core stream will play a strong role in establishing the transfer and propulsive efficiencies of 

the propulsion system, and will therefore be critical for efficient utilization of battery electric 

power. Alternative architectures alleviating this potential inefficiency have been identified for 

exploration.

Figure 2.5 – Booster Stall Margin with Electric Fan Power Augmentation

For a fixed thrust setting, as electric power is added to the LP spool, the turbofan core will roll 

back, leading to a loss of core thrust. Consequently, a conventionally designed fan must over 

speed to maintain the given thrust requirement. However, the excessive fan speed becomes 

-10

-5

0

5

10

15

20

25

-7000 -6000 -5000 -4000 -3000 -2000 -1000 0 1000 Cmp023.SMW

HP Extraction

LPC Stall Margin, Cruise

No Var A8

Var A8

Var A8, VBV

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inefficient at this off-design operating condition, as well as possibly mechanically prohibitive. 

The fan speed increase trend can be seen below in Figure 2.6. To correct this condition, a larger 

fan must be designed, accounting for the deficit in core thrust while preserving the intended 

maximum electric augmented operating speed. Alternatively, electric augmentation could be 

reserved for cruise, in which case the unaugmented fan and core would be adequate to 

produce climb thrust. The interwoven nature of design and control philosophies for the hybrid 

propulsion system emphasizes the need for a highly integrated airframe/engine manufacturer

design process. 

Figure 2.6 – Fan Speed with Electric Power Augmentation

The addition of electric power will decrease the fuel consumption of the propulsion system at a 

specified thrust level. Electric fan power augmentation causes the fuel flow to cut back, 

resulting in substantial improvements in SFC, as shown in Figure 2.7. At the cruise condition, a 

6,000HP electric motor results in roughly a 75% SFC reduction. 

96

98

100

102

104

106

108

110

-7000 -6000 -5000 -4000 -3000 -2000 -1000 0 1000 Cmp020.NcPct

HP Extraction

Percent Corrected Fan Speed, Cruise

Var A8, VBV

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Figure 2.7 – Specific Fuel Consumption with Electric Power Augmentation

The electric fan augmentation allows for substantial improvements in SFC for the Hybrid 

Electric propulsion system. While this improvement in SFC does not necessarily represent a 

reduction in specific energy consumption, it does represent a shift from generating the 

mechanical power in flight to generating it on the ground, where there is significant flexibility 

and potential for cost and emissions reduction.

2.1.2.3 hFan+ Advanced Hybrid Electric Turbofan

The propulsion system for the SUGAR Volt airframe is a hybrid electric engine architecture that 

utilizes an electric motor to distribute power to the LP spool of the turbofan. The propulsion 

system is also required to satisfy the SUGAR Volt thrust requirements while operating strictly as 

an advanced turbofan, without aid of the electric motor. In SUGAR phase II, Boeing provided GE 

with aircraft fuel burn sensitivities to engine specific fuel consumption, weight, and drag for the 

SUGAR Volt aircraft. These sensitivities are summarized in Figure 2.8. 

-80.0%

-70.0%

-60.0%

-50.0%

-40.0%

-30.0%

-20.0%

-10.0%

0.0%

-7000 -6000 -5000 -4000 -3000 -2000 -1000 0 1000 Delta SFC (%) 

HP Extraction

%SFC Reduction, Cruise

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Figure 2.8 – SUGAR Volt Aircraft Sensitivities

The concept of utilizing batteries in conjunction with an electric motor to add power to the LP 

spool of a gas turbine for aviation use was explored in phase I of the SUGAR program. In Phase 

II, the concept was investigated with increased fidelity. For Concept 1, the hFan+, the team 

completed a parametric study utilizing derivatives from Boeing found above in Figure 2.8. The 

study’s result is a direct-drive turbofan with a 1.45 fan pressure ratio. This selection yields a 

sacrifice in SFC to realize a lighter weight, lower drag engine. The resulting design minimizes 

fuel burn while meeting the thrust requirements of both a 3,500nm and 900nm mission. An 

electric motor translates energy from the batteries via a power conditioning unit to add power 

on the LP spool. In discussion with Boeing, the electrical system bookkeeping was divided into 

two sections separated at the pylon interface. GE is responsible for the electric motor, 

propulsion system line losses, and power conditioning, while Boeing will account for the 

batteries, aircraft line losses, and turnaround efficiency. An overview of the key characteristics 

of the hFan+ and the electrical system bookkeeping is shown below in Figure 2.9. 

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Figure 2.9 – Hybrid Electric hFan+ Architecture

As a result of discussions with Boeing, GE completed four electrical system layouts with design 

electric motor outputs of 2,000HP, 4,000HP, 6,000HP, and 8,000HP. This sweep in electrical 

power would provide Boeing the option to use electric power to partially or fully drive the LP 

spool. As a result, the core of the turbofan could be slowed to an idle or windmilling condition, 

if desired. A summary of the propulsion system key characteristics is provided in Table 2.1. 

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Table 2.1 – hFan+ Key Characteristics

2.1.2.4 Emissions

The LTO NOx emissions for the hFan+ propulsion system were assessed. The CAEP/6 

requirements, characteristic LTO NOx, and margined estimates are listed below in Table 2.2. 

Calculations for the LTO NOx emissions were performed for both the non-electric and electric 

modes of operation.

Table 2.2 – hFan+ LTO NOx Emissions

2.1.2.5 hFan+ Conventional Electrical System

In agreement with Boeing and NASA, the team decided to investigate both conventional and 

superconducting electrical systems for application on the SUGAR Volt. The hFan+ utilizes a 

conventional electrical system. On the propulsion side, the system requires a power 

conditioner, solid state circuit breaker, heat exchanger, and an electric motor. The conventional 

system utilizes a Switched Reluctance Motor (SRM). The motor sizing assumptions are based on 

a high power density SRM with liquid cooling for continuous peak electromagnetic power 

operation. Advanced composite materials are used for the shaft and motor case. The power 

hFan+ (2000HP Electric Motor) hFan+ (4000HP Electric Motor)

Fan Diameter 78.6" Fan Diameter 78.6"

Length 129" Length 129"

Propulsion System Weight 7572 lbf Propulsion System Weight 8422 lbf

Thrust (lbf) SFC (lbm/lbf/hr) Thrust (lbf) SFC (lbm/lbf/hr)

SLS 24048 0.180 SLS 24048 0.159

TKOF 18025 0.271 TKOF 18025 0.244

TOC 3605 0.320 TOC 3605 0.214

Cruise 2884 0.407 Cruise 2884 0.257

hFan+ (6000HP Electric Motor) hFan+ (8000HP Electric Motor)

Fan Diameter 78.6" Fan Diameter 78.6"

Length 129" Length 129"

Propulsion System Weight 9272 lbf Propulsion System Weight 10122 lbf

Thrust (lbf) SFC (lbm/lbf/hr) Thrust (lbf) SFC (lbm/lbf/hr)

SLS 24048 0.139 SLS 24048 0.120

TKOF 18025 0.218 TKOF 18025 0.192

TOC 3605 0.112 TOC 3605 0.000

Cruise 2884 0.000 Cruise 2884 0.000

LTO NOx Emissions - Round 1 hFan+

hFan Non-Electric hFan Electric

CAEP/6, g/kN 66.2 66.2

Characteristic LTO NOx, g/kN 13.5 7.9

Margined Estimate, % CAEP/6 22.6% 13.2%

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conditioning unit utilizes high voltage SiC devices, advanced high-temperature plastic film 

capacitors, and an advanced composite liquid-cooled heat sink. Additionally, the power 

conditioner has low loss inductors and an all composite enclosure. The resulting conventional 

electric system has a specific power in the class of 2-3 HP/lbm and an overall efficiency of 

~0.93. An overview of the conventional electrical system is displayed in Figure 2.10. 

Figure 2.10 – Hybrid Electric Architecture with Conventional Electric Technology

In order to generate high-density propulsion data across the mission flight envelope for various 

gas generator power settings and battery-to-gas generator power splits, an off-design electrical 

system performance model was developed. Each electrical system (2,000HP, 4,000HP, 6,000HP, 

and 8,000HP) received a dedicated model accounting for power conditioning efficiency, line 

losses, and electric motor efficiency. An example of the 8,000HP conventional electric system 

performance map is shown below in Figure 2.11. 

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Figure 2.11 – Conventional 8,000HP Electric System Efficiency Map

2.1.2.6 hFan+ Superconducting Electrical System

A superconducting electrical system has also been analyzed. This system also requires a power 

conditioner, solid state circuit breaker, heat exchanger, and an electric motor. The electrical 

system utilizes a superconducting electric motor, wires, and power conditioner. The motor 

sizing assumptions are based on the availability of liquid natural gas to the system. The motor 

includes conductors that are held at LNG temperatures and are superconducting with 

alternating current. The motor requires high strength insulating structural materials and an 

evacuated ironless design. The power conditioning unit employs an advanced composite LNG 

cooled heat sink and high voltage SiC devices. It also takes advantage of advanced low loss 

inductors and advanced plastic film capacitors. The resulting superconducting electric system 

has a specific power in the class of 5-6 HP/lbm and an overall efficiency of ~0.99. An overview 

of the superconducting electrical system is displayed below in Figure 2.12. The superconducting 

architecture was used in Task 1 (reported separately) and was also modeled in Task 3.3, but 

was not part of the SUGAR Volt performance and sizing analysis that follows.

Electric Power (HP)

LP Shaft Speed (RPM)

 y p

1000 2000 3000 4000 5000 6000 7000 8000

500

1000

1500

2000

2500

3000

3500

4000

4500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

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Figure 2.12 – Hybrid Electric Architecture with Superconducting Electric Technology

2.1.2.7 Regenerating Descent Energy into the Battery System

The best fuel burn is achieved when a continuous descent approach is utilized, with minimal 

energy expended during the descent and speed is varied to maintain a high L/D ratio 

throughout the descent. For the SUGAR N+3 timeframe, it is assumed that air traffic 

management is highly efficient and that continuous descent approaches are the norm. 

Extracting power during the descent will lower the aircraft L/D and steepen the descent angle.

This requires the aircraft to have flown further in cruise and consume energy before the 

descent. This is illustrated in Figure 2.13. 

Figure 2.13 - Sample Trajectories for Continuous Descent and Descent Regeneration

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

0 20 40 60 80 100 120 140 160 180

Altitude (ft)

Descent Distance (NM)

Descent Regeneration

Continuous Descent

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Like many trades involving the hybrid electric propulsion system on the SUGAR Volt, the 

additional degrees of freedom that result from having an electric motor or motor/generator 

and energy storage create a complex situation to evaluate the benefits or optimize operation.

In this case, energy recovered during descent is stored in the battery, but additional fuel energy 

is expended during the extended cruise. So, generally, in an ideal case, there is nothing to be 

gained by extending the cruise and then recovering energy. As losses are included for using the 

fan for regeneration, the case for regeneration only gets worse.

The efficiency of the energy extraction from the fan also is lower than normal fan operation, as 

the fan needs to be designed to produce thrust at high efficiency and any compromise to the 

fan design for energy extraction comes at the expense of thrust efficiency. The fan efficiency on 

the hFan is high at cruise, when the fan airfoils are making lift the as-designed direction (low 

pressure on the side that’s closer to the nose). Regeneration mode puts low pressure on the 

side that’s closer to the tail, so the airfoils are operating upside down, while the fan is still 

turning the same direction. Also, there are the electrical system losses and charging losses to 

consider. If energy cost is considered, then the energy regenerated is displacing relatively low 

cost electricity at the airport recharging with additional relatively expensive jet fuel that is 

consumed during the extended cruise segment.

However, there are elements to complicate the assessment. If a non-continuous descent is 

needed, then there are inefficiencies (extra drag needed) that could benefit the regeneration 

case. The idle fuel flow of the engine has an impact on the fuel used in both cases. 

If you did have recharging capability in the engine, it could be used for emergency power, 

unusually steep descents, or holding to the glide slope instead of using spoilers. However, these 

benefits are small. 

For these reasons, the baseline SUGAR Volt hFan concept is not assumed to extract power from 

the air steam to charge the batteries during flight. Doing so could add some flexibility to the 

system, but will not benefit fuel burn or likely produce significant benefits to energy use or cost. 

2.2 Configuration Description

The configuration for the SUGAR High aircraft (765-096 Rev A) is derived from the 765-095 Rev 

D configuration and should be considered the first revision from the Phase I (1) SUGAR effort 

(765-096). The primary difference between the 095 and 096 is the addition of hybrid electric 

systems and wing mounted battery pods.

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Table 2.3 – 765-095 Revision History

Rev. Description of Major Changes Date

New SUGAR Phase I Exit 6/4/2009

A Configuration brought to SUGAR 765-095-RD layout 11/11/12

2.2.1 Integration

The aircraft general arrangement drawing and principal characteristics are shown in Figure 2.14

and Table 2.4 respectively. Airplane components for this configuration are identical to the 765-

095-RevD SUGAR High configuration, thus, the Wing, Strut, Jury Strut, Vertical Fin, Horizontal 

Stabilizer, Fuselage, and Landing Gear will not be repeated in this volume of the final report. 

The engine configuration and battery pods are the only changes as compared to the SUGAR 

High.

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Figure 2.14 – SUGAR Volt (765-096-RevA) General Arrangement

35’ – 0.09”

(420.09)

OVERALL

169’ – 11.50”

(2,039.50)

SPAN

193.27 188.23

~118’

(1,416.00)

FOLDED SPAN

116.11

~517”

272.29

612.35

1,497

139’ – 8.68”

(1,676.68)

OVERALL

11.11°

TAIL DOWN

GEAR EXTENDED

11.5°

GEAR EXTENDED

38’ – 5.83”

(461.83)

SPAN

148.7

863.64

707.13

Parameter Units Total

Projected

Horizontal 

Stab.

Vertical

Fin

Area ft.^2 1477.11 296.24 297.69

Aspect Ratio 19.552 5.000 1.000

Span in. 2039.301 461.833 207.043

Taper Ratio 0.346 0.350 1.000

Root Chord in. 130.313 136.839 207.043

Tip Chord in. 45.135 47.894 207.043

Sweep 25% deg. 12.52 20.63 41.00

MAC in. 110.286 99.504 207.043

Volume Coeff. 1.5631 0.687

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Table 2.4 – Principal Characteristics

Model 765-095 Rev. D (As Drawn) Balanced

1,380 Hp

Core 

Shutdown

7,150 Hp

Weights

Maximum Takeoff Weight (MTOW) 150,000 lb 190,000 lb

Maximum Landing Weight (MLW) 143,300 lb 185,800 lb

Maximum Zero Fuel Weight (MZFW) 135,300 lb 177,800 lb

Maximum Battery Pod Weight (MBPW) 15,700 lb 47,900 lb

Operating Empty Weight (OEW) 88,800 lb 91,900 lb

Engine Type hFan+2 hFan+2

Rated Horse Power 1,380 hp 7,150 hp

Boeing Equivalent Thrust (no HP 

addition)

21,000 lbf 20,700 lbf

Fan Diameter 72.1 in 79.6 in

Overall Dimensions

Length 139.7 ft

Height 35 ft

Fuselage Length 124.8 ft

Fuselage Cross Section (Height x Width) 166.5 x 148.7 in

Passenger Cabin Length 1127 in

Wing

Reference Area 1477.11 ft2

Span 169.3 ft

Projected Sweep 12.52 deg

Passenger, Baggage, Fuel Capacities

Passenger Count (Dual Class) 154

Class Distribution (FC / EC) 12 / 142

Cargo Capacity Bulk

Fuel Capacity 5,416 USG

Landing Gear

Wheel Base 612.35

Main Track 272.29

Main Tire Size 45x17R21

Nose Tire Size 30x10.5R15

2.2.2 Battery Pods

Aside from the change to hFan engines, the SUGAR Volt is the same as SUGAR High. However, 

the SUGAR Volt has the addition of wing hard points for battery pod addition. Electric energy is 

inexpensive as compared to jet fuel however, batteries can be costly. It was decided that to 

minimize DOC, the SUGAR Volt would carry its battery pods as external components making 

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them rapidly exchangeable thus allowing slow charge rates and increasing battery life without 

interfering with conventional ground servicing operations or increasing aircraft turn time. In 

addition to the rapid change capability, the battery pod location helps spanload the 

configuration reducing the OEW penalty for carrying their weight. These benefits come with 

extra drag and possible lateral imbalance issues (depending on pod weight). It should be noted 

that the location of the batteries has not been traded. The pods could be palletized and 

contained within the cargo bay. This would increase the OEW because the configuration is less 

spanloaded and would require a palletized cargo handling system. The current design currently 

carries only bulk (non-palletized) cargo. 

The battery pods are aerodynamically tailored for the wing to minimize interference. They 

contain a passive cooling system consisting of an inlet, ducting, and a nozzle. Depending on 

battery heat generation and atmospheric conditions, this passive system may be insufficient for 

some combinations of electrical system use. These constraints have not been analyzed and are 

most likely at high temperature, low speed, and/or low altitude. The batteries are not part of 

the base airplane and thus will not be reflected in the aero databases. The pod drag is booked 

as a delta f in the performance analysis.

2.2.3 Propulsion

Upon completion of the first round of SUGAR Volt sizing and economic analysis, two follow-on 

studies were conducted. Boeing developed two revised SUGAR Volt aircraft, the 750 Balanced 

and 750 Core Shutdown. “750” refers to the assumption of 750 Wh/kg battery technology. The 

objectives of these follow-on studies were to further reduce energy consumption and also to 

improve the propulsion system efficiency during core shutdown operation. The thrust 

requirements for the 750 Balanced and 750 Core Shutdown can be found in Figure 2.15 and 

Figure 2.16. The Balanced engine contained a 1,380 HP electric motor, while the Core 

Shutdown engine contained a 7,150 HP electric motor. 

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Figure 2.15 – 750 Balanced Thrust Requirements

Figure 2.16 – 750 Core Shutdown Thrust Requirements

The thrust requirements and aircraft sensitivities were again used to conduct a parametric 

optimization study to size two hybrid electric advanced turbofan architectures. These “hFan+2” 

propulsion systems leverage the conventional electrical system and gas turbine technologies 

from the hFan+. Using weight, drag, SFC, and energy-to-fuel burn trades, the ideal fan pressure 

ratio and diameters of the 750 Balanced and 750 Core Shutdown engines were calculated. 

Propulsion system performance data was transmitted to Boeing for the two architectures. 

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Additionally, GE provided engine performance data for a 750 Balanced hFan+2 with an electric 

motor rated for 1,750HP. The supplementary 1,750 HP data set allowed Boeing to optimize the 

750 Balanced aircraft and propulsion system through interpolation. A summary of the 

propulsion system key characteristics is provided in Table 2.5. The performance quotes include 

the respective electric motors running at full power throughout the flight envelope.

Table 2.5 - hFan+2 Key Characteristics

2.3 Configuration Analysis and Final Performance

This section includes the discipline analyses of the configuration documented in Section 2.1.2.7

of this report and Section 2.2 of the Truss Braced Wing report. The results of the analysis are 

not used in the synthesis of the SUGAR Volt configuration but would be incorporated in the

next configuration cycle.

2.3.1 Aerodynamics

The aerodynamic buildup of the SUGAR Volt (765-096-RA) is identical to the SUGAR Volt (765-

095 Rev) D with the inclusion of a drag increment for battery pods mounted on the wings. The 

airplane delta f for a given battery pod size is shown in Table 2.6. 

Table 2.6 – Battery Aero Characteristics

Pod_1 Pod_2 Pod_3

Battery

Energy Density kWh/ft3 34 34 34

Specific Energy Wh/kg 750 750 750

Battery Energy kWh 1,360 2,723 4,081

Pod Weight lb 4,818 9,646 14,456

Airplane Delta F ft2 1.59 2.49 3.23

750 Balanced (hFan+2) 750 Core Shutdown (hFan+2)

Fan Diameter 72.1" Fan Diameter 79.6"

Length 129" Length 129"

Propulsion System Weight 7030 lbf Propulsion System Weight 10025 lbf

Thrust (lbf) SFC (lbm/lbf/hr) Thrust (lbf) SFC (lbm/lbf/hr)

SLS 17394 0.161 SLS 22992 0.113

TKOF 14276 0.251 TKOF 18320 0.186

TOC 3308 0.300 TOC 4310 0.080

Cruise 2780 0.272 Cruise 3595 0.000

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2.3.2 Mass Properties

The group weight statements for the 765-096-Rev A are shown in Table 2.7. These weights are 

generated starting with the 765-095-Rev D, changing the propulsion system to the hFan, 

including the hard points for the batteries, and adding electric systems. The weight data is 

generated at 150,000 pounds for the low horsepower aircraft and 190,000 pounds for the high 

horsepower aircraft.

Table 2.7 – 765-096-RA Group Weight Statement

GROUP WEIGHT (LB)

1,380 HP 1,750 HP 7,150 HP

WING 16,986 17,011 18,808

TAIL 3,157 3,157 3,157

FUSELAGE 16,929 16,928 17,307

WING STRUT & MLG SUPT INSTL 5,077 5,077 6,584

LANDING GEAR 5,073 5,116 6,264

NACELLE & PYLON 3,682 3,682 3,682

PROPULSION 11,028 11,314 16,429

ENGINES 9,300 9,586 14,698

FUEL SYSTEM 1,728 1,728 1,731

FLIGHT CONTROLS 2,652 2,652 2,652

POWER SYSTEMS 4,068 4,068 4,068

AUXILIARY POWER UNIT 1,014 1,014 1,014

HYDRAULICS 758 758 758

ELECTRICAL 2,297 2,297 2,297

INSTRUMENTS 773 773 773

AVIONICS & AUTOPILOT 1,504 1,504 1,504

FURNISHINGS & EQUIPMENT 9,115 9,115 9,115

AIR CONDITIONING 1,441 1,441 1,441

ANTI-ICING 121 121 121

MANUFACTURER'S EMPTY WEIGHT (MEW) 81,607 81,960 91,907

OPERATIONAL ITEMS 7,207 7,207 7,207

OPERATIONAL EMPTY WEIGHT (OEW) 88,814 89,167 99,114

USABLE FUEL 14,716 13,298 12,178

MAX BATTERY POD 15,670 16,735 47,908

DESIGN PAYLOAD 30,800 30,800 30,800

TAKEOFF GROSS WEIGHT (TOGW) 150,000 150,000 190,000

2.3.3 Performance and Sizing

Performance and sizing analysis for a hybrid electric airplane is very complex as compared to a 

conventionally fueled configuration. Heritage performance routines have various levels of 

flexibility in there sizing capability however, neither of Boeings codes (there are two 

commercial transport performance routines at Boeing; Douglas heritage CASES and Boeing 

heritage BMAP) can directly accommodate electric aircraft. For this reason, a ModelCenter 

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environment was created that allows mission performance to be calculated. BMAP was 

selected for this environment for consistency with the fuel energy airplane performance 

analyses.

Performance is analyzed for two missions, the long range mission used for sizing, and the 

average range economic mission which is used for block fuel comparison. The long range sizing 

mission is run in BMAP conventionally with the mission defined and executed in one 

performance evaluation which assumes an all fuel energy source. Some aircraft (the core 

shutdown hybrid electric) are held at a Maximum Takeoff Weight (MTOW) of 190,000 lbs.

Calculating the performance for the hybrid average range mission is significantly more 

complicated. To accomplish this, BMAP was configured to analyze segments rather than an 

entire mission. The mission was broken into 8 segments:

• Taxi Out

• Takeoff & Climbout

• Climb

• Cruise (Two segments for core shutdown cases)

• Descend

• Approach and Land

• Taxi In

• Reserves

Engine data is generated for each of the mission segments based on a presumed electric power 

code (how much electric horsepower is applied to the low pressure spool) for that mission 

segment. The segment performance is then executed in isolation with care taken to ensure that 

final conditions are appropriately passed to the next segments initial conditions. Battery weight 

is booked with OEW and is assumed not to change over the mission duration (which may not 

necessarily be true for some potential future battery chemistries). After fuel burn performance 

is calculated, electric energy is integrated by looking up the power demand at each Mach, 

altitude, airspeed, thrust, and electric power code at each time step reported by BMAP. The 

battery energy required to fly the mission is then converged with the battery energy available. 

This requires resizing the long range mission because of the OEW change associated with a 

change in maximum battery pod weight (the short range mission is assumed to utilize the 

maximum available battery energy). The original intent was to optimize reference horsepower, 

wing area, thrust, MTOW, and each segments electric power code to minimize fuel burn. 

Unfortunately, due to tolerance of the performance convergence and the long runtime of the 

environment, the aircraft were hand optimized assuming maximum electric power code for 

takeoff, climb, and cruise. Core shutdown aircraft use hybrid mode with maximum electric 

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effort for takeoff and climb, then an all fuel cruise segment (to burn of gross weight), followed 

by the remaining cruise segment with the core shutdown. 3% of the battery energy is carried as 

fuel using a fuel energy conversion ratio of 18,580 BTU / LB and assuming that 50% of the heat 

energy is converted to thrust energy.

Three hybrid aircraft are sized at 1,380, 1,750, and 7,150 reference horsepower. A sizing plot is 

shown for each airplane in Figure 2.17, Figure 2.18, and Figure 2.19 respectively. Each response 

surface represents a wing area and takeoff parameter (wing loading divided by thrust to weight 

ratio) variation with an MTOW converged on the long range mission distance (or target MTOW 

for the heavy airplanes). These aircraft are constrained by takeoff field length and the ability to 

fly at optimum altitude (thrust initial cruise altitude capability (thrust ICAC)) for all missions. The 

plots are calculated for a fixed Maximum Battery Pod Weight (MBPW) making them valid only 

at the sizing point illustrated on the chart. If the MBPW were converged at all points on the 

chart, those points above the sizing point would get higher in fuel burn and those below would 

get lower in fuel burn. This effectively scales the charts about the sizing point and would not 

change the location of the minimum fuel burn. These plots verify the airplane is at the optimum 

conditions. 

Table 2.8 shows the performance summary of the three hybrid electric aircraft in addition to 

the SUGAR High (fuel powered) and SUGAR Free configurations. It is shown that the low 

horsepower aircraft have lower fuel burn at the economic mission range and remain 

competitive from an energy use perspective. Inspection of the sizing chart (Figure 2.19) for the 

7,150 HP configuration shows that the engine is sized by the power required to shut down the 

core at the cruise condition (labeled thrust). Table 2.9 shows additional sizing options: the “No 

Ele. Thrust Const.” column shows a hypothetical airplane that ignores the constraint and runs 

to the intersection of the short range ICAC constraint and the TOFL constraint. This is 

hypothetical because the energy use at cruise requires more horsepower than the one supplied 

with the GE engine data can absorb but should approximate the benefits of installing a larger 

motor on the configuration (balancing top of climb thrust with takeoff thrust). The final column, 

“No Shutdown”, is a trade showing the fuel burn without shutting down the core during the 

cruise segment. This shows that the fuel burn vs. throttle setting curve is quite flat as the 

vehicle is better than the current sizing point (primarily achieved by balancing TOFL with top of 

climb thrust without an electric cruise sizing requirement for shutdown).

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Figure 2.17 - 765-096-RA 1380 HP sizing Plot

Figure 2.18 - 765-096-RA 1750 HP sizing Plot

Long Range Short Range

Long Range Short Range

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Figure 2.19 - 765-096-RA 7150 HP sizing Plot

Long Range Short Range

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Table 2.8 – 765-096 Rev A Mission Performance Comparison

Model

Sizing Level

765-093

FREE

765-095

SUGAR High

Balanced

(Ref Hp 1,380)

Balanced

(Ref Hp 1,750)

Core Shutdown

(Ref Hp 7,150)

PASSENGERS / CLASS 154 / DUAL 154 / DUAL 154 / DUAL 154 / DUAL 154 / DUAL

MAX TAKEOFF WEIGHT LB 182,600 138,300 140,700 139,700 190,000

MAX LANDING WEIGHT LB 149,400 135,700 138,800 139,300 189,300

MAX ZERO FUEL WEIGHT LB 140,400 127,700 130,800 131,300 181,300

MAX BATTERY WEIGHT LB - - 15,700 16,700 47,900

OPERATING EMPTY WEIGHT LB 94,400 81,700 84,300 83,800 102,600

FUEL CAPACITY REQ / AVIL USG 9,633/9,633 4,213/4,213 4,185/4,194 4,121 / 4,605 4,426 / 6,340

ENGINE MODEL CFM56 gFan+2 hFan+2 hFan+2 hFan+2

FAN DIAMETER IN 62 66 69 67 79

HORSEPOWER HP - - 1,260 1,502 7,053

BOEING EQUIVALENT THRUST (BET) LB 27,900 19,400 19,200 18,000 20,700

WING AREA / SPAN FT2 / FT 1,406 / 121 1,195 / 153 1,190 / 153 1,290 / 159 1,680 / 181

ASPECT RATIO (EFFECTIVE) 10.41 19.56 19.56 19.56 19.56

OPTIMUM CL (SR / LR) 0.584 0.759 0.769 / 0.798 0.728 / 0.730 0.730 / 0.733

CRUISE L/D @ OPT CL (SR / LR) 17.997 23.995 23.047 / 0.77 23.537 / 24.213 25.223 / 25.933

LONG MISSION RANGE NM 3,680 3,500 3,499 3,500 3,500

PERFORMANCE CRUISE MACH 0.79 0.70 0.70 0.70 0.70

LONG RANGE CRUISE MACH (LRC) 0.79 0.70 0.70 0.70 0.70

THRUST ICAC (MTOW, ISA) (SR / LR) FT 36,200 37,700 39,500 / 38,000 40,200 / 37,700 38,000 / 41,900

TIME/DIST (MTOW,35k FT,ISA)(SR/LR) MIN / NM 23 / 148 23 / 147 (18/25)/(108/149) (22/28)/(130/172) (17/23)/(100/135)

OPT. ALTITUDE (MTOW, ISA)(SR/LR) FT 34,900 37,700 37,700 / 37,600 38,200 / 38,400 37,200 / 41,100

BUFFET ICAC (MTOW, ISA) (SR / LR) FT 36,200 43,000 42,700 / 42,600 44,300 / 44,400 43,300 / 47,000

TOFL (MTOW, SEA LEVEL, 86 DEG F) FT 8,190 7,680 8,170 8,180 6,740

APPROACH SPEED (MLW) KT 126 109 111 106 109

BLOCK FUEL / SEAT (900 NM) LB 91.51

(0%)

42.46

(-53.6%)/(0%)

37.82

(-58.7% / -10.9%)

36.56

(-60% / -13.9%)

33.25

(-63.7% / -21.7%)

BLOCK ENERGY / SEAT (900 NM) kBTU 1,700 (Base) 789 (-53.6%) 801 (-52.9%) 784 (-53.9%) 917 (-46.0%)

FUEL ENERGY FRACTION % 100% 100% 87.8% 86.6% 67.3%

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Table 2.9 – 765-096 Rev A Core Shutdown Trade

Model

Sizing Level

Core Shutdown

(Ref Hp 7,150)

No Ele Thrust Const.

(Ref Hp 8,080)

No Shutdown

(Ref Hp 7,150)

PASSENGERS / CLASS 154 / DUAL 154 / DUAL 154 / DUAL

MAX TAKEOFF WEIGHT LB 190,000 190,000 190,000

MAX LANDING WEIGHT LB 189,300 189,500 189,400

MAX ZERO FUEL WEIGHT LB 181,300 181,500 181,400

MAX BATTERY WEIGHT LB 47,900 50,900 50,900

OPERATING EMPTY WEIGHT LB 102,600 99,700 99,700

FUEL CAPACITY REQ / AVIL USG 4,426 / 6,340 4,148/ 5,568 4,143/ 5,568

ENGINE MODEL hFan+2 hFan+2 hFan+2

FAN DIAMETER IN 79 Unk. (77+) 77

HORSEPOWER HP 7,053 7,585 6,880

BOEING EQUIVALENT THRUST (BET) LB 20,700 19,700 19,700

WING AREA / SPAN FT2 / FT 1,680 / 181 1,511 / 172 1,516 / 172

ASPECT RATIO (EFFECTIVE) 19.56 19.56 19.56

OPTIMUM CL (SR / LR) 0.730 / 0.733 0.772 / 0.767 0.801 / 0.767

CRUISE L/D @ OPT CL (SR / LR) 25.223 / 25.933 24.496 / 25.737 24.463 / 25.766

LONG MISSION RANGE NM 3,500 3,500 3,500

PERFORMANCE CRUISE MACH 0.70 0.70 0.70

LONG RANGE CRUISE MACH (LRC) 0.70 0.70 0.70

THRUST ICAC (MTOW, ISA) (SR / LR) FT 38,000 / 41,900 36,200 / 37,200 38,700 / 37,200

TIME/DIST (MTOW,35k FT,ISA)(SR/LR) MIN / NM (17/23)/(100/135) (19/34) / (114/203) (20/34) / (119/205)

OPT. ALTITUDE (MTOW, ISA)(SR/LR) FT 37,200 / 41,100 36,200 / 36,300 37,100 / 36,300

BUFFET ICAC (MTOW, ISA) (SR / LR) FT 43,300 / 47,000 41,100 / 41,300 41,200 / 41,400

TOFL (MTOW, SEA LEVEL, 86 DEG F) FT 6,740 8,180 8,180

APPROACH SPEED (MLW) KT 109 115 115

BLOCK FUEL / SEAT (900 NM) LB 33.25

(-63.7% / -21.7%)

32.12

(-64.9% / -24.4%)

32.69

(-64.3% / -23.0%)

BLOCK ENERGY / SEAT (900 NM) kBTU 917 (-46.0%) 915 (-46.2%) 926 (-45.5%)

FUEL ENERGY FRACTION % 67.3% 65.2% 65.6%

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A summary of the fuel burn and energy reduction from the SUGAR Free configuration is shown 

in Figure 2.20. It is shown that the hybrid electric configurations do save fuel for the economic 

mission. The 1,750 Hp configuration is quite good and meets NASA’s goal for fuel burn but 

shows only a minor improvement in the energy use. The magnitude and sign of electric energy 

savings over an all fuel configuration has vacillated for hybrid solutions and, with the present 

understanding of the design, appears to be a push or a slight negative. Even with no energy 

advantage on the economic mission, the hybrid architecture may allow for a greener source of 

energy as many environmentally friendly ground charging options exist. It is also possible to 

alter where in the atmosphere emissions are being deposited (cruise altitude or ground power 

station). 

Figure 2.20 - Economic Mission Fuel and Energy Reduction

The payload range curves for each hybrid airplane are illustrated in Figure 2.21. This figure also 

shows the all fuel truss braced configuration (SUGAR High 765-095-RD) for comparison. The 

7,150 Hp configuration is sized to 190,000 lb while the lower horsepower configurations are 

sized for the 3,500 nautical mile maximum range requirement. This is clearly depicted by the 

figure as the high horsepower configuration exceeds the range capability requirement and is 

fuel volume limited even without the 1,000 gallon body tank it carries (this curve is 

approximated). It should be noted that the payload range curves illustrated represent the all 

fuel capability of the airplane, thus, any mission under the payload range envelope can be 

performed in hybrid mode.

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

765-095-RD

SUGAR High

765-096-RA

1380 Ref Hp

765-096-RA

1750 Ref Hp

765-096-RA

7150 Ref Hp

Goal Reduction (Relative to SUGAR Free)

Block Fuel Block Energy

NASA Goal

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Figure 2.21 - Payload v. Range Chart for Hybrid Electric Aircraft

The fuel burn and energy use per segment for the hybrid electric airplanes is shown in Figure 

2.22 and Figure 2.23 respectively for the 900 nautical mile economic mission. These metrics 

aren’t particularly telling as the range of each segment varies from configuration to 

configuration. Figure 2.24 normalizes the energy use per segment by the range of the segment 

(specific range calculated with energy rather than fuel). This figure provides a metric to 

compare the efficiency of each airplane segment with respect to energy use. The 1,750 

horsepower configuration shows equal cruise efficiency as compared to the SUGAR High UDF

(UnDucted Fan). The fuel burn increment from the SUGAR High UDF and the SUGAR High (with 

the gFan+2 ducted fan) could be applied to any hybrid configuration.

0

5

10

15

20

25

30

35

40

45

50

0 1 2 3 4 5 6 7

Payload ~ 1,000 lb

Still Air Range ~ 1,000 nmi

Full Passenger Payload

765-095-RD 7150 HP

1380 HP

1750 HP

Approximate

7150 HP

No Body Fuel

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Figure 2.22 - Fuel Burn per Segment for Hybrid Electric Aircraft

Figure 2.23 - Energy Use per Segment for Hybrid Airplanes

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Figure 2.24 - Specific Range (Energy) per Segment for Hybrid Airplanes

Fuel burn as a function of range is illustrated by Figure 2.25. This shows the reduction of fuel 

burned at shorter ranges for hybrid electric airplanes. It should be noted that the hybrid electric

1,750 Hp engine has better performance than its all fuel counterpart even at the 3,500 

maximum range. This shows that the airplane is favoring the cycle with a slightly larger fan than 

the baseline gFan+2 engine. Figure 2.26 shows energy verses range for the same aircraft. The 

large MTOW core shutdown airplane has similar energy use on a 400 nm mission but shows 

much worse energy use for a 900 nm mission. This becomes important later when fleet fuel 

burn is discussed. Figure 2.27 shows the optimum battery weight for fuel burn minimization for 

each hybrid airplane as a function of range. The aircraft operate in different modes depending 

on the size of the electric motor and the mount of battery that can be carried. Figure 2.28

illustrates what mode of operation each hybrid airplane is operating in as a function of range.

The less than MTOW and less than max battery weight is a region where the airplane flies in 

hybrid mode but is at less than gross weight. This usually occurs because the electric motor 

cannot deplete the battery energy in the flight duration. The airplane operates at less than 

MTOW. This region followed by a region that can utilize MBPW but is not so long that the 

battery energy density is limiting range. Following this, a MTOW region is encountered. This 

region is where the battery energy density is traded for higher fuel energy density. The low 

MTOW airplanes (1,380 and 1,750 HP configurations) trade in this region from roughly the 

economic 900 nautical mile mission to the maximum range 3,500 nautical mile mission. The 

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7,150 HP, 190,000 pound configuration reaches a point where no battery energy is used. This 

region is a less than MTOW and non-hybrid. The cost of carrying the battery pod is outweighed 

by the drag penalty of carrying battery pods.

The average fleet frequencies verses range (previously reported (1)) can be used in 

coordination with these fuel and energy verses range charts to determine the fleet fuel burn 

and energy use (reported in Figure 2.29). This shows that, with the current concepts and engine 

architectures, the fleet energy use can be maintained at a fuel burn advantage. This can be a 

benefit in areas with a clean power grid or can be used to trade where the emissions are being 

deposited into the atmosphere.

Figure 2.25 - Fuel Burn v. Range for Hybrid Aircraft

0

5,000

10,000

15,000

20,000

25,000

0 500 1,000 1,500 2,000 2,500 3,000 3,500

Fuel Burn (lb)

Range (nm)

SUGAR High SUGAR Volt 1,380 Hp SUGAR Volt 1,750 Hp SUGAR Volt 7,150 Hp

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Figure 2.26 – Energy v. Range for Hybrid Aircraft

Figure 2.27 – Battery Weight v. Range for Hybrid Aircraft

0

500

1,000

1,500

2,000

2,500

3,000

3,500

0 500 1,000 1,500 2,000 2,500 3,000 3,500

Energy (kBTU)

Range (nm)

SUGAR High SUGAR Volt 1,380 Hp SUGAR Volt 1,750 Hp SUGAR Volt 7,150 Hp

0

10,000

20,000

30,000

40,000

50,000

60,000

0 500 1,000 1,500 2,000 2,500 3,000 3,500

Battery Weight (lb)

Range (nm)

SUGAR Volt 1,380 Hp SUGAR Volt 1,750 Hp SUGAR Volt 7,150 Hp

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Figure 2.28 – Modes of operation for hybrid airplanes

Figure 2.29 – Fleet Fuel and Energy Reduction as compared to NASA Goal

2.3.4 Acoustic Analysis

Acoustic analysis was completed for the SUGAR High (non-electric) and SUGAR Volt (HE) in 

order to benchmark the noise characteristics of the electric configuration and assess the 

potential for achieving NASA’s stated noise goal for a vehicle entering service in the 2030 time 

frame. The analysis methodology is similar to that used to conduct the noise analysis for the 

Environmentally Responsible Aviation (ERA) – Preferred System Concept (PSC) (3) study which 

strived to achieve a similar noise goal, although for a larger class aircraft. The results of the 

Boeing ERA study estimated the PSC vehicle could achieve the N+2 goal (-42 dB relative to Stage 

IV) by 2020, but would require some breakthrough technologies, as yet unidentified, if greater

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

765-095-RD

SUGAR High

765-096-RA

1380 Ref Hp

765-096-RA

1750 Ref Hp

765-096-RA

7150 Ref Hp

Goal Reduction (Relative to SUGAR Free)

Block Fuel Block Energy

NASA Goal

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noise reduction is needed. The SUGAR Hybrid Electric (HE) concept does have many features 

that are beneficial for noise reduction including advanced engine design, state of the art 

acoustic liner technology as well as excellent low speed aircraft performance.

2.3.4.1 Summary of Acoustic Results

The SUGAR High total aircraft noise at Approach (AP), Cutback (CB), and (SL) conditions is 

provided in Figure 2.30. The cumulative margin to Stage 4 of 22.8 EPNdB is noted as well.

Similarly, Figure 2.30 also provides the total aircraft noise for the SUGAR Volt at 1,860 electric 

horsepower, yielding 24.3 EPNdB margin to Stage 4. Both airplanes have an acoustically treated 

engine, but do not have advanced noise reduction technology. The small differences in noise 

levels & margin is due to small differences in flight performance, and similarity in the engine 

noise between the non-electric and electric engine. With aggressive airframe and engine noise 

reduction technology, respectively, -3.0 EPNdB & -2.0 EPNdB, the SUGAR High achieves 30.1 

EPNdB margin and SUGAR Volt achieves 31.4 EPNdB margin.

Figure 2.30 – Noise Levels & Stage 4 Margins for the SUGAR High & Volt

2.3.4.2 Configuration & Performance 

The main geometric parameters of interest for noise calculation are the landing gear geometry 

(wheel diameter, strut diameter, etc.), and high-lift system geometry (chord, span, deflection, 

etc.). Details for the specific values will be provided below in the detailed analysis discussion.

Noise analysis was determined for a prescribed FAR 36 flight performance profile/condition, 

depicted in Figure 2.31. The standard parameters are: (a) SL 77 deg F, 77 RH, (b) climbout speed

min = V2+10 kts, (c) distance to the microphone = 21,325 ft from brake release, (d) start of 

engine cutback = 3,500 ft before the microphone and ending at 23,000 ft from brake release, 

(e) noise trajectory ending at 3,000 ft altitude, and (f) Sideline noise measured at 1,000 ft 

altitude

22.8 CUM Margin to Stage 4 24.3 CUM Margin to Stage 4 

SUGAR High SUGAR Volt

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Figure 2.31 – FAR 36 Noise Profile

The relevant flight performance parameters and values for the Approach, Cutback, and Sideline 

noise profiles are provided below, in Table 2.10, for both the SUGAR High and Volt. The TOGW 

noted in the table was used to derive the Stage 3 noise limits.

Table 2.10 – Flight Performance Parameters for Noise Analysis

2.3.4.3 Overall Method of Analysis 

The noise analysis was conducted in 3 major parts, namely, engine noise, airframe noise, and 

total aircraft (system noise). The overall noise analysis process is shown in Figure 2.32. 

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Figure 2.32 – Overall Noise Analysis Process

The design specification inputs entail the airframe design, flight profiles, engine type, and 

engine power conditions. These parameters served as the inputs for the engine and airframe 

noise predictions. They provided the specific geometry values for the subcomponents, such as 

chords, spans, deflections, etc., and also the flight operating conditions for the specific noise 

certification points, such as altitude, velocity, and others.

The engine noise was developed and provided by GE, using their in-house “GE Decomposition” 

method. Non-treated (hardwall) in-flight Noise-Power-Distance (NPD) engine noise data was 

provided for the baseline (non-electric) engine and for all electric power settings. It included 

subcomponent 1/3rd octave spectral data for the Fan-Inlet, Fan-Exhaust, Jet, and Combustor.

Airframe noise was derived by Boeing’s component noise predictions which have been 

discussed and validated in References (4), (5), (6), and (7). Airframe noise included 1/3rd octave 

spectral data for the nose gear, main gear, slat, and flap. Trailing edge noise was not analyzed.

Engine and airframe noise data was extrapolated from flight to 150 foot polar-arc condition and 

then back to certification SL/CB/AP flight conditions. The data was then log summed to yield 

the total aircraft noise, and system metrics of PNLT, PNL and EPNdB. The extrapolation from 

flight to 150 ft polar-arc, and then back to flight was done so that all the predicted data would 

be processed through a standard in-house extrapolation processes.

2.3.4.4 Detailed Acoustic Analysis

CFD Analysis for Noise

CFD analysis was performed for the SUGAR HE configuration using CFD++ with an unstructured 

grid. The analysis was conducted at the power-on, reference noise certification low-speed 

Design 

Specification 

Inputs 

Airplane 

Geometry

Flight 

Performance

Engine 

Component 

Noise 

Prediction

Airframe 

Component 

Noise 

Prediction

Incoherent 

Summation

Certification 

Noise Metrics

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conditions. The configuration geometry was modeled using CATIAv5. The approach condition 

geometry entailed: (a) extended main gear, (b) extended nose gear, (c) leading edge Krueger 

(slat) set to 40o, and (d) trailing edge (TE) flaps deflected to 35o. The take-off (cutback) and 

sideline configuration entailed: (a) retracted landing gear, (b) slat set at 40o, and (c) TE flaps set 

at 5o

. The surface grids were generated using CATIAv5 Advanced Mesh Generation workbench 

tools. Care was taken to resolve all the TE surfaces as well as the flap and slat surfaces. High 

grid density was enforced using background source functions near the expected regions of large 

flow gradients. These regions included:

• Nacelle/pylon region to adequately resolve the powered flow field

• Around both the nose and main landing gears

• Around the wing and near LE Krueger, and TE flap

The Advancing Front Local Remeshing (AFLR) hybrid volume grid size grew to ~125 million cells.

The unstructured grid geometry of the SUGAR configuration at approach is shown in Figure 

2.33. The power-on boundary conditions were applied at the fan inlet, fan exit, and core exit 

faces. Finally, the CFD++ N-S analysis was performed at the appropriate flight conditions. The 

approach condition was set at M = 0.2, Altitude = 394ft, and AOA = 6.859o

. The sideline and 

takeoff (cutback) condition were set at M = 0.268, AOA = 6.000o, and altitudes of 1,000 feet and 

1,730 feet, respectively. The power-on boundary condition engine parameters (mass flow & 

temperature) for the inlet primary and bypass exhaust flows were interpolated from the engine 

cycle deck.

Figure 2.33 – SUGAR Geometry at Approach

Areas of high density gridding included the LE slat and TE flap. It is noted that, in the takeoff 

position, the leading edge of the flap is tucked under the wing such that the integrated lift of 

the flap alone had to be approximated. The solution iteration history for CL, CM, and CD 

parameters is shown in Figure 2.34. Note that the solution converged by ~600 iterations.

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Figure 2.34 – CFD++ Solution Convergence

Flow parameter data was extracted from the CFD Q files for the airframe noise component 

prediction codes. These included total Cl, Cp, and Mach number. Figure 2.35 shows locations of 

streamwise and spanwise cuts used to calculate these parameters.

Figure 2.35 – CFD++ Computed Mach Cuts

For gear noise the flow field in front of the main and nose gears was used to calculate the local 

Mach which was an important parameter for the gear noise prediction.

Since CFD++ uses unstructured grids, sectional Cl was not directly available from the results.

Hence, two Matlab routines were written, one to sort, filter, and smooth the CFD data, and 

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another to perform numerical integration, and then sum CPs to get sectional Cls. This process is 

illustrated in Figure 2.36. 

Figure 2.36 – Post-Processing of CFD++ Data for Computing Sectional Lift Coefficient

In the first Matlab routine, filter_v3.m, CFD results are filtered by Cp to separate wing upper 

surface and lower surface. The data points are then smoothed and *.int files are generated. The 

second Matlab routine, integration_v2.m, computes summations of Cp over the upper and 

lower surfaces. The magnitudes of ΣCp are added to obtain total Cl. The sectional lift coefficient 

cl is obtained by dividing Cl by chord length.

Airframe Noise Analysis

The airframe noise analysis included the nose gear, main gear, slat, and flap components. The 

trailing edge noise was not included. The approach condition included these four components, 

but the sideline and cutback conditions only included the slat and flap noise. It was assumed 

that the nose and main gear is stowed during cutback and sideline.

Both the SUGAR High & Volt are essentially a conventional tube-and-wing configuration other 

than the truss-braced wing aspect. Hence the methodologies developed for predicting the

airframe components were relevant since they were validated for current generation aircraft.

Moreover, the basis for the methods is aerodynamic sound generation theory, hence they are 

not limited to any particular aircraft type. Therefore, any effects specific to the SUGAR HE 

configuration were captured by the input parameters.

The airframe noise analysis process is depicted in Figure 2.37 below. In the first step, the 

airframe main-gear, nose-gear, flap, and slat subcomponents are predicted at in-flight condition 

for AP/CB/SL, using the “As-Drawn” airplane parameter inputs. The subcomponents are then 

merged (log summed) to yield the total airframe data at AP/CB/SL. This total airframe data is 

then extrapolated to 150 ft polar-arc condition at flight speed. This data is then scaled to the 

“Sized” airplane, using the scaling factor SF = SQRT(Wing Area Ratio) = 0.923, with Sized Area = 

1260 ft^2 and As-Drawn Area = 1478 ft^2. The SPL correction is then 20LOG(SF) = -0.693, which 

is a constant/fixed SPL adjustment for all angles and frequencies. The final step is extrapolation 

of the total airframe data to the AP, CB, and SL conditions noted in Table 2.10 above. 

CFD 

Solutions filter_v3.m integration_v2.m CL

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Figure 2.37 – Airframe Noise Analysis Process

The main & nose gear geometric input parameters for both the SUGAR High & Volt are 

provided in Table 2.11 below. The approach condition flight performance for the gear noise 

prediction is provided above, in Table 2.10. Aside from geometry, the most important input 

parameter for the gear noise prediction is the local Mach in proximity of the gear. This was 

derived, as noted above using CFD analysis with CFD++.

Table 2.11 – Main Gear & Nose Gear Noise Prediction Input Parameters

The slat system is also the same for the SUGAR High and Volt, and consists of an inboard 

leading edge Krueger flap and several outboard elements which are all Krueger flaps. The 

deflection angle for the slat is the same for approach, cutback, and sideline, and is depicted in 

Figure 2.38 below. Note in Figure 2.38, that all the outboard elements deploy together.

Effectively then, it can be considered as only 2 slat elements, an inboard and outboard element.

In-Flight Airframe 

Component Prediction

Merge Airframe 

Components

Extrapolate to 150ft 

Polar-arc

Scale to “Sized” 

Airplane

Extrapolate to Flight

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Figure 2.38 – SUGAR Slat System Deployment for Approach/Cutback/Sideline

However, because there is a large change in sweep and chord length from station 587.9, hence 

the slat was treated as 3 elements, namely, element 1 up to the engine centerline, element 2 

from the engine center line to station 587.9 and element 3 from station 587.9 and onwards.

This 3 element representation is illustrated in Figure 2.39 below.

Figure 2.39 – SUGAR Slat System 3-Element Definition for Noise Analysis

The geometric input parameters for the slat system are provided in Table 2.12. The flight 

performance conditions for approach, cutback and sideline conditions for the slat noise

prediction are provided in Table 2.10 above. The CFD++ analysis provided the slat lift coefficient 

data, and the total lift aerodynamic inputs required for the slat noise prediction.

Table 2.12 – Slat Noise Prediction Input Parameters

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The flap system is also the same for the SUGAR High and Volt, and consists of an inboard flap 

element, a flaperon, and 2 outboard flap elements. Both the inboard and outboard are single 

element fowler flaps. The flap system deflection for approach is illustrated in Figure 2.40. The 

deployment for cutback/sideline is depicted in Figure 2.41. 

Figure 2.40 – SUGAR Flap System Deployment at Approach

Figure 2.41 – SUGAR Flap System Deployment at Cutback/Sideline

Note in Figure 2.40 and Figure 2.41 that all the flap elements deploy together, which implies 

that there are only two edges, one edge between the body and inboard side of the inboard flap 

and one edge between the outboard edge of the outboard flap and the aileron. These 2 edges 

are schematically noted in Figure 2.42. 

Figure 2.42 – SUGAR Flap System Definition for Noise Analysis

Inboard

Flap

Outboard

Flap

Flaperon

EDGE

Aileron

EDGE

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The SUGAR configuration geometric input parameters for the flap at approach are provided in 

Table 2.13 . The parameters for Cutback are provided in Table 2.14. Note that the only 

differences are in the deployment angle and CLs. The sideline parameters are provided in Table 

2.15. Note that the cutback and sideline parameters are quite similar.

Table 2.13 – Flap Noise Prediction Input Parameters at Approach

Table 2.14 – Flap Noise Prediction Input Parameters at Cutback

Table 2.15 – Flap Noise Prediction Input Parameters at Sideline

The airframe noise levels will be shown later on in comparison to the engine noise levels.

Engine Noise Analysis

In this study, engine noise prediction and analysis was provided by GE. The data included 

spectral engine noise component predictions and final Noise-Power-Distance (NPD) EPNL levels 

at the noise certification conditions. All the data was at in-flight condition and for a Hardwall 

Engine. The methodology and technology assumptions GE used for their predictions are shown 

in Table 2.16. The engine noise was derived from a “parent” engine database using the most 

advanced engine technology that was applicable to this design, namely the GE GENX-1B engine.

This acoustic database had to be scaled to the operating conditions of the SUGAR HE as well as 

account for any differences in the engine design.

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Table 2.16 – GE Noise Modeling Assumptions for the SUGAR HE Volt Engine

Figure 2.43 below, gives the noise certification EPNL NPD levels at sideline, cutback and 

approach for the baseline (no electric motor) and for 2000 – 10,000 Hp electric motor / engine 

designs. The noise curves shown here are for total engine noise without the airframe noise and 

for hardwall engine (no acoustic liners). As noted in the figure, the addition of electric power at 

AP, CB and SL has very minimal impact or benefit on the noise levels.

Figure 2.43 – GE Noise-Power-Distance (NPD) Predictions for Hardwall Engine

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Engine treatment was defined and analyzed by Boeing. The GE supplied hardwall engine data 

was used as input. Detailed liner treatment analysis/design was out of scope for this study, 

hence a simpler approach was adopted, as illustrated in Figure 2.44. 

Figure 2.44 – Process for Derivation of SUGAR HE Treated Engine Noise

The first step is to extrapolate the SUGAR in-flight data to 150 ft polar-arc with speed and not 

for static condition since flight effects can’t be undone. Then the inlet, aft-fan, jet and 

combustor engine subcomponents are extracted separately. Next the jet, inlet, and aft-fan are 

adjusted. The jet subcomponent adjustment is based on the Boeing database of HBR engines

and is consistent with the standard system noise process. The fan-inlet and fan-exhaust are 

adjusted using the GEnx-1B data. Similarity of engine treatment was assumed. This engine was 

chosen for derivation of attenuations since it was also the engine used as the starting point to 

define the hardwall SUGAR engine model provided by GE. The input is the GEnx-1B engine 

component static test data. This data is scaled to the SUGAR engine size using the scale factor 

SF = Fan Diameter Ratio = 0.64, in which SUGAR = 71.2 in fan diameter and GEnx-1B = 111.1 in.

SUGAR In-Flight Engine 

Noise Prediction (by GE)

Extrapolate to 150ft 

Polar-arc

Extract Engine 

Components

Apply Adjustments / 

Attenuations

Scale to

“Sized” Engine

Merge Engine 

Components

Extrapolate to Flight

GEnx-1B

Engine 

Component Static 

Data 

Scale to Sugar 

Engine Size

Derive Inlet &

Aft-fan 

Attenuations

Adjust SUGAR 

Inlet & Aft-Fan 

with Attenuations

Adjust 

SUGAR JET

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The noise attenuations are then derived by subtracting the hardwall data minus the treated 

data for the aft-fan and inlet subcomponents. The derived spectral attenuations are then used 

to adjust the SUGAR aft-fan and inlet subcomponent data. Care was taken to align the correct 

power setting conditions between the scaled GEnx-1B data and the SUGAR HE data so that 

tones aligned correctly when applying the attenuations. Next, the adjusted jet, inlet, and aft-fan 

subcomponents are remerged (log summed) with the combustor component to derive the 

adjusted (noise attenuated) total engine noise which includes lining effects. Next, the adjusted 

data is scaled by thrust to derive appropriate RPMS for AP/CB/SL. Finally, the selected RPM 

spectral data is extrapolated to AP/CB/SL flight conditions noted in Table 2.10 above.

Total Aircraft (System Noise)

With all the airframe and engine subcomponents properly scaled, adjusted, and projected to inflight conditions, comparisons can now be made of the subcomponent noise levels relative to 

each other. Figure 2.45 shows the PNLT noise level contribution of each subcomponent for the 

SUGAR Volt at AP condition. Note that at AP, the main-gear is the dominant component. The 

next dominant component is the flap followed by the nose gear. Although not shown, the 

results are similar for the SUGAR High.

Figure 2.45 – PNLT Contribution at AP from All Subcomponents for SUGAR Volt

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Similarly, Figure 2.46 shows the PNLT comparison at CB condition. Here the jet subcomponent 

dominates in the forward-arc (up to overhead). From overhead to about 16 sec after overhead, 

the aft-fan dominates, followed closely by jet. From then on the core noise becomes dominant.

Again, similar results were noted for the SUGAR High.

Figure 2.46 – PNLT Contribution at CB from All Subcomponents for SUGAR Volt

Finally, Figure 2.47 shows the PNLT comparison at SL condition. Here the jet subcomponent 

clearly dominates and by a far margin. After the jet subcomponent, the flap dominates up to 

overhead and the aft-fan dominates after overhead. Similar results noted for the SUGAR High

Figure 2.47 – PNLT Contribution at SL from All Subcomponents for SUGAR Volt

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Having looked at the comparison of all the noise subcomponents on a PNLT basis, the total 

engine noise and total airframe noise are compared on an integrated EPNdB basis. Hence all 

the airframe subcomponents are merged (LOG summed) to derive total engine noise and all the 

engine subcomponents are merged to derive the total engine noise. Figure 2.48 provides the 

EPNdB comparison for the total engine and airframe noise at AP, CB, and SL for the SUGAR Volt.

On a total basis, the airframe dominates at AP only and the engine dominates at CB and SL.

Similar results were noted for the SUGAR High.

Figure 2.48 – Comparison of Total Engine and Airframe Noise for the SUGAR Volt

With the final subcomponent and total noise levels discussed so far, the SUGAR High and Volt 

yield the total aircraft (system) noise levels and margins shown in Figure 2.30. These noise 

levels and margins can be considered baseline. Recall, that the SUGAR High achieves a CUM 

margin to Stage 4 = 22.8 and the SUGAR Volt achieves a CUM margin = 24.3. The SUGAR High 

and Volt have similar noise levels and hence achieve similar CUM margins because of small 

differences in flight speeds, thrusts, and altitude at cutback between the airplanes, as well as 

similar engine noise between the electric and non-electric variants.

Parametric Noise Reduction

A conceptual level parametric noise reduction study was conducted to assess what further 

noise reduction can be achieved beyond the “baseline” noise levels and CUM margins. The 

engine and airframe components were considered separately and each was assigned a range of 

potential total component noise reduction that seemed reasonable and feasible to achieve in 

the future. However no detailed analysis was conducted to assess/identify which specific 

technology would account for the total component noise reduction, as this was outside the 

scope of the analysis. Thus the total component noise reduction levels were merely 

“placeholders” to then assess what would be the resultant reduction in the total system noise, 

and the gained reduction in the CUM noise margin. Table 2.17 provides the parametric study 

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results for the SUGAR High with a total airframe noise reduction from 0.0 to -3.0 EPNdB and a 

reduction in total engine noise from 0.0 to -2.0 EPNdB. Note that the 0.0 reduction in both the 

airframe and engine corresponds to the baseline 22.8 EPNdB CUM margin noted in Figure 2.30

and above. In contrast, looking at the highest potential reduction in airframe noise of -3.0 

EPNdB and highest potential reduction in engine noise of -2.0 EPNdB, the maximum achievable 

CUM margin to Stage 4 is 30.1 EPNdB.

Table 2.17 – Parametric Noise Reduction & Stage 4 CUM Margins for SUGAR High

Similarly Table 2.18 provides the parametric study results for the SUGAR Volt. Note 24.3 EPNdB 

CUM that the 0.0 reduction in both the airframe and engine corresponds to the baseline 24.3 

EPNdB baseline CUM margin for no reduction as noted in Figure 2.30 above. In contrast, at the 

highest reduction levels (airframe = -3.0, and engine = -2.0), the maximum achievable CUM 

margin to Stage 4 is 31.4 EPNdB.

Table 2.18 – Parametric Noise Reduction & Stage 4 CUM Margins for SUGAR Volt

SUGAR High

CUM Margins 

to Stage 4

Engine Noise Reduction (EPNdB)

0.0 -0.5 -1.0 -1.5 -2.0

Airframe Noise Reduction 

(EPNdB)

0.0 22.8 23.8 24.7 25.7 26.6

-0.5 23.4 24.4 25.4 26.3 27.2

-1.0 23.9 24.9 25.9 26.9 27.8

-1.5 24.5 25.5 26.5 27.5 28.4

-2.0 25.0 26.0 27.0 28.0 29.0

-2.5 25.4 26.5 27.5 28.5 29.6

-3.0 25.9 27.0 28.1 29.1 30.1

SUGAR Volt 1860 (2000HP)

CUM Margins 

to Stage 4

Engine Noise Reduction (EPNdB)

0.0 -0.5 -1.0 -1.5 -2.0

Airframe Noise Reduction 

(EPNdB)

0.0 24.3 25.4 26.3 27.3 28.4

-0.5 24.8 25.8 26.9 27.9 28.9

-1.0 25.3 26.4 27.3 28.4 29.4

-1.5 25.7 26.8 27.9 29.0 30.0

-2.0 26.3 27.2 28.3 29.5 30.6

-2.5 26.6 27.8 28.9 29.8 31.0

-3.0 27.0 28.1 29.3 30.4 31.4

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With the margins identified for each potential airframe/engine noise reduction pairing, a 

brainstorming was conducted to identify some concepts that might help to achieve these 

component noise reductions. The gear, flap jet and fan were focused on since they were the 

dominant subcomponents. Table 2.19 provides the brainstormed noise reduction ideas for the 

airframe components. Table 2.20 provides the brainstormed noise reduction ideas for the 

engine components. This is by no means an exhaustive list but rather a sampling of potential 

ideas.

Table 2.19 – Potential Airframe Noise Reduction Technology

Table 2.20 – Potential Engine Noise Reduction Technology

2.3.4.5 Summary of Acoustic Analysis 

Acoustic analysis was completed for the SUGAR High and SUGAR Volt. Engine Noise prediction 

and analysis was provided by GE. Airframe noise, and engine treatment noise prediction and 

analysis were conducted by Boeing. Final integration of engine and airframe noise and 

derivation of system noise was conducted by Boeing as well. With respect to the Phase 1 results 

and the preliminary Phase 2 results, the final results are drawn from higher fidelity noise 

analysis, and from use of flight performance and a refined more accurate baseline (reference).

The SUGAR High & Volt were found to have similar noise levels and CUM margins due to 

similarity in flight performance and small influence on the engine noise due to use of electric 

power. The SUGAR High achieved a CUM margin to Stage 4 of 22.8 EPNdB, while the SUGAR 

Volt achieved 24.3 EPNdB CUM margin. With “placeholder” values for advanced airframe and 

engine noise reduction technology, the Sugar High achieved 30.1 EPNdB CUM margin to Stage 4 

and the Sugar Volt achieved a CUM margin of 31.4 EPNdB. All results were far short of meeting 

the 52 EPNdB CUM margin goal.

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2.3.5 hFan+2: Emissions

The LTO NOx emissions for the hFan+2 propulsion systems for the 750 Balanced and 750 Core 

Shutdown aircraft were assessed. The CAEP/6 requirements, characteristic LTO NOx, and 

margined estimates are listed below in Table 2.21. GE also provided estimates of cruise NOx 

emissions compared to a CFM-56. Propulsive and thermal efficiency gains in combination with 

improved thrust lapse characteristics have resulted in diminished LTO NOx emissions for the 

hFan+2 engine architectures. For LTO NOx, both the hFan+2 propulsion systems were better 

than the goal (20% of CAEP/6). For cruise NOx the 1380 and 1750 HP aircraft are close to the 

80% reduction goal and the 7150 HP core shutdown aircraft has essentially no NOx emissions 

over approximately 50 percent of the cruise segment of the 900 nm mission.

Table 2.21 – 750 Balanced and Core Shutdown NOx Emissions

2.3.6 Energy Cost Analysis

The objective of the Energy Cost Study was to investigate the total energy cost of operating the 

SUGAR Volt by parametrically varying battery performance, fuel cost, battery life and cost, and 

electricity cost. 

In addition to comparing the SUGAR Free and SUGAR High, two versions of the SUGAR Volt 

were analyzed: SUGAR Balanced (Ref HP 1,750) and SUGAR Core Shutdown. Both planes 

assumed a battery performance level of 750 Whr/kg. Each plane had a different design goal and 

approach: 

LTO NOx Emissions - 750 Balanced

Non-Hybrid Electric Hybrid Electric

CAEP/6, g/kN 75.4 75.4

Characteristic LTO NOx, g/kN 13.1 7.4

Margined Estimate, % CAEP/6 19.2% 10.9%

LTO NOx Emissions - 750 Core Shutdown

Non-Hybrid Electric Hybrid Electric

CAEP/6, g/kN 78.4 78.4

Characteristic LTO NOx, g/kN 11.6 5.2

Margined Estimate, % CAEP/6 16.3% 7.4%

1380 HP ICAC 

Constrained

1750HP ICAC 

Inactive

7150HP ICAC 

Inactive

Absolute NOx Reduction 73.5% 73.9% 99.3%

Mid-Cruise Emissions (CFM56-7B Baseline)

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• Balanced (Ref HP 1,750) Design: 

o MTOW Sized by 3,500 NM mission (assuming an all-fuel mission)

o Net energy density (fuel + batteries) is traded for range while maintaining MTOW

for missions longer than 900 nm

o Ref HP 1,750 was chosen to fully discharge the 16,700 lb of batteries over the 

duration of the 900 nm mission

• Core Shutdown Design: 

o MTOW sized to 190,000 lb to maximize fuel burn savings on the 900 nm mission

o Results in larger electric motors and greater battery size and weight capability

o The electric horse power was sized (7,150 engine HP) to enable core shutdown 

at cruise. 

An Excel-based tool was used to parametrically explore combinations of energy and battery 

cost. Electricity, fuel, and battery life assumptions do not change the aircraft weight and thus 

do not require the vehicles to be resized.

We evaluated the following costs:

• Fuel + electricity (“energy cost”)

• Fuel + electricity + battery replacement (“energy + batteries cost”)

• Fuel + electricity + battery replacement + other weight & complexity based costs (from 

public domain cost models)(8) (“total cost”)

To conduct the parametric analysis, we needed to establish a range of assumptions for the 

major drivers on aircraft costs. These included fuel cost, electricity cost, battery energy per 

mass, battery cost, and battery life. Based on expert judgment and historical trends, we 

established a range of values for each (low, middle, high), shown in Table 2.22. For battery 

specific energy, we used 750 Wh/kg (the reference N+3 level) where we have available sized 

aircraft configurations. It was our expectation, based on Phase I and Phase II Year 1-2 results 

that values below 750 Wh/kg would not yield any cost benefits due to the increased battery 

weight. Battery Life/Cost values were calculated by using the highest and lowest combinations 

of battery life and cost.

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Table 2.22 – Cost Assumptions

Parameter Units Low 

Value

Middle 

Value

High 

Value

Nominal More 

Optimistic

Most 

Optimistic

Fuel Cost $/gal 3.00 5.00 6.00 5.00 5.00 6.00

Electricity Cost $/kwh 0.03 0.07 0.10 0.07 0.07 0.03

Battery Energy Wh/kg 750 1000 750 750 750

Battery Cost $/kg 44 88 220 88 44 44

Battery Life Cycles 1,000 2,000 5,000 2,000 5,000 5,000

Battery 

Life/Cost

Cycles 

kg/$

4.5 23 114 23 114 114

2.3.6.1 Results for Nominal Assumptions

Using the nominal cost assumptions shown in Table 2.22, results were generated and are 

shown in Figure 2.49, Figure 2.50, and Table 2.23. These results indicate that the balanced 

SUGAR Volt (Ref HP 1750) is the better option. The percentages shown in Table 2.23 are 

computed as differences relative to SUGAR High. The balanced Volt has a 6% reduction in 

Energy Cost, a 1% reduction in Energy + Battery Cost, and a 2% increase in Total Cost compared 

to SUGAR High. The core shutdown plane shows a slight increase in Energy Cost, a 16% increase

in Energy + Battery Cost, and a 15% increase in Total Cost. The core shutdown Volt, which had 

nearly triple the battery weight of the balanced Volt, clearly reflects this in the increased 

battery costs.

Table 2.23 – Cost Results for Nominal Cost Assumptions

Category

SUGAR Balanced

750 Wh/kg

(Ref HP 1,750)

SUGAR Core 

Shutdown

750 Wh/kg

Energy Cost -6.3% 0.1%

Energy + Batteries Cost -0.8% 15.9%

Total Cost 1.8% 15.3% 

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Figure 2.49 – Energy + Battery Cost Results for Nominal Assumptions

Figure 2.50 – Total Cost Results for Nominal Assumptions 

2.3.6.2 Results for More Optimistic Battery Assumptions

Using the more optimistic battery cost and life assumptions shown in Table 2.22, results were 

computed and are shown in Figure 2.51, Figure 2.52, and Table 2.24. The percentages in Table 

2.24 are taken relative to SUGAR High. Again, the balanced Volt looks best. The balanced 

airplane shows a 6% reduction in Energy Cost, a 5% decrease in Energy + Battery Cost, and a 1% 

Total Cost increase compared to SUGAR High. The core shutdown airplane has a slight increase

in Energy Cost, a 3% increase in Energy + Battery Cost, and a 12% increase in Total Cost. The 

core shutdown airplane is heavily penalized for its significantly increased MTOW over SUGAR 

High. 

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SUGAR Free SUGAR High SUGAR Balanced

750 Whr/kg (Ref

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SUGAR Core

Shutdown 750

Whr/kg

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Electricity

Fuel

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Table 2.24 – Cost Results for More Optimistic Battery Assumptions

Category

SUGAR Balanced

750 Wh/kg

(Ref HP 1,750)

SUGAR Core 

Shutdown

750 Wh/kg

Energy Cost -6.3% 0.1%

Energy + Batteries Cost -5.2% 3.3% 

Total Cost 0.6% 11.6%

Figure 2.51 – Energy + Battery Cost Results for More Optimistic Battery Assumptions

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SUGAR Free SUGAR High SUGAR Balanced

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HP 1750)

SUGAR Core

Shutdown 750

Whr/kg

Cost/Seat (900 nmi flight)

Batteries

Electricity

Fuel

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Figure 2.52 – Total Cost Results for More Optimistic Battery Assumptions

2.3.6.3 Results for Most Optimistic Assumptions for Electric Aircraft

Using the most optimistic battery cost and life assumptions, as well as a high fuel cost and a low 

electricity cost (see Table 2.22), the following results were generated and are shown in Figure 

2.53, Figure 2.54, and Table 2.25 (relative to SUGAR High). The balanced airplane shows an 11% 

reduction in Energy Cost, a 10% decrease in Energy + Battery Cost, and a 1% reduction in Total 

Cost compared to SUGAR High. The core shutdown aircraft has a 14% reduction in Energy Cost,

an 11% reduction in Energy + Battery Cost, and a 6% increase in Total Cost. Using the most 

optimistic assumptions, the balanced aircraft shows a lower Total Cost than SUGAR High. 

Table 2.25 – Cost Results for Most Optimistic Battery Assumptions

Category

SUGAR Balanced

750 Wh/kg

(Ref HP 1750)

SUGAR Core 

Shutdown

750 Wh/kg

Energy Cost -11.2% -13.9%

Energy + Batteries Cost -10.3% -11.3% 

Total Cost -1.4% 6.4% 

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SUGAR Free SUGAR High SUGAR Balanced

750 Whr/kg (Ref

HP 1750)

SUGAR Core

Shutdown 750

Whr/kg

Cost/Seat (900 nmi flight)

Aircraft

Batteries

Electricity

Fuel

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Figure 2.53 – Energy + Battery Cost Results for Most Optimistic Battery Assumptions

Figure 2.54 – Total Cost Results for Most Optimistic Battery Assumptions

2.3.6.4 Energy Cost Analysis Summary

These results are sensitive to fuel and electricity costs, battery energy density, battery cost, 

battery life, and weight. 

SUGAR High reduces fuel and electricity cost by 54% and total cost by 29%, when compared to 

the SUGAR Free (current technology non-electric). SUGAR High includes all SUGAR N+3 

technologies except the use of a hybrid electric battery gas turbine propulsion system. The 

SUGAR High is used as the reference for the hybrid electric SUGAR Volt aircraft results below. 

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The balanced SUGAR Volt shows a 6-11% reduced direct energy cost (fuel + electricity) and a 1-

10% reduced energy (fuel + electricity + battery) cost relative to SUGAR High. However, 

including costs for increased aircraft weight and complexity reveals minimal cost difference 

(+2% to -2%) relative to SUGAR High. The balanced SUGAR Volt aircraft was found to be the 

best option of the two SUGAR Volt airplanes in this study.

The SUGAR Volt maintains an emissions advantage over the SUGAR High. NOx emissions are 

better because of reduced gas turbine throttle and reduced fuel use. Life cycle CO2 emissions 

can also be improved, but only if battery-charging electricity is from a clean, low-carbon source. 

2.4 Technology Plans and Roadmaps

The team has updated some of the technology plans that were developed in Phase I. The 

following updated technology plans are included in this section:

• Hybrid Electric Engine

• High Performance Batteries

These plans are not included in this report but are included in Final Report Volume I Truss 

Braced Wing Design Exploration:

• NextGen Air Traffic Management

• Aerodynamic Technologies for Performance

• Airframe Acoustic Technologies

• Engine Acoustic Technologies

• Advanced Subsystems

• Structural Materials

• Structural Concepts

• Advanced Engine Technologies

• High Span TWB Technology Integration

2.4.1 Hybrid Electric Engine

Goals and Objectives: 

Develop high performance, flight weight, and prime-reliable electric power components 

suitable for flight propulsion applications.

Performance Area and Impact: 

Fuel burn and emissions will be reduced by using energy stored in batteries that is generated 

from alternative energy sources, such as solar, wind, or nuclear. The battery energy will be 

leveraged to aid in hybrid turbo-electric propulsion of the aircraft.

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Noise will be reduced by using stored battery energy to replace some of the energy generated 

by gas engines, thereby reducing the production of noise from the core of the engines. Hybrid 

electric systems also could enable distributed propulsion architectures which could enable BLI 

(Boundary Layer Ingestion) technology.

Technical Description:

Hybrid turbo-electric propulsion enabled by energy stored in batteries has the potential to 

reduce fuel burn, emissions, and noise. Savings are dependent on battery energy density as 

well as the performance, efficiency, and weight of the electric power components. Efficient, 

high power, and light weight motors and motor controllers need to be developed. Light weight 

radiators and surface coolers are also needed to maintain the electric power components at 

temperatures conducive to high efficiency. A sustained program to develop high voltage 

conductors and insulators is also needed to support development of the necessary electric 

power components. A variable core nozzle needs to be developed to allow the engine to 

operate with more widely varying levels of load introduced by the option to switch to electric 

power. These components need to be integrated in a hybrid engine system that can be 

demonstrated in flight. Superconducting components should be considered as possible system 

enhancing technologies and this would require additional development, design, and testing of 

cryocooler systems for aircraft. Battery technology development is included in a separate 

technology plan.

Risk Assessment:

If hybrid engine performance and weight do not reach the levels assumed in the vehicle 

analysis, this technology will not contribute the projected benefits in fuel burn, emissions, and 

noise.

1

2

3

4

5

1 2 3 4 5

Consequence

Likelihood

 

 

 

 

 

 

 

a

b

c

d

e

Low

Moderate

High

 

 

 

 

 

 

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Major Milestones:

Maturation Plan: 

TRL 2 (a) Current

• Some analysis of the engine system has been performed. 

• Some mission and sizing analysis has been conducted to assess fuel burn, energy, and 

global life cycle emissions benefit.

TRL 3 (b)

• A life-cycle energy study will examine net benefit to fuel burn and emissions including 

generation of energy on the ground

• A study will assess the potential reductions in airport noise and emissions

• 3 motor design cycles

• 3 surface cooler/radiator design cycles

• 3 motor controller/power electronics design cycles

• Sustained program for lightweight high voltage conductors and insulators, with offramps every ~18 months

• Lightweight variable core exhaust nozzle design

• A design developed for a small-scale hybrid electric propulsion system

TRL 4 (c)

• 3 motor build, test, report-out cycles

• 3 surface cooler/radiator build, test, report out cycles

• 3 motor controller/power electronics build, test, report out cycles

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• Sustained program for lightweight high voltage conductors and insulators, with off 

ramps every ~18 months

• Lightweight variable core exhaust nozzle build, test

• An integrated ground test of a small scale hybrid engine

TRL 5 (d)

• Integration of components into 1st full-scale demonstration engine

• 1st demonstration engine test

• Flight test of a small scale hybrid engine integrated into a small aircraft

TRL 6 (e)

• Integration of components into 2nd full-scale demonstration engine

• 2nd demonstration engine test

• Optional flight test

Dependency:

• High energy density battery technology is required to harness the benefit of hybrid 

engine technology.

• A suitable off-the-shelf engine asset is needed to support testing.

Success Criteria:

TRL Success Criteria Alternate Steps if Unsuccessful

3 

Analysis shows hybrid engine system will 

have performance (fuel burn, emissions, 

noise) and weight consistent with meeting 

goals

Continue to optimize and improve design of 

system and components

Consider application to smaller, shorter 

range aircraft

4 

Tests of hybrid engine system components 

show performance (fuel burn, emissions, 

noise) and weight consistent with goals

Redesign components with shortfalls

5 

Hybrid engine system components 

integrated and successfully tested

Initial system performance (fuel burn, 

emissions, noise) and weight indicates goals 

can be met with some redesign

Redesign system to meet goals

Accept meeting reduced goals

6 

Hybrid engine system demonstrates 

performance (fuel burn, emissions, noise) 

and weight consistent with goals

Accept meeting reduced goals

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Volume II – Hybrid Electric Design Exploration

Figure 2.55 – Hybrid Electric Engine Technologies*

TRL Task 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028

1 Lightweight flightworthy high voltage enabling materials

3 1.1 Insulator Materials Program

3 1.2 Conductors and Connectors Program

3 1.3 Lightweight Magnetics & Support Structure

2-3-4 2 Flight weight, prime-reliable motor

2.1 Design, Modeling & Analysis

2.2 Controller Fabrication & Bench Test

2-3-4 3 Motor Controller / Power Electronics

3.1 Design, Modeling & Analysis

3.2 Controller Fabrication & Bench Test

2-3-4 4 Light weight, low loss cooler/radiator

4.2 Design, Modeling & Analysis

4.3 Design Fabrication & Bench Test

2-3-4 5 Variable Core Nozzle

5.1 Design, Modeling & Analysis

5.2 Nozzle Fabrication & Component Tests

6 Engine Design Studies

7 Flight Demo (optional)

3 7.1 Integrated Ground Demo

4 7.2 Flight Demo Small Scale Reduced Scale ecoDemonstrator?

8 Full Scale Demo

8.1 Demo Engine Design & Integration

8.2 Demo Build 1 Component Fabrication & Assembly

5 8.3 Demo Build 1 Test

8.4 Demo Build 2 Design & Integration

8.5 Demo Build 2 Component Fabrication & Assembly

6 8.6 Demo Build 2 Test

*The roadmap schedule shown is notional, suitable for overall program planning 

purposes only, with no implied guarantee or commitment on the part of GE Aviation 67
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Volume II – Hybrid Electric Design Exploration

2.4.2 High Performance Batteries

Goals and Objectives: 

Foster development of high energy density modular batteries. Work with one or more battery 

manufacturers to produce batteries that achieve aviation safety requirements and are tailored 

for aviation performance requirements and usage patterns. Integrate them in flight propulsion 

applications when the batteries are at an appropriate level of development.

Performance Area and Impact: 

Fuel burn and emissions will be reduced by using energy stored in batteries that is generated 

from alternative energy sources, such as solar, wind, or nuclear. High efficiency of electrical 

components may reduce total energy usage and emissions relative to conventional liquid fuels. 

Life cycle studies will be needed to confirm these savings.

Technical Description:

Multiple battery technologies have potential to produce the energy densities needed to reduce 

fuel burn and emissions in an aircraft application. Low-level studies are needed to produce 

requirements and data that can be provided to battery manufacturers to encourage the 

development of battery technology that can support such application. Once suitable batteries 

are available, a substantial development program will be required to integrate and test these 

batteries in combination with hybrid-electric engine technology and aviation specific 

requirements.

Risk Assessment:

If battery energy densities do not reach the levels assumed in the vehicle analysis, this 

technology will not contribute the projected benefits in fuel burn, emissions, and noise.

1

2

3

4

5

1 2 3 4 5

Consequence

Likelihood

 

 

 

 

 

 

 

a

b

c

d

e

Low

Moderate

High

 

 

 

 

 

 

*The roadmap schedule shown is notional, suitable for overall program planning 

purposes only, with no implied guarantee or commitment on the part of GE Aviation 68
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Volume II – Hybrid Electric Design Exploration

Major Milestones:

Maturation Plan: 

TRL 2 (a) Current

• Theoretical estimates and some small-scale experiments indicate feasibility of reaching 

the needed energy density

TRL 3 (b)

• A life-cycle energy study will examine net benefit to fuel burn and emissions including 

generation of energy on the ground

• Aircraft system studies will define requirements for battery technology (including safety 

and charge/discharge rate)

• Battery manufacturers will develop the basic technology to achieve the required energy 

density. Develop approach to achieve aviation specific battery life, charge/discharge 

rate, and safety.

TRL 4 (c)

• Battery components will be tested for meeting aircraft power, life, charge/discharge 

rates, and safety requirements including operation in a relevant environment

TRL 5 (d)

• Battery components will be integrated and packaged for testing in flight

• A battery package of representative size will be tested in flight or simulated flight 

conditions

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TRL 6 (e)

• A battery power system suitable for a demonstrator aircraft will be assembled

• The battery power system will be integrated with a hybrid-electric engine

• The combined hybrid-electric engine and battery power system will be tested in flight

Dependency:

Aviation batteries are dependent on dramatic improvements in battery technology for other 

applications such as ground transportation. Hybrid-electric or all-electric propulsion is required 

to harness the benefit of aviation battery technology.

Success Criteria:

TRL Success Criteria Alternate Steps if Unsuccessful

3 

Analysis shows battery technology will result 

in fuel burn and emissions reductions 

through a complete energy life-cycle

Battery energy density reaches required 

levels

Continue basic battery development

Switch to alternative battery option*

Switch to alternative technology option*

Accept meeting reduced goals**

4 

Component testing of batteries show 

suitability for aircraft application (including 

life, charge/discharge rate, and safety)

Resume basic battery development

Switch to alternative battery option*

Switch to alternative technology option*

5 

Batteries successfully packaged for use in 

flight

Battery package successfully tested in flight 

conditions

Redesign battery packaging

Switch to alternative technology option*

6 Battery power system successfully tested 

with hybrid-electric engine in flight

Accept meeting reduced goals**

Switch to alternative technology option*

* Baseline battery technology is assumed to be Lithium-Ion, but this chemistry may reach a 

plateau in performance before needed levels are reached. Alternative battery options include 

Zinc-Air, Lithium-Air, and liquid electrolyte slurries which would require additional systems.

Alternative technology options include hybrid batteries (multiple chemistries), capacitors, 

hybrid battery capacitor, and flywheels. Zinc-Air and Lithium-air batteries require design of air 

induction and exhaust system which would require updated roadmap tasks to be added. Quick 

modular battery swap out or mechanically rechargeable components could be used if charge 

rates are not fast enough for quick gate turn requirements.

** Lower performance batteries could be suitable for smaller and especially shorter range 

aircraft

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Figure 2.56 – High Performance Battery Technology Roadmap

TRL Task

2013

2014201520162017201820192020202120222023202420252026

20272028

20292030203120322033203420352036203720382039

2-3-4 1 Battery Development

1.1 Basic Technology 

Development

Generation 0 Generation 1 Generation 2 Generation 3 Product Improvement

1.2 Aviation Req. Testing

Test Cycle 1 Test Cycle 2 Test Battery Life, etc. Test Battery Life, etc.

3 2 Life Cycle Study

2.1 Perform Life-Cycle Study

3 3 Aircraft System Studies

3.1 Perform Aircraft System 

Studies

Update Requirements Update Requirements Update Requirements

3.2 Define Battery 

Requirements

3-4-5 4 Integrated Power System 

Testing

4.1 Design and Modeling for 

Integration and Packaging

4.2 Hardware buildup

4.3 Perform Integrated 

Testing

6 5 Aircraft Demonstrator

5.1 Design Battery Power 

System

5.2 Integrate Battery Power 

System on Aircraft

5.3 In Flight Testing

Early Battery Transition 

to Smaller / Shorter 

Range Aircraft

Product 

Improvement 

N+4.5?

N+3 

Battery 

Pathway

N+4 

Battery 

Pathway

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3.0 Hybrid Electric Propulsion Modeling Environment & Sizing Studies

The modeling environment documentation is broken into two separate sections. The first 

section covers the modeling environment while the second covers sizing studies conducted 

within the environment.

3.1 Hybrid Electric Modeling Environment

In Task 3.3 Georgia Tech developed a set of generic NPSS elements that can be used to 

construct a modeling environment that will enable assessment of various electric and hybrid 

electric architectures, including batteries, fuel cells, and ducted and unducted fans.

The NPSS models were constructed using public domain methods in order to ensure their 

usability and traceability. When possible, the models were developed such that proprietary 

data or modeling algorithms could be easily implemented should that be required. After an 

initial period of discussion with Boeing and NASA, Georgia Tech finalized the list of modeling 

elements to be developed. The list of NPSS elements developed includes an SOFC fuel cell, 

generic battery, a suite of electrical distribution elements, motors, generators, and an 

augmentation to the base NPSS propeller performance element that allows for parametric 

generation of propeller performance maps for single and counter rotating applications.

Following the completion of the individual NPSS modeling elements, Georgia Tech completed 

Task 3.3.4 in 2013. Some of the individual elements were combined to construct a nonproprietary model similar to General Electric’s hFan. This required construction of a parametric 

turbofan model within NPSS complete with appropriate power management logic. After 

noticeable performance differences were resolved between the GE and Georgia Tech NPSS 

models, Georgia Tech added detailed efficiency prediction capabilities to the motor, inverter, 

and battery elements in order to predict hybrid electric engine performance parametrically 

throughout the flight envelope. Finally, cycle trade studies were performed using Boeing 

generated aircraft trade factors to examine the influence of cryogenic and conventional 

components on engine cycle selection.

Individual element modeling has been completed and is described in more detail in the sections 

following the NPSS engine model description. Where further enhancements were made to 

model the hFan architecture, they are described in the NPSS Element sections 3.1.1.6 and 

3.1.1.7. Additionally, each developed element contains reference sheets included in the 

Appendix A of this report.

The propeller performance element ran into some difficulty during the validation phase. An 

attempt was made to construct an algorithm that allows for parametric propeller maps to be 

generated as a function of basic design parameters suitable for high speed propellers. This 

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effort attempted several different routes, none of which yielded acceptable results. The 

corresponding section describes in detail the steps taken and why they were not successful.

3.1.1 Electric Model

The Electric Element set allows all electric or hybrid electric propulsion systems to be modeled 

in NPSS. The elements were constructed with relatively simple models which can be extended 

as needed, but form a complete set which interconnect as required to allow an aircraft to be 

modeled with any arbitrary number of power sources and sinks. The superconducting models 

were based on prior work by P. Masson, however the other models were constructed from first 

order models or approximations. This subtask first attempted a bottoms-up approach in which 

electrical elements such as inverters and rectifiers were modeled using their constituent 

components. This approach was somewhat successful but significant challenges regarding the 

complexity and setup of this approach were identified. As a result the approach was changed to 

a top-down approach which allows for generic elements consisting of an inverter, rectifier, 

connection cable, motor, and generator were constructed which make use of NPSS sockets to 

allow the modeler to use custom modeling routines as needed.

3.1.1.1 Nomenclature

Acronyms

NPSS – Numerical Propulsion System Simulation

AC – Alternating Current

DC – Direct Current

Equation Terms

f – Electrical Frequency

Kv– Motor Voltage constant

Kt– Motor Torque constant

I – Current

L– Inductance

Np – Number of Pole Pairs

P – Power

R – Resistance

T – Torque

V – Voltage

3.1.1.2 Introduction

A hybrid electric propulsion system for transport aircraft use several components which are not 

found in any conventional gas turbine based system. Before the hybrid system can be modeled 

in NPSS, elements representing each new component must be added to NPSS. These elements 

include the motors, generators, power cables, and power electronic converters. The elements 

must be connected electrically, using DataPorts for lack of native electric connections in NPSS, 

and must be arranged such that the solver can successfully converge on a solution. The 

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DataPorts are used to represent electrical ports which transfer voltage, current, frequency, and 

phase information between electrical elements.

3.1.1.3 Element Modeling

3.1.1.3.1 Electric Motor/Generator Modeling 

The electric motor and generator were modeled in both superconducting and nonsuperconducting models. This allows use in both near term and cryogenically equipped 

propulsion systems. The conventional motor operation is based on several parameters 

including motor constant (Kv), motor Resistance (R), motor inductance (L) and the number of 

pole pairs in the motor (Np). The complete sequence from torque (T) and speed (w) to voltage 

(V), current (I), electrical frequency (f) and the phase between voltage and current (phase) is 

given in Equation 3.1 through Equation 3.8 below.

BackEmf = Kv ∗ w

Equation 3.1 

Kt = .73756214837/Kv

Equation 3.2 

I = T ∗ Kt

Equation 3.3 

f = w ∗ Np

Equation 3.4 

Vimaginary = I ∗ L

Equation 3.5 

Vreal = BackEmf + I ∗ R

Equation 3.6 

V = Sqrt(Vreal2 + Vimaginary2)

Equation 3.7 

Phase = arctan (Vimaginary, Vreal)

Equation 3.8 

The generator is essentially a motor run ‘backwards’ at this level of abstraction and is identical 

except for the Vreal formula and the sign on Vimaginary

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Vreal = BackEmf − I ∗ R

Equation 3.9 

Vimaginary = −I ∗ L

Equation 3.10 

The cryogenic varients are adapted from Matlab models of a High Temperature Superconductor 

(HTS) motor developed by P. Masson (9). Unlike the conventional motor model, the HTS motor 

model begins by specifying the geometry of the motor and works out its operation as a physics 

based model. The motor can be scaled geometrically to increase or decrease its performance 

during the sizing phase of the simulation. The HTS generator is an adaptation of the HTS motor 

to convert torque into electricity, similar to the reversal of the conventional motor.

3.1.1.3.2 Power Cable 

The power cables considered in the DC distribution system are either superconducting cables, 

which are lossless below their associated max current and temperature limits, or conventional 

cables, which in DC are simply resistances. The current entering the cable is as the exit current 

and the voltage drop is given by Ohm’s Law, shown in Equation 3.11. 

∆V = I ∗ R

Equation 3.11 

The resistance of the cable is heavily dependent on its length, and requires the layout of the 

system to be known to estimate the length of relevent cables. In the test cases modeled, the 

length of the cables carrying AC between motors/generators and their power electronics is 

considered small compared to the DC cabling connecting the motors to the generators, as the 

electronics is assumed to be mounted near the motors.

3.1.1.3.3 Power Electronics Modeling

The power electronics devices in the system convert power from one voltage to another and 

from AC to DC or back if necessary. An inverter converts DC into AC, for example to drive a 

motor. A rectifier converts DC into AC; for example to charge a DC bus from an AC generator. A 

DC transformer converts one voltage of DC into another. This would be used with battery 

systems or fuel cells. The following paragraphs describe the original approach to model these 

elements which was later abandoned for a more simplified approach. Please see the reference 

sheets for a complete listing of the inputs and outputs of each element.

The original approach to power electronics modeling was to construct them from the same 

basic electronic elements- transistors, diodes, inductors, capacitors and resistors. Each of these 

elements pass current through themselves based on the voltage at each of their nodes and 

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their internal state, which can be a function of their history or of time. Accordingly the circuits 

were constructed as a set of nodes with voltages as independent variables, connected by 

elements which calculated their currents from the node voltages. The currents were summed at 

the junction points between the subelements where the NPSS solver was used to drive the net 

current do zero at every node by varying the voltage at each node. The voltages and currents 

were passed between elements using DataInputPorts and DataOutputPorts. To manage the 

execution order the nodes were split into voltage and current nodes, with voltage nodes 

executing first and containing the independents, while current nodes contained the 

dependents. This required protection measures to make sure that the components were 

properly attached to each pair of voltage/current nodes, accomplished by adding a node 

identifier to the array passed between data ports. For transient simulations the system 

increments the time and repeats the entire process after it converges. The transistor was 

approximated as a two terminal device with internal logic to determine if it should turn on or 

off, based on the simulation time and a predetermined switching waveform.

Each of the circuit elements are relatively simple, so the first attempt at modeling the power 

electronics was to take models of the individual circuit elements and assemble them into the 

required circuits to make inverters rectifiers and DC transformers. These circuits do not operate 

with a steady state current flow, instead having transistors switch on and off and AC waveforms 

oscillating. To calculate the overall currents into and out of the devices their operation would 

have to be simulated over several periods of the AC waveforms and switching cycles and the 

performance averaged to find total energy into and out of the system at each voltage and 

frequency. Accordingly transient models were constructed of notional inverters, rectifiers, and 

DC transformers. An example circuit model of a rectifier and a graph of its operation are shown 

in Figure 3.1. The results are as expected. The sinusoidal AC current, represented in blue, is 

converted correctly to a stable DC current, represented in red.

Figure 3.1 – Half Wave Rectifier

This approach was abandoned as a result of two problems that were discovered when 

constructing other circuits. First, every time a transistor turned on or off, this created a 

-10

-5

0

5

10

15

0 0.02 0.04 0.06

Voltage 

(Volts)

Time (sec)

Vin

Vout

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discontinuity that the NPSS solver struggled to adapt to. These discontinuities also occur in the 

physical system so they had to be accurately modeled. Attempts to solve this problem 

exacerbated the second problem: the execution time of the transient model was on the order 

minutes. This is required since the time steps necessary to solve a transient circuit are on the 

order of microseconds. This meant that for every pass of the overall model (trying to find the 

fuel flow in the gas turbine to meet a thrust requirement for example) each inverter would take 

minutes, making the overall convergence time unacceptable. 

An alternative approach was adopted, modeling each piece of power electronics from the top 

down. Each was first modeled as a black box with a fixed efficiency, where power went in and 

flowed out at the appropriate frequencies and voltages. After these had successfully been 

integrated with the other electrical elements refinements were added in the form of sockets to 

calculate efficiency depending on the design power, and further refinements can similarly be 

added to meet whatever modeling fidelity is desired.

3.1.1.3.4 Electrical Bus

The only element which has more than two electrical connections, both in and out, is the bus. It 

can receive and send multiple currents at a constant DC voltage. The bus is essentially a model 

of the junction point where multiple power cables combine and allows the currents to be 

divided in any user defined manner; the only constraint being a net current of zero. As the bus 

is a model of a single connection point it currently contains no losses and is primarily a way for 

the solver to distribute currents.

3.1.1.4 Model Integration and Solver 

NPSS contains a vast library of gas turbine and rocket engine components, and many ports with 

which to interconnect them. However, there is currently no electrical port available for one 

component to pass an electrical signal to another. To overcome this DataPorts were adapted as 

these allow a single object to be passed between elements. Data ports have two significant 

drawbacks compared to fluid or other ports- first there is no error when a DataOutputPort is 

not connected to a DataInputPort. In the electrical case this could allow a motor to run without 

drawing power from the bus if it isn’t properly connected. The second drawback is that 

DataPorts only pass a single object. This was overcome by passing an array of voltage, current, 

phase, and frequency. A DC current can be represented by the voltage and current, while an AC 

additionally has a phase and a potential phase difference between the current and voltage 

waveforms, therefore a four element array was used with a frequency of zero signifying a DC 

current.

The solver layout is done such that there is a flow of information from the devices using power 

to the sources to minimize solver loops. Furthermore, the elements have been set up in a 

manner that the autosolver should automatically adjust to the number of elements within the 

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system without too much user modification. A notional system with one motor, one battery 

and one generator is shown below in Figure 3.2. In this example the speed of the constant 

speed generator, the voltage of the DC bus, and the speed and torque load on the motor are all 

specified as inputs. The motor executes, calculating its AC voltage and current, which are 

passed to the inverter. The Inverter calculates the efficiency loss and guesses what its DC input 

voltage is, not knowing the bus voltage or how much voltage will be lost in the power cable. It 

passes the DC voltage and current to the cable, which passes the entire current and a voltage 

updated due to the resistance to the bus. The bus checks if the passed voltage is equal to its 

own voltage, a dependent to match the independent in the inverter, before splitting the 

current and sending it up cables to the DC transformer and the rectifier. The independent 

torque in the generator is chosen and it sends its electric power to the rectifier, which 

compares the input and output powers to its efficiency. The battery current and DC transformer 

dependent evaluate simultaneously to complete the execution.

Figure 3.2 – Solver layout diagram

3.1.1.5 Verification

The majority of the models included here is based on very simple equations or black box 

efficiencies, and can be easily demonstrated to match Ohm’s Law or the motor equations 

25

Solver Diagram

Generator

Bus

Cable

Rectifier

Motor

Cable

Inverter

Speed and torque: Inputs

Voltage, 

Current, 

Frequency,

Phase DC Voltage, 

Current, 

DC Voltage, 

Current, 

Independent in Inverter: 

DC Input Voltage

Dependent in Bus: Voltage of Inverter 

Cable = Bus Voltage

Bus Voltage: Input DC Voltage, 

Current, 

DC Voltage, 

Current, 

Voltage, 

Current, 

Frequency,

Phase 

Dependent in Rectifier:

Output Power = Input Power*eff

Speed: Input

Independent in 

Generator:

Input Torque

Data passing from 

one element to 

another

Independents and Dependents 

Inputs

Battery

Cable

DC 

DC V, I Transformer

Dependent in DC Transformer:

Output Power = Input Power*eff

Independent in Battery:

Output Current

DC Voltage, 

Current, 

DC V, I 

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detailed above for the conventional motor. However the HTS models were adapted into NPSS 

from Matlab models by P. Masson which were complex enough to warrant a detailed check to 

ensure that the calculations were identical to the original. Below are two of the sample cases 

where the same inputs were put into the original and the NPSS model to confirm that they had 

the same output in every category. The resulting NPSS model showed an exact match, 

indicating no error resulting from the conversion to NPSS.

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Table 3.1 – NPSS HTS Motor Validation

Inputs Case1 (Default) Case2

Machine Power (W) 8000000 8000000

Machine Speed (RPM) 13000 8000

Machine Shape Factor 5 4

N pole Pairs 2 2

Stat Ave Rad (m) 0.12 0.2

Stat Elec Load (A/m) 500000 600000

Stat Op Fac 0.6 0.6

Stat AcLossDim (m) 0.00002 0.00002

HTS Temp (K) 20 20

BkFe Field Density (T) 1.9 2

Cryostat Wall Thick (m) 0.004 0.004

EMShield Thick (m) 0.002 0.002

Cryocooler Carnot eff 

(%) 20 20

Outputs Matlab NPSS %error Matlab NPSS %error

TotBearWeight (kg) 31.49 31.49 0 212.96 212.96 0

EMShield Weight (kg) 2.916 2.916 0 6.8788 6.8788 0

Shaft Weight (kg) 6.9999 6.9999 0 13.4588 13.4588 0

Rotor Sup Weight (kg) 8.7184 8.7184 0 33.2 33.2 0

rotor HTS weight (kg) 0.9548 0.9548 0 0.627 0.627 0

Stator ClPrWeight (kg) 2.0466 2.0466 0 5.4405 5.4405 0

Rotor ClPrWeight (kg) 4.7402 4.7402 0 24.8334 24.8334 0

Cryostat weight (kg) 26.4791 26.4791 0 59.4239 59.4239 0

Stator Torq T weight 

(kg) 117.856 117.856 0 384.753 384.753 0 

Stator Sup Weight (kg) 3.45 3.45 0 12.5318 12.5318 0

Stator HTS Weight (kg) 1.39 1.39 0 3.315 3.315 0

BkFe Weight (kg) 96.85 96.85 0 377.243 377.243 0

Total Machine Weight 

(kg) 303.902 303.902 0 1134.7 1134.7 0 

Machine T.Tube HL (W) 3342.9 3342.9 0 10563 10563 0

Stator CL Tot Loss (W) 98.3546 98.3546 0 118.025 118.025 0

Rotor CL Tot Loss (W) 115.158 115.158 0 153.218 153.218 0

Machine Ac loss op 

temp (W) 21792 21792 0 7786.6 7786.6 0 

Total Machine losses 

(W) 25349 25349 0 18621 18621 0 

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3.1.1.6 Inverter Efficiency Subelement Development

In order to capture the effect of motor speed and power load on the inverter, a parametric 

model was constructed that allows for inverter efficiency to be predicted as a function of these 

parameters. The inverter efficiency is implemented via the efficiency socket in the developed 

inverter element. In addition to predicting inverter efficiency, the element converts the DC 

current coming from the battery into AC current that the motor element can then use to 

produce torque. The following sections describe the efficiency prediction algorithm. Parameters 

used to simulate cryogenic and conventional components. References used to determine the 

parameters in Table 3.2 are marked with reference in parentheses.

3.1.1.6.1 Model Inputs:

• Inverter max current

• Motor speed (voltage frequency)

• Voltage

• Input power

• Inverter rated power

3.1.1.6.2 Converter Parameters:

Some characteristics of the inverter must be known for the loss calculation. The variables that 

must be set are:

• Switch turn-off fall-time (tf) 

• Link capacitance (C)

• Switching frequency (fs) 

• Power factor (pf) (Estimated using polynomial regression)

• Switch voltage drop (Vt) 

• Diode voltage drop (Vd) 

• Switch on-resistance (Rs) 

• Diode resistance (Rd) 

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Table 3.2 – Parameter Estimates

Parameter Room-Temperature Cryogenic

Switch turn-off fall time 2*10-7 (10) 18.2*10-9 (11)

Link capacitance bus voltage

105.6∗fs

2∗source inductance (12) bus voltage

105.6∗fs

2∗source inductance (12)

Switching frequency 10000 – 20000 (Hz) (13) 10000 – 20000 (Hz) (13)

Switch voltage drop 5.3 (V) (14) n/a

Diode voltage drop 3.75 (V) (15) n/a

Switch on-resistance .11 (14) n/a

Diode resistance .02 (15) n/a

Power Factor (16)

−1.782 ∗ % load2 + 1.9898

∗ % load

+ 0.1571

−1.782 ∗ % load2 + 1.9898

∗ % load

+ 0.1571

% load is the percentage is power/rated power

3.1.1.6.3 Efficiency Calculation:

There are two main types of losses in the converter: conduction losses and switching losses. 

Conduction losses are caused by the resistance of the switches and diodes in the converter. 

Switching losses are caused by the transient effect of rapid changes in the voltage and current 

profile across the switch.

Conduction losses (room-temperature) (13): 

Pc−sw = 1

2 ImVs �

1

π

+

m

4 pf� + Im

2 Rs(

√3

8π

+

m

3π pf)

Pc−d = 1

2 ImVd �

1

π

+

m

4 pf� + Im

2 Rd(

√3

8π

+

m

3π pf)

For cryogenic conduction losses just divide each equation by 10 (11). 

Where m is the modulation index, which is:

m = voltage frequency

switching frequency

Switching losses (13): 

Psw =

1

2 �Im

2 tf

2�

24C fs

Assuming the converter has 6 switches and 6 diodes, total losses are (13): 

Ploss = 6(Pc−sw + Pc−d + Psw)

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The efficiency of the inverter is then:

η = power input − Ploss

power input

3.1.1.7 Motor Efficiency Subelement Development

The basic motor element is fairly simple. The incoming AC power is combined with an efficiency 

number to predict the total torque on the output shaft, given a rotational speed. In order to 

add realism and the ability to perform parametric studies a detailed efficiency loss buildup is 

needed. This buildup allows the motor efficiency to change realistically throughout the 

operational envelope and across different rated motor sizes. Furthermore, a loss buildup model 

is desired that can be easily switched between a conventional and cryogenic operational mode. 

The modeling framework described in the following sections is based primarily on section 3.3 of 

(17) with elements incorporated from (18), (19), (20), (21) and (22). A switched reluctance 

motor type was chosen since that type is consistent with what GE assumed for the hFan 

configuration.

3.1.1.7.1 Motor Loss Definitions

Figure 3.3 shows a summary of major losses present in an electric motor. The major losses can 

be broken down into three groups, windage, copper, and iron losses. Windage loss results from 

the friction associated with cooling air flowing between the gap between the motor rotor and 

stator. Copper losses are a result of resistance within the wiring and coils. Iron losses results 

from eddy currents formed in the motor core as a result of moving magnetic fields.

Figure 3.3 – Motor Loss Buildup

Motor

Pelec Pshaft

Ploss

Pwindage

Pcopper

Piron

Peddy

Physteresis

ηmotor = Pshaft / ( Pshaft + Ploss )

Pelec = Pshaft + Ploss

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3.1.1.7.2 Motor Sizing Process

Since the modeling of the loss mechanisms in switched reluctance machines is well 

documented, the focus of this section will be on modeling elements specific to the NPSS 

implementation and verification of the resulting element. The primary consideration is how to 

size the motor. Sizing the motor is essential to predicting the individual losses. To understand 

the sizing process, one must first understand the operational behavior of a switched reluctance 

motor (SRM). For those not familiar with electrical devices, torque is proportional to current 

and voltage is proportional to speed. The torque-speed relationship is notionally shown in 

Figure 3.4. Starting from low rotational speed, the current is at a maximum (indicated by the 

red line), limited by the heat removal capability and wire gauge used in the motor coils. As 

speed increases, the current, and therefore torque is held constant. Since voltage and speed 

are increasing, the motor’s power increases linearly up until the point that the maximum power 

is reached (indicated by the green line). The maximum motor power is a function of both the 

maximum current capability of the motor, and the ability of the upstream inverter to deliver 

power. As speed continues to increase, the torque, and therefore current must decrease in 

order to limit the motor to its maximum power. Eventually the point of maximum voltage, 

dictated by upstream conversion equipment is reached, and torque falls off rapidly beyond this 

point. Of course the motor may also operate at intermediate speeds below rated torque and 

power.

Figure 3.4 – SRM Torque-Speed Characteristic

This understanding of the SRM torque-speed characteristic is important because the loss 

models require operating current and voltage as inputs. Fortunately, the motor current and 

voltage can be estimated using Kt and Kv coefficients, shown in Equation 3.12 and Equation 

3.13. Kt and Kv can be assumed constant for a given motor design.

Max Current

Max Power

MaxVoltage

1)Vmax - Specified

Calculate i

Calculate Kv

2)Imax

CalculateV & I from Kv

Calculate Kt

Break Speed - Specified

Rotational Speed

Torque

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Kv = (V − i ∗ R0)

ω

Equation 3.12 – Kv Definition

Kt = T

i

Equation 3.13 – Kt Definition

Using the Kt and Kv definitions it is not hard to see how, given an operating current and voltage 

that torque (and therefore power) can be determined. The question now becomes how to 

determine Kv and Kt for a given SRM. The first step is to parameterize the curve shown in Figure 

3.4. It is assumed the maximum power will come from the user as a design parameter and the 

maximum speed can also be calculated from the fan shaft in this application. If it is further 

assumed that operating in the blue region where torque rapidly falls off is undesirable then 

point (1) in Figure 3.4 can be defined. Since power, speed, and therefore torque are all known 

at this point, the operating current can be calculated using a specified maximum operating 

voltage. With these parameters defined Kv can be calculated using Equation 3.14, where R0 is 

an assumed motor winding resistance.

Kv = (Vmax − i ∗ R0)

ωmax

Equation 3.14 – Kv Calculation

To predict Kt, it is first necessary to define a parameter, named ωbase, as the ratio of the break 

speed (intersection of maximum power and torque) to ωmax. This is a user specified parameter 

which will depend on the specifics of the problem. Then, a system of equations consisting of Kv, 

the maximum specified voltage, Vmax, ωbase, ωmax, and the maximum power can be solved to 

estimate the current at point (2) in Figure 3.4. First the voltage at point (2) is solved using 

Equation 3.15. Then the torque is calculated using the break speed and maximum power. 

Finally Kt can be calculated using imax and the torque at point (2) in Figure 3.4. 

imax = 0.5 ∗

�Kv2 ∗ ωbase

2 + 1.63299 ∗ Pmax ∗ R0 − Kv ∗ ωbase

R0

Equation 3.15 – Solution for Voltage at Point 2

Now that Kv and Kt are defined for a ‘sized’ motor, voltage and torque can be estimated for any 

required speed and torque the motor is commanded to provide. Current and voltage are the 

primary inputs into the individual loss models listed in Figure 3.3. 

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3.1.1.7.3 Motor loss prediction

Once the motor is ‘sized’ (i.e, Kt and Kv calculated), the windage, copper, and iron losses can be 

estimated. This is done every time the motor element is executed within NPSS since Kv and Kt 

are held constant. While full details of the loss estimation process are provided in the 

aforementioned references, the following equations offer a brief summary of the final loss 

estimation algorithm.

In order to estimate the copper losses, the motor diameter and length is needed. These two 

parameters are determined using Equation 3.16 and Equation 3.17. The diameter equation was 

generated by iterating through the sizing process to estimate power as a function of diameter. 

The equation was then solved for diameter. Length is estimated by holding a constant length to 

diameter ratio based on the published GE hFan drawings.

Diameter = 0.0713950 ∗ log(Pmax) − 0.1572667

Equation 3.16 – Motor Sized Diameter

Length = 0.4

0.48 ∗ Diameter

Equation 3.17 – Motor Sized Length

Once motor dimensions are determined, the number of turns of coil are estimated using 

Equation 3.18. The resistance per turn is estimated using the regression shown in Equation 3.19

which is based off of AWG wire sizing charts.

Tph = As ∗ π ∗ Diameter

imaxnphase ∗ 2.0

Equation 3.18 – Estimated Number of Coil Turns

rper,turn = 48.83 ∗ imax

−1.001259

Equation 3.19 – Resistance Per Turn

Finally, total resistance is calculated by calculating total wire length, shown in Equation 3.20. 

Current power losses are then estimated using Equation 3.21 and the model’s operating 

current.

R = Diameter ∗ π ∗ rper,turn

1000

Equation 3.20 – Wire Resistance

Pcopper = I2 ∗ R ∗ Tph

Equation 3.21 – Copper Power Losses

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Once the copper power losses are estimated, the iron losses are calculated as follows. First the 

motor operating frequency is estimated via Equation 3.22, and then the motor weight is 

estimated using Equation 3.23 assuming the core is manufactured from laminated steel. Note 

that this weight is suitable for predicting changes in efficiency since motor iron losses are 

proportional to weight/volume; however, it is not indicative of the entire motor installed 

weight.

Frequency = RPM ∗ 2 ∗ nphase ∗ 2

120

Equation 3.22 – Motor Operating Frequency

weight = 7650.0 ∗ Length ∗ �Diameter

2 �

2

Equation 3.23 – Motor Core Loss Weight Estimation

Finally iron losses are estimated using the method from (21), replicated in Equation 3.24. Bp

represents the peak magnetic flux. Georgia Tech found a value of 1.5 T worked well for motors 

applicable to the SUGAR requirements.

Kh = 0.0275

Kc = 0.0000183

Ke = 0.0000277

Piron = �Kh ∗ Bp

2 ∗ Frequency + Kc�Bp ∗ Frequency�

2

+ Ke�Bp ∗ Frequency�

1.5

� �weight

176 �

Equation 3.24 – Motor Iron Losses

Finally, the windage loss is estimated using Equation 3.25 where the coefficient of friction is 

estimated from (20). 

Pwindage = cfriction ∗ π ∗ ω2 ∗ �Pmax

0.3 ∗

Diameter

2 �

3

Equation 3.25 – Windage Loss Estimation

Once the loss model is defined, the motor efficiency is calculated using Equation 3.26. 

eff = Pmech

Pmech + Piron + Pcopper + Pwindage

Equation 3.26 – Motor Efficiency Definition

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Examples of the motor performance for three different sized motors, with ωbase set to 0.6 are 

shown in Figure 3.5. 

Figure 3.5 – Motor Off-Design Operation

3.1.2 Fuel Cell Model

3.1.2.1 Nomenclature

Abbreviations

ASR – Area Specific Resistance

FC – Fuel Cell

GT – Gas Turbine

SOFC – Solid Oxide Fuel Cell

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

3500 3700 3900 4100 4300 4500 4700

Motor Efficiency

Speed (RPM)

10000 Volts 2000 hp

10000 volts 8000 hp

5000 volts 8000 hp

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Equation Terms

A – Area

Deff – Effective diffusivity

E – Reversible voltage, Energy

F – Faraday’s constant

H – Enthalpy

h – Plank’s constant

i – Current

j – Current density

k – Boltzman’s constant, installation factor, pre-exponent factor

N – Number

n – Number of electrons exchanged per molecule

P – Power

p – Pressure 

Q – Heat

R – Universal gas constant

T – Temperature

t – Thickness

U – Fuel Utilization

V – Voltage

Vol – Volume

W – Weight

x – Mole fraction

α – Reaction transfer coefficient

∆G – Change in Gibb’s free energy

∆ĥ – Enthalpy of reaction

λ – stoichiometric coefficient

η – efficiency

ν – molar flow rate

ρ – Density

σ – Conductivity

Superscripts

0 – Standard state, bulk fluid property

* – Component interface property

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Subscripts

0 – Exchange

a – Anode property

act – Activation

c – Cathode property

cell – Cell property

conc – Concentration

design – Design point property

e – Electrolyte property

f – Formation

flow – pertaining to the anode and cathode flows

gen – Generated

i – For component

L – Limiting

op – Property while in operation

stack – Stack property

total – System property

volt – voltaic

3.1.2.2 Introduction

One of the advanced technologies that are considered in this study is a solid-oxide fuel cell 

(SOFC), an electrochemical device that produces electricity from hydrogen and oxygen, 

producing water as a byproduct. Many models exist for analyzing fuel cells, but they focus on 

varying aspects of the cell. Severson describes an SOFC model based on first principles used to 

parametrically analyze the electrochemical performance of a fuel cell in (23). Many papers have 

also been found documenting design and off-design analysis of a hybrid SOFC/gas turbine 

systems performed using parametric models (24; 25; 26; 27). Tornabene’s model also performs 

a mass and volume calculation, important for aerospace applications (27). Lastly (28) provides a 

detailed description of a spreadsheet model used to analyze the electrical and heat 

performance of a fuel cell/gas turbine system.

The SOFC was chosen over other fuel cells because it operates at a high temperature (500 to 

1000 °C), which can be used to preheat air for the combustor and it can use many different 

compounds, including hydro-carbons, as fuel. The fuel cell is able to use such a range of fuels 

either indirectly, using a fuel reformer, or directly, as the SOFC is able to use internal reforming 

to produce the hydrogen-rich gas necessary to function inside the anode (29). The model is 

developed in the NPSS which allows it to use the Chemical Equilibrium with Applications (CEA) 

code and JANAF thermodynamic data built-in to the system (30). The model is developed using 

the electrochemical equations from (23; 24; 31), heat equations from (29), and mass and 

volume equations from (27). Though the mass and volume equations are not explicitly stated in 

the paper, it is possible to ascertain them by comparing the numbers presented for densities, 

cell geometry, and the numbers given as the mass and volume results. In the following sections, 

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background information on solid oxide fuel cells is covered, and then the model development

and finally the validation of the model using published data are described. 

3.1.2.3 Background 

3.1.2.3.1 Electrochemistry

A fuel cell consists of three main components, the anode, cathode, and electrolyte. A SOFC is so 

called because the electrolyte is a solid ceramic, as opposed to an aqueous solution (29). In an 

SOFC the most common chemical reaction used is the combustion of hydrogen and oxygen to 

produce water. In the fuel cell the total chemical reaction can be split into two half reactions 

that occur at each electrode. The oxidation of the hydrogen occurs at the anode, while the 

reduction of oxidizer, commonly air, occurs at the cathode. The electrons then travel from the 

cathode, through the load to the anode where the reaction is completed (Figure 3.6).

Figure 3.6 – Diagram of SOFC

3.1.2.3.2 Architectures

The literature shows three different manners in which the three components of a fuel cell can 

be combined, with benefits and detriments to each. The cross sections of all three are shown in 

Figure 3.7, with the anode in yellow, the cathode in blue, and the electrolyte in green.

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Figure 3.7 – SOFC Architectures

The tubular design is known for its ease of manufacturing, and for less thermal cracking from 

thermal properties mismatching. Another benefit is that there is no need for sealing to prevent 

the fuel gases from combining with the oxidant. However, the tubular design does yield a lower 

power density than the others and current collection from one tube to the next is complex to 

organize. The planar design increases the power density and there are lower fabrication costs 

than the tubular and monolithic, but it does require sealing between the anode and cathode 

inputs to create separate flows. The planar design also requires strict matching between the 

thermal expansion coefficients in order to prevent cracking and delamination. The monolithic 

design has the highest power density potential of all three architectures, and the flow 

arrangement of the anode and cathode gases is simple because of the natural gaps that form 

between the electrodes and the interconnect. However, like the planar design, the monolithic 

architecture requires strict matching of the thermal properties of the materials and sealing to 

prevent the anode and cathode gases from mixing. It also requires a difficult fabrication process 

to produce the honeycomb shape (32). 

3.1.2.3.3 Losses

There are three primary losses in a fuel cell. These losses occur as functions of the current 

density of the cell and each loss is prominent in a specific region of current density. The first 

type of loss is activation loss. This occurs as the result of energizing the fuel at the anode and 

the oxidizer at the cathode, enabling the two half reactions to take place. This type of loss is 

prominent at low current densities. The second type is ohmic or resistance losses. This occurs 

because an electromotive force (voltage difference) needs to exist to move the electrons and 

the ions. Resistance losses are important in the middle region of current densities. The final 

type of losses, concentration or mass transfer losses, give an upper limit to the current density 

that can be present in a fuel cell. They come about when current density is high enough to 

deplete the hydrogen or oxygen, leaving no source for further current. The voltage of a fuel cell 

is determined by calculating the ideal voltage of the cell, and subtracting the sum of all of the 

losses as shown in Figure 3.8. This final curve that gives the voltage as a function of current 

density is known as the polarization curve of a fuel cell. The polarization curve is the main 

metric of fuel cell performance (31). 

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Figure 3.8 – Formation of the polarization curve

3.1.2.4 Model 

The NPSS fuel cell model is a 1-D performance model that computes the electrical output of the 

fuel cell based on the desired current density. The performance model is wrapped into a sizing 

algorithm to output the size of a stack of fuel cells given a design current density and a design 

power load. Once a stack is sized, the model can output the performance of the cell over 

varying current densities, operating pressures, temperatures, and utilization rates based either 

on constant stoichiometry, where the fuel cell is fed the fuel and oxidizer in a set ratio, or 

constant fuel flow. The model is not dependent upon the architecture of the fuel cell, and the 

only calculation in the model that is absolutely dependent upon the geometry is the weight and 

volume calculation, which assumes a planar architecture.

3.1.2.4.1 Assumptions

In order to enable the parameterization of the model, the following assumptions were made:

• Only 1-dimensional flow across fuel cell is considered implying that any gradients across 

a cross section of the fuel cell are ignored

• Convective transport intricacies are ignored this assumption eliminates the 

consideration of any flow structures in the electrodes. Only the diffusion across the 

electrodes and electrolyte are analyzed (31). 

• H2O only exists as water vapor simplifies the analysis to a single phase product which 

eliminates the higher heating value from the analysis (31). 

• Fuel reforming has already taken place and no internal reforming happens or anode 

recycling occurs (24). 

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3.1.2.4.2 Model Calculations 

The model calculates the performance of the fuel cell based on the cell temperature, pressure, 

and the current density. During design analysis, a specified current density and operating 

conditions are used to determine the performance of a single cell, which is subsequently used 

to calculate the number of cells necessary to meet a power output requirement. Once the cell 

size is determined, the weight and the volume of the stack can be calculated using the density 

of the materials for each component, and the component thickness.

3.1.2.4.3 Electrochemistry

To analyze the electrochemical performance of a fuel cell, the ideal voltage is calculated using 

Equation 3.27, called the Nernst equation. This voltage is also known as the reversible voltage 

or the Nernst voltage. It is the highest voltage that can be produced by a fuel cell for a given 

reaction. There is some debate in the literature on what values to use for the partial pressures 

since they change across an electrode in a fuel cell (24; 29). This is important because partial 

pressures taken at the inlet will give a higher ideal voltage, and pressures taken at the outlet 

will give a lower ideal voltage (23). 

VIdeal = ΔGf

0

nF + RT

nF

ln �

pH2pO2

1 2⁄

pH2O

�

Equation 3.27

Once the ideal voltage is calculated, the values for each of the losses need to be determined. 

Equation 3.28 through Equation 3.30 gives the voltage losses related to activation, resistance, 

and mass transfer (23). The operating voltage (Equation 3.31) is calculated by subtracting the 

losses from the ideal voltage.

Vact = RT

αnF

ln � j

2j0

+ �� j

2j0

�

2

+ 1�

Equation 3.28

Vohmic = j ∗ ASR

Equation 3.29

Vconc = RT

nF

ln �1 − j

jL

�

Equation 3.30

Vop = Videal − (Vact + Vohmic + Vconc)

Equation 3.31

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Previously, the activation loss was calculated using Equation 3.32. The natural logarithm in this 

equation caused inaccuracies below the exchange current density. During testing and validation 

this proved problematic as the voltage at lower current densities was consistently higher than 

the value in literature. After further investigation, Equation 3.28 was used in the model for the 

activation losses, and the model performed significantly better.

Vact = RT

αnF

ln � j

j0

�

Equation 3.32

Once the operational voltage is known, the fuel cell is sized based on a design power load and 

an active area of the cell. The total area necessary to produce the required power is calculated 

using the current density and the operational voltage of the cell at the specified current density 

(Equation 3.33). The number of cells required to achieve the design power is the total area 

necessary divided by the area of a single cell (Equation 3.34).

Atotal = Pdesign

�Vop ∗ j�

Equation 3.33

Ntotal = Atotal

Acell

Equation 3.34

If the detailed material properties are known, such as diffusion of the reactants and products 

across the electrodes, the thicknesses of each component, and the conductivity of each 

component, Equation 3.35 through Equation 3.38 can be used to calculate the values needed 

for Equation 3.28 through Equation 3.30. The exchange current density can be calculated using 

Equation 3.35 and Equation 3.36 (23). For Equation 3.35 and Equation 3.36, the constants m 

and n are typically 1 and 0.25, respectively. The pre-exponent factor k and the activation energy 

can be found in literature (23) and the reference pressure can be set to 1 atmosphere for 

simplicity of calculation. Since the equations used for the exchange current density calculate it 

at each of the electrodes, then activation overpotentials also need to be calculated separately 

at each of the electrodes and summed to determine the total activation loss.

j0,a = ka �

PH2

Pref� �PH2OPref �

m

e−Eact,a⁄(RT)

Equation 3.35 

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j0,c = kc �

PO2

Pref�

n

e−Eact,c⁄(RT)

Equation 3.36

Area specific resistance is calculated as the sum of the resistances of each of the components, 

the electrodes and the electrolyte. For a stack of cells, the interconnect would also need to be 

included in the calculation. The resistivity of the cell is equivalent to the inverse of the 

conductivity, σ, and t in Equation 3.37 is the thickness of the component (23). 

ASR = � t

σ components

Equation 3.37

The limiting current density, jL is found using Equation 3.38, which uses the bulk mole fraction 

that is the most constraining, the effective diffusivity and the thickness of the electrode to 

determine the maximum current density possible at an electrode. This calculation should be 

performed at each electrode, and the lowest current density found should be used for jL.

jL = nFDeff

RT ∙

pxi

0

t

Equation 3.38

The hydrogen molar flow is dependent on the amount of current output by the fuel cell and the 

fuel utilization, as shown by Equation 3.39. The oxygen molar flow is determined by the 

stoichiometric ratio between oxygen and hydrogen as shown by Equation 3.40. The water 

molar flow is only dependent on the amount of current, shown in Equation 3.41 (31). 

νH2 = itotal

nF ∙ 1

UH2

Equation 3.39

νO2 = itotal

nF ∙

λO2

UH2

= νH2 ∙ λO2

Equation 3.40

νH2O = itotal

nF

Equation 3.41

For a fuel cell, there are a few different measures of efficiency. The voltaic efficiency is 

measured as the voltage equivalent to the total available energy of the reaction divided by the 

operating voltage (Equation 3.42). The enthalpy of reaction of water vapor is used for the total 

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available energy as one of the assumptions states that only water vapor is produced. Another 

measure of efficiency is the fuel efficiency or utilization. This is defined as the ratio of fuel input 

to the cell to fuel used by the cell (Equation 3.43). The overall efficiency of the fuel cell is the 

product of the fuel efficiency and the voltaic efficiency (Equation 3.44) (29). 

ηvolt = Vop

Δhf⁄nF

Equation 3.42 

ηfuel = UH2 = itotal⁄nF

νH2

Equation 3.43 

ηfc = ηfuelηvolt

Equation 3.44 

3.1.2.4.4 Heat

The heat generated by the fuel cell is calculated using the voltaic efficiency as that gives the 

amount of energy from the reaction that is not used to generate the cell voltage (Equation 

3.45). The heat transfer that occurs by the flow of reactants and products is calculated based on 

the enthalpy of the flows into and out of the electrodes (Equation 3.46). In this specific model, 

this calculation is enabled by the chemical equilibrium analysis (CEA) which is part of NPSS. The 

net heat out of the fuel cell is the heat generated less the heat absorbed by the cathode and 

anode flows (Equation 3.47) (29). 

Qgen,total = � Vop

ηvolt

− Vop� itotal

Equation 3.45 

Qflow = � xiHi

out

− � xjHj

in

Equation 3.46

Qnet = Qgen − Qflow

Equation 3.47 

3.1.2.4.5 Weight and Volume 

The weight and volume are calculated by “building up” the fuel cell, component by component. 

First the weight is determined by adding up the weight of each of the components, calculated 

using the density, thickness and area of each (Equation 3.48). The total number of cells could be 

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split up into multiple stacks of cells. These stacks would be wired together to achieve the 

desired electrical output. The weight of one stack is the number of cells multiplied by the 

weight of a single cell, and an installation factor (Equation 3.49). The total weight is the number 

of stacks multiplied by the weight of a single stack and another installation factor (Equation 

3.50). The volume of the fuel cells is calculated in the same manner, first with a buildup of the 

volume of a single cell using the thickness and area of each of the components (Equation 3.51); 

then the calculation of the volume of a stack using an installation factor and the number of cells

(Equation 3.52). Finally, the total volume is the product of the volume of a stack, the number of 

stacks, and an installation factor (Equation 3.53) (27). 

Wcell = � ρitiAcell

components

Equation 3.48 

Wstack = kcell,W ∗ Ncell ∗ Wcell

Equation 3.49 

Wtotal = kstack,W ∗ Nstack ∗ Wstack

Equation 3.50 

Vcell = � tiAcell

components

Equation 3.51 

Vstack = kcell,Vol ∗ Ncell ∗ Vcell

Equation 3.52 

Vtotal = kstack,Vol ∗ Nstack ∗ Vstack

Equation 3.53 

3.1.2.5 Validation 

To validate the model, data from (23) and (24) was used. Three tests were performed focusing 

on varying different inputs to the model. The first was to determine the effect of pressure on 

the performance, the second is a test to analyze the effect of varying the fuel composition, and 

the last test measures the effect of varying temperature. The first test was performed using 

inputs from (24), shown in Table 3.3. The cell pressure was varied between 1 and 15 

atmospheres for the test, with the model results for 1 atmosphere and 15 atmospheres shown 

here. Some values are not present in the literature and had to be inferred. These are denoted 

by an asterisk (*) in the table. These parameters were used to match the outcome of the model 

to the published data. The values used in to fill in the missing information were also verified 

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against typical values for those parameters from other literature sources (23; 24; 28). The final 

validation plot is shown in Figure 3.9. For the validation, the average partial pressures of the 

fuel and air were used to calculate the Nernst voltage.

Table 3.3 – Validation data used for pressure variation tests

Inputs Used Inputs from Literature

Description 1atm 15atm 1atm 15atm

P_op (atm) 1 15 1 15

T_op (K) 1273 1273

iL (A/cm^2) 0.65 1.677 0.65 1.677

i0 (A/cm^2) 0.15 * 

Alpha 0.5 *

ASR (ohmcm^2) 0.3 * 

xH2 0.89 0.89

xH2O 0.11 0.11

xO2 0.21 0.21

Utilization 0.85 0.85

Stoichiometric 

Ratio of Air 6 6 

Figure 3.9 – Comparison of model pressure test output with output from (24) 

The composition test used the inputs present in Table 3.4. The excel model indicates the NPSS 

algorithm results. The Freeh model indicates the validation data from the literature. Based on 

tests, pressure raises the voltage of the cell uniformly across all current densities. i0 raises and 

lowers the voltage, but also affects the slope in the middle region of the graph. As the exchange 

current density is raised, the slope becomes closer to a constant across all current densities. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6

V

o

l

t

a

g

e

Current Density (A/cm^2)

Excel Model 1 atm

Excel Model 15 atm

Freeh Model 1 atm

Freeh Model 15 atm

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The transfer coefficient, alpha, also affects the slope of the plot, but does not change the 

curvature of the plot. Using only the data in the literature, the best match to the validation data 

is shown in Figure 3.11. Tests were also performed to determine if other combinations provided 

a closer match. The best result occurred with the values in Table 3.5. Different partial pressures 

for the Nernst voltage were also used to increase the fit of the data. The data at 10% H2 uses 

the output partial pressure, while the data at 97% H2 uses the average partial pressure for the 

ideal voltage calculation.

Table 3.4 – Validation inputs for composition variation tests

Inputs Used Inputs from 

Literature

Description 10% H2 97% H2 10% H2 97% H2

P_op (atm) 1 *

T_op (K) 1023 1023

iL (A/cm^2) 0.325 7 0.325 7 

i0 (A/cm^2) 0.25 0.25 * 

Alpha 0.3 0.3 *

ASR (ohmcm^2) A*exp(E/T) A*exp(E/T)

A (ohm-cm^2) 2.1*10^(-6) 2.1*10^(-6)

E (K) 10000 10000

xH2 0.1 0.97 0.1 0.97

xH2O 0.03 0.03

xN2 0.87 0.0 0.87 0.0

xO2 0.21 0.21

Utilization 0.9 *

Stoichiometric 

Ratio of Air 6 * 

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Figure 3.10 – Comparison of model composition test outputs with results from (24) 

Table 3.5 – Inputs used for improved matching of composition tests

Inputs Used

Description 10% H2 97% H2

iL (A/cm^2) 0.31 7

i0 (A/cm^2) 0.15 0.15

Alpha 0.41 0.41

ASR (ohmcm^2) A*exp(E/T) A*exp(E/T)

A (ohmcm^2)7.0*10^(-

6)

7.0*10^(-

6)

E (K) 10000 10000

Figure 3.11 – Model output from improved composition matching with results from (24) 

0.5

0.6

0.7

0.8

0.9

1

1.1

0 0.5 1 1.5

V

o

l

t

a

g

e

Current Density (A/cm^2)

Excel Model 10% H2

Excel Model 97% H2

Freeh Model 10% H2

Freeh Model 97% H2

0.5

0.6

0.7

0.8

0.9

1

1.1

0 0.5 1 1.5

V

o

l

t

a

g

e

Current Density (A/cm^2)

Excel Model 10% H2

Excel Model 97% H2

Freeh Model 10% H2

Freeh Model 97% H2

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The temperature variation tests were performed at 600, 700, and 800C. The input data is taken 

from (23) and is shown in Table 3.6. ASR was calculated as the sum of the resistances of the 

components. Equation 3.54 through Equation 3.56 is the equations given in the literature to 

calculate the resistance of each component as a function of temperature. The only value not 

present in the literature is the limiting current density, iL. The value used is the same as the 

value from the composition tests in (23) because the data in (23) comes from the same authors 

as that data. For the analysis, Equation 3.35 and Equation 3.36 are used for the exchange 

current density since they coordinate with inputs given in the literature. As a result, the 

activation voltage was initially calculated for each of the electrodes and summed to determine 

the total activation losses.

Figure 3.12 shows the initial model output with the published data. The resistance is 

determined to be the main source of error as it has a large effect on the slope of the 

polarization curve. The published equation for the resistances comes from a different source 

than the other inputs, so there is the possibility that it is not completely accurate to the tested 

fuel cell. Since the electrolyte resistance is the main source of resistance losses in fuel cells, 

according to the literature (29), this was adjusted to better match the experimental data. For 

the plot shown in Figure 3.13, Equation 3.57 was used for the resistance of the electrolyte. To 

be sure the matching was not just mathematical, but also validated conceptually, typical values 

for the resistance of the electrolyte material, yttria-stabilized zirconia (YSZ) were researched 

and plotted in an arrhenius plot with the updated resistance equation in Figure 3.14. Linear 

trend lines were fit to the data and compared to the changed equation which seems to fit in the 

same area as the other resistances. A more detailed legend is located in Table 3.7 and gives the 

properties of the trend lines for comparison. The other source of error is the Nernst voltage. 

The ideal voltage calculated by the model is less than the published data. One source of error 

could be the usage of partial pressures. For this test, average partial pressures were used in the 

Nernst voltage calculation. The actual partial pressures would change across the electrodes, but 

in the literature there is nothing pointing to using the partial pressures at one point as opposed 

to another.

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Table 3.6 – Inputs for temperature variation validation

Inputs Used Inputs from Literature

Description 600 C 700 C 800 C 600 C 700 C 800 C

P_op (atm) 1 1

T_op (K) 873 973 1073 873 973 1073

iL (A/cm^2) 7 *

Alpha 0.5 0.5

k (A/cm^2) 5.5*104 5.5*104

Eact (J/mol) 1*105 1*105

ta (cm) 0.102 0.102

tc (cm) 0.007 0.007

te (cm) 0.0008 0.0008

xH2 0.97 0.97

xH2O 0.03 0.03

xO2 0.21 0.21

Utilization 0.5 0.5

Stoichiometric 

Ratio of Air 10 10

ρa = 1 �

95×104

T exp �

−1150

T � ��

Equation 3.54 

ρc = 1 �

42×104

T exp �

−1250

T � ��

Equation 3.55 

ρe = 1 �

3.34×102

T exp �

−10300

T � ��

Equation 3.56 

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Figure 3.12 – Initial comparison of model output with experimental data from (23) 

ρe = 1 �

2.5×102

T exp �

−10700

T � ��

Equation 3.57 

Figure 3.13 – Comparison of improved matching output with experimental data (23) 

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4

V

o

l

t

a

g

e

( V)

Current Density (A/cm^2)

Data 600C

Data 700 C

Data 800C

Model 600C

Model 700 C

Model 800C

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4

V

o

l

t

a

g

e

( V)

Current Density (A/cm^2)

Data 600C

Data 700 C

Data 800C

Model 600C

Model 700 C

Model 800C

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Figure 3.14 – Arrhenius plot of YSZ resistances

Table 3.7 – Trend line parameters for YSZ resistances

Number Description Slope Y-intercept

1 Bulk YSZ -4.87 3.30

2 Thin film YSZ on Pt (across plane) -4.91 3.33

3 Thin film YSZ on Sapphire -5.18 3.5

4 Thin film YSZ on Al2O3 (in-plane) 200nm 

grains -4.38 3.68

5 Thin film YSZ on Al-2O3 (in-plane) 20nm 

grains -4.43 3.99

6 Thin film YSZ on Si (in-plane) -5.98 5.73

7 Thin film on Sapphire (in-plane) -5.35 3.10

Experimental -4.65 2.40

3.1.3 Battery Model

The battery model is an NPSS element that calculates the size, layout, and performance of a 

battery stack. This calculation is based on the performance, which is given by a parameterized 

discharge curve. The discharge curve is determined by published data, but can also be modified 

to fit projections for future battery performance. To determine the size and layout of the stack, 

an operating profile and power load are used as the sizing criteria. Once the stack is sized, the 

model calculates the weight and volume of the stack, and can also determine the stack voltage 

and heat output at off-design points if given a state of charge and current load.

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

0.5 1 1.5 2

log(conductivity [log[s/cm]

1/Temperature [1000/K]

1

2

3

4

5

6

7

Experimental

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3.1.3.1 Nomenclature

Equation Terms

A – Exponential zone amplitude

B – Exponential zone time constant inverse

E – Voltage

i – Current 

K – Polarization voltage

k – Technology factor

N – Number

P – Power

Q – Battery Charge, Heat

R – Internal resistance

SOC – State of Charge

t – Time

V – Voltage

W – Weight

η – Voltaic efficiency

ρenergy – Specific energy

ρpower – Power density

Subscripts

batt – For a single Battery

dis – Discharge

exp – At end of exponential zone

L – Discharge limit

N – Nominal, as declared in literature

nom – At end of nominal zone

P – At power design point

parallel – For parallel batteries

3.1.3.2 Introduction

In a hybrid electric configuration, batteries can serve two roles. The first is as a power source 

connected to the power systems in order to provide electricity while discharging. The other is 

as a load-leveling device which would charge using any excess current from the system at low 

load times, and then discharge when the load on the system requires more energy than the 

primary power generation equipment can produce (33). In either case, it is necessary that the 

battery is sized properly to be able to provide the proper amount of power at the correct time 

without being over-designed and thereby too heavy. This model serves to determine the 

battery’s performance and sizes the battery stack accordingly. The model also provides the 

capability to determine the weight and volume of the battery stack in addition to the heat 

produced and the electric performance characteristics of the battery.

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3.1.3.3 Model Development

The main metric of battery performance is the discharge curve. This gives the voltage of the 

battery as a function of the state of charge and the current load. A functional model not based 

on first principles is desired as that simplifies the calculations of the model in addition to 

making the model usable without requiring intimate knowledge of the battery to be modeled. 

Using an empirically derived method also allows the NPSS model to easily accommodate 

varying battery chemistries. As the purpose of the project is to analyze future technologies, the 

model must also provide the ability to modify the battery attributes to predict future 

performance characteristics. Since these characteristics are not known currently, modifiers can 

be used to change the performance of current batteries to provide an estimate of future 

capabilities. These modifiers are essentially the technology factors. Using technology factors 

requires a general equation for the discharge curve that can be parameterized, but is still based 

on current capabilities. Reference (33) offers a generic equation that uses published data to 

determine parametric performance characteristics of a battery and can easily be modified by 

technology factors. 

3.1.3.3.1 Assumptions

The model has some assumptions and limitations to its operation and results (33): 

• The internal resistance is assumed to be constant and is independent of the amplitude 

of the current

• The parameters used in the model are determined by the discharge performance and 

are assumed to be the same for the charging performance

• The capacity of the battery is independent of the amplitude of the current

• The performance of the battery is independent of temperature

• The self-discharge of the battery is not taken into account

• The battery has no memory effect

This model outputs the point performance of the battery. It does not model battery 

degradation over time, or degradation due to over-charging or deep-discharge. The 

performance is assumed to be the same over all charge/discharge cycles.

3.1.3.3.2 Generic Performance curve

To model the performance of a battery as it discharges, a generalized discharge curve 

formulation in (33) is used. The curve models battery voltage as a function of SOC and current 

load on the battery. A discharge curve from a commercial data sheet or research literature can 

be generalized into the equation to be used in the model. Three points on the curve are needed 

to calculate the parameters for the generalized equation: the battery voltage at full charge, the 

battery voltage and charge at the end of the exponential zone, and the battery voltage and 

charge at the end of the nominal zone. The end of the exponential zone is determined by the 

point where the slope of the curve changes from a highly negative slope to close to constant. 

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There is no mathematical formulation to calculate the end of the exponential zone, but through 

iterative comparison to the original discharge curve, this value can be tested and changed to 

achieve better accuracy. The end of the nominal zone is the point where the discharge curve 

crosses the nominal voltage given by the battery documentation. An example curve is shown in 

Figure 3.15 with the three points identified. The values necessary to calculate the parameters 

for the generic equation are shown in Table 3.8. 

Figure 3.15 – Example battery discharge curve

Table 3.8 – Necessary values from discharge curve

Description Voltage Charge

Fully Charged Voltage Efull

End of Exponential 

Zone Eexp Qexp = i * texp

End of Nominal Zone Enom Qnom = i * 

tnom

To calculate the equation parameters, Equation 3.58 through Equation 3.62 is used with the 

values extracted from the discharge curve. First the voltage drop over the exponential zone and 

the time constant of the exponential zone are determined:

A = Efull − Eexp

Equation 3.58 

B = 3

Qexp

Equation 3.59 

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Next, using the first two parameters and the point at the end of the nominal zone from the 

discharge curve, the polarization voltage is calculated.

K = �Efull − Enom + A[exp(−B ∗ Qnom) − 1]� ∗ (QN − Qnom)

Equation 3.60 

QN in Equation 3.60 is the nominal capacity stated by the manufacturer. The value for the 

internal resistance of the battery can be used to match the calculated curve to the curve given 

by the battery manufacturer. If there is a value available in the manufacturer’s literature, it can 

be used as a starting value for this process, or one can be calculated using Equation 3.61:

R = VN ∙

1 − η

iN

Equation 3.61 

Equation 3.61 uses an assumed value of 99.5% for the efficiency (33), and the current used to 

determine the discharge curve in the manufacturer’s documentation. To calculate E0, the noload constant voltage of the battery, the previous parameters are used:

E0 = Efull + K + Ri − A

Equation 3.62 

The final equation for the battery discharge curve uses the parameters and the amount of 

charge already lost to give the voltage output of the battery:

V = E0 − K Q

Q − it + Aexp(−B ∗ it) − Ri

Equation 3.63

The charge already lost is formulated as current multiplied by time if a constant current is used. 

If the current is not constant this value can also be calculated as the integral over time of the 

current. The parameters used represent physical characteristics of the battery and the 

materials from which it is composed. Adding in technology factors on the parameters allows the 

user to adjust for advances in battery technology projected to occur in the future. This results in 

Equation 3.64: 

V = k1 ∙ E0 − k2 ∙ K

k3 ∙ Q

k3 ∙ Q − it + k4 ∙ Aexp(−k5 ∙ B ∗ it) − k6 ∙ Ri

Equation 3.64 

The performance model also outputs the heat generated by the cell, which is equivalent to the 

power output of the internal resistance, given by Equation 3.65. 

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Qout = i2 ∙ R

Equation 3.65 

3.1.3.4 Sizing Algorithm

The performance model is wrapped up into a sizing procedure to calculate the size and layout 

of a battery stack necessary to produce the required power over a given operating profile. The 

algorithm uses a design power load and current operating profile input by the user. Figure 3.16

shows the flow of information and calculations.

First, the number of parallel cells required in the stack is determined based on the current 

capacity calculated from the current load profile. The profile is integrated using the trapezoidal 

method to determine the total energy discharge over the profile. The model then calculates the 

current load on each cell using the number of parallel cells. The current at the design power 

load is found in the operating profile at the time of the design power load. The capacity 

discharged before the design power load is calculated as the integral of the load profile from 

the start to the time of the design power load. Again, the integration is done using the 

trapezoidal method. The performance model described previously calculates the voltage output 

based on the energy depleted, the current load, and the parameters input by the user. The 

voltage output multiplied by the current load gives the power output of the battery. The design 

power load divided by the power output of a single battery gives the entire size of the battery 

stack. To find the number of cells in series, the total number of cells is divided by the number of 

cells in parallel which is then rounded up. To form the complete stack as a full array of cells, the 

number of cells in series is multiplied by the number of cells in parallel. This gives the final 

number of total cells in the stack.

Once the battery stack is sized by the performance algorithm, the volume and weight of the 

battery stack are determined by the specific energy and the power density. The volume and 

weight calculations are performed in a subelement in NPSS. The final results of the sizing are 

the number of cells in the battery stack, the number of cells in parallel, and the weight and 

volume of the stack.

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Figure 3.16 – Data flow for Sizing algorithm

3.1.3.5 Validation 

For validation, data from (33) is input to the performance model and the discharge plots are 

compared with published results. The inputs to the model taken from the literature are shown 

in Table 3.9. These inputs represent the Panaxonic HHR650D3, a 6.5 amp-hour, 1.2 volt nickel 

metal-hydride battery. The nominal current capacity of the battery tested is 6.5 Amp-hours, but 

the data sheet for the battery gives the average capacity as 6.8 Amp-hours. On the data sheet 

the internal resistance is given as 2 mΩ, but (33) found that a value of 4.6 mΩ matches the 

model to the manufacturer’s plots better.

Table 3.9 – Validation Inputs

Parameter Value

E0 (Volts) 1.2848

R (Ohms) 0.0046

K (Volts) 0.01875

A (volts) 0.144

B (Ah)-1 2.3077

Figure 3.17 shows the output plots from the model with the literature plots overlaid. For the 

currents tested the model matches the published results exactly, validating the model.

To test the sizing algorithm, small stacks were sized using the inputs from the validation. An 

operating profile was generated in MATLAB to test the integration calculation in addition to the 

algorithm’s functionality. The results from the integration of the profile are displayed in Figure 

( ) ( ) dis Pbatt

N dis Pbatt

N

P

QBkAk

QQk

Qk KkEkE

4 5 ,,

3 ,,

3

201

exp ⋅⋅−⋅

+ −⋅

⋅ ⋅−⋅=

PPbatt Pbatt , 6 iRkEV , ⋅⋅−=

∫ = = end t

t dis Q idt

0

∫ = = P

parallel

t

t N

i Qdis Pbatt dt

0 ,,

PbattPbattPbatt, iVP ,, ⋅=

( ) N L

dis

parallel qQk

Q N −⋅⋅ = 1 2

, = PPbatt Nii parallel

cells = PPN ,PbattP

energy

W cells N QkVN N

ρ

⋅⋅⋅ = 2

power

batt NP cells Vol

ρ

⋅ =

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3.17. Compared to the trapezoidal integration function in MATALB, the trapezoidal integration 

in the battery model obtains the same answer, proving its accuracy.

Figure 3.17 – Battery performance model validation plots.

Table 3.10 – Comparison of integration results

Description MATLAB Battery 

Element

Energy discharged before design power 

load (Amp-hrs) 5.139 5.139

Total energy discharged (Amp-hrs) 10.10 10.10

The model also correctly sized the battery stack to the design power load and the operating 

profile as shown in Table 3.11. The number of cells in parallel multiplied by the capacity is 

greater than the total energy discharged divided by the usable capacity of the battery (1 – % 

reserves). Also the nominal power output multiplied by the number of total cells is greater than 

the design power load. The most demanding requirement is the total capacity necessary, for 

which the stack is sized.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 20 40 60 80

V

o

l

t

a

g

e

Time (min)

Model: 6.5 A

Model: 13 A

Model: 32.5 A

[1]: 6.5 A

[1]: 13 A

[1]: 32.5 A

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Table 3.11 – Sizing test inputs and results

Input Value

Discharge Duration (Hours) 2

Design Power Load (Watts) 7

Nominal Battery Power 

(Watts) 7.8

Nominal Voltage (Volts) 1.2

Capacity 6.8

Charge Reserves (%) 10

Time of Design Power 

(Hours) 1 

Specific Energy (Watthours/kg) 300

Power Density (W/m3) 150

Output Value

Cells in Parallel 1

Cells in Series 2

Total Cells 2

Stack Mass (kg) 0.052

Stack Volume (m3) 0.104

3.1.4 Cryocooler Model

3.1.4.1 Introduction

One of the major components needed to model the hybrid electric engine is a cryogenic cooler 

or cryocooler. The cryocooler is used to reject excess heat from electrical components and 

reduce their temperature to cryogenic levels in order to attain superconductivity. To 

accomplish this, a specific variant of cryocooler was chosen. More specifically, the reverse turbo 

brayton cycle was chosen due to the ease of modeling in NPSS as well as the simplicity of being 

a steady state analysis along with its potential for high cooling capacity to weight ratios. With 

the model built in NPSS, it was validated using two different fluids, neon and helium. Following 

the validation, it was tested for off design conditions.

3.1.4.2 Problem Definition

There exist many variants of cryocooler systems. Each is based on a different thermodynamic 

cycle. In the beginning of this effort all the cycle types were surveyed. It was found that the 

main types were regenerative cycles and recuperative cycles. Within the regenerative types 

existed Stirling cycle, Pulse Tubes, and Gifford McMahon. For the Recuperative type there was 

the Joule Thomson and Reverse Turbo Brayton cycle. A summary of the cycle types can be seen 

in the table below. 

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Table 3.12 – Thermodynamic Cycle Summary

Type Process 1-2 Process 2-3 Process 3-4 Process 4-1

Stirling Isothermal Isochoric Isothermal Isochoric

Pulse Tube Isothermal Isobaric Isothermal Isobaric

Gifford McMahon Isothermal Isobaric Isothermal Isobaric

Joule Thomson Adiabatic Isobaric Adiabatic Isobaric

Reverse Turbo Brayton Adiabatic Isobaric Adiabatic Isobaric

In general, many of the cryocoolers used today are designed for low power applications, used

sensors and space for example. Each of the options offer particular advantages. The Stirling 

cryocooler, which works off of the Stirling cycle was one of the first cryogenic cycle discovered 

and used. One of its main advantages is compactness; however this advantage quickly 

disappears when applications require multiple components cooled simultaneously. Another 

solution frequently used, is the Gifford-McMahon cryocooler which is a variant of the Stirling 

type. It uses extra valves to isolate compression and expansion unit in order to increase 

compression ratio. This solution is more robust, allows separation of compressor from cold 

section, and allows ease of multi-staging. However the system suffers issues related to being a 

source of vibration, heat loss due to the shuttle, and lower efficiency. Another more recent 

type of cryocooler is the Pulse Tube. The Pulse Tube works by using pressure waves, within the 

system, to shuttle heat back and forward to create cool conditions. A big advantage of this 

system is that no moving parts are used in the cold region. However, this system cannot be 

scaled while maintaining efficiency. Also, extra space is required for the pulse tube. The last 

type, which is among the recuperative cycles, is the Reverse Turbo Brayton Cryocooler. This 

type of system is analogous to turbojet systems, except heat is removed where the combustor 

would be in a conventional heat engine. One of the particular benefits which is of interest to 

aerospace applications is the lower specific weight as seen in the figure below. 

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Figure 3.18 – Power Capacity vs Specific Mass Cryocooler Survey

While it has poor specific mass in the lower regimes, as capacity increases the system quickly 

becomes the lightest option available. This is also the reason few reverse Brayton cryocoolers 

are seen today; most cryo applications are relatively low power. In a SUGAR application the 

amount of heat that must be rejected is two or more orders of magnitude larger than current 

cryo cooler applications.

So far, the types of cryocoolers available have been shown and each offer specific advantages 

and disadvantages given specific requirements. But in order to narrow down, it is important to 

assess the impact of these systems given the aerospace application of interest. It is expected 

that heat rejection on the order of 100s of kW will be required.

Figure 3.19 – Power vs Mass for Cryocooler Types

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In the above figure, SUGARs application is indication by the red circle. While it is seen the only 

current solutions offered are Stirling types, these units can be bulky due to reasons described 

earlier.

Figure 3.20 – Power Capacity vs Percent Carnot for Cryocooler Types

Similarly, in the figure above, SUGARs application is indicated with red circle showing options 

only available for Stirling cycles. While currently Reverse Turbo Brayton Cycles do not exist for 

the current application in mind, it is expected that options would become available for the N+4 

timeframe.

In further evaluating each of the selections, it was found that the Regenerative types would 

require a transient analysis while Recuperative only required steady state. Along with only 

needing steady state analysis, the recuperative type offered the advantage of being able to use 

elements prebuilt in NPSS (compressor, duct, flowstart, etc.)

Among this survey, it was also found that typically Helium, Hydrogen, Nitrogen, Neon, and 

Methane were used as the working fluid. Helium was used for applications requiring 7 R, 

Hydrogen for 36 R, Neon for 135 R, Nitrogen for 144 R, and Methane for 216 R.

3.1.4.3 On Design Approach

In order to begin modeling, all the major inputs and outputs were needed. Along with these 

variables, it is also important to determine how such a system is thermodynamically sized, 

because the thermodynamic size will eventually be related to the physical size and mass of the 

system. Also, how do the characteristics vary at different conditions? This is important because 

the initial sizing represents the ideal case; however the system is more likely to experience off 

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design conditions. Then the system must be validated in order to prove the results coming out 

of the system to be reasonable. To determine the inputs/outputs a representative model was 

defined and can be seen below.

Figure 3.21 – Closed Circuit Reverse Turbo Brayton Cycle

The model defined above is the general representation of the Reverse Turbo-Brayton Cycle. In 

this model, the blue arrows represent the direction of the flow. For the cycle, the fluid flows 

through the compressor where it is adiabatically compressed. Next, it flows through the heat 

rejection element where heat is removed isobarically. Following heat rejection, the fluid moves 

through the recuperator (depicted by the topmost box in the figure above), which transfers 

heat to the colder line. Afterwards, the flow is adiabatically expanded which decreases the 

temperature to cryogenic levels which then proceeds through the heat absorption section and 

absorbs the heat of the superconducting components. From this model, it is determined that 

the system can be represented as five elements, as seen in the figure below. 

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Figure 3.22 – Reverse Turbo Brayton Diagram (from Creare Contract #NAS5-31281) 

The model was broken down into the elements above, because this form can be most easily 

translated into the language of NPSS. As seen in the figure above, the major inputs of the 

system are temperature, pressure, heat absorbed (Qload) and performance and efficiency 

factors. The major outputs were work in and out of the system and heat rejection (Qreject).

Next, it was determined that the thermodynamic size is a strong function of massflow. The 

massflow is sized in order to pump a given amount of heat load away from the superconducting 

components. This was translated to an independent/dependent relationship of massflow to the 

desired cryogenic temperature. Along with this, in order to complete the cycle an 

independent/dependent relation was defined between the turbine pressure ratio and the 

difference of pressures between the end and beginning of the cycle (before compressor, after 

turbine).

With the cycle successfully defined, implementation within NPSS began. From the figure above, 

the model would use a compressor, turbine, three heat exchangers, three flowstarts, three 

flowends, as well as multiple ducts and shaft elements. For the first iteration, a simple model 

was created which did not use the recuperator. This was done initially to assess potential 

difficulties that may be associated with developing a non-turbojet model in the NPSS 

framework. Following the successful implementation, the recuperator was added. Due to the 

nature of how NPSS calculates each block in the model file sequentially, the flow was broken 

after the recuperator adding an additional flowstart and flowend. Schematically this final model 

can be seen in the figure below. The flow broken is represented by FlowStart4 and FlowEnd4, 

which initially was connected as one line. ‘Breaking’ the model in this manner and enforcing 

continuity between FlowEnd4 and FlowStart4 via the NPSS solver helps to remove circular 

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pathways. NPSS tends to have numerical issues with circular pathways; therefore, this method 

is applied to increase numerical stability.

Figure 3.23 – NPSS Block Schematic of Reverse Turbo Brayton Cycle

In addition to the model above, Load elements were added to the shaft ports of HPC and HPT 

representing the compressor and turbine respectively. This was done to separate the 

compressor from the turbine because a cryocooler is not a power producing device. To 

complete the disconnect between the compressor and turbine, the independents and 

dependents from autoSolverSetup() needed to be modified. Along with this modification, other 

independent and dependent elements were needed to account for the extra flowstarts. The list 

of independents and dependents can be seen in Table 3.13. 

Table 3.13 – NPSS Model Independents and Dependents

Independent Dependents

Compressor Load Compressor Shaft Speed

Turbine Load Turbine Shaft Speed

Massflow into FlowStart 4 Massflow Balance between FlowStart FlowEnd 4

Temperature into FlowStart 4 Temperature Balance between FlowStart 

FlowEnd 4

Pressure into FlowStar 4 Pressure Balance between FlowStart FlowEnd 4

Turbine Pressure Ratio Pressure Balance between FlowStart 1 FlowEnd 3

Internal Massflow Desired Cryogenic Temperature

Massflow across Heat Rejection (HEX) Heat Balance across the system

Massflow across Heat Absorption (CEX) Desired cold side ambient temperature

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The model described above was initially built using the NPSS thermodynamic package 

“GasTbls”. However, in order to define a realistic model, other fluids were required.

From the earlier survey, it was found that the fluids used in most cryogenic systems were 

Hydrogen, Helium, Nitrogen, Neon, and Methane. Since none of these tables exist within NPSS, 

Fluid Property Tables or FPTs were created. First, thermodynamic properties for each of the 

fluids were obtained from the National Institute of Standards and Technology (NIST) website. 

The website contained various thermodynamic data such as enthalpy, entropy, heat capacity, 

density, and all other relevant thermodynamic properties necessary to construct FPT tables. 

Using examples provided in NPSS and the Rocket Supplement included with NPSS, the structure 

of FPTs were created for each of the fluids. The result was five tables each containing over 

150,000 points of data for interpolation and extrapolation. The ranges of these tables 

depended on the fluid; however these tables represent a temperature range of 100R -2,000R 

where some had somewhat smaller ranges. Finally, the range of pressures extended from 1 PSI 

to 10,000 PSI.

With the completion of the FPT, the thermodynamic package being was switched over for 

validation. However, this resulted in difficulties. While the model was converging with GasTbls, 

it was not converging with FPTs. First it was assumed the FPT tables were incorrect or too large. 

Even when the ranges of the tables were reduced and the intervals between each point were 

also reduced, NPSS still would not converge. After much testing and recreation of the tables, a 

bug was found within the version of NPSS being used. For some of the NPSS elements,

specifically the turbine and compressor, there are functions used to determine the 

thermodynamic properties at a given station for a fluid. These functions are setTotalTP, 

setTotalh_P, and setTotalSP. Each of these functions require two inputs to define the conditions 

at a station, temperature and pressure for setTotalTP, enthalpy and pressure for setTotalh_P, 

and entropy and pressure for setTotalSP. Specifically, within the compressor and turbine 

elements, the code was failing with the setTotalSP function.

Further testing outside the NPSS model showed that not only the custom built tables were 

failing, but also the example cases provided with NPSS were also failing. In order to bypass this 

issue, a work around was created using the Secant Solver function. The specific code can be 

seen in the figure below

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Figure 3.24 – setTotalSP workaround using Secant Solver

The code works by comparing the known entropy Sout and the computed entropy Fl_Otemp.S. 

For each iteration, the Otemp entropy is computed using setTotalTP and the loop continues 

until convergence is found. Simply it works by taking the known pressure, and the known 

entropy, and iterates on temperature until a temperature and pressure is found which would 

relate to the known entropy.

When these changes were added to the Compressor and Turbine element, the model began

converging and validation could proceed.

3.1.4.4 Results: Validation

Validation was performed using two different cryocooler models. The first model used came 

from a study titled “Preliminary Design for a Reverse Brayton Cycle Cryogenic Cooler” (34); this 

model used Neon as the working fluid. The second model was a study titled “Second-Law 

Analysis of a Hybrid Reverse Brayton Stirling Cryocooler” (35), which the working fluid used was 

Helium.

For the first validation, the model was a single stage Reverse Turbo Brayton cryocooler. The

original model was developed by Creare Inc. The purpose of the model was to cryogenically 

cool sensing components on the Hubble Space Telescope. While the purpose was utilized for 

space, the thermodynamic cycle used for either Aircraft or Spacecraft would be the same.

For the validation, either the parameters were directly given in the paper, or information was 

calculated from the flowstation data. Major inputs as well as model comparison can be seen in 

the table and figure below. 

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Table 3.14 – Comparison NPSS inputs to Source Inputs First Validation

Type NPSS Value Source Value

Fluid Neon Neon

Input Pressure (PSI) 15.87 15.872

Input Temperature (R) 527.0 527.0

Pressure Ratio 1.602 1.602

Compressor Polytropic 

Efficiency 0.73 0.73

Heat Rejector Effectiveness 0.95 0.95

Recuperator Effectiveness 0.994 0.993

Turbine Adiabatic Efficiency 0.602 0.602

Heat Absorption (BTU/S) 0.0052 0.0052

High Pressue Line Drop 

(dPqP1) 0.0058 0.0058

Low Pressure Line Drop 

(dPqP2) 0.0093 0.0093

Figure 3.25 – NPSS Model Comparison to Actual Data First Validation

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Figure 3.26 – First Validation H-S Diagram

The resulting validation data resulted in a close match between the model and the actual data. 

Most of the points resulted in an error of <1% and the highest error was the work out of the 

turbine at around 3%.

The next validation was done with a hybrid Reverse Brayton Stirling cryocooler which uses 

helium as the working fluid. This model was developed by the Air Force Research Laboratory 

AFRL and Raytheon Space and Airborne Systems. The purpose of the model was to cool infrared 

sensors for space applications. While the paper uses a Stirling cycle, it uses it as a precooler 

which was used for validation. Similarly to the previous model, most of the efficiency 

information was given. However, only a T-S diagram was provided. In order to determine the 

flowstation data, the T-S diagram was digitized and temperature was obtained. The general 

inputs can be seen in the Table below. 

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Table 3.15 – Comparison NPSS inputs to Source Inputs Second Validation

Type NPSS Value Source Value

Fluid Helium Helium

Input Pressure (PSI) 15.87 ----

Input Temperature (R) 44.55 44.55

Pressure Ratio 1.485 1.5

Compressor Polytropic 

Efficiency 0.395 0.4

Heat Rejector Effectiveness 0.99 0.99

Recuperator Effectiveness 0.984 0.986

Turbine Adiabatic Efficiency 0.495 0.5

Heat Absorption (BTU/S) 2.84 E-05 2.84 E-05

Recuperator Pressure Drop 0.02 0.02

HEX Pressure Drop 0.01 0.01

Scaled Factor 50000 ----

While validating this model, it was found that NPSS did not perform well when using extremely 

small values of flow. While the previous validation also used small values for massflow, the 

helium model used significantly smaller values which were 100 times smaller. To bypass this, 

the system’s mass flow and heat load was scaled by 50,000 in order to help reduce the relative 

error. The result from the process will not affect the outputs as long as everything is 

consistently scaled. After the analysis is ran, dividing the result with the scaling factor again will 

produce the desired results. The second validation including the scaling factor can be seen in 

the Figure 3.27. 

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Figure 3.27 – NPSS Model Comparison to Actual Data Second Validation

Figure 3.28 – Second Validation H-S Diagram

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3.1.4.5 Results: Off Design Analysis

Primarily, the previous sections dealt with sizing a system thermodynamically. Once it is sized, it 

is important next to assess how a system would behave under off design conditions. Before 

developing the Off Design for NPSS, it was determined that a cryocooler would experience 

three differing conditions during operation. The ambient temperature will vary, the required 

temperature of the cold side could change, and the cooling load could change. 

To control the ambient conditions, massflow of the HEX and CEX will become independents. 

Internal massflow will be used to match the varying cooling load. This leads to a new solver 

setup which can be seen in the table below. 

Table 3.16 – NPSS Off Design Dependent and Independent Setup

Independent Dependents

Compressor R Line Compressor Error Term

Compressor Load Compressor Shaft Speed

Turbine Load Turbine Shaft Speed

Massflow into FlowStart 4 Massflow Balance between FlowStart FlowEnd 4

Temperature into FlowStart 4 Temperature Balance between FlowStart 

FlowEnd 4

Pressure into FlowStar 4 Pressure Balance between FlowStart FlowEnd 4

Turbine Pressure Ratio Pressure Balance between FlowStart 1 FlowEnd 3

Internal Massflow Desired Cryogenic Temperature

Massflow across Heat Rejection (HEX) Heat Balance across the system

Massflow across Heat Absorption (CEX) Desired cold side ambient temperature

Following the successful validation of the model, off design testing began. Using the Off Design 

solver setup described previously, the model was tested by keeping the massflow across the 

heat exchanger constant for three conditions. Along with the three conditions, the cooling load 

was varied. Results from this analysis can be seen in the figures below. 

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Figure 3.29 – Rejection Temperature

Figure 3.30 – Heat Rejection Load

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Figure 3.31 – HPC Power

Figure 3.32 – HPT Power

For the heat rejection temperature, it shows that as the cooling load is increased the resulting 

temperature increases. Also, as the heat flow is varied the line shifts upward, suggesting that 

less massflow forces the system to work harder to reject the inefficiencies from the 

compressor. Similarly for the HPC power which is the compressor, as the cooling load increases, 

the amount of work into the system increases. With the compressor, changing the massflow 

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causes the line to shift down which means the amount of work required increases. The turbine 

shows the same trend as the compressor. The results of the off design analysis shows that 

either increasing the cooling load on the CEX or decreasing the massflow across the HEX forces 

the system to work harder in order to maintain the cycle.

The full off design assessment can be seen in the trends below. Each trend line represents a 

different ambient massflow across the HEX. Blue line is the design condition, red line is 

massflow reduced by 10%, and the green line is massflow reduced by 20%. 

Figure 3.33 – FS_2 Temperature Figure 3.34 – FS_2 Pressure

Figure 3.35 – FS_2 Massflow

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Figure 3.36 – FS_3 Temperature Figure 3.37 – FS_3 Pressure

Figure 3.38 – FS_3 Massflow

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Figure 3.39 – FS_5 Temperature Figure 3.40 – FS_5 Pressure

Figure 3.41 – FS_5 Massflow

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Figure 3.42 – FS_6 Temperature Figure 3.43 – FS_6 Pressure

Figure 3.44 – FS_6 Massflow

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Figure 3.45 – FS_7 Temperature Figure 3.46 – FS_7 Pressure

Figure 3.47 – FS_7 Massflow

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Figure 3.48 – RC_4 Temperature Figure 3.49 – RC_4 Pressure

Figure 3.50 – RC_4 Massflow

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Figure 3.51 – HX_1 at HEX Figure 3.52 – CX_1 at CEX

Figure 3.53 – CX_3 Heat Absorption Figure 3.54 – HX_1 Across HEX

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Figure 3.55 – CX_1 Across CEX Figure 3.56 – Turbine Pressure Ratio

Figure 3.57 – Compressor Pressure Ratio

3.1.5 Generating High Speed Propeller Performance Map

3.1.5.1 Introduction

There is an increase of interest in civilian aircraft designs propelled by open rotors due to the 

increase in fuel prices as there was in 1980s (36). Open rotors have less fuel consumption 

compared to modern civilian jet engines. However, highly swept propellers are necessary to 

operate a typical jet engine powered civilian aircraft cruise Mach numbers. Although there 

were several projects (37; 38) which built prototypes in 1970s and 1980s, there is significantly 

less information on highly swept propellers compared to typical propellers. Therefore, higher 

order analysis capabilities are required to estimate open rotor performance at the conceptual 

and preliminary design phases.

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Higher order analyses are computationally expensive and sometimes they are not available in 

the conceptual and preliminary design phases. Therefore, an approach for estimating open 

rotor performance with acceptable computational expense and accuracy is necessary for the 

conceptual and preliminary design.

To provide a method with acceptable computational expense and accuracy, this study aimed at 

generating a parametric propeller performance prediction based on empirical data on highly 

swept propellers. For this purpose, two attempts were made. The first one was repopulating 

the parametric propeller performance prediction charts for typical, low-speed propellers with 

highly swept propeller data. The first method is based on a NACA report (39). The second 

attempt was aimed at adjusting a family of propeller performance maps which were created for 

propellers designed in the same time frame with the highly swept propellers. The second 

method is based on a Hamilton-Standard (HS) report (40). 

First, the validation process used for evaluating the methods is introduced. Then, the NACA 

method and how it was adjusted are explained. Later, the validation test results for this 

approach are presented. After covering the NACA method, the HS method is introduced and 

the validation test results for the HS method are discussed. Finally, the reason why the HS 

method was not updated with the highly swept propeller data as a third attempt is discussed.

3.1.5.2 Method Validation Process 

As previously mentioned, two methods were tried to create an empirical parametric advanced 

propeller performance map, namely NACA and HS methods. In this section the validation 

process devised for these methods is explained. The following sections go over how these 

methods work, how they are applied, and their validation results in detail.

3.1.5.2.1 Validation Data and Range of Interest 

Since the final goal of this research study is to develop an accurate and parametric method 

using empirical data to predict advanced counter-rotating propeller performance, F7A7 

performance data (38) was chosen for validation. The validation data in terms of design and 

operational variables are given in Table 3.17. 

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Table 3.17 – F7A7 Design and Operational Variable Values

Design or Operational Variable Value

Counter Rotating Yes

Number of Rows 2

Number of Blades per Row 8

Blade Activity Factor (AF) 150

Integrated Design Lift Coefficient (CLi) 0.4

Mach Number 0.24 – 0.80

Pitch Angle (β) 37.9 – 63

Advance Ratio (J) 0.85 – 4.1

The CPQA vs. advance ratio map for F7A7 is shown in Figure 3.58. 

Figure 3.58 – F7A7 CPQA vs. Advance Ratio Map

Figure 3.59 shows the other aspect of performance, namely efficiency.

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Figure 3.59 – F7A7 Efficiency vs. CPQA / J3 Map 

An ideal parametric performance prediction method should predict all of the validation data 

with acceptable accuracy. However, acceptable accuracy in a certain portion of the validation 

data is more important than other portions. For high speed propellers, this more important 

portion of the data corresponds to the advance ratio range between 2 and 3 which correspond 

to cruise conditions. This portion covers most of the operational envelope, such as cruise and 

the efficiency which is highest in this region. In other words, since the efficiency peaks in this 

region, most of the operation must be in this region for economic viability.

After Figure 3.58 and Figure 3.59 which show the performance in the whole data range, it is 

necessary to zoom in on the region in which the advance ratio is between 2 and 3 to observe 

the behavior of the validation data in the most important portion. Figure 3.60 presents the CPQA

vs. advance ratio behavior.

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Figure 3.60 – F7A7 CPQA vs. Advance Ratio for 2 ≤ J ≤ 3

Initially, Figure 3.60 shows that two pitch angle values, namely 37.9° and 63° do not have any 

points where the advance ratio is between 2 and 3. As it is shown in Figure 3.58, the curves 

shift upper right as the pitch angle value increases. Furthermore, the curves shift lower left as 

the Mach number increases at constant pitch angle value. However, all of the pitch angle values 

in Figure 3.60 except 61.2° have several curves because of experiments conducted at several 

Mach numbers. Figure 3.60 shows CPQA whereas Figure 3.61 shows efficiency.

Figure 3.61 – F7A7 Efficiency vs. CPQA / J3 for 2 ≤ J ≤ 3

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3.1.5.2.1.1 Relation between CP and CPQA

For the reader’s reference, it is necessary to derive the relation between CPQA and CP. Generally, 

propeller performance is shown in terms of CP or power coefficient. However, here CPQA is used 

to follow the convention in the source document (38). The relation between CP and CPQA can be 

derived as follows and is given in Equation 3.66. 

CP = P

ρ(nD)3D2 and CPQA = Pρ(nD)3A

whereas A = 

πD2

4

then

CPQA = 4

π

∙ CP

Equation 3.66 Relation between CPQA and CP

Generally, methods for predicting propeller performance parametrically use power coefficient 

instead of CPQA. The result of the prediction method is corrected based on the relation in 

Equation 3.66 before the validation test.

3.1.5.2.2 Acceptable Level of Error for the Validation Test 

All models have errors when compared with the actual data. However, models which have 

acceptable level of error for the purpose in mind are useful. The problem is how to determine 

the acceptable level of error for the given task. Determining the acceptable error is a heuristic 

process.

In this study the goal is to model the on-design and off-design performance of an advanced 

propeller parametrically and accurately. Although there is not a standard or recommended 

practice about the acceptable error for the goal of this study, a recommend practice for a more 

challenging application can be used to err on the side of caution. For this purpose, a 

recommended practice for gas turbine engine real-time steady state modeling (41) can be used. 

The acceptable error for real-time gas turbine engine steady state model is ±2%.

3.1.5.3 NACA Method for Parametric Propeller Performance Estimation

The NACA method (39) depends on correcting the ideal propeller efficiency computed using the 

actuator disk theorem for blade number adjustment, compressibility effects, and counterrotation if applicable. This approach is for constant speed propellers. Therefore, the propeller 

operation is always at peak efficiency conditions by changing the pitch angle appropriately. The 

constant speed operation concept is shown in Figure 3.62. The NACA method predicts only the 

efficiency since the pitch angle is not handled explicitly to predict the power coefficient (CP). 

The main formulation of the approach is given in Equation 3.67. 

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η = ηiηo where ηi = ηi

′ + Δηi + 0.6 Er

P

Equation 3.67 NACA Propeller Efficiency Prediction Model

ηi is the adjusted induced efficiency and ηi

’ is the ideal efficiency computed from the actuator 

disk theorem. On the other hand, Δηi is the adjustment for the number of blades and blade 

activity factor, in other words how the blade thickness is distributed along the blade radius. 

Because actuator disk theorem does not account for the effect of the blades, the efficiency 

prediction has to be adjusted for that effect. ηo is the variation in peak efficiency with Mach 

number, in other words compressibility correction. Finally, Er / P is the fractional energy loss 

due to slipstream rotation. Based on experience (39) 60% of the slipstream energy loss can be 

recovered with counter-rotation.

NACA approach does not account for the blade camber effect, in other words the integrated 

design lift coefficient (CLi). Integrated design lift coefficient and activity factor are the most 

important propeller design variables. Since NACA method does not take into account the effect 

of the integrated design lift coefficient, the validation process was adjusted to observe if the 

integrated design lift coefficient could be neglected as NACA method suggests.

Figure 3.62 – Constant Speed Propeller Operation

After the brief introduction of the NACA method, it is necessary to introduce how each effect is 

accounted for in greater detail. Before covering the effects and corrections one by one, the 

method to calculate ideal efficiency based on actuator disk theory will be reiterated for reader’s 

reference. Then, the correction for the blade number and AF is discussed. Later, the variation of 

peak profile efficiency with Mach number is covered. After that, the counter-rotation effect is 

accounted for and presented. After discussing the corrections, modifications to the NACA 

method are discussed. Finally, the validation results for the adjusted NACA method are 

provided.

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3.1.5.3.1 Ideal Propeller Efficiency based on Actuator Disk Theorem 

For the reference of the reader, the ideal efficiency (42) of a propeller based on the actuator 

disk theorem is given in Equation 3.68. 

ηi

′

(1 − ηi

′

)1/3 = �

π

3

�

1/3

� J

CP

1/3�

Equation 3.68 Actuator Disk Theory Ideal Efficiency

The equation above is an implicit nonlinear equation. Therefore, nonlinear Richardson iteration 

with over-relaxation was used to ensure numerical solution stability at every operating 

condition. 

3.1.5.3.2 Blade Geometry Correction 

As it can be seen in Equation 3.68, the actuator disk theorem does not account for the blade 

number and geometry. Therefore, corrections are necessary. For this purpose NACA method 

has the chart given in Figure 3.63. 

Figure 3.63 – NACA Method Correction for Blade Number and Geometry (39) 

Figure 3.63 shows how the geometry correction changes as a function of advance ratio and 

total activity factor (blade number multiplied by the blade activity factor). Although this 

correction is for blade geometry, it doesn’t account for the camber effect as mentioned before. 

The six bladed propeller is set as the baseline and the other blade numbers are drawn with 

respect to the six bladed propeller. The range of blade activity factor in this figure is between 

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100 and 125 for an eight bladed propeller. If Figure 3.63 is used as is, the predictions for the 

validation data is extrapolated. Furthermore, each line in this figure is for constant speed 

operation at the design speed. Values read from this chart are added to the ideal propeller 

efficiency as the blade geometry correction.

3.1.5.3.3 Compressibility Correction

After the blade geometry effects are accounted for, it is necessary to consider compressibility 

effects on the propeller performance at high Mach numbers. For that purpose the NACA 

method uses another chart which shows how the peak efficiency varies with the Mach number. 

This chart is provided in Figure 3.64. 

Figure 3.64 – NACA Method Compressibility Correction (39) 

In Figure 3.64 the variation in the peak efficiency is a function of Mach number and advance 

ratio. Constant advance ratio curves are loci of peak efficiencies as a function of the Mach 

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number. The correction from Figure 3.64 is used as a multiplier on the ideal efficiency corrected 

for blade geometry and counter-rotation.

3.1.5.3.4 Counter-Rotation Effect 

The last effect to account for is the counter-rotation effect. The NACA method captures this 

effect with another provided chart. As mentioned before, the counter-rotation effect is 

captured by estimating the amount of recovered energy loss due to slipstream rotation. Figure 

3.65 shows how the fractional energy loss due to slipstream rotation changes as a function of 

total activity factor and advance ratio.

Figure 3.65 – NACA Method Counter-Rotation Correction (39) 

Figure 3.65 is for six and eight bladed propellers and the blade activity factor range is again 

between 100 and 125. Therefore, if this chart is used as is, the predictions for the validation 

data are extrapolations. The values read from this chart is multiplied by 0.6 (39) and added to 

the propeller efficiency.

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The original NACA method was developed in 1940s and 1950s. However, F7A7 used for the 

validation test was designed in 1980s. Therefore, the charts in the original NACA method must 

be updated to predict the performance changes due to the significantly different designs with 

acceptable accuracy. The data necessary to repopulate the NACA charts were gathered from 

the reports of Advanced Turboprop Project (ATP) (37). In ATP different highly swept propellers 

were designed and tested from 1970s to 1980s. After covering all of the corrections in the 

original NACA method, the NACA method adjustments to account for ATP data is discussed 

next.

3.1.5.3.5 The Adjusted NACA Method

As previously mentioned, the original NACA charts must be repopulated with more modern 

data to reproduce high speed propeller performance. For that purpose data from the ATP were 

used. In ATP several highly swept propellers were designed, built, and tested. These propellers 

(37; 43; 44) were SR-1, SR-2, SR-3, SR-5, SR-6, SR-7L and SR-7A. SR-2 (45) was an unswept 

propeller. Therefore, the data for SR-2 were discarded. Performance data for SR-5 and SR-6 

were not found during the literature review. SR-7A (44) is a 2/9 scale model of SR-7L built for 

acoustics test with very limited performance data in the literature. Therefore, data for SR-7A 

were also discarded.

The data for the remaining propellers, namely SR-1 (45; 46), SR-3 (47; 48; 49), and SR-7L (43; 

50; 51; 52) were stored for updating the NACA charts. When all of the NACA charts are 

considered, the available data can only be used to repopulate the blade geometry correction 

and the compressibility correction charts. In other words, the counter-rotation chart is used as 

is for the adjusted NACA and HS methods although it must be extrapolated. Finally, NACA 

approach does not account for blade camber effects as mentioned several times before.

Before using all of the data for populating the charts, it was necessary to test the accuracy of 

neglecting the integrated design lift coefficient effect on the performance. If this assumption is 

incorrect, then the method fails the validation test before any further investigation. Therefore, 

two data sets, namely SR-1 and SR-3 were selected. Because SR-1 and SR-3 have one row of 

blades, the accuracy of the assumption can be tested without introducing the extrapolation 

error for the counter-rotation. Only SR-1 data were used to repopulate the charts and SR-3 

chart were used for validation of the assumption.

3.1.5.3.6 Updating Compressibility Correction

As was shown in Equation 3.67, the compressibility correction is used as a multiplier on the 

ideal efficiency after being corrected for blade geometry and counter-rotation. Therefore, the 

compressibility correction has a significant effect on the accuracy. As a result, it is updated first. 

The blade geometry correction is updated later based on difference between the SR-1 data and 

the efficiency prediction only corrected for compressibility effects.

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To generate the compressibility effect curves, the peak efficiencies for all Mach number and 

pitch angle combinations were used. These points are given in Figure 3.66 and Figure 3.67. 

These figures show that efficiency goes down as the Mach number increases as expected. Also, 

higher Mach number values correspond to higher pitch angle and advance ratio values. At these 

points the compressibility correction was determined to minimize the difference between 

experimental SR-1 efficiency and the ideal efficiency.

Figure 3.66 – SR-1 Peak Efficiency Points based on Pitch Angle

Figure 3.67 – SR-1 Peak Efficiency Points based on Advance Ratio

In Figure 3.67 it can be seen that there are few points in the low advance ratio values. Thus, 

generating the compressibility correction curves for relatively low advance ratio values were 

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harder compared to relatively higher advance ratio values. Using close data points the 

compressibility correction curves were created to minimize the difference between the ideal 

efficiency and the measured efficiency. The generated compressibility correction curves are 

shown in Figure 3.68. 

Figure 3.68 – Repopulated Compressibility Correction Chart based on SR-1 Data

The compressibility correction is a function of the Mach number and advance ratio. As Figure 

3.68 shows, the compressibility curves cover the advance ratio region between 2 and 3.5. 

Therefore, the compressibility correction can be used for the advance ratio values between two 

and three which constitutes the most important operational range as discusses before. 

However, curves for advance ratios lower than 2 and larger than 3.5 were not available.

3.1.5.3.7 Blade Geometry Correction 

As was explained in the section about the original NACA method blade geometry correction, 

the blade geometry correction is plotted for a constant rotational speed for a given blade 

number and activity factor. Typically, the performance map data does not include the rotational 

speed. As mentioned before, a typical propeller performance map has Mach number, pitch 

angle, advance ratio, power coefficient, and efficiency. From these values the rotational speed 

must be derived to determine the blade geometry correction curve.

3.1.5.3.7.1 Creating the Blade Geometry Correction Curve

Since SR-1 data were used to create the adjusted NACA method and SR-3 data were used to 

validate it, the counter-rotation effect can be neglected. Therefore, the NACA efficiency 

prediction model takes the form given in Equation 3.69 for the blade geometry correction. 

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η = (ηi

′ + Δηi)ηo then Δηi = (η − ηi′

ηo)/ηo

Equation 3.69 Blade Geometry Correction Model

For the points at which the corrected rotational speed is about 1225 RPM, the blade geometry 

correction is computed using Equation 3.69. The first step of the computations was determining 

the compressibility correction for each of these points based on the chart provided before. 

Then, the ideal efficiency corrected for compressibility was subtracted from the measured 

efficiency at each of these points to get the blade geometry correction. The blade geometry 

correction is given in Figure 3.69. 

Figure 3.69 – Blade Geometry Correction based on SR-1 Data

After discussing how each of the corrections were updated based on SR-1 data for the adjusted 

NACA method, the results of the validation test based on SR-3 data is presented in the next 

section.

3.1.5.3.8 The Adjusted NACA Method Validation Test Results 

For the adjusted NACA method validation test, SR-3 data between advance ratio values of two 

and three were used. For the sake of brevity, the results only at three Mach numbers are 

reported. These Mach numbers are 0.2, 0.6, and 0.75. The efficiency predicted at these Mach 

numbers as a function of pitch angle and advance ratio is compared with measured efficiencies. 

The predicted efficiency at Mach 0.2 and its percent error are given in Figure 3.70 and Figure 

3.71. 

Figure 3.70 shows that the adjusted NACA method fails to capture the trend in the SR-3 data 

and the error increases as the advance ratio increases. The increase in the error as the advance 

ratio increases is given in Figure 3.71. The percent error values are far from being acceptable. 

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At this point it is trivial that the adjusted NACA method failed the validation test. However, for 

the sake of completeness, the results at the other Mach numbers are also presented.

Figure 3.70 – Validation Test Efficiency vs. Advance Ratio for the Adjusted NACA Method at Mach 0.2

Figure 3.71 – Validation Test Efficiency Error for the Adjusted NACA Method at M=0.2 

After Mach 0.2, the next Mach number is 0.6. The validation test results for this Mach number 

are given in Figure 3.72 and Figure 3.73. Figure 3.72 shows that the adjusted NACA method fails 

to capture the trends in most of the pitch angle values except 60.5°. This situation is similar to 

Mach 0.2. The divergence in error for 51.5° and unacceptable level of errors are given in Figure 

3.73. 

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Figure 3.72 – Validation Test Efficiency vs. Advance Ratio for the Adjusted NACA Method at M=0.6 

Figure 3.73 – Validation Test Efficiency Percent Error for the Adjusted NACA Method at M=0.6 

The final Mach number for the validation test is 0.75. The results for this Mach number are 

shown in Figure 3.74 and Figure 3.75. Compared to the previous two cases, Figure 3.74 shows 

that the adjusted NACA method captured the trends in SR-3 data for Mach 0.75. Furthermore, 

it looks like the predicted efficiencies are off by a constant. The percent error values in Figure 

3.75 also suggest that the accuracy was very close to ±2% limit and the prediction are almost off 

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by a constant. Moreover, the error does not diverge. Although the percent error is the best 

among the test results, predictions at Mach 0.75 are still unacceptable.

Figure 3.74 – Validation Test Efficiency vs. Advance Ratio for the Adjusted NACA Method at M=0.75 

Figure 3.75 – Validation Test Efficiency Percent Error for the Adjusted NACA Method at M=0.75 

Overall, it can be concluded that the adjusted NACA method failed the validation test although 

it managed to capture some of the trends at certain conditions.

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3.1.5.4 Hamilton – Standard Method for Parametric Propeller Performance 

Estimation

The Hamilton – Standard (HS) method (40) is based on a collection of propeller performance 

maps for ranges of number of blades, activity factor (AF) and integrated design lift coefficient 

(CLi). These maps are for high Mach number turboprops designed in the 1960s and early 1970s. 

The performance of a given propeller design is predicted using linear interpolation or 

extrapolation of the data available in the collection of propeller performance maps. The 

available ranges for the design variables are given in Table 3.18. 

Table 3.18 – Hamilton – Standard Performance Maps Design Variable Ranges

Design Variable Range

Number of Blades 3 and 4

Blade Activity Factor (AF) 80 – 220

Integrated Design Lift Coefficient 

(CLi) 0.150 – 0.700

As Table 3.18 shows the propeller performance maps available in HS method cover the validation 

data in terms of blade activity factor and integrated design lift coefficient. However, the HS 

method does not cover the validation data in terms of the number of blades and counterrotation. Finally, HS method provides correction for compressibility effects.

3.1.5.4.1 Hamilton – Standard Method Steps

Each map in the HS method is for a given number of blades, blade activity factor, and 

integrated design lift coefficient. There are series of maps with different integrated design lift 

coefficients for a given blade number and blade activity factor. For each blade number there 

are a series of map groups with different blade activity factors. For the reader’s reference, one 

performance map for a propeller with a blade number of four, a blade activity factor of 140, 

and an integrated design lift coefficient of 0.3 is reproduced in Figure 3.76

Figure 3.76 shows a typical propeller performance map used in this method. The maps used for 

the application of this method are given in the appendices. The axes are power coefficient (CP) 

and advance ratio (J). Constant pitch angle curves and isoefficiency curves are plotted. From 

this map for given advance ratio and pitch angle values, it is possible to obtain the 

corresponding power coefficient and efficiency values.

When the given design is not one of the maps in the collection, linear interpolation or 

extrapolation is used to predict performance. This is done by interpolating on integrated design 

lift coefficient and activity factor for a given number of blades.

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Figure 3.76 – Sample Hamilton – Standard Performance Map (40)

3.1.5.4.2 Adjusted Hamilton – Standard Method 

As mentioned before, the original Hamilton – Standard method is for three and four blades, and 

single row propellers whereas the purpose of this study is to predict the performance of a 

counter-rotating propeller with eight blades per row. The difference between the number of 

blades per row and number of rows affect the power consumption of the propeller. On the 

other hand, having multiple rows affect the propulsive efficiency of the propeller. These effects 

must be accounted for accurate performance predictions.

In this subsection of the report each of these effects are accounted for is discussed. First, the 

difference in the number of blades and rows is addressed and explained. Second, the 

compressibility effects are discussed. Third and last, the effect of the counter-rotation on 

efficiency is captured is presented.

3.1.5.4.2.1 Power Coefficient Corrections

The original HS method is for three and four blades whereas F7A7 has eight blades in each row. 

This difference in blade number causes a change in power coefficient for the same operating 

conditions as expected. Although there are nonlinear effects on power consumption due to the 

interaction between blades when the number of blades increases, the only possible way of 

accounting for this effect in an empirical approach is to multiply the power coefficient by the 

ratio of the blade numbers. For instance, if the maps for four blades are used, then the power 

coefficient values are multiplied by two to match the eight bladed case.

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In summary, only maps for four bladed propellers in the original HS method were used instead 

of the three bladed ones because F7A7 has eight blades in each row. Moreover, the power 

coefficient values in these maps were multiplied by two to account for the difference in the 

number of blades per row. Then, they were multiplied by two to capture the difference in the 

number of rows. Finally, they were multiplied by (4 / π) to convert power coefficient values into 

CPQA values.

3.1.5.4.2.2 Compressibility Corrections

The original HS method has correction for compressibility effects. This correction was used 

exactly in the adjusted HS method for this study. The correction is based on Mach number and 

the Mach number correction for the effect of blade camber. Figure 3.77 shows the general 

compressibility correction.

The general compressibility correction is a multiplier for the efficiency value read from the 

maps as given in Equation 3.70. It is a function of the effective Mach number and advance ratio. 

Effective Mach number is the flight Mach number corrected for the blade camber. Figure 3.78

gives the Mach number correction for the blade camber.

The blade camber correction for the Mach number is added to the flight Mach number. The 

camber correction is a function of the integrated design lift coefficient. As the integrated design 

lift coefficient increases (camber increases), the compressibility loss increases.

ηCorrected = ηMap × Ft

Equation 3.70 Hamilton – Standard Compressibility Correction

Figure 3.77 – Hamilton – Standard General Compressibility Correction (40)

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Figure 3.78 – Hamilton – Standard Mach Number Adjustment for Blade Camber (40) 

3.1.5.4.2.3 Counter-Rotation Effect

Counter-rotation effects in the HS method were included using the same approach as the NACA 

method (39). The effects of counter-rotation were modeled as recovery of the fractional energy 

loss due to rotation of slipstream. A fraction of the energy loss was added to the compressibility 

corrected efficiency. Experience showed that 60% of the loss can be recovered (39). Because 

the fractional energy loss chart for NACA method was used here, the chart was extrapolated 

linearly due to blade activity factor values as was the case for NACA method. The chart was 

provided in the NACA section of this report. When all of the effects are accounted for, the 

efficiency prediction can be formulated as in Equation 3.71. 

η = ηCorrected + 0.6 ∙

Er

P = ηMap ∙ Ft + 0.6 ∙

Er

P

Equation 3.71 Adjusted Hamilton – Standard Method Efficiency Prediction Model

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3.1.5.4.2.4 Adjusted Linear Interpolation and Extrapolation Scheme

In the original HS method values from the closest four maps were interpolated or extrapolated 

for each given pitch angle and advance ratio. Instead of repeating this process for every pitch 

angle and advance ratio combination in the validation data, the common pitch angle and 

advance ratio combinations among the closest four maps were interpolated once to generate 

the estimated performance maps for F7A7. Pitch angle and advance ratio combinations in the 

estimated performance maps were chosen based on the most constraining map. In other 

words, if the combination does not exist in all of the maps, then that combination was not used 

for interpolation. The interpolation was based on F7A7 blade activity factor and integrated 

design lift coefficient. The design variables of the four propeller maps chosen for interpolation

are given in Table 3.19. 

Table 3.19 – Hamilton – Standard Performance Maps Selected for F7A7 Performance Estimation

Blade 

Number

Blade Activity 

Factor

Integrated Design Lift 

Coefficient

4 140 0.3

4 140 0.5

4 180 0.3

4 180 0.5

These maps were chosen because their design variables envelop F7A7 design variables, namely 

blade activity factor of 150 and integrated design lift coefficient of 0.4. Before the interpolation 

their power coefficient values were corrected for blade number and number of rows as 

explained in the power coefficient correction subsection. The interpolated CPQA vs. advance 

ratio map for F7A7 is shown in Figure 3.79. 

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Figure 3.79 – Interpolated CPQA vs. Advance Ratio Map for F7A7

Figure 3.79 shows the estimated CPQA behavior for a counter-rotating propeller with eight 

bladed two rows. When the pitch angle values in the interpolated performance map are 

compared with the pitch angle values in the validation data, most of the validation data is 

outside the interpolated map in terms of the pitch angle values. Thus, error due to 

extrapolation is introduced in the estimation. The other performance map, efficiency vs. 

advance ratio is given in Figure 3.80. 

Figure 3.80 – Interpolated Efficiency vs. Advance Ratio Map for F7A7

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3.1.5.4.3 Validation Test of the Hamilton – Standard Method

After interpolating the performance maps for F7A7 performance estimation, the next step was 

the validation test. For the validation test, F7A7 data presented in the method validation 

process section were used. For each pitch angle and advance ratio combination in the 

validation data, the interpolated maps were used to estimate the CPQA and efficiency at each of 

these conditions. The outputs of the maps were corrected for compressibility effects and 

counter-rotation. This process is illustrated schematically in Figure 3.81. 

Figure 3.81 – Output Generation Steps for the Validation Test

The corrected outputs, namely CPQA and efficiency were compared with the validation data for 

each pitch angle and advance ratio combination in the validation data. The results are 

presented in terms of absolute and relative errors. The results are presented for each pitch 

angle value to divide the results into easily understandable portions. The first set of results is 

for a pitch angle of 55.7° and they are given in Figure 3.82, Figure 3.83, Figure 3.84, and Figure 

3.85. 

Figure 3.82 shows that at a pitch angle of 55.7°, the performance estimation map does not 

capture the trend in the validation data but as Figure 3.83 shows, percent error at each of these 

points is almost in a ±10% band and obviously that is above the validation test passing criterion 

of ±2%. At this point it was concluded that the adjusted HS approach could not pass the 

validation test because it had to meet the accuracy requirement at each point. Furthermore, 

the pitch angle of 55.7 is the pitch angle which requires the smallest extrapolation from the 

estimation map. Therefore, if the estimation map has unacceptable error at this level of 

extrapolation, then the error in the rest of the data will probably fail the test. However, for the 

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sake of completeness, the error in the rest of the validation data was computed and they are 

presented in this report.

Figure 3.82 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 55.7°

Figure 3.83 – Validation Test Efficiency Percent Error at a Pitch Angle of 55.7°

After analyzing the error in the efficiency prediction, the error in CPQA prediction must be 

observed. Figure 3.84 shows that the estimation map captured the trend in the validation data 

to a certain extent but the errors between the predictions and the actual data increase as the 

advance ratio increases. Figure 3.85 shows this divergence in the errors explicitly. The error in 

CPQA prediction reaches almost 90% and the amount of divergence increases as the Mach 

number increases. This amount of difference is not surprising given the very crude assumptions 

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made to account for the differences between the prediction data and the validation data in 

terms of the number of blades and the number of rows. CPAQ is overestimated throughout the 

whole validation data for 55.7°. Obviously, the adjusted HS method is better at predicting the 

efficiency than predicting CPQA. 

Figure 3.84 – CPQA vs. J Validation Test at a Pitch Angle of 55.7°

Figure 3.85 – Validation Test CPQA Percent Error at a Pitch Angle of 55.7°

After covering the accuracy of the prediction map at a pitch angle of 55.7°, the results at the 

next pitch angle value, namely 56.9° are given in Figure 3.86, Figure 3.87, Figure 3.88, and 

Figure 3.89. Figure 3.86 shows the predicted efficiency trend against the validation data trend 

whereas Figure 3.87 shows the relative error of the predicted efficiency. In terms of efficiency 

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results for a pitch angle of 56.9° look similar to those of 55.7°. The prediction fails to capture 

the trend but the relative error is limited between -6% and 12%. It is not surprising to see 

similar trends between predictions for two close pitch angle values. Although 56.9° requires 

further extrapolation than 55.7°, the relative error is contained within a narrower band. Next 

the prediction results for CPQA at a pitch angle of 56.9° are discussed.

Figure 3.86 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 56.9°

Figure 3.87 – Validation Test Efficiency Percent Error at a Pitch Angle of 56.9°

Figure 3.88 compares the predicted trend for CPQA against the validation data trend and Figure 

3.89 shows the relative error in the predicted CPQA. As it was the case for efficiency, CPAQ results 

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at 56.9° look similar to those of 55.7°. The predicted trend is offset from the validation data by 

almost a constant for the most of the data range. As the advance ratio increases, the difference 

between prediction and validation data increases. This divergence is shown in Figure 3.89. As it 

turns out, the relative error in CPQA at 56.9° is not as large as 55.7° case although 56.9° requires 

further extrapolation. However, CPQA was constantly overestimated as it was in the case of 55.7° 

and the divergence increases as the Mach number increases.

Figure 3.88 – CPQA vs. Advance Ratio Validation Test at a Pitch Angle of 56.9°

Figure 3.89 – Validation Test CPQA Percent Error at a Pitch Angle of 56.9°

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After covering the validation test results for the first two pitch angle settings, the results for the 

third pitch angle value, namely 58.5° is shown in Figure 3.90, Figure 3.91, Figure 3.92, and 

Figure 3.93. The order of the figures is the same with previous two cases. Figure 3.90 is for 

efficiency prediction absolute error comparison at 58.5° and Figure 3.91 is for the relative error 

of the efficiency prediction. Similar to the previous cases, the prediction fails to capture the 

validation data trend and the relative error is between almost ±15%. The width of the relative 

error band increased compared to the previous two cases due to further extrapolation 

probably.

Figure 3.90 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 58.5°

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Figure 3.91 – Validation Test Efficiency Percent Error at a Pitch Angle of 58.5°

Figure 3.92 shows that the predicted CPQA trend captures some of the validation data at 58.5°. 

However, the difference between the prediction and the validation data increases as the 

advance ratio increases. Since the predicted trend captures some of the data, not all of the 

validation data is overestimated at 58.5° for the first time as shown in Figure 3.93. As expected, 

the diverging trend in the relative error is also shown in Figure 3.93 and the divergence 

increases as the Mach number increases. 

Figure 3.92 – CPQA vs. Advance Ratio Validation Test at a Pitch Angle of 58.5°

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Figure 3.93 – Validation Test CPQA Percent Error at a Pitch Angle of 58.5°

For the validation test results of 60.2°, Figure 3.94, Figure 3.95, Figure 3.96, and Figure 3.97 are 

provided. Figure 3.94 shows that the predicted efficiency trend is not same with the validation 

data. The relative error is larger than the previous cases as a result of further extrapolation. The 

relative error is between -10% and 25% as given in Figure 3.95. For the first time the predicted 

efficiency is more than one at three points in Figure 3.94. This unreasonable and unrealistic 

result is a result of slopes in the prediction data and the distance of the extrapolated point.

Figure 3.94 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 60.2°

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Figure 3.95 – Validation Test Efficiency Percent Error at a Pitch Angle of 60.2°

Figure 3.96 – CPQA vs. Advance Ratio Validation Test at a Pitch Angle of 60.2°

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Figure 3.97 – Validation Test CPQA Percent Error at a Pitch Angle of 60.2°

Figure 3.96 and Figure 3.97 are for the CPQA validation test results at 60.2°. Figure 3.96 shows 

that the predicted CPQA trend crosses through the validation data for the first time. However, 

the relative error is not acceptable as in all of the cases so far. The relative error is between -

10% and 25% and the error increases as the Mach number increases according to Figure 3.97. 

There is still a diverging trend in the relative error as the advance ratio increases.

There is only one more pitch angle value left in the validation data range of interest. The 

validation test results at 61.2° are presented in Figure 3.98, Figure 3.99, Figure 3.100, and 

Figure 3.101. Pitch angle setting of 61.2° has only three points in the validation data range of 

interest. Compared to the other settings, this setting has the smallest number of points. Figure 

3.98 shows that all of the points are overestimated but the amount of overestimation is less 

than 10% as given in Figure 3.99. 

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Figure 3.98 – Efficiency vs. CPQA / J3 Validation Test at a Pitch Angle of 61.2°

Figure 3.99 – Validation Test Efficiency Percent Error at a Pitch Angle of 61.2°

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Figure 3.100 – CPQA vs. Advance Ratio Validation Test at a Pitch Angle of 61.2°

Figure 3.101 – Validation Test CPQA Percent Efficiency at a Pitch Angle of 61.2°

Figure 3.100 and Figure 3.101 show the validation test results for CPQA at 61.2°. In Figure 3.100

the predicted trend overestimates almost by a constant relative error. The relative error is 

about 10% for all of the three points in Figure 3.101. 

After reviewing the HS method performance predictions for the validation data, it was obvious 

that the method failed the validation test. Efficiency prediction was significantly better than 

CPQA prediction although both of them failed the validation test.

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One of the main contributors to the error was the generation difference between the 

prediction data used and the validation data. In other words, the prediction data used were for 

the turboprops in 1960s whereas the validation data used were for an open rotor designed in 

1990s. The open rotor blades are swept more than turboprop blades. The difference in sweep 

affects the efficiency and power consumption behavior.

The last main contributor was the counter-rotation effect. The prediction data had only one 

row of blades whereas the validation data had two. Accounting for the effect of this difference 

on the power consumption is not available. Therefore, the power consumption for one row was 

multiplied by two as another crude assumption. Moreover, the NACA chart used to predict the 

effect of counter-rotation on efficiency was extrapolated due to larger blade AF values. 

Therefore, error due to extrapolation was introduced.

3.2 Hybrid Electric Sizing Studies

Once the basic building blocks for modeling hybrid electric propulsion systems has been created 

in NPSS, they can be used to construct a hybrid electric NPSS engine model similar to the hFan 

architecture developed by GE. While many specific modeling details of the hFan are 

proprietary, there is enough data published in this report, to create a public domain model and 

use it to conduct trade studies. Since the hFan architecture contains additional degrees of 

freedom (electric power and core power settings) it is first necessary to define the modeling 

assumptions and methodology, described in section 3.2.1. After that, comparisons to public 

domain GE engine performance data are made to verify functionality of the GE hybrid electric 

model. Finally, a study is conducted using cryogenic and non-cryogenic components to assess 

the engine cycle trades associated with the hybrid electric engine architecture. Additional 

degrees of freedom also present themselves during the sizing process. The trade study will 

address the impact of sizing the engine with and without the electric motor ‘turned on’. In 

order to give context to the trade study, vehicle trade factors for drag, fuel burn, energy usage, 

and weight are used as provided by Boeing for the SUGAR Volt configuration.

3.2.1 Modeling Methodology

This research seeks to create a public domain representation of the GE hFan engine 

architecture shown in Figure 3.102. While technologically advanced compared to today’s 

turbofans, the underlying architecture is fundamentally similar. It is a two-shaft, high bypass, 

separate flow turbofan designed for podded applications. However, the hybrid electric portion 

manifests itself through an electric motor embedded in the tailcone of the engine. This motor is 

coupled to the low pressure spool and provides the ability to drive the fan either in addition to, 

or in lieu of the core.

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Figure 3.102 – Hybrid Electric Engine Architecture (53) 

The electric motor is a switched reluctance motor (SRM) design that allows for robust operation 

in the event of a failure of one or more of the motors phases. Conventionally, SRM designs may 

suffer from so-called torque stutter (17). This occurs when the magnetic fields align between 

the rotor and stator resulting in a momentary a period of no torque until the shaft inertia 

carries the motor poles out of alignment; however, this is not an issue for such a large machine 

with the inertia of the fan and low pressure compressor pulling the motor. Furthermore, by 

using a switched reluctance machine the motor can be switched off simply by de-energizng the 

stator poles. This allows flexible operation and use of the turbofan in either all fuel, all electric, 

or hybrid modes depending on the needs of the mission.

The electric motor is connected to an inverter that converts power from DC to AC. Behind that 

is a transmission line that connects the motor to a series of batteries that are charged before 

flight. While inflight charging is certainly possible, this study does not include any provisions for 

that capability. The following sections will describe modeling aspects of the integrated system.

3.2.2 Gas Turbine Modeling Considerations

In order to understand modeling considerations associated with the operation of a hybrid 

electric system, it is first necessary to establish and extend standard definitions used in aircraft 

engine power management. First, the establishment of a power code for the electric system 

must be defined, similar to the power code system for a conventional propulsor. Table 3.20 lists 

the standard power rating codes for non-augmented turbofan applications (54). It is up to the 

OEM to determine the exact operational constraints associated with each rating code. Often 

lifing considerations are associated with each power code and there may be other certification 

constraints associated with assignment of engine operational state to a power code. For 

example, there are FAA requirements regarding engine acceleration time from flight idle to 

maximum power in the event of a go-around.

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Table 3.20 – Conventional Power Code Definitions

Power Code Definition

50 Maximum Takeoff

45 Maximum Continuous

40 Maximum Climb

35 Maximum Cruise

22-35 Intermediate Rated Power

21 Flight Idle

20 Ground Idle

In a practical modeling sense the above rating codes can be simulated through the use of NPSS 

independent and dependent variables and constraints, shown in Table 3.21. Essentially, given a 

power code for a sized engine, NPSS will vary the independent until the dependent condition is 

satisfied, or a constraint is activated. Note that only the commonly active constraint is shown in 

Table 3.21; however, the engine model used in this, and most applications, includes physical 

shaft speed constraints, compressor exit temperature constraints, minimum nozzle pressure 

ratio constraints, and others necessary to keep the engine operable. Note that power code 20 is 

removed, since in a steady state simulation there is not a reasonable method to predict the 

difference between ground and flight idle.

Table 3.21 – Conventional NPSS Power Management Scheme

Power 

Code Definition NPSS Independent NPSS Dependent Constraint(s)

50 Maximum Takeoff Burner FAR 100% Corrected 

Fan Speed T4.1 Limit

45 Maximum 

Continuous Burner FAR T4.1 Limit –Margin1

Fan Corrected 

Speed

40 Maximum Climb Burner FAR T4.1 Limit –

Margin2

Fan Corrected 

Speed

35 Maximum Cruise Burner FAR T4.1 Limit –

Margin3

Fan Corrected 

Speed

22-35 Intermediate Rated 

Power Burner FAR Thrust (% of PC 35) Fan Corrected Speed

21 Flight Idle Burner FAR Thrust (5% of PC 

50)

Minimum Burner 

FAR

While Table 3.21 is fairly straightforward, it is constructed with the mindset that the engine 

core creates all necessary power for thrust, evidenced, by the burner fuel-to-air (FAR) ratio 

being the only independent in the solver. For the hybrid electric system being modeled, an 

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additional term is defined in agreement with the GE defined terminology: the electric power 

code (EPC). The EPC NPSS implementation is shown in Table 3.22 along with additional 

constraints that must be added to the solver in addition to those shown in Table 3.21. Since this 

is a new definition it merits further discussion. For a given operating state (altitude and Mach), 

a turbofan will produce a certain amount of thrust for a specified power code. Also associated 

with this state will be a shaft speeds and combustor operating temperatures. While not entirely 

accurate of reality or NPSS, it is suitable for the purposes of discussion to assume that the fan 

produces all of the thrust. In order to simplify the power management structure, it is further 

defined that for a given power code, the thrust level will be held constant regardless of the 

amount of electric power sent to the motor. In other words EPC=50 and EPC=0 will both 

provide the same net thrust at PC=45. Defining EPC in this manner allows it to be orthogonal to 

the PC definition. In essence, PC sets thrust, and EPC sets the amount of electric power (torque) 

applied to the low pressure shaft. While this is certainly not the only way to define EPC, it is 

perhaps the easiest to understand.

Table 3.22 – Electric Power Code Definitions

EPC Definition NPSS 

Independent NPSS Dependent Constraint(s)

50 Maximum Motor 

Power Motor Torque Minimum Burner FAR (core idle)

Rated Motor 

Power

0-50 Intermediate 

Motor Power Motor Torque Intermediate Motor Power (% of Rated) None

0 Motor Off Motor Torque Minimum Motor Power None

In the absence of other operability constraints, which will be discussed later, one primary 

limiter in maximum allowable motor power is the core idle limit. As more motor power/torque 

is applied for a given PC, the NPSS solver will reduce burner FAR, and thereby core power 

output, to maintain thrust. Eventually, the core will drop below acceptable stability limits and 

could blowout. While this may eventually be allowable in the presence of an electrical drive 

system, it is assumed in this analysis that the core must remain lit at all times. Therefore, EPC 

50 is defined as the motor power required to achieve minimum burner FAR for a given PC, or 

the maximum rated motor power, whichever happens earlier. EPC 0 through 50 is then defined 

as a percent of the EPC = 50 power. The general power management algorithm is as follows:

1. Specify Mach, altitude, PC, and EPC

2. Temporarily set EPC to 0

3. Run engine to PC according to Table 3.21

4. Save off thrust

5. Vary motor torque to hit core idle condition, save power

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6. Apply motor torque consistent with Table 3.22 and (5), then run model to hold thrust 

at (4)

7. Repeat for flight envelope

Of course, this simplistic explanation while accurate leaves out real operability features that 

constrain hybrid electric operation. One primary limiter on application of electrical power to 

the shaft is the resulting flow mismatch between the high and low pressure spools. Since thrust 

is fixed for a specified PC it can be assumed that fan speed, flow, and pressure ratio all remain 

relatively constant. As motor torque is increased, the core no longer needs to produce as much 

high temperature and pressure gas to drive the low pressure turbine. From an engine and NPSS 

solver perspective, the burner FAR will be reduced to compensate for additional power being 

supplied by the motor. Correspondingly, the high pressure shaft speed will decrease as applied 

motor power increases and core power is decreased. Since, for a two-spool architecture, the 

low pressure compressor and fan are on the same shaft the amount of flow being sent to the 

high pressure compressor (HPC) will remain high, even as high pressure spool speed decreases. 

This flow mis-match between the HPC and low pressure compressor (LPC) results in reduced 

margin and ultimately surge of the LPC. To alleviate this, an operability bleed must be placed aft 

of the LPC to control surge. The operability bleed is controlled via a discrete state variable (DSV) 

in NPSS that opens the bleed valve whenever the LPC stall margin falls below a specified 

margin. In addition to the operability bleed, variable area fan nozzle logic is defined that varies 

the bypass nozzle area to maintain peak fan efficiency along the operating envelope unless 

constrained by a minimum stall margin constraint. Variable area core nozzle logic is 

implemented; however, it was found to be difficult to achieve consistent convergence with 

NPSS when the engine included a variable area core nozzle, variable area bypass nozzle, and 

operability bleed aft of the LPC. The reason for this is that NPSS converges the system through a 

Newton-Rhapson solver. When there are collinear dependents, which are metrics that are 

affected equally by more than one independent, the solver is singular and cannot converge. For 

example, core nozzle area and the operability bleed flow can both be used to control LPC stall 

margin; however, the solver can only vary one at a time. In a final design the two variables 

would be scheduled (i.e., set from lookup tables) to provide maximum operability and 

performance; however, since the objective of the integrated NPSS model is to perform 

parametric studies, the solver must be used to dynamically set operability features so that the 

engine can adapt to different cycle designs without extensive user adjustment. In order to 

accomplish this goal the parametric sizing comparisons between the cryogenic and 

conventionally driven system were performed without a variable area core nozzle.

Weight estimation of the engine was performed using the flow scaling method defined in (55)

with parameters tuned to match available GE hFan baseline data. Engine parametric size and 

efficiency effects were also used using the methods described in (55). 

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3.2.3 Verification of the NPSS Hybrid Electric Model to GE Public Domain Data

In order to validate the performance trends of the Georgia Tech NPSS model, a comparison was 

performed with the GE hFan model developed in year 2 of the SUGAR project. The NPSS model 

was calibrated to match the engine TSFC and performance information presented in (1). The 

calibrated engine cycle is shown in Table 3.23. Cruise is defined as Mach 0.7 at 40,000 feet ISA.

Table 3.23 – Georgia Tech hFan Calibration Point

Cycle Parameter Value

FPR at cruise 1.45

BPR at cruise 11.4

LPCPR at cruise 1.45

HPCPR at cruise 28

T41max 3500 deg R

T41 at top of climb 2500 deg R

Cooling Uncooled advanced CMC

HP Shaft HPX 256 HP

LP Shaft HPX 150 HP

Maximum Motor Power 8000 HP

The GE generated powerhook, recreated in Figure 3.103, was used as a basis for comparison.

Figure 3.103 – GE hFan Powerhook

The output from the Georgia Tech model, using the operability features described in Section 

3.2.1 and detailed motor and inverter elements described in Sections 3.1.1.6 and 3.1.1.7 is 

shown in Figure 3.104. The trends are similar in both magnitude and shape suggesting the 

Georgia Tech model is appropriate for use in parametric studies.

GE

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Figure 3.104 – Georgia Tech Hybrid Electric Trends

It is also useful to look at the Georgia Tech hybrid electric performance averaged across all 

flight Mach numbers and altitudes, shown in Figure 3.105. 

Figure 3.105 – Georgia Tech Average Performance

Marked in Figure 3.105 are several red circles that show the point at which the operability 

bleed opens. For each EPC from 0 to 50, there are two similar colored lines for each EPC setting. 

The lighter shaded one represents the engine cycle with the operability bleed logic turned on, 

whereas the darker line represents the NPSS results with the operability logic completely 

disabled. (e.g., the LPC is allowed to surge in a numerical sense). The overall trends are still in 

good agreement with the GE generated data with the core idle limit constraining electric motor 

0

0.1

0.2

0.3

0.4

0.5

0.6

20 30 40 50

TSFC (lbm/lbf-hr)

Power Code

40,000 ft / Mach 0.7 / ISA

EPC = 0

EPC = 20

EPC = 40

EPC = 50

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

TSFC

Power Code

No Op Bleed - 0

No Op Bleed - 10

No Op Bleed - 20

No Op Bleed - 30

No Op Bleed - 40

No Op Bleed - 50

With Op Bleed - 0

With Op Bleed - 10

With Op Bleed - 20

With Op Bleed - 30

With Op Bleed - 40

With Op Bleed - 50

EPC Operability Bleed Opening

Average Performance Across all MN, alt

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power addition at lower power codes. Furthermore, the operability bleed opens earlier as 

electric power is increased. While Figure 3.105 is only indicative of average performance, it 

verifies the desired functionality within the engine model.

3.2.4 Hybrid Electric Engine Sizing Studies

Once the baseline parametric NPSS hybrid electric model is created, it can be used to perform a 

series of studies to examine the coupling between the engine thermodynamic cycle, electrical 

system including the motor, and the aircraft. Both a cryogenic and conventional motor and 

inverter setup are examined. The effect of sizing the engine with and without the electric motor 

is also investigated for both the cryo and non-cryo applications. The study uses the following 

assumptions for power system specific weights, listed in Table 3.24 and based on (56). The

vehicle trade factors in Table 3.24 were provided by Boeing for the 750 Balanced configuration 

of the SUGAR Volt and show the percent fuel burn change for the noted engine trade factor. 

The Energy percent change is calculated relative to the baseline configuration using a 1,380 hp 

motor. Engines results are compared relative to a common baseline to assess which cycle is 

optimal; however, this does not provide an absolute level of fuel burn reduction which requires 

cycling the detailed vehicle sizing; this was outside the scope of this study.

Table 3.24 – NPSS Hybrid Electric Weight Assumptions

Cryo NonCryo

Motor Power Density (hp/lb) 13 3

Inverter Power Density (hp/lb) 15 10

Transmission Weight (lbm) 20 20

Cryo Cooler Weight (lbm/hp-input) 5 NA

Cryo COP (Percent Carnot) 0.3

Battery Energy Density (Wh/kg) 750

Weight Trade Factor (per 1000 lbm) 0.86

Drag Trade Factor (per 1" fan diameter) 0.09

SFC (per %) 1.01

Energy (per %) 0.31

Component efficiencies for the engine and electrical system are calculated using the methods 

described in previous sections. The first set of studies focused on varying fan pressure ratio 

varied from 1.35 to 1.55 with overall pressure ratio held constant at 58. This was accomplished 

by varying the low pressure compressor pressure ratio and holding the high pressure 

compressor pressure ratio at 28. T41 was not changed and extraction ratio was held at 1.15. 

Unless otherwise stated, all results are shown for the mid-cruise condition.

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3.2.4.1 Non-cryogenic Results – Sized With Motor 

First, the engine was sized with full electric horsepower addition from the motor. In other 

words, the fan was sized by top of climb requirements provided by Boeing for the 750 Wh/kg 

balanced scenario at maximum core power output and full electric motor power addition. 

Figure 3.106 shows the trade factor results for this scenario for varying levels.

Figure 3.106 – Motor Sized Non-Cryogenic Fan Sizing Breakdown

Starting at the upper left of Figure 3.106, the results are shown with varying FPR. Since 

extraction ratio (Pt19/Pt9) is held constant, lowering FPR leads to increased BPR, resulting in 

decreased TSFC. Sizing the fan with the electric horsepower does carry a penalty in all fuel 

mode which will be fully examined later.

On a fuel efficiency basis alone, one would choose the highest BPR (lowest FPR) cycle with the 

highest electric motor available; however, as the BPR increases, so does the weight of the 

engine, shown in the upper right portion of Figure 3.106. The primary driver of weight is bypass 

ratio, evidenced by the steep slope of fuel burn change with FPR. Moving to the lower right of 

Figure 3.106, bypass ratio is the primary driver on engine diameter and size; increasing bypass 

ratio leads to larger engine diameters.

Finally, adding the individual fuel burn sensitivities from Figure 3.106 yields the overall fuel 

burn and cycle trade, shown in the lower right. The minimum fuel burn occurs near a FPR of 

1.42 corresponding to a BPR of approximately 15.

-15.00

-13.00

-11.00

-9.00

-7.00

-5.00

-3.00

-1.00

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Delta FB (TSFC) Sized With Motor

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Delta FB (Weight) Sized With Motor

-2.00

-1.80

-1.60

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Delta FB (Drag) Sized With Motor

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

1.00

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Delta FB (Total) Sized With Motor

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3.2.4.1 Cryogenic Results

The cryogenically driven system results are similar in nature to the non-cryogenic ones; 

therefore, an in-depth investigation will be avoided; however, the benefit to the cycle is 

apparent, as shown in Figure 3.107. Again, the deltas are relative to the same points as shown 

in Figure 3.106 to facilitate comparison between the cryogenic and non-cryogenic systems.

Figure 3.107 – Cryogenic Cycle Trades

Figure 3.108, shows the breakdowns between the various trades underlying Figure 3.107. Of 

note is that the increased efficiency of the cryogenic components results in the elimination of 

approximately 3% of the energy usage relative to conventional components. This trades as just 

over 0.5% vehicle fuel burn, shown in the lower right of Figure 3.108. However, this efficiency 

increase is offset somewhat by the need to power the cryocooler and carry the heavier cooling 

equipment.

-5.00

-4.50

-4.00

-3.50

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Delta FB (Total) Sized With Motor

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Figure 3.108 – Cryogenic System Sizing

The cryogenic system provides a 1.5% decrease in fuel burn for the motor-sized case.

3.2.4.2 Engine Sizing Method Trades

In order to provide a clearer picture of the trades associated with sizing the hybrid engine and 

the differences between cryogenic and conventional electric systems, four sets of data have 

been overlaid in Figure 3.109. The engine has been sized with and without the motor turned on 

at the sizing condition (top of climb). Evaluations have been made for both the cryogenic and 

conventional systems. Since extraction ratio is held constant, lowering FPR leads to a rise in 

bypass ratio. A few observations are immediately apparent. First, there is little variation in the 

slopes of the four engines with varying fan pressure ratio which means switching to a cryogenic 

system has little impact on the choice of FPR. Second, sizing with the motor turned on results in 

a 2% fuel burn reduction for the conventional motor system and a 1% reduction in fuel burn for 

the cryogenic variant.

The first observation is more easily explained. From an engine viewpoint the electric system 

manifests itself as a torque applied to the low pressure shaft. The engine cycle is invariant to 

where the power to drive the motor comes from, or how efficient the electric systems are. As 

FPR (and therefore BPR) is varied, the electric system remains the same size for a given motor 

horsepower. These two effects mean that electric system differences in weight and efficiency 

manifest themselves through the vehicle level trades, as shown in Table 3.24. 

-15.00

-13.00

-11.00

-9.00

-7.00

-5.00

-3.00

-1.00

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Delta FB (TSFC) Sized With Motor

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Delta FB (Weight) Sized With Motor

-1

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Delta FB (Energy) Sized With Motor

-2.00

-1.80

-1.60

-1.40

-1.20

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Delta FB (Drag) Sized With Motor

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Figure 3.109 – FPR Electric and Sizing Trades

The second trend showing decreased TSFC with increasing motor power merits a more in depth 

explanation. Table 3.25 a more detailed listing of relevant cycle parameters for the 

conventional system at FPR 1.45 from Figure 3.109. Only the conventional electric system is

shown for simplicity of discussion. The trends and underlying explanation is valid for the 

cryogenic system too. Since the engine is being sized to a constant top of climb thrust, the 

addition of 1,380 hp of motor power during sizing means a smaller engine core is required to 

provide fan power.

Table 3.25 – Core Sensitivity to Sizing With Motor (60 OPR, 2500R T41, FPR 1.45)

Parameter Conv-W Conv-W/O

BPR (design) 13.3 10.6

Cruise TSFC (lbm/lbf-hr) 0.340 0.345

Corrected Core Size (lbm/s) 43.8 47.8

Cruise Core Flow (% max) 79.4 77.6

OPR (cruise) 39.46 33.2

T41 (deg R, cruise) 2265 2130

As a result the bypass ratio of the engine increases, leading to a decrease in TSFC. This explains 

the benefit of sizing the core with the motor turned on; however, there are other benefits. In 

Table 3.25, the corrected core size indicates the corrected weight flow entering the high 

pressure compressor at the top of climb condition. Both top of climb conditions have a 2500 

degree Rankine T4.1. In the case of the motor turned on during sizing, Conv-W, the flow is 

smaller since the power needed to drive the fan is being partially supplied by the electric 

motor. In the engine sized without the motor, Conv-W/O, the core is larger since it must supply 

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

1.3 1.35 1.4 1.45 1.5 1.55 1.6

Delta FB (%)

FPR

Conventional W Motor Cryo W Motor

Conventional W/O Motor Cryo W/O Motor

1.9%

0.9%

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all of the fan power. The difference in core size has a large effect on part-power operation 

(cruise). Since the Conv-W core is smaller, it can run closer to its full power, peak efficiency 

point at cruise when the motor power is turned on. This leads to higher OPR and higher 

component efficiency. Conversely, the Conv-W/O engine reacts differently at part-power. Since 

the core was sized without motor power it must be operated at a lower power setting at cruise. 

This results in lower percentage flow and lower OPR, leading to a less thermally efficient core at 

cruise which lowers the TSFC benefit of electric operation. As a side note, the smaller core, 

while more efficient, does have a higher T41 by almost 130 degrees. This is a maintenance and 

operational cost that cannot be neglected; however, full assessment of this impact is outside 

the scope of this study.

There are negative consequences to sizing the core with the motor turned on; however, to fully 

understand these issues, the overall pressure ratio, T41 space must also be explored. The 

results of varying T41 between 2,500 and 3,000 degrees Rankine and OPR from 50 to 65, both 

at the top of climb sizing point, are shown in Figure 3.110. Sensitivities were run at a FPR of 

1.45, since it corresponds to the optimum FPR from Figure 3.109. 

Figure 3.110 – OPR / T41 Trade Space

As was the case for the FPR trade, there is no discernable effect of optimal cycle selection as a 

function of the electrical system. The cryogenic system is about 1% better depending on cycle, 

as is the case in the FPR trade space. In order to better understand the results the T41/OPR 

trade space is reduced to four runs. OPR sweeps are generated for 2,500 and 3,000 degrees 

T4.1 while sizing the engine with and without the electric motor, designated as W and W/O 

respectively. The results are shown in Figure 3.111. Some of the trends are familiar, the blue 

dots, indicating the 2,500 degree T41, show an optimum OPR of approximately 60. The higher 

T41 of 3,000 degrees leads to an optimum OPR of ~80 if the trends are extrapolated. 

Conventional sizing trends dictate higher T41 is needed to maximize core specific power and 

minimize weight as OPR is increased. This trend is independent of whether or not electric 

motor power is applied during sizing, suggesting that the core may be designed and optimized 

in isolation of hybrid electric propulsion. Since the electric motor off-design performance is 

-7.00

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

45 50 55 60 65 70

Delta FB (%)

OPR

Delta FB (Total) Sized With Motor 

OPR/FPR Trade 2500 2600

2700 2800

2900 3000

-7.00

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

45 50 55 60 65 70

Delta FB (%)

OPR

Delta FB (Total) Sized With Motor 

OPR/FPR Trade 2500 2600

2700 2800

2900 3000

Non-Cryo Cryo

T4.1 T4.1

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primarily driven by flow capacity through the core, it could be possible to design a core for a 

non-hybrid-electric engine and then adapt it to a hybrid electric configuration by changing its 

flow capacity via the HPT stage one nozzle area.

Figure 3.111 – OPR/T41 Electric and Sizing Trades

Also apparent in Figure 3.111 is that sizing with the motor turned on is approximately 

equivalent to 500 degrees of firing temperature. This is most evident at the 55 OPR point where 

the red and blue markers overlap, but is also present throughout the space. This suggests that 

while the smaller core sized with the motor tends to burn hotter in cruise, it may still burn 

cooler than the equivalent higher T4 design point. At this point it is worth recalling that these 

results are all for the case of a highly advanced, uncooled turbine. Additional cooling required 

at higher temperatures may offset the effects from higher T4, meaning sizing with the electric 

motor may look even more favorable.

Finally, there are negative consequences of sizing the core with the electric motor included. 

Table 3.26 shows data extracted for the 50 and 60 OPR points shown in Figure 3.111. Recall, the 

results shown up to this point show fuel burn benefits assuming the electric motor is operating 

at cruise; however, this will not always be the case. In Table 3.26, the Cruise Penalty column 

shows the fuel burn detriment of running the engine sized with the motor in all fuel mode. In 

other words, since the core is small, it must operate in an overspeed condition to drive the fan. 

For this study, the amount of overspeed varies according to the cycle, but is approximately 

102% corrected speed at the cruise condition and 108% at the top of climb condition. While 

including the motor power when sizing the core leads to up to a 1.6% fuel burn benefit when 

flying in all electric mode, the downside is up to a two percent increase in fuel burn when flying 

-7.00

-6.00

-5.00

-4.00

-3.00

-2.00

-1.00

0.00

1.00

45 50 55 60 65 70

Delta FB (%)

OPR

2500W 3000W 2500WO 3000WO

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in all electric mode. The top of climb fuel consumption is increased up to 6% when flying in all 

fuel mode.

Table 3.26 – All Fuel Penalties for Smaller Core

OPR Design 

T41

Cruise 

Penalty 

(All Fuel)

Cruise Benefit 

(All Electric)

50

2500 

R 1.6% 1.56%

3000 

R 1.3% 2.07%

60

2500 

R 1.6% 1.6%

3000 

R 1.1% 1.96%

3.2.4.3 Hybrid Electric Trade Study Conclusions and Recommendations 

The parametric investigation into the cycle design space of the hybrid electric turbofan 

configuration has revealed many interesting results. First, the impacts of electric system 

efficiency and weight manifest themselves at the aircraft level since they are independent of 

the engine cycle. As a result the engine cycle can be selected without regards to the electrical 

system setup. Further studies should be examined with varying motor sizes to verify this trend, 

but is expected to remain consistent with the results presented here. As a result the differences 

between cryogenic and conventional electric systems are relatively small. The differences are 

expected to be larger for larger electric motors since they will contribute to a larger amount of 

vehicle weight.

Second, the impact of the sizing method of the core on fuel burn has been investigated. Sizing 

the core with the electric motor turned on leads to a smaller core that is more efficient at cruise 

since it can operate closer to its design point. Unfortunately, this leads to a fuel burn penalty 

when operating in all fuel mode since the core must overspeed in order to provide cruise and 

top of climb thrust. While this study examined sizing the core with all or none of the electric 

power available, further design iterations should focus on determining the optimum 

intermediate values of electric power applied during sizing. This should be optimized in 

consideration with the entire vehicle sizing process.

It was also found that smaller high temperature cores are less sensitive to the effects of core 

sizing method since the flow lapse is smaller and more energy comes from combustion than 

mass flow. This leads to the conclusion that advanced materials and cooling techniques will 

remain important with hybrid electric architectures.

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Finally, this engine trade study was performed for a fixed motor size and optimized for a single 

aircraft mission. Since the amount of electric power available on board the aircraft will be a 

function of flight length, a full optimization over all mission lengths is suggested for future 

studies. It may be worth taking a fuel burn penalty in all fuel mode, which would be required for

longer stage lengths, if the fuel savings on more numerous short range missions proves 

beneficial from a fleet perspective. Performing such a study requires a wholly integrated engine 

and airframe sizing tool of which this parametric NPSS tool suite is an important step.

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4.0 Summary

4.1 Performance and Sizing Analysis

The SUGAR Volt aircraft was analyzed and the battery and electrical energy usage were 

optimized to minimize fuel burn and manage battery energy. Two distinct versions of the 

aircraft were developed:

A “Balanced” aircraft sized the electric motor and batteries to be used relatively evenly 

over most of the 900 nm mission. This aircraft was approximately 140,000 lb TOGW,

used a 1750 HP electric motor, and assumed 750 Wh/kg batteries. This aircraft reduced 

fuel burn by 60% & energy use by 54% (Goal 60% reduction from the SUGAR Free 

current technology baseline). 

A “Core shutdown” version sized the electric motor for cruise on 100% electric power 

for at least part of the cruise leg and used more batteries. The TOGW was allowed to 

grow up to 190,000 lb TOGW. The aircraft used a larger 7150 HP electric motor. This 

aircraft was able to reduce fuel burn by 64% & energy use by 46% (Goal 60% reduction). 

4.2 Emissions Analysis

Landing and takeoff emissions analysis conducted by GE resulted in levels 89%-93% below 

CAEP/6 (Goal 80% reduction). Compared to the reference CFM-56 engine, cruise NOx was 

reduced 74% (Goal 80% reduction) for 1750 HP “Balanced” aircraft and eliminated for 7150 HP 

“Core shutdown” aircraft for part of the cruise segment. For the “Core shutdown” aircraft with 

the 900 nm range mission, approximately 50% of the cruise leg can be flow with electric power 

and no emissions. The SUGAR Volt maintains a total lifecycle emissions advantage (CO2 and 

NOx) if charging electricity is from a clean source. 

4.3 Noise analysis

The more detailed noise analysis resulted in quieter aircraft (24 to 31 dB below stage 4) than 

the less detailed assessment in Phase I. The engine noise was dominated by the fan and jet 

sources, so reducing the core noise from more electric use resulted in only a small benefit

compared to a conventional propulsion system. Although a range of additional airframe noise 

reduction technologies were identified, the aircraft was not able to achieve the NASA noise goal

of 52 dB below stage 4.

4.4 Energy cost analysis

The SUGAR High aircraft was shown to reduce fuel and energy cost by 54% and total cost by 

29% relative to SUGAR Free Baseline. The SUGAR Volt showed an additional 6-14% reduced 

direct energy cost (fuel + electricity) relative to SUGAR High, depending on the future energy 

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cost scenario selected. However, including costs for increased aircraft weight and complexity 

reveals little change in SUGAR Volt total cost relative to SUGAR High. 

4.5 Technology Plans and Roadmaps

Technology plans and roadmaps for hybrid electric engines and batteries were updated.

4.6 Hybrid Electric Propulsion Modeling Environment

Highly detailed propulsion component models were developed as NPSS elements. These 

elements were integrated into a hybrid electric modeling environment and exercised on sample 

engine trade studies.

4.7 Conclusions

The SUGAR Volt, with hybrid electric propulsion, produces significant additional emissions and 

fuel burn reductions beyond those levels achieved by SUGAR High. 

Total energy usage is not decreased with hybrid electric propulsion, but reduced energy cost 

can be achieved for some energy cost scenarios.

The SUGAR study has identified technologies to reduce noise, but was not able to define a path 

to meeting the NASA noise goals. 

4.8 Recommendations

1) Define in detail the next technical maturation steps for the hybrid electric engine 

technology. Determine what can be done on a modest initial budget.

2) Understand shared technologies with HEDP (Hybrid Electric Distributed Propulsion). 

3) Conduct a battery/electric system integration study to address integration issues 

(including inlets and exhausts for air batteries and fuel cells). 

4) Perform an installation study with a lower fan pressure ratio engine and other airframe 

and engine technologies for improved acoustics. Determine how close to the noise goal

is achievable. Consider revising the noise goal.

5) Consider support of small aircraft hybrid electric demonstrations. Understand the

tradeoffs of cost and technical risk reduction value and learning. 

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54. Gas Turbine Engine Steady-State and Transient Performance Presentation for Digital 

Computer Programs. 1997. AS681H.

55. Welge, R., et al. N+2 Supersonic Concept Development and Systems Integration. 2010. 

NASA/CR-2010-216842.

56. Brown, G.,. Efficient Flight-Weight Electric Systems. 2012. Technical Conference: NASA 

Fundamental Aeronautics Program, Clevland.

 192
NASA Contract NNL08AA16B – NNL11AA00T – Subsonic Ultra Green Aircraft Research – Phase II

Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

Subsonic Ultra Green Aircraft Research – Phase II

Volume II – Appendix A – Georgia Tech Propeller Performance Maps and 

Reference Sheets

Contract Number: NNL08AA16B

Task Order: NNL11AA00T

6/30/2014

Prepared by:

GEORGIA INSTITUTE OF TECHNOLOGY

 

Prepared for:

Erik Olson

NASA Langley Research Center
NASA Contract NNL08AA16B – NNL11AA00T – Subsonic Ultra Green Aircraft Research – Phase II

Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

Propeller Performance Maps

Figure 1 Hamilton - Standard Propeller Performance Map for 4 Blades, AF 140, and CLi 0.3

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Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

Figure 2 Hamilton – Standard Propeller Performance Map for 4 Blades, AF 150, and CLi 0.5

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Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

Figure 3 Hamilton - Standard Propeller Performance Map for 4 Blades, AF 180, and CLi 0.3

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Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

Figure 4 Hamilton - Standard Propeller Performance Map for 4 Blades, AF 180, and CLi 0.5

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NPSS Element Reference Sheets

Battery

 --------------------------

| | 

| | 

| Battery | 

| | 

| | 

 --------------------------

 | 

 | 

 | 

 | 

Socket Name: S_weight

Socket Type: BATTERY_WEIGHT

Returns: StackWeight, StackVol

 

Socket Name: S_heat

Socket Type: BATTERY_HEAT

Returns: 

Socket Name: TB_current

Socket Type: Function

Returns:

Battery calculates the size, layout, weight, and volume of a battery stack and evaluates 

the performance of a stack given the layout and a current load.

Variables

Variable Description Default Units IO 

Status

Capacity

CapDis Percent of battery charge left as reserves 0 none input

CapDisBattDesPower Nominal capacity of one cell 0 none input

CapDisLimit Total current draw during design power load 0 none input

CurrBatt Current drawn from each cell output

Current Current drawn from the stack 0 none input

DesPower Design power load 0 none input

dischargeInterval Time interval for performance analysis

ExpZoneAmp Voltage lost over exponential zone of discharge curve 0 none input

ExpZoneTimeConst Time constant for exponential zone of discharge curve 0 none input

FlightTime Duration of the period when batter will be operating 0 none input

k_1 Technology factor for 1 none input

k_2 Technology factor for 1 none input

k_3 Technology factor for 1 none input

k_4 Technology factor for 1 none input

k_5 Technology factor for 1 none input

Ncells Number of cells in sized battery stack 1 none output

NewStateOfCharge State of charge of the battery after output

NoLoadVoltage No-load constant voltage of battery 0 none input

Nparallel Number of cells in parallel in sized battery stack 1 none output

Nseries Number of cells in series in sized battery stack 1 none output

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PolarizationVoltage Voltage lost due to polarization in discharge curve 0 none input

Qgen Heat generated by battery output

Resistance Internal resistance of individual cell 0 none input

StackWeight Weight of sized battery stack 0 none output

StackVol Volume of sized battery stack 0 none output

StateOfCharge Initial charge remaining in battery

TimeDesPower Time of design power load 0 none input

timeStep Interval for trapezoidal integration of load profile

Voltage Stack voltage output

VoltageBatt Cell voltage output

Option Variables

Name Description Variables IOStatus Affected Default Allowed Values

switchDes Design/Offdesign switch Ncells, Nseries, Nparallel, Current DESIGN DESIGN, OFFDESIGN

Functions

Prototype Description

void calculate() None

void variableChanged(string name, any oldVal) None

Ports and Internal Stations

Port/Station Type Description

OUTPUT DataOutputPort Electric signal from battery

Independents

Name Description Default Active When

ind_current Current draw on the battery Current Always

Sockets

Socket Description socketType Sets Values

TB_current Current load profile table Function

S_weight Stack volume and weight socket BATTERY_WEIGHT StackWeight, StackVol

Usage Notes

Battery

- In design mode, the element provides a single-pass sizing calculation using a current 

load profile and a design power load. The load profile is input as a table and is 

required to have values for the current until the end of the flight. Extrapolation of 

values is strongly discouraged. A weight and volume calculation is performed in the 

BatteryWeight subelement based on the parameters calculated during sizing. 

- The energy discharged over the flight is calculated using a trapezoidal numerical 

integration of the load profile with constant time intervals set by the variable 

“timeStep.” This integration is performed twice, once over the entire flight, and once 

from the beginning of the flight to the time of the design power load. 

- The number of cells in the stack arranged in parallel is determined by the energy 

capacity necessary for the flight. The total number of cells is calculated from the 

design power load. The values are rounded up to the nearest cell. The number of cells in 

series is calculated based on those two values. The final sized stack is assumed to be a 

full array. This is achieved through a recalculation of the total number of cells as a 

product of the number in series with the number in parallel

- Ncells = Nseries * Nparallel

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- In offdesign mode the voltage calculation is performed as a function of the current 

load and the state of charge of the battery. The calculation determines the voltage 

output of a single cell, and then the voltage of the entire stack is found based on the 

input layout.

 

- Technology factors can be applied to battery performance parameters to accommodate 

projections of future batteries. Default values of the factors are one, signifying 

present performance.

- The output port transfers a four-element array of voltage, current, frequency, and 

phase. Since a battery provide direct current (DC) electricity, the frequency and the 

phase are both zero (0).

Battery has a baseType of Element.

BatteryHeat

 --------------------------

| | 

| | 

| BatteryHeat | 

| | 

| | 

 --------------------------

BatteryHeat calculates the heat generated due to the operation of the battery stack.

Variables

Variable Description Default Units IO Status

Qgen Heat generated by the battery during operation 0 none OUTPUT

Functions

Prototype Description

void calculate() None

Usage Notes

BatteryHeat

- Heat is calculated as the power output by a single battery due to internal resistance. 

This is multiplied by the number of cells to obtain the heat generated by the stack.

- Qgen = Ncells * CurrBatt**2 * Resistance;

BatteryWeight

 --------------------------

| | 

| | 

| BatteryWeight | 

| | 

| | 

 --------------------------

 

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BatteryWeight calculates the weight and volume of the battery stack using the specific 

energy and power density. 

Variables

Variable Description Default Units IO 

Status

SpecEnergy Specific energy of battery 0 none input

PowerDensity Power density of battery 0 none input

PowerBattNom Nominal power of battery 0 none input

VoltageNominal Nominal voltage of battery 0 none input

Functions

Prototype Description

void calculate() None

Usage Notes

SOFC_new

 --------------------------

| | 

| | 

| SOFC_new | 

| | 

| | 

 --------------------------

 | 

 | 

 | 

 | 

Socket Name: S_heat

Socket Type: SOFC_HEAT

Returns: qGen, qNet, qNet_total

Socket Name: S_weight

Socket Type: SOFC_WEIGHT

Returns: totalWeight, totalVol

Battery calculates the size, layout, weight, and volume of a battery stack and evaluates 

the performance of a stack given the layout and a current load.

Variables

Variable Description Default NPSS Units 

(Actual Units)

IO 

Status

Capacity Nominal capacity of a single cell 6.8 none (amp-hr)

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Option Variables

Name Description Variables IOStatus Affected Default Allowed Values

switchDes Design/Offdesign switch None DESIGN DESIGN, OFFDESIGN

switchMode Design mode switch CELL CELL, PWR, EFF

switchOdesMode Offdesign mode switch None STOICH STOICH, FUELFLOW

Functions

Prototype Description

void calculate() None

void variableChanged(string name, any oldVal) None

Ports and Internal Stations

Port/Station Type Description

Fl_I1 FluidInputPort Incoming Anode Flow

Fl_I2 FluidInputPort Incoming Cathode Flow

Fl_O1 FluidInputPort Exiting Anode Flow

Fl_O2 FluidInputPort Exiting Cathode Flow

Fl_Cathode1 FlowStation Cathode air after 1st heat addition and equilibrium

Fl_Cathode1 FlowStation Cathode air after electrochemistry and equilibrium

Fl_Anode1 FlowStation Anode gas after 1st heat addition and equilibrium

Fl_Anode2 FlowStation Anode gas after electrochemistry and equilibrium

Electric_Out DataOutputPort Electric signal from fuel cell

Dependents

Name Description eq_lhs eq_rhs Active When

dep_RefFuel Molar flow through the anode M_H2required M_H2_Anode1 switchOdesMode != 

FUELFLOW

dep_CathAir Molar flow through the cathode M_O2_Cathode1 M_O2required switchOdesMode != 

FUELFLOW

dep_xH2O Mole fraction of water in flow into anode yH2O_Des yH2O_Anode0 switchOdesMode != 

FUELFLOW

dep_xN2 Mole fraction of nitrogen in flow into anode yN2_Des yN2_Anode0 switchOdesMode != 

FUELFLOW

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Sockets

Socket Description socketType Sets Values

S_heat System heat socket SOFC_HEAT qGen, qNet, qNet_total

S_weight System volume and weight socket SOFC_WEIGHT totalWeight, totalVol

Usage Notes

SOFC_new

- SOFC_new calculates the performance of a solid oxide fuel cell by determining the ideal 

voltage of the cell and subtracting electrochemical losses from it. The losses calculated 

in the model are activation losses, ohmic or resistive losses, and concentration or mass 

transfer losses. 

- The losses are calculated based on either direct inputs or first principles. For direct 

inputs, exchange current density, area specific resistance, and limiting current density 

are input as values. If first principles are used, the values are calculated using 

material properties such as diffusion and resistivity of the fuel cell components. The 

equations that use first principles can be “turned on” individually based on what 

properties the user knows. 

- For all calculations with partial pressures, the model is currently set to use the 

partial pressures at the outlet of the electrodes. If the user desires to change this the 

source code would have to be changed, there is no switch built in.

-The sizing of the fuel cell stack is based on the power output at a design current 

density. The stack is sized to a design power load at the design point. The sizing not 

only gives the stack size, but also the fuel and oxidizer flows that match the operating 

conditions declared by the user.

-For design analysis the model can determine the size of the stack given a power load and 

an interconnect efficiency, the power output given a stack and efficiency, or an 

efficiency if the stack size and power required are input. 

-For off-design, the model can keep a constant stoichiometry, or ratio between the fuel 

and the air, or constant flows. These flows would need to be determined by the sizing 

code first.

- The oxygen and hydrogen fuel flows and mole fractions are dependents whenever 

stoichiometric analysis is performed, both for design and off-design conditions. For 

constant input flow conditions, the dependents are not included in the solver. 

- The SOFC outputs a four element array consisting of voltage, current, frequency and 

phase to the data output port. The frequency and phase are zero because the fuel cell 

generates direct current (DC). 

SOFC_new has a baseType of Element.

SOFCHeat

 --------------------------

| | 

| | 

| SOFCWeight | 

| | 

| | 

 --------------------------

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SOFCHeat calculates the heat transferred through flows, the heat generated by the fuel 

cells, and the net heat of the fuel cell system.

Variables

Variable Description Default Units IO 

Status

qGen Heat generated by a single fuel cell 0 none input

qNet Heat transferred through anode and cathode flows 0 none input

qNet_total Net heat of the fuel cell 0 none input

Functions

Prototype Description

void calculate() None

Usage Notes

SOFCHeat

-SOFCHeat calculates the heat as the excess energy from the reaction that is not used to 

produce electricity. The enthalpy of the reaction is used as the total energy available 

for power production.

-The heat transfer due to the anode and cathode flows is the difference in the enthalpy 

at the inlet and the enthalpy at the outlet. qNet is the net heat generated for one cell. 

If qNet is positive, there is excess heat that has not been absorbed by the flows. If it 

is negative, the flows have absorbed more heat from the cell than the cell has generated.

SOFCHeat has a basetype of Subelement

SOFCWeight

 --------------------------

| | 

| | 

| SOFCWeight | 

| | 

| | 

 --------------------------

 

SOFCWeight calculates the weight and volume of the fuel cell system using densities and 

geometric values. 

Variables

Variable Description Default Units IO 

Status

cellArea Total cell area 0 none input

cellInstallFactorV Volumetric installation factor for cells 0 none input

cellInstallFactorW Weight installation factor for cells 0 none input

cellVol Volume of one cell 0 none output

cellWeight Weight of one cell 0 output

numCellPerStack Number of cells in one stack 1 input

numStack Number of stacks that make up the total fuel cell system 0 output

rhoAnode Density of the anode 1 input

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rhoCathode Density of the cathode 1 input

rhoElectrolyte Density of the electrolyte 1 input

rhoInterconnect Density of the interconnect 1 input

stackInstallFactorV Volumetric installation factor for the stack 1 input

stackInstallFactorW Weight installation factor for the stack 1 input

stackVol Volume of a stack in the system 0 output

stackWEight Weight of a stack in the system 0 output

t_Anode Thickness of the anode 1 input

t_Cathode Thickness of the cathode 1 input

t_Electrolyte Thickness of the electrolyte 1 input

t_Interconnect Thickness of the interconnect 1 input

totalWeight Weight of the SOFC system 0 output

totalVol Volume of the SFC system 0 output

Functions

Prototype Description

void calculate() None

Usage Notes

SOFCWeight

-SOFCWeight calculates the weight and volume by buiding up a cell using the cell area, 

the component densities, and the component thicknesses. The cell area is the total cell 

area, not just the active area used to calculate the current. 

-Each cell is assumed to consist of an anode, a cathode, an electrode, and an 

interconnect. There are installation factors for both weight and volume for other parts 

not accounted for in the calculation.

-The number of stacks is calculated from the total number of cells determined through the 

sizing algorithm in the SOFC_new element. 

SOFCWeight has a baseType of Subelement.

BatteryWeight

- Weight is calculated as a division of the total energy capacity of the stack by the 

specific energy. Volume is calculated as the total power of the battery stack divided by 

the power density. Nominal metrics are used for the calculations since voltage and power 

can vary. 

- StackWeight = Ncells * VoltageNominal * k_2 * CapNominal / SpecEnergy;

- StackVol = Ncells * PowerBattNom / PowerDensity;

BatteryWeight has a baseType of Subelement.

Inverter

Electrical connections are made using Data Ports passing a real[4] array, consisting of:

{Voltage, Current, Frequency, Phase}

Voltage and Current are the magnitudes of the sine waves in an AC system, Phase is the difference in phase between the 

voltage and current waveforms

DC currents have Frequency = 0 and do not use the value of Phase

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Electrical units not defined in NPSS, so most calculations done unitless.

Input = AC Electricity generated (to drive a Motor)

Output = DC required to generate it (Power draw of inverter from Bus)

Sockets: 

Socket Name: S_Efficiency

Socket Type: Inverter _Efficiency

Returns: Efficiency

The Inverter element calculates the DC power required to generate the AC waveforms required by the Motor. 

Variable Description Default Units (not implemented) IO Status

Efficiency Power 

Out/Power in

1 None INPUT

OutputVoltage Amplitude of 

AC output

120 (Volts) INPUT

OutputCurrent Amplitude of 

AC output 

current

2 (Amps) INPUT

InputVoltage Voltage at DC 

input

500 (Volts) INPUT

InputCurrent DC Input 

current

.48 (Amps) OUTPUT

OutputFrequency Frequency at 

AC output

60 (Hz) INPUT

DesignPower Design Output 

Power in watts

12000000 (Watts) INPUT

OutputPower Output Side 

Power

12000000 (Watts) OUTPUT

Option Variables used:

Variable Description Variables Affected Default Allowed Values

switchDes Design/Off –

Design Switch

Efficiency DESIGN DESIGN, 

OFFDESIGN

Functions

Prototype Description

Void calculate() None

Ports

Port Type Description

INPUT DataInputPort Power Requirements from Motor

OUTPUT DataOutputPort Power Requirements to Bus

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Sockets

Socket Description socketType Sets Values

S_Efficiency Calculates efficiency from ratio 

between DesignPower and 

OutputPower

Inverter _Efficiency Efficiency

Independents

Name Description Variable Active When

ind_Voltage Attempts to match voltage to bus 

voltage through a resistive cable

InputVoltage always

Usage Notes: 

The socket included is currently a table lookup of efficiency based on OutputPower/DesignPower.

The DC input voltage is the bus voltage – the losses in the cable, which are a function of the Input current. The 

Independent is required to guess what the input voltage is such that the dependent in the bus will find that 

the bus end of the power cable is at the bus voltage.

Details of Calculate: Read in inputs, set DesignPower=OutputPower if switchDes=Design, calculate efficiency 

using S_Efficiency, InputPower = OutputPower/Efficiency, format outputs & end. 

Rectifier

Electrical connections are made using Data Ports passing a real[4] array, consisting of:

{Voltage, Current, Frequency, Phase}

Voltage and Current are the magnitudes of the sine waves in an AC system, Phase is the difference in phase between the 

voltage and current waveforms

DC currents have Frequency = 0 and do not use the value of Phase

Electrical units not defined in NPSS, so most calculations done unitless.

Inputs 

AC Electricity (from Generator)

DC Electricity (required from Bus)

Sockets: 

Socket Name: S_Efficiency

Socket Type: Rectifier _Efficiency

Returns: Efficiency

The Inverter element calculates the DC power required to generate the AC waveforms required by the Motor. 

Variable Description Default Units (not implemented) IO Status

Efficiency Power 

Out/Power in

1 None INPUT

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OutputVoltage Amplitude of 

DC output

500 (Volts) INPUT

OutputCurrent Amplitude of 

DC output 

current

.48 (Amps) INPUT

InputVoltage Voltage at AC 

input

120 (Volts) INPUT

InputCurrent AC Input 

current

2 (Amps) INPUT

InputFrequency Frequency at 

AC Input

60 (Hz) INPUT

DesignPower Design Output 

Power in watts

12000000 (Watts) INPUT

OutputPower DC Output 

Power

60 (Watts) INPUT

InputPower AC Input 

Power

60 (Watts) INPUT

Option Variables used:

Variable Description Variables Affected Default Allowed Values

switchDes Design/Off –

Design Switch

Efficiency DESIGN DESIGN, 

OFFDESIGN

Functions

Prototype Description

Void calculate() None

Ports

Port Type Description

INPUT DataInputPort Power From Generator

OUTPUT DataInputPort Power Requirements to Bus

Sockets

Socket Description socketType Sets Values

S_Efficiency Calculates efficiency from ratio 

between DesignPower and 

OutputPower

Rectifier _Efficiency Efficiency

Dependents

Name Description Eq_lhs Eq_rhs Active When

dep_Power Makes sure that generator 

power*efficiency = power 

to bus

InputPower*Efficiency OutputPower always

Usage Notes: 

The Dependent is the one ensuring that the Generator power corresponds to the Motor power and the system 

efficiency.

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Details of Calculate:Read in inputs, set DesignPower=OutputPower if switchDes=Design, calculate efficiency

using S_Efficiency. Dependent takes care of the rest.

The socket included is currently a table lookup of efficiency based on OutputPower/DesignPower.

DCTransformer

Electrical connections are made using Data Ports passing a real[4] array, consisting of:

{Voltage, Current, Frequency, Phase}

Voltage and Current are the magnitudes of the sine waves in an AC system, Phase is the difference in phase between the 

voltage and current waveforms

DC currents have Frequency = 0 and do not use the value of Phase

Electrical units not defined in NPSS, so most calculations done unitless.

Inputs = DC Electricity (from Battery)

DC Electricity (required from Bus)

Sockets: 

Socket Name: S_Efficiency

Socket Type: DCTransformer _Efficiency

Returns: Efficiency

The DC Transformer element compares the power draw from the battery and the power required by the bus

Variable Description Default Units (not implemented) IO Status

Efficiency Power 

Out/Power in

1 None INPUT

OutputVoltage Amplitude of 

DC output

500 (Volts) INPUT

OutputCurrent Amplitude of 

DC output 

current

.48 (Amps) INPUT

InputVoltage Voltage at AC 

input

120 (Volts) INPUT

InputCurrent AC Input 

current

2 (Amps) INPUT

DesignPower Design Output 

Power in watts

12000000 (Watts) INPUT

OutputPower DC Output 

Power

60 (Watts) INPUT

InputPower AC Input 

Power

60 (Watts) INPUT

Option Variables used:

Variable Description Variables Affected Default Allowed Values

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switchDes Design/Off –

Design Switch

Efficiency DESIGN DESIGN, 

OFFDESIGN

Functions

Prototype Description

Void calculate() None

Ports

Port Type Description

INPUT DataInputPort Power From Generator

OUTPUT DataInputPort Power Requirements to Bus

Sockets

Socket Description socketType Sets Values

S_Efficiency Calculates efficiency from ratio 

between DesignPower and 

OutputPower

DCTransformer _Efficiency Efficiency

Dependents

Name Description Eq_lhs Eq_rhs Active When

dep_Power Makes sure that battery 

power*efficiency = power 

to bus

InputPower*Efficiency OutputPower always

Usage Notes: 

The Dependent is the one ensuring that the Batery power corresponds to the Motor power and the system 

efficiency.

Details of Calculate: Read in inputs, set DesignPower=OutputPower if switchDes=Design, calculate efficiency 

using S_Efficiency. Dependent takes care of the rest.

The socket included is currently a table lookup of efficiency based on OutputPower/DesignPower.

PowerCable

Electrical connections are made using Data Ports passing a real[4] array, consisting of:

{Voltage, Current, Frequency, Phase}

Voltage and Current are the magnitudes of the sine waves in an AC system, Phase is the difference in phase between the 

voltage and current waveforms

DC currents have Frequency = 0 and do not use the value of Phase

Electrical units not defined in NPSS, so most calculations done unitless.

Inputs = Electricity

Output = Electricity 

The Power Cable models a DC power cable with a specified resistance

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Variable Description Default Units (not implemented) IO Status

Resistance Resistance of 

cable

0 (Ohms) INPUT

OutputVoltage Amplitude of 

DC output

500 (Volts) OUTPUT

OutputCurrent Amplitude of 

DC output 

current

2 (Amps) INPUT

InputVoltage Voltage at AC 

input

500 (Volts) INPUT

InputCurrent AC Input 

current

2 (Amps) INPUT

Functions

Prototype Description

Void calculate() None

Ports

Port Type Description

INPUT DataInputPort Power From Generator

OUTPUT DataOutputPort Power Requirements to Bus

Calculate: Process Inputs from DataInputPort, OutputVoltage = InputVoltage – Current*Resistance, Load 

outputs into DataOutputPort.

Bus

Electrical connections are made using Data Ports passing a real[4] array, consisting of:

{Voltage, Current, Frequency, Phase}

Voltage and Current are the magnitudes of the sine waves in an AC system, Phase is the difference in phase between the 

voltage and current waveforms

DC currents have Frequency = 0 and do not use the value of Phase

Electrical units not defined in NPSS, so most calculations done unitless.

Inputs = DC power draws (from Inverters or other power loads)

Output = DC power demands to power sources. 

The Bus receives all the power loads of the system and distributes their power draw between all the power 

sources. 

Variable Description Default Units (not implemented) IO Status

Voltage Target Voltage 

of Bus

10000 (Volts) INPUT

NetCurrent Net Current 

into bus from 

inputs

[15] (Volts) INPUT

PowerSourceSplit Division of 

power loads 

between 

[] None INPUT

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power sources

Functions

Prototype Description

Void calculate() None

void postcreate( string name) Processes DataInputPorts, DataOutputPorts, and Dependents 

declared in the .mdl file

Ports

Port Type Description

dataInputPortList[] String array of pointers to 

DataInputPort objects

Power Demanded by each load

dataOutputPortList[] String array of pointers to 

DataOutputPort objects

Power Required from each source

Dependents

Name Description Eq_lhs Eq_rhs Active When

dependentList[]

(string array of 

pointers to the 

dependents)

Makes sure that each load 

is receiving the bus voltage 

at its DC input

InputVoltage[i] Voltage always

Usage Notes: 

The DataInputPorts, DataOutputPorts, and Dependents must be declared in the .mdl file. The Dependents 

eq_lhs and eq_rhs are handled in the postcreate() function but there must be as many dependents as 

datainputports. In addition the division of power between the sources is even unless PowerSourceSplit[] has 

as many entries as there are DataOutputPorts and has at least one non-zero element. The split is normalized 

to ensure that the total current into and out of the bus sums to zero. 

Details of Calculate: Read in inputs, sum currents into NetCurrent, normalize PowerSourceSpit and check to 

see if it is the same length as DataOutputPortList and nonzero, then divide power into each of the outputs. 

Motor 

Inputs = Shaft Speed and Torque

Outputs = AC Electricity required to turn motor

Calculates the performance of a conventional motor with specified Kv, R, and L

Variable Description Default Units (not implemented) IO Status

Resistance Resistance of 

Stator

0 (ohms) INPUT

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Inductance Motor 

Inductance

0 (Henrys) INPUT

Current AC input 

current 

magnitude

2 (Amps) OUTPUT

Voltage AC voltage 

across motor

500 (Volts) OUTPUT

Phase Phase offset 

between 

Current 

&Voltage

0 (Radians) OUTPUT

Frequency Frequency at 

AC Input

60 (Hz) OUTPUT

Torque Output Torque 1000 (ft-lb) INPUT

Speed Output Shaft 

Mechanical 

speed

50 (rad/s) INPUT

Kv Speed/Volt 60 ((rad/s)/volt) INPUT

PolePairs Number of 

Pole Pairs in 

the motor

4 None INPUT

Functions

Prototype Description

Void calculate() None

Ports

Port Type Description

Sh_O ShaftOutputPort Shaft Port Connection

ElectricOutput DataOutputPort Power Requirements to Inverter

Independents

Name Description Variable Active When

ind_Torque Matches torque required to spin 

shaft

Torque Should have 

autoSetup=true for 

solver to work

Usage Notes: 

Uses Kv and a calculated Kt to generate ideal motor, to which resistive and inductive losses are then added

Details of Calculate: Read in inputs, calculate Kt from Kv, Frequency from PolePairs and ShaftSpeed, Current 

from Kt&torque, ideal voltage from Kv and speed, resistive and inductive behavior from frequency and 

current, and the output voltage as the sum of the ideal, resistor and inductor voltages. Calculate phase 

difference from inductor voltage and output the electricity. 

Generator

Electrical connections are made using Data Ports passing a real[4] array, consisting of:

{Voltage, Current, Frequency, Phase}

A-20
NASA Contract NNL08AA16B – NNL11AA00T – Subsonic Ultra Green Aircraft Research – Phase II

Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

Voltage and Current are the magnitudes of the sine waves in an AC system, Phase is the difference in phase between the 

voltage and current waveforms

DC currents have Frequency = 0 and do not use the value of Phase

Electrical units not defined in NPSS, so most calculations done unitless.

Outputs = AC Electricity to system

Calculates the performance of a conventional generator with specified Kv, R, and L

Variable Description Default Units (not implemented) IO Status

Resistance Resistance of 

Stator

0 (ohms) INPUT

Inductance Generator

Inductance

0 (Henrys) INPUT

Current AC input 

current 

magnitude

2 (Amps) OUTPUT

Voltage AC voltage 

across motor

500 (Volts) OUTPUT

Phase Phase offset 

between 

Current 

&Voltage

0 (Radians) OUTPUT

Frequency Frequency at 

AC Input

60 (Hz) OUTPUT

Torque Output Torque 1000 (ft-lb) INPUT

Speed Output Shaft 

Mechanical 

speed

50 (rad/s) INPUT

Kv Speed/Volt 60 ((rad/s)/volt) INPUT

PolePairs Number of 

Pole Pairs in 

the Generator

4 None INPUT

Functions

Prototype Description

Void calculate() None

Ports

Port Type Description

ElectricOutput DataOutputPort Power Requirements to Inverter

Independents

Name Description Variable Active When

ind_Torque Determines how much power 

generator is producing

Torque Should have 

autoSetup=true for 

A-21
NASA Contract NNL08AA16B – NNL11AA00T – Subsonic Ultra Green Aircraft Research – Phase II

Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

solver to work

Usage Notes: 

Uses Kv and a calculated Kt to generate ideal generator, to which resistive and inductive losses are then 

added. Currently doesn’t include a shaft, eventually a shaft would be added which could be attached to a gas 

turbine. 

Details of Calculate: Read in inputs, calculate Kt from Kv, Frequency from PolePairs and ShaftSpeed, Current 

from Kt&torque, ideal voltage from Kv and speed, resistive and inductive behavior from frequency and 

current, and the output voltage as the difference of the ideal, resistor and inductor voltages. Calculate phase 

difference from inductor voltage and outputs the electricity. 

HTSMotor 

Electrical connections are made using Data Ports passing a real[4] array, consisting of:

{Voltage, Current, Frequency, Phase}

Voltage and Current are the magnitudes of the sine waves in an AC system, Phase is the difference in phase between the 

voltage and current waveforms

DC currents have Frequency = 0 and do not use the value of Phase

Electrical units not defined in NPSS, so most calculations done unitless.

Inputs = Shaft Speed and Torque

Outputs = AC Electricity required to turn motor

Calculates the performance of a HTS superconducting motor.

Variable Description Default Units (not implemented) IO Status

MachinePower Nominal power 

of motor

10728.1767

(80) 

(HP)

(MW)

INPUT

MachineSpeed Speed of motor 13000 (RPM) INPUT

MachineShapeFactor Ratio of length 

to radius

5 None INPUT

NPolePairs Number of pole 

pairs in the 

motor

500 (Volts) INPUT

StatAveRad Radius of the 

stator

.3937

(.12)

(feet)

(meters)

INPUT

StatElecLoad Current loading 

of stator

152400

(500000)

(Amps/ft)

(Amps/m)

INPUT

StatOpFac Stator operating 

factor 

.6 (none) INPUT

StatorACLossDim Reference 

distance for AC

losses

.0007874

(.00002)

(feet)

(meters)

INPUT

HTSTemp Temperature of 36 (Degrees Rankine) INPUT

A-22
NASA Contract NNL08AA16B – NNL11AA00T – Subsonic Ultra Green Aircraft Research – Phase II

Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

superconductor 20 (Kelvin)

BkFeFDensity Magnetic field 

strength within 

the back iron

1.9 Tesla INPUT

CryoWallThick Wall thickness 

of cryocooler

.1575

(.004)

(inches)

(meters)

INPUT

EMShieldTh Thickness of EM 

shield

.0787

(002)

(inches)

(meters)

INPUT

MachineLosses Thermal and EM 

losses within 

motor

29.22

(21.79)

(Hp)

(kW)

Output

ElectricPower Total electric 

power drawn

10750 (Hp) Output

Weight Mass of HTS 

motor

670

304

(lbm)

(kg)

Output

OffDesignPower Current power 

setting of motor

10728.1767

(80) 

(HP)

(MW)

OUTPUT

OffDesignSpeed Current speed 

that motor is 

running at

13000 (RPM) OUTPUT

OffDesignTorque Current torque 

of motor

38000 (ft-lb) INPUT

Current stator current 

magnitude 3 

phase

200 Amps OUTPUT

Voltage Stator phase to 

phase voltage

220 Volts OUTPUT

Functions

Prototype Description

Void calculate() None

Ports

Port Type Description

Sh_O ShaftOutputPort Shaft Port Connection

ElectricOutput DataOutputPort Power Requirements to Inverter

Independents

Name Description Variable Active When

ind_desPower Sizes motor to match required 

power

MachinePower When switchdes = 

“DESIGN’

Usage Notes: 

Based on Masson Motor Model

Details of Calculate: Finds geometry of motor and does physics based modeling of magnetic fields from there. 

HTSGenerator

Electrical connections are made using Data Ports passing a real[4] array, consisting of:

A-23
NASA Contract NNL08AA16B – NNL11AA00T – Subsonic Ultra Green Aircraft Research – Phase II

Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

{Voltage, Current, Frequency, Phase}

Voltage and Current are the magnitudes of the sine waves in an AC system, Phase is the difference in phase between the 

voltage and current waveforms

DC currents have Frequency = 0 and do not use the value of Phase

Electrical units not defined in NPSS, so most calculations done unitless.

Inputs = Shaft Speed and Torque

Outputs = AC Electricity out

Calculates the performance of a HTS superconducting generator

Variable Description Default Units (not implemented) IO Status

MachinePower Nominal power 

of motor

10728.1767

(80) 

(HP)

(MW)

INPUT

MachineSpeed Speed of motor 13000 (RPM) INPUT

MachineShapeFactor Ratio of length 

to radius

5 None INPUT

NPolePairs Number of pole 

pairs in the 

motor

500 (Volts) INPUT

StatAveRad Radius of the 

stator

.3937

(.12)

(feet)

(meters)

INPUT

StatElecLoad Current loading 

of stator

152400

(500000)

(Amps/ft)

(Amps/m)

INPUT

StatOpFac Stator operating 

factor 

.6 (none) INPUT

StatorACLossDim Reference 

distance for AC 

losses

.0007874

(.00002)

(feet)

(meters)

INPUT

HTSTemp Temperature of 

superconductor

36

20

(Degrees Rankine)

(Kelvin)

INPUT

BkFeFDensity Magnetic field 

strength within 

the back iron

1.9 Tesla INPUT

CryoWallThick Wall thickness 

of cryocooler

.1575

(.004)

(inches)

(meters)

INPUT

EMShieldTh Thickness of EM 

shield

.0787

(002)

(inches)

(meters)

INPUT

MachineLosses Thermal and EM 

losses within 

motor

29.22

(21.79)

(Hp)

(kW)

Output

ElectricPower Total electric 

power drawn

10750 (Hp) Output

Weight Mass of HTS 

motor

670

304

(lbm)

(kg)

Output

A-24
NASA Contract NNL08AA16B – NNL11AA00T – Subsonic Ultra Green Aircraft Research – Phase II

Volume II – Appendix A – Georgia Tech Propeller Performance Maps and Reference Sheets

OffDesignPower Current power 

setting of motor

10728.1767

(80) 

(HP)

(MW)

OUTPUT

OffDesignSpeed Current speed 

that motor is 

running at

13000 (RPM) OUTPUT

OffDesignTorque Current torque 

of motor

38000 (ft-lb) INPUT

Current stator current 

magnitude 3 

phase

200 Amps OUTPUT

Voltage Stator phase to 

phase voltage

220 Volts OUTPUT

Functions

Prototype Description

Void calculate() None

Ports

Port Type Description

Sh_O ShaftOutputPort Shaft Port Connection

ElectricOutput DataOutputPort Power Requirements to Inverter

Independents

Name Description Variable Active When

ind_desPower Sizes motor to match required 

power

MachinePower When switchdes = 

“DESIGN’

Usage Notes: 

Based on Masson Motor Model

Details of Calculate: Finds geometry of generator and does physics based modeling of magnetic fields from 

there. 

A-25
REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Contractor Report

 4. TITLE AND SUBTITLE

Subsonic Ultra Green Aircraft Research: Phase II – Volume II – Hybrid 

Electric Design Exploration

5a. CONTRACT NUMBER

NNL08AA16B

 6. AUTHOR(S)

Bradley, Marty K.; Droney, Christopher K.

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

 

The Boeing Company 5301 Bolsa Ave, MC H017-D334 

Huntington Beach, California 92647-2099

 

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

National Aeronautics and Space Administration

Washington, DC 20546-0001

 8. PERFORMING ORGANIZATION

 REPORT NUMBER

10. SPONSOR/MONITOR'S ACRONYM(S)

NASA

13. SUPPLEMENTARY NOTES

Final Report

Langley Technical Monitor: Erik D. Olson

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category 05

Availability: NASA STI Program (757) 864-9658

19a. NAME OF RESPONSIBLE PERSON

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

14. ABSTRACT

This report summarizes the hybrid electric concept design, analysis, and modeling work accomplished by the Boeing Subsonic Ultra Green Aircraft Research 

(SUGAR) team, consisting of Boeing Research and Technology, Boeing Commercial Airplanes, General Electric, and Georgia Tech.Performance and sizing 

tasks were conducted for hybrid electric versions of a conventional tube-and-wing aircraft and a hybrid wing body. The high wing Truss Braced Wing (TBW) 

SUGAR Volt was updated based on results from the TBW work (documented separately) and new engine performance models. Energy cost and acoustic 

analyses were conducted and technology roadmaps were updated for hybrid electric and battery technology. NOx emissions were calculated for landing and 

takeoff (LTO) and cruise. NPSS models were developed for hybrid electric components and tested using an integrated analysis of superconducting and 

non-superconducting hybrid electric engines. The hybrid electric SUGAR Volt was shown to produce significant emissions and fuel burn reductions beyond 

those achieved by the conventionally powered SUGAR High and was able to meet the NASA goals for fuel burn. Total energy utilization was not decreased 

but reduced energy cost can be achieved for some scenarios. The team was not able to identify a technology development path to meet NASA’s noise goals. 

15. SUBJECT TERMS

Acoustics; Aircraft performance; Batteries; Cryogenics; Emissions; Fuel burn; Fuel cells; Generator; Truss-braced wing; 

Unducted fan

18. NUMBER

 OF 

 PAGES

233

19b. TELEPHONE NUMBER (Include area code)

(443) 757-5802

a. REPORT

U

c. THIS PAGE

U

b. ABSTRACT

U

17. LIMITATION OF 

 ABSTRACT

UU

Prescribed by ANSI Std. Z39.18

Standard Form 298 (Rev. 8-98)

3. DATES COVERED (From - To)

February 2012 - June 2014

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

5e. TASK NUMBER

NNL11AA00T

5f. WORK UNIT NUMBER

081876.01.07.02 

11. SPONSOR/MONITOR'S REPORT

 NUMBER(S)

NASA/CR-2015-218704/Volume II

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1. REPORT DATE (DD-MM-YYYY)

01- 04 - 2015