Jeffrey J. Berton

Glenn Research Center, Cleveland, Ohio

Mark D. Guynn

Langley Research Center, Hampton, Virginia

Multi-Objective Optimization of a Turbofan 

for an Advanced, Single-Aisle Transport

NASA/TM—2012-217428

April 2012
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Jeffrey J. Berton

Glenn Research Center, Cleveland, Ohio

Mark D. Guynn

Langley Research Center, Hampton, Virginia

Multi-Objective Optimization of a Turbofan 

for an Advanced, Single-Aisle Transport

NASA/TM—2012-217428

April 2012

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135
Acknowledgments

We would like to thank the other members of the NASA Intercenter Systems Analysis Team who worked on the initial 

ultra-high bypass ratio engine study that preceded this analysis. William Haller, Douglas Thurman, Kenneth Fisher, 

and Michael Tong, your work is appreciated. We would also like to thank the Subsonic Fixed Wing Project of 

NASA’s Fundamental Aeronautics Program for supporting this effort.

Available from

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076–1320

National Technical Information Service

5301 Shawnee Road

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

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

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

either expressed or implied, by the National Aeronautics and 

Space Administration.

This work was sponsored by the Fundamental Aeronautics Program 

at the NASA Glenn Research Center.

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

This report is a formal draft or working 

paper, intended to solicit comments and 

ideas from a technical peer group.

This report contains preliminary fi ndings, 

subject to revision as analysis proceeds.
NASA/TM—2012-217428 1 

Multi-Objective Optimization of a Turbofan for an Advanced, 

Single-Aisle Transport

Jeffrey J. Berton

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135 

Mark D. Guynn 

National Aeronautics and Space Administration

Langley Research Center

Hampton, Virginia 23681 

Abstract

Considerable interest surrounds the design of the next generation of single-aisle commercial 

transports in the Boeing 737 and Airbus A320 class. Aircraft designers will depend on advanced, nextgeneration turbofan engines to power these airplanes. The focus of this study is to apply single- and multiobjective optimization algorithms to the conceptual design of ultrahigh bypass turbofan engines for this 

class of aircraft, using NASA’s Subsonic Fixed Wing Project metrics as multidisciplinary objectives for 

optimization. The independent design variables investigated include three continuous variables: sea level 

static thrust, wing reference area, and aerodynamic design point fan pressure ratio, and four discrete 

variables: overall pressure ratio, fan drive system architecture (i.e., direct- or gear-driven), bypass nozzle 

architecture (i.e., fixed- or variable geometry), and the high- and low-pressure compressor work split. 

Ramp weight, fuel burn, noise, and emissions are the parameters treated as dependent objective functions. 

These optimized solutions provide insight to the ultrahigh bypass engine design process and provide 

information to NASA program management to help guide its technology development efforts.

1.0 Introduction

NASA sets aggressive, strategic, civil aircraft performance and environmental goals and develops 

ambitious technology roadmaps to guide its technology research efforts. Under NASA’s Fundamental 

Aeronautics Program, the Subsonic Fixed Wing Project has adopted fuel efficiency, community noise, 

exhaust emissions, and takeoff field length goals for the new, subsonic, single-aisle, civil aircraft expected 

to replace the current Boeing 737 and Airbus A320 families of airplanes. Relative to B737-800/CFM56-

7B performance levels, NASA goals call for 33% reductions in block fuel burn and takeoff field length. 

The NASA goal for oxides of nitrogen (NOX) is 60% below the landing and takeoff emission stringencies 

set in 2004 by the Committee on Aviation Environmental Protection (CAEP/6) (Ref. 1). Additionally, 

these aircraft are to achieve certification noise levels 32 cumulative EPNdB under current FAA stage 4 

(Ref. 2)/ICAO chapter 4 noise limits (Ref. 3).

Since these performance metrics are aggressive and often conflicting, achieving goal levels for the 

fuel burn, noise, emissions, and field length metrics simultaneously may not be possible. These goals 

therefore may represent distinct “corners” of the airplane design trade space. A balanced, profitable 

business-case airplane design may satisfy one or more of these goals, but is unlikely to meet the goal of 

every metric at once. 

The multidisciplinary design and analysis of an advanced, single-aisle civil airplane lends itself well 

to single- and multi-objective optimization. In this study, the NASA goal metrics for performance, noise, 

and emissions serve as the basis for this practical optimization problem with important implications for 

the product’s design and expectations. 
NASA/TM—2012-217428 2 

This assessment leverages tools, methods, and results from earlier NASA system concept studies 

conducted in 2008 and 2009, in which ultrahigh bypass (UHB) turbofan engines were examined for a 

notional, single-aisle transport (Refs. 4 to 6). A parallel acoustics study of the airplane was also 

performed (Ref. 7). The objective of the NASA UHB engine concept studies was to determine if the fuel 

consumption and noise benefits of engines having lower fan pressure ratios (and correspondingly higher 

bypass ratios) translate into overall aircraft system-level benefits for a 737-class vehicle. Such 

information is important to help NASA program management guide its own technology development 

efforts.

In References 4 to 6, the propulsion system conceptual design trade space was examined by designing 

a representative family of 48 UHB engines, analytically installing them on a common airframe model, 

and performing aircraft mission performance and sizing analyses. The independent propulsion design 

variables investigated were aerodynamic design point fan pressure ratio, overall pressure ratio, fan drive 

system architecture (i.e., direct- or gear-driven), bypass nozzle architecture (i.e., fixed- or variablegeometry), high- and low-pressure compressor compression work split, and cruise Mach number. 

In this study, all of the above design variables (except for the design cruise Mach number) are treated 

as design variables subject to optimization. Analytic computations of aircraft performance characteristics, 

including the NASA Project metrics (fuel burn, NOX emissions and noise) as well as ramp weight are the 

objective functions. 

Significant fuel and cost reductions are necessary to justify the development of a new, single-aisle 

transport, or to justify retrofitting current aircraft with new engines. Substantial reductions in noise and 

emissions are required to enable unconstrained aviation growth without negatively impacting the 

environment. The expectation of this analysis is to broaden the solutions obtained during the studies of 

References 4 to 6 with the additional objectives of fuel, noise, and exhaust emissions included in the 

optimization. This single- and multi-objective optimization provides insight to the engine design options 

that will be necessary to achieve multiple goals.

2.0 Nomenclature

DL landing field length

DTO takeoff field length

fi optimization objective functions

FN,SLS sea level static thrust

gi optimization inequality design constraints

h ̇ pot,toc potential rate of climb at top of climb conditions

NMCum cumulative noise margin relative to the stage 4/chapter 4 rule

NOX oxides of nitrogen (nitric oxide and nitrogen dioxide) exhaust emissions 

SW reference trapezoidal wing area

xi optimization design variables

vapp approach velocity

Wblock fuel block fuel weight 

Wramp ramp weight 

ΔFN,mapp missed approach excess net thrust

ΔFN,ss second segment climb excess net thrust 

ΔWexcess fuel excess fuel weight
NASA/TM—2012-217428 3 

3.0 Method of Analysis

Once expected to enter service as early as 2015, it now appears a new single-aisle aircraft in the 

150-seat class will be delayed for several more years. Airbus has elected to proceed with an engine 

retrofit program with their A320neo. And as of this writing, Boeing may yet proceed with a 737 engine 

retrofit program of their own rather than design a new, “clean sheet” airplane. Nonetheless, a notional 

airplane intended to represent an all-new, 737 replacement equipped with new UHB turbofans is modeled 

here. Engine component and subsystem performance, hot section cooling levels, and material 

technologies appropriate for an approximate 2015–2020 service entry date are assumed. Airframe 

technologies commensurate with a 2015–2020 service entry date are also assumed. 

The tools and procedures described in Reference 4 have essentially been automated so that the entire 

multidisciplinary analysis may be driven by single- and multi-objective optimizers. This is no small feat, 

as many heuristic design rules are added to remove the engineer from the loop without introducing errors. 

Each disciplinary analysis is summarized below. Numerical accuracy and numerical solution errors for 

each analysis are very small and are insignificant relative to the uncertainties introduced by the 

assumptions made.

3.1 Propulsion System

The basic engine architecture is a boosted, two-spool, separate-flow turbofan. Three-spool engines 

represent another potentially viable turbofan architecture for this aircraft class, but they are not 

investigated here. The propulsion system independent design variables subject to optimization are the sea 

level static thrust, fan pressure ratio (FPR) at the aerodynamic design point (ADP), the overall pressure 

ratio (OPR) at the ADP, the fan drive system architecture (i.e., direct- or gear-driven), the bypass nozzle 

architecture (i.e., fixed- or variable-geometry), and the high- and low-pressure compressor compression 

work split. 

A multiple design point analysis is performed on the engine cycle to meet several performance 

requirements such as airplane thrust demand, as well as to set flow rates, cycle temperatures, pressures, 

spool speeds, and cooling levels. 

An advantage that a new, “clean sheet” airplane enjoys is that it may be designed to accommodate 

high-diameter nacelles. This is unlike an existing airplane undergoing an engine retrofit, where underwing 

clearance is limited and fan diameter must be constrained. To exploit this benefit, a multiple-point 

analysis is used to design each engine cycle to meet targets for thrust that the airplane requires at the end 

of the runway and at the top of its climb. In other words, the thrust lapse from takeoff to the top of climb 

is identical for every engine designed. In an analysis without a fan diameter constraint, engine airflow and 

the ratio of combustor temperatures from takeoff to top-of-climb may be varied to satisfy the airplane 

thrust demand. Of course, if the thrust lapse is held constant, other cycle parameters lapse instead. 

Perhaps most important of these is temperature. High-bypass cycles have higher hot section temperatures 

during cruise than lower bypass cycles, and thus a hot-section component life analysis would be necessary

in a later design stage.

The cycle ADP reference is at the top-of-climb condition (Mach 0.80, 35kft, standard temperature), 

and the rolling takeoff condition is at sea level, Mach 0.25, 86 °F/30 °C. Turbomachinery is represented 

by scaled component performance maps. Additional details on the engine design, material selections, and 

engine technology level assumptions may be found in References 4 to 6. 

The thermodynamic engine cycle performance is analyzed using the Numerical Propulsion System 

Simulation code (NPSS, Refs. 8 and 9). NPSS is a variable-fidelity, object-oriented, engine cycle analysis 

tool developed jointly by NASA and U.S. industry. It is currently the accepted, state-of-the-art software 

for airbreathing engine cycle performance analysis for U.S. aerospace industry, academia, and NASA. 

Aeromechanical design, flowpath, and engine weight analyses are performed with the Weight Analysis of 

Turbine Engines code (WATE, Ref. 10). WATE has been significantly upgraded since its initial
NASA/TM—2012-217428 4 

introduction in the 1970s and is currently implemented as a suite of NPSS interpretive analysis elements. 

At NASA, WATE is coupled with NPSS to provide a complete modeling capability of turbofan engines. 

The design FPR has a large influence over an engine’s performance, dimensions, and weight. Its 

value, along with the ratio of pressure levels in the bypass and core exhaust ducts (the extraction ratio), 

sets the bypass ratio—a major determinant of an engine’s fuel consumption characteristics. Bypass ratio 

is inversely proportional to FPR; as FPR is reduced, fan airflow must increase to maintain thrust 

requirements. Constant design fan loading is assumed in this analysis, therefore the choice of FPR sets the 

fan tip speed and the rotation rate of the low-pressure spool. FPR is also a major consideration in setting 

the design airflow; thus, it factors into the overall diameter, weight, wetted area, and drag of the 

propulsion system. The choice of FPR also has a considerable effect on the low-pressure turbine design, 

particularly in cases where no fan gearbox is present. It is selected as a design variable for the 

optimization. The range of values considered is 1.35 to 1.70; these are approximate practical limits for 

large, single-stage fans.

The design OPR also has a large influence over cycle performance, with higher OPRs providing the 

benefits of higher engine thermal efficiencies. It also has implications in exhaust NOX emissions, as 

discussed below. Treating OPR as another continuous design variable, however, leads to excessive 

computational time. Instead, it is treated here as a discrete variable with two options: a “high” value of 42 

at the ADP and a “low” value of 32 at the ADP. An ADP OPR of 42 is the approximate upper limit for 

this type and class of engine. With reasonable compressor disk and compressor exit Mach number design 

assumptions, an OPR of 42 leads to compressor annular exit passage heights of approximately one-half 

inch (a typical limiting constraint used in axial turbomachinery design).

An engine design variable related to OPR is the compression work split between the high- and lowpressure compressors. For a given fan pressure ratio and overall pressure ratio, the “low work” engines 

have a lower pressure rise across the low-pressure compressor (and a correspondingly higher pressure rise 

across the high-pressure compressor) compared with the “high work” engines. For example, in engines 

having an ADP OPR of 42, the high-pressure compressor pressure ratio is 17.7 for the “low work” 

designs, but it is only 12.0 for the “high work” designs. The low-pressure compressor pressure ratio is 

selected to produce the desired OPR as FPR varies. The compression work split is also represented as a 

two-valued discrete design variable, rather than by a continuous real variable. More information on how 

the work split logic is implemented may be found in References 4 to 6. 

Low FPR engine cycles generally require some type of variable geometry for proper operation 

throughout the flight envelope. Without variable geometry, a sufficiently low ADP FPR at altitude will 

lead to a fan surge problem when operating near sea level. The most attractive way to solve this problem 

may be with a variable-geometry bypass nozzle. The nozzle exit opens when operating near sea level, and 

the resulting increase in flow area shifts the fan operating point away from the surge line. The variablegeometry nozzles in the analysis are assessed a 10% weight penalty relative to equivalent fixed-geometry 

designs. The exit areas of the variable nozzles are varied at off-design using an NPSS solver balance to 

maintain a constant fan operating line. Engines equipped with variable-geometry nozzles therefore enjoy 

operation near peak fan efficiencies, albeit at the cost of added nozzle weight. Whenever the optimizer 

naively “creates” an engine by coupling a fixed-geometry nozzle with a low-pressure fan, the fan design 

surge margin is automatically increased by the optimizer’s heuristic rule set so that the fan always 

operates with an adequate surge margin in off-design conditions. This results in the fan operating at very 

low fan efficiencies during cruise. In reality, a low-pressure fan would never be designed with a fixedgeometry nozzle, but it is possible in the analytical world. The punishing effects of a very high design fan 

surge margin should naturally cause the optimizer to avoid these designs. 

The choice of the fan drive system architecture is a major propulsion design consideration. At 

reasonable fan loadings, a fan having a low design pressure ratio spins relatively slowly. Without a 

gearbox, the low-pressure turbine (LPT) must rotate at the same low speed. This sets up the classical shaft 

speed mismatch for two-spool turbofans having low design fan pressure ratios. The LPT diameter cannot 

simply be increased to maintain high, efficient tip speeds without weight penalties and without 

obstructing the flow in the bypass duct. The stage count of the LPT in a conventionally-driven turbofan 
NASA/TM—2012-217428 5 

must increase as the design FPR is reduced if reasonable LPT loadings are maintained, which adds 

considerable weight and length to the engine. Exacerbating the problem is the high gas temperatures in 

the hot section of the engine (relative to those in the fan), which effectively elevates the speed of sound 

and lowers the tip Mach numbers of the LPT. The shaft speed mismatch can be altogether avoided by 

using a gearbox, which enables the fan and the low-pressure spool to operate at different rotational 

speeds. Use of a gear system does, however, introduce a separate set of concerns such as gearbox weight, 

reliability, and cost. In this study, a binary discrete design variable determines if a fan gearbox is present. 

The optimizer will frequently (and naively) seek to combine a directly-driven fan with a low FPR. Such 

poorly-designed systems will have a large number of LPT stages (and low-pressure compressor stages, 

particularly in the case of the “high work” engines). But, like the situation discussed above with the 

variable-geometry bypass nozzle design switch, the resulting poor objective values should steer the 

optimizer away from these designs.

3.2 Airplane

It is not sufficient to determine aircraft benefits from engine characteristics alone. Improvements in 

fuel consumption, for example, often come at the expense of engine size and weight. Only by combining 

the engines with an airframe model can the net impact of an engine design be captured. Aircraft synthesis 

and sizing analyses are carried out using NASA’s Flight Optimization System (FLOPS, v8.0, Ref. 11) 

computer program. The weights, dimensions and flight envelope performance of each propulsion system 

design described above are passed to FLOPS via tabular input files. FLOPS scales the engine and wing 

sizes to meet the airplane performance constraints described in Section 4.0. Special sizing considerations 

introduced by large, UHB engines with unconstrained fan diameters are addressed through enhancements 

to the FLOPS analysis. Spreadsheet analyses are used to determine landing gear length and weight, 

engine-out drag, and required vertical tail size so that impacts of large-diameter engines are properly 

captured. Enhancements to basic FLOPS capabilities are also made in the structural weight and 

aerodynamics areas. The wing and fuselage structural weight estimates of FLOPS are replaced with 

estimates from PDCYL (Ref. 12). PDCYL offers a less empirical, more analytical weight estimation 

methodology that is more sensitive to parameters such as engine weight and location. FLOPS 

aerodynamic predictions are enhanced through a model calibration process incorporating details of the 

737-800 high-speed and low-speed aerodynamic performance.

3.2.1 Baseline Airframe Model

The notional airplane is based on an analytical evolution of the currently-in-service Boeing 737-800 

with winglets. In Reference 4, a reference 737 analytical model was developed based on publiclyavailable 737-800 geometry, weight, and performance information; proprietary low-speed and cleanconfiguration aerodynamic data; and a NASA NPSS representation of the CFM56-7B engine. The 

CFM56-7B was analytically modeled in NPSS using data available from several public-domain sources, 

such as FAA type certification data sheets, manufacturer-provided operating documents, technical reports, 

Jane’s Aero-Engines, and manufacturer’s websites. No company-proprietary propulsion data were used. 

The reference 737 carries 162 passengers in a single-aisle, two-class seating arrangement. Minor 

calibrations to the FLOPS-computed component weights and aerodynamics were performed to match 

published operating empty weight and range capability of 3060 nm at a 32,400 lb payload. The FLOPS 

model was set up to perform a basic wing-engine sizing analysis. Scaling the wing and engine sizes of the 

reference model for minimum ramp weight subject to active aircraft performance constraints led to results 

that were consistent with the actual 737 aircraft.
NASA/TM—2012-217428 6 

Figure 1.—Planform view of the notional, 

advanced, single-aisle transport.

3.2.2 Advanced Airframe Model

To transform the reference 737 into the advanced vehicle 

model, the design cruise Mach number is increased from 

0.785 to 0.800, with an appropriate increase in wing quarterchord sweep (from 25 to 27°) to reflect the higher airspeed. 

The wing aspect ratio and taper ratio are unchanged. The 

162-passenger, mixed-class, single-aisle cabin arrangement 

is maintained, but the design range at the 32,400 lb payload 

point is increased from 3060 to 3250 nm. The performance 

improvements in airspeed and range are considered 

appropriate for a future vehicle in this class. 

Climb and descent flight segments are optimized to 

minimize time while respecting a calibrated airspeed limit of 

150 kt at altitudes less than 10,000 ft. An initial cruise altitude 

of 35,000 ft is required. The cruise segment is a fixed Mach 

(0.80), optimum-altitude climbing cruise which reflects the 

advanced air traffic management capabilities expected circa 

2020. 

Broad use of composite structural materials is assumed 

relative to the predominantly-metal construction of the 737. This is comparable to the structure of the new 

Boeing 787, where as much as 50% of the primary structure is made of composites. Composite 

construction of primary structures is assumed to result in a 15% reduction in the component weights 

computed for the wing, fuselage, and empennage. This weight reduction represents an assumed benefit 

for future composite structures and is not necessarily a claim for what has been achieved to date. 

Additional technology improvements similar to those found on the 787 include an increase in hydraulic 

pressure to 5000 psi and an assumed 1% reduction in drag due to trailing edge variable camber and drag 

cleanup. A planform schematic of the vehicle is shown in Figure 1. This vehicle is the common airframe 

model for all of the propulsion systems analyzed. 

3.3 Takeoff and Landing Trajectory Analysis 

An important aspect of the aircraft-engine system not always considered in noise certification 

predictions is the influence of airplane trajectory and engine throttling on noise. UHB turbofans would 

have a lower specific thrust than a conventional or current turbofan; as a result, they have significantly 

different thrust characteristics. Even though the thrust lapse from takeoff to the top of the climb is 

identical for every engine designed, the thrust of UHB engines lapses more quickly in the takeoff regime 

as airspeed increases. To capture this difference, the throttle settings during the takeoff and approach 

trajectories require proper modeling to more appropriately predict certification noise values. Detailed 

takeoff and approach trajectory calculations are automatically made for every airplane and engine 

designed by the optimizer. These trajectory calculations are used to establish the field length of each 

airplane as well as for its certification noise levels.

Detailed, low-speed takeoff and landing assessments are made using FLOPS’s built-in, time-stepping 

trajectory analysis module. Proprietary low-speed aerodynamic data for several flap and slat settings, 

thrust performance, and aircraft weights are inputs to the analysis. Compliance with the airworthiness 

requirements described in Part 25 (Ref. 13) and Part 36 (Ref. 2) of the Federal Aviation Regulations are 

observed. The low-speed trajectory analysis is validated for the baseline airframe model using 

performance data (Ref. 14) of a 737 taking off and landing under standard day, dry runway conditions. 

Takeoff and landing distances for the baseline airframe model match to within approximately 1% of the 

reported values. The same analysis is used to predict the trajectories of the advanced airframe model.
NASA/TM—2012-217428 7 

A noise abatement throttle cutback occurs in all cases between 16,000 and 17,000 ft from brake 

release. The engine climb thrust at this distance is reduced to the minimum level permitted by regulation 

(i.e., Ref. 2 requires a minimum climb gradient of 4% with both engines operating, or level flight with 

one engine inoperative.). The power cutback typically takes place at approximately 1200 ft above field 

elevation, depending on the characteristics of the engine and airplane designed by the optimizer. This 

is always above the minimum altitude permitted (i.e., 300 m/984 ft for a twinjet), in an attempt to 

gain additional altitude and reduce the noise at the flyover noise measurement point (located at 

6500 m/21,325 ft from brake release). Because of its lower specific thrust relative to the CFM56-7B, a 

throttle cutback for a UHB engine designed to meet a rolling takeoff thrust target is typically not as deep 

(in percent thrust), and a typical UHB-powered airplane does not climb as high over the flyover 

measurement point as the 737 reference model does. These thrust-related effects have an impact on 

flyover noise and their impact should be captured. Likewise, the UHB engine throttle setting on the 

3° glide slope approach is typically higher than the CFM56-7B (in percent thrust). This also has an impact 

on the approach noise that should be taken into account. This analysis captures all of these trajectoryrelated effects on noise.

3.4 Exhaust Emissions 

Oxides of nitrogen pose a health hazard to animal and plant life and are a potential ozone destruction 

risk in the stratosphere. In gas turbine engines, they are predominantly produced thermally via the 

Zeldovich chain reaction when ordinary nitrogen in the air comes into contact with high-temperature 

regions inside the combustor. There, nitrogen oxidizes into nitric oxide (NO), and much of it 

subsequently oxidizes further into nitrogen dioxide (NO2), which are collectively known as NOX. NOX is 

one of the commercial jet engine exhaust emittants regulated by international standards (Refs. 15 and 16). 

The landing and takeoff (LTO) cycle defined in these regulations is intended to represent a single airplane 

operational cycle near airports. This cycle consists of four operational segments, each having a different 

throttle setting. The parameter regulated, LTO NOX, is the amount of NOX (in grams) per kilonewton of 

maximum takeoff rated sea level thrust over the four segments of the operational cycle. LTO NOX is one 

of the objectives in this study.

Empirical correlation models are often used in conceptual design studies to predict an engine’s NOX

emission index (EINOX, defined as masses of NOX emitted per thousand masses of fuel burned). EINOX

values are then used to make LTO NOX certification predictions. In this study, a correlation model 

developed during NASA’s Ultra-Efficient Engine Technology Project is used to predict EINOX. The 

model is a strong function of combustor average reaction temperature, represented in the correlation by 

the fuel-air ratio. But, significant increases in NO have also been observed to be a function of combustor 

entrance temperature (Ref. 17) and, to a lesser extent, combustor entrance pressure (Refs. 18 and 19). 

Therefore, the correlation is also a function of combustor entrance total temperature and total pressure. 

Each of these properties are predicted by NPSS as noted above. Other constants in the correlation model 

are calibrated to represent next-generation combustor emissions technology levels and are fixed. 

Most of the LTO NOX is produced during the two cycle segments having the highest thrust settings, 

when the combustor reaction temperatures are highest. The combustor temperature during maximum 

takeoff rated conditions is fixed across the engine design space and is limited by hot section materials 

constraints. Therefore, changes in combustor entrance conditions bring about the largest changes in LTO 

NOX. The discrete design variable OPRADP will be shown to have a large influence on LTO NOX. 

LTO NOX is entirely independent of any airplane characteristic (indeed, in certification, it is 

measured on an engine test stand). To the first order, even the influence of engine size is removed from 

the metric via normalization by thrust. It is linked in this study to airplane-related metrics (such as 

airplane ramp weight) only by way of our multi-objective system optimization. 
NASA/TM—2012-217428 8 

3.5 Certification Noise

The certification noise analysis approach, assumptions, and tools used in this study have been 

examined by acoustics experts from NASA Glenn, NASA Langley, U.S. industry, and academia as part of 

a comprehensive, multifidelity, NASA acoustic tool benchmarking activity (Ref. 20). The Aircraft Noise 

Prediction Program (ANOPP, Release Level 27 (Refs. 21 and 22)) is a systems-level code used in this 

study to compute certification noise for the airplanes. The certification noise predictions of the reference 

737 described in Reference 20 serve as a validation of the methods and tools used in this vehicle study.

Freefield, lossless, one-third-octave band frequency component source noise levels are computed 

using predictive modules within ANOPP. The UHB engine’s thermodynamic, aeromechanical, and 

geometry data are used as inputs to ANOPP’s propulsion source noise prediction methods. At NASA, the 

NPSS and WATE airbreathing component element libraries have functions, viewers, and case files coded 

in interpretive language to produce engine data to be used as ANOPP input parameters. These so-called 

“engine state tables” are the preferred method to transfer engine state data to ANOPP’s source noise 

prediction modules. Engine state data, consisting of pressure, temperature, flow area, spool speed, and 

fuel and air flow rates, are computed by NPSS for a range of airspeeds, altitudes, and throttle settings at 

standard acoustic day (77 °F/25 °C) conditions. As the airplane traverses its flight path, engine data at the 

appropriate airspeed, altitude, atmospheric conditions, and throttle setting are interpolated from the state 

tables and are delivered to the source noise prediction modules.

The UHB engine’s hardwall fan, jet, and core noise sources are predicted using ANOPP’s Heidmann

(Ref. 23), Stone et al. (Ref. 24), and Emmerling et al. (Refs. 25 and 26) methods, respectively. Propulsion 

noise reduction technologies applied in this analysis include nozzle chevrons, conventional doubledegree-of-freedom fan acoustic liners, soft vane stators (Ref. 27), and over-the-rotor foam metal treatment 

(Ref. 28). The latter two fan noise reduction technologies are anticipated to have matured enough for 

incorporation in the UHB engine assumed in this study. These technologies are also assumed to be 

relatively lightweight, inexpensive, low-maintenance, and free of aerodynamic performance penalties 

such that an engine manufacturer would be willing to make use of them on their product. In engine 

designs equipped with variable-area bypass nozzles, chevrons are assumed present for the central core 

nozzle only (chevrons are not applied to the bypass nozzle due to potential conflict with the actuation 

system needed for the nozzle design).

Freefield, lossless, one-third-octave band spectra for flap, slat, landing gear, and trailing edge 

airframe noise sources are predicted using a method developed by M.R. Fink for the FAA (Ref. 29). The 

Fink method accepts gross airframe dimensions such as span, flap chord lengths, and gear configuration 

and dimensions. The noise reduction technologies applied to the airframe are landing gear fairings, slat 

cove fillers, and flap porous tips. These technologies are considered mature enough to be available in the 

2020 timeframe. 

Descriptions of how each of these noise reduction technologies are used and their assumed acoustic 

benefits are described in detail in (Refs. 4 and 7). 

Using an assumption of acoustic superposition, the free-field, lossless spectra for all of the noise 

sources described above are analytically summed in the vicinity of the aircraft. Real noise sources are, of 

course, complex, distributed signals that are affected by other acoustic sources, aircraft external surfaces, 

and the environment. No provisions are made to adjust the component spectra for acoustic near-field 

phenomena such as source interactions, reflections, refraction, diffraction, or other effects.

The summed spectra are propagated to the three certification observers on the ground in accordance 

with the specifications for certification measurements. Noise propagation effects accounted for include 

spherical spreading, Doppler shift and convective amplification, atmospheric attenuation, ground 

reflections based on data for grass-covered ground, and extra ground attenuation. More complex 

propagation phenomena such as scattering, weather effects, and terrain are not modeled. The airplane 

trajectory, computed as described earlier, is fed into the ANOPP simulation. Vector geometry analyses for 

the airplane relative to the three certification microphone measurement locations are performed within 

ANOPP as functions of source time. The propagated acoustic spectra are predicted at half-second 
NASA/TM—2012-217428 9 

intervals at each of the three certification locations on the ground. From these propagated spectra, 

ANOPP computes several noise metrics of interest as functions of observer time. The Effective Perceived 

Noise Level (EPNL) certification noise metric is computed from the noise-time history at each observer 

as prescribed in (Ref. 2). 

In noise certification parlance, the cumulative, or algebraic, sum of the three certification EPNLs is 

often used to capture the range of operating conditions. The cumulative noise margin with respect to the 

Stage 4/Chapter 4 regulatory standard is used for the overall noise metric in this paper.

3.6 Single- and Multi-Objective Optimization

Single-objective and multi-objective optimal solutions are sought, in various combinations, for block 

fuel burned, ramp weight, cumulative Stage 4 noise margin, and LTO NOX emission objectives. 

Single-objective optimal solutions are straightforward. They are obtained using one of the searchstrategy optimizers built into the FLOPS code. In NPSS and WATE, the continuous engine design 

variable FPR is parametrically varied while permuting each of the two-valued discrete engine design 

variables. In this manner, several hundred engine designs are computed and passed to FLOPS for singleobjective optimization of the engine size and wing size independent variables. The minimum value of the 

objective function of interest is chosen by inspection. The objective space is well-behaved; there are never 

multiple objective peaks and the airplane performance constraints are smooth. The constrained global 

minima are easily found by examination. Feasibility with respect to each inequality constraint is easily 

checked. The minima are verified by plotting graphical sizing diagrams for each permutation of the 

discrete engine design variables. The FLOPS optimizer used is the quasi-Newton Broyden-FletcherGoldfarb-Shanno method along with a Fiacco-McCormick penalty function strategy to account for 

constraints. 

The multi-objective, Pareto-optimal solutions are handled differently. Pareto-optimal solutions are 

nondominated sets where the value of one objective cannot be improved without punishing another. A set 

of designs is sought that represent the best possible tradeoffs among the objectives considered. Classical 

methods of identifying Pareto-optimal solutions are commonly of the “preference” type, where 

information regarding the relative importance of each objective is assumed a priori by the designer. A 

scalarization method, for example, often begins by defining a single composite objective using the 

problem’s multiple objective functions. The composite objective is optimized while varying the 

preferences for each of the underlying objectives until a Pareto solution is computed. 

However, this problem has a combination of continuous and discrete design variables. The discrete 

variables create a mathematically disconnected objective space and constraint functions where gradients 

are not available to traditional optimizers. In addition, there is unavoidable mathematical “noise” due to 

the internal iterations and convergence tolerances in the disparate tools that define the objective space 

(i.e., NPSS, FLOPS, ANOPP, etc.). These problems may confound many search-strategy optimizers by 

causing them to improperly center over artificial local optima or by making function evaluations 

discontinuous. This particular problem is also burdened by occasional “nonconverged” points—most 

commonly in the engine aeromechanical analysis—where the optimizer is left to deal with no information 

returned from the analysis. It therefore may be difficult to use any classical, composite-objective, 

scalarization method for this problem. For these reasons, an evolutionary multi-objective algorithm is 

chosen. The Nondominated, Sorting Genetic Algorithm, NSGA-II (Ref. 30), is selected for its speed 

(relative to many other evolutionary optimizers) and its ability to control crowding and obtain solution 

diversity. NSGA-II uses a constrained tournament selection process consisting of crossover and mutation 

variation operators to define each generation. Binary crossovers involve simple exchanges of genes 

between parent members, while real-parameter crossovers use a Simulated Binary Crossover method

(Ref. 31). In problems involving simultaneous optimization of discrete and continuous variables, the two 

crossover approaches are combined. Random changes are also introduced in each generation using real 

and binary mutation operators. The method’s no-penalty parameter approach to constraint handling has 

been shown to achieve convergence while maintaining good population diversity (Ref. 32). 
NASA/TM—2012-217428 10

The use of NSGA-II, or any evolutionary multi-objective algorithm, is not without its disadvantages. 

Although NSGA-II ranks among the fastest of evolutionary methods, it is still computationally expensive 

when compared to search-strategy methods; typically requiring about two weeks on a modest platform (an 

Intel Core 2 Duo (Intel Corporation, Santa Clara, CA)) to run the problems presented here to completion. 

Potentially, a search-strategy optimizer (set to run a multi-objective optimization for each permutation of 

the discrete, two-valued design variables) may have led to faster convergence times. Other approaches, 

such as variable-fidelity meta-modeling, or a simplifying reformulation of the problem statement could 

also be applied to reduce computational time. NSGA-II is used, however, because it is expected to 

provide good results, largely without regard for its computational efficiency. 

Like search-strategy methods, NSGA-II may also be hampered by nonconverged cases. In a 

nonconverged case, large numerical values are assigned to each objective function, which has the effect 

of steering the optimizer away from that area of the design space. These occurrences have the potential to 

cause difficulties in the NSGA-II child selection process and to slow down the convergence to the Paretooptimal solution.

4.0 Results and Discussion

The automated multidisciplinary analysis described above provides objective and constraint function 

evaluations for the following optimization problems. The objectives are, in various combinations, ramp 

weight, block fuel burned, cumulative Stage 4 noise margin, and LTO NOX emissions.

4.1 Minimum Ramp Weight Solution (One Objective)

The solution for minimum ramp weight is an interesting optimization problem in aircraft design. 

Minimizing ramp weight is of particular interest because ramp weight is often used as a proxy for vehicle 

cost. Classical treatments of this problem rarely have included simultaneous optimization of both 

propulsion system and airframe design variables. Note that, in this context, ramp weight refers to the 

maximum permissible airplane gross weight “on the ramp” before takeoff. FLOPS determines ramp 

weight by iterating the design maximum gross weight (and the resulting structural weights, etc.) until the 

available fuel weight is sufficient to complete the specified design mission.

The problem is formally stated as follows. Mathematical nomenclature similar to that used in

Reference 30 is used. The single objective function, f1, Equation (1), the independent continuous design 

variables, x1 through x3, Equation (2), the discrete design variables, x4 through x7, Equation (3), and the 

inequality constraints, g1 through g7, Equation (4), are written as

1 = ramp Wf (lb) 150,000 (1) 

(ft ) 1400 2

1 = W Sx = ,2 SLSN Fx (lb) 26,000 FPRADP x3 = (2) 

x1 ≤≤ 6.18.0 6.18.0 x2 ≤≤ .1 35 .1 70 x3 ≤≤

x4 = Fan Drive System OPRADP x5 = x spoolLow PR Split 6 = x Variable Nozzle 7 = (3)





 = DriveGear

Direct Drive x4





 = )42( 

)32( 

5 High

Low x





 = High

Low x6





 = False

True x7
NASA/TM—2012-217428 11 

01

7000

(ft) )( 1  

DTO g x 01

7000

(ft) )( 2 

DL

g x 01

150

(kt) )( 3   app v

g x

0

300

(ft/min) 1)( ,

4  

hpot toc g



x 0

10,000

(lb) )( 5  

ΔWexcess fuel g x 0

1000

(lb) )( ,

6  

ΔFN ss g x (4)

0

1000

(lb) )( ,

7  

ΔFN mapp g x 

Note that all objectives, design variables and performance constraints are made dimensionless and are 

normalized to equivalent orders of magnitude by dividing by appropriate constants. The design variables 

x1 and x2 represent wing size and engine size and are the most important variables in a classic airframeengine sizing problem. x3 through x7 are the propulsion design variables discussed in the previous section. 

The inequality constraints g1 through g7 represent typical airplane performance sizing requirements for 

field length, approach velocity, potential climb rate at top-of-climb (service ceiling) conditions, excess 

fuel weight (wing fuel capacity), and excess thrust for the second-segment climb and missed approach, 

respectively. All of the gi must be non-positive for the solution to be feasible. g1 and g2 are defined to 

ensure that field length never exceeds 7000 ft; g3 is defined so that approach airspeed never exceeds 

150 kts; and g4 is defined so that the climb rate at the top of climb never falls short of 300 ft/min at Mach 

0.80, 35,000 ft. g5 through g7 represent “excess” functions for wing fuel capacity and thrust, where values 

of zero just satisfy the sizing requirement. Note that range is not one of the constraints as the ramp weight 

is sized to meet the required range independent of the optimization. 

A single-objective optimum is easily found by conventional means and does not necessarily require 

the NSGA-II evolutionary optimizer. Indeed, it is often preferable to perform the optimization using a 

conventional search-strategy optimizer to more precisely locate the optimum, or to use graphical means to 

gain insight to the problem. The analysis is performed by running a single-objective constrained 

optimization of x1 and x2 in FLOPS (that is, a classical wing and engine sizing) while parametrically 

changing x3 for the sixteen permutations of x4 through x7 in NPSS and WATE. Solutions are chosen by 

examination and are confirmed graphically as described in Section 3.6. 

The propulsion system representing the minimum ramp weight solution (at 150,800 lb) has a directlydriven fan, a “high” OPR, a “low” work split, and a fixed-geometry bypass nozzle. The ADP FPR is at 

the maximum value allowed (1.70) for a single-stage fan, which sets the bypass ratio at about 10. This 

engine is noted in (Ref. 6) and is shown in Figure 2 (left). The block fuel burned for this design is 

31,250 lb. Although they are calculated outcomes rather than objectives, its Stage 4 cumulative noise 

margin and LTO NOX emissions are –8.4 EPNdB and 25.6 g/kN, respectively. 

Figure 2.—Turbofan for the minimum ramp weight airplane (left); turbofan for the airplane having the lowest block 

fuel while minimizing ramp weight (right). 

 
NASA/TM—2012-217428 12

Figure 3.—Sizing diagram for the airplane having 

the lowest block fuel while minimizing ramp 

weight.

However, with very little sacrifice in ramp 

weight, an interesting compromise solution exists 

that results in the lowest block fuel while 

minimizing ramp weight. Note this solution differs 

slightly from the solution presented immediately 

below, where block fuel is cast as f1 in a singleobjective optimization. The compromise solution 

is discovered by running parametric sweeps in 

engine designs (i.e., varying x3 through x7) while 

minimizing ramp weight, and then simply 

choosing the design having the lowest block fuel. 

This propulsion system has a gear-driven fan, a 

“high” OPR, a “high” work split, a fixed-geometry 

bypass nozzle, and an ADP FPR of 1.48. The FPR 

is high enough that a variable-geometry bypass 

nozzle does not appear to be required and its extra 

weight is not justified. Here, the block fuel is at its lowest (30,400 lb), while the ramp weight (151,200 lb) 

is only 0.3% higher than the ramp weight global minimum. The Stage 4 cumulative noise margin and 

LTO NOX emissions are –21.1 EPNdB, and 22.1 g/kN, respectively. This “compromise solution” engine, 

shown in Figure 2 (right), is referred to in Reference 6 as perhaps the best balanced engine design when 

all performance metrics are considered. A graphical sizing diagram for this solution is shown in Figure 3. 

Ramp weight objective function contours are shown (in klb), and the design solution is shown by the 

circular marker. Five of the seven inequality constraints (gi) considered are also plotted, but takeoff field 

length is the only active (i.e., binding) constraint. Infeasible design space is shaded. When a singleobjective search-strategy optimizer is used, the optimum point may be found quite accurately. Any 

constraint violation is insignificant.

4.2 Minimum Block Fuel Solution (One Objective)

The minimum block fuel weight solution is usually important in finding the minimum cash-direct 

operating cost design. It is also exactly the same as minimizing block CO2 emissions. The single objective 

function, f1, is written as

1 = block fuel Wf (lb) 30,000 (5)

The independent design variables, xi, and the inequality constraints, gi, are the same as Equations (2) 

through (4). The FLOPS optimizer is used as above, with block fuel as the single objective. The 

propulsion system representing the minimum block fuel solution (at 29,800 lb) has a gear-driven fan, a 

“high” OPR, a “high” compression work split, and a variable-geometry bypass nozzle. The ADP FPR is 

1.36: very nearly at the minimum value allowed (1.35). Ramp weight is 154,900 lb; only 3% higher than 

the case for minimum ramp weight. The Stage 4 cumulative noise margin and LTO NOX emissions are

27.8 EPNdB and 19.2 g/kN, respectively. The engine diagram is similar in overall architecture and 

turbomachinery stage counts to the engine shown in Figure 2 (right), so it is not shown here. 

It is interesting to note the dramatic differences in engine design for the minimum ramp weight and 

minimum block fuel cases. The minimum ramp weight design prefers a conventional architecture, lowweight, compact, high-FPR engine, while the minimum block fuel design prefers an alternative, geardriven fan architecture with excellent fuel efficiency, a very low FPR, a high-diameter fan, and a variablegeometry bypass nozzle. This is perhaps to be expected, since a minimum-fuel airplane design should 

demand the most fuel-efficient engine possible, and would tolerate—to a degree—any reasonable 

penalties corresponding to such an engine (such as engine weight, diameter, landing gear weight, and 

nacelle drag penalties).
NASA/TM—2012-217428 13

With no fan diameter constraint, a FPR of only 1.36 leads to a rather large, 83-inch diameter fan. 

Very long landing gear are required to provide adequate ground clearance for the engine nacelle. 

Although changes in gear length and weight are accounted for in this analysis, gear integration and 

internal wing packaging are not. A more detailed analysis would be required to determine if this engine 

design could really be accommodated in an underwing configuration. 

Also worth noting is that, when considering ramp weight and block fuel objectives, it is always 

preferable to select the “high” OPR logical design switch. The higher OPR designs (42 at the ADP) have 

thermal efficiency benefits that are not offset by turbomachinery weight and length penalties. 

4.3 Minimum Block Fuel and Noise Solution (Two Objectives)

The NSGA-II evolutionary algorithm is used for this two-objective problem. Some initial trial runs 

helped define the population size to ensure convergence without involving excessive computational 

effort. With this guidance, the population size is set at 48 members. The objectives are defined as

1 = block fuel Wf (lb) 30,000 f 2 = NM Cum (EPNdB) 25 (6) 

f2 is the Stage 4 cumulative certification noise margin (NMCum) normalized to the same order of 

magnitude as f1. The independent design variables, xi, and the inequality constraints, gi, are the same as 

Equations (2) through (4).

The analysis was stopped after 167 generations (with over 8000 designs analyzed) when it became 

apparent that the solution was converging exclusively on low-FPR, geared engine designs. This should be 

expected, since the lowest noise designs and the lowest block fuel designs both occur at very low FPRs. 

Low-FPR, high-bypass-ratio engine cycles have very little jet noise. And, with the authors’ assumption of 

constant design fan loading, low-FPR cycle designs also have quite low, subsonic fan tip speeds and are 

thus free of rotor-shock-related fan noise sources. With propulsion noise reduction technologies being 

equal, the minimum noise solution naturally occurs at the lowest possible FPR values. 

Therefore, the simultaneous solution of both objectives focuses on a narrow range of FPR between 

1.35 (the minimum allowed) and 1.37. Only a very limited Pareto-optimal solution is possible. In 

mathematical terms, the cardinality of this Pareto-optimal set is approximately unity.

This is excellent news, since it is widely believed at NASA that meeting our aggressive noise and fuel 

burn goals simultaneously is not possible. It is for that reason that the NASA Subsonic Fixed Wing 

Project’s goals are often called “corners of the design trade space,” where one or two goals may be met 

simultaneously, but only at the expense of another. These results show that there is not a significant 

tradeoff between minimizing fuel and minimizing noise when selecting the cycle design characteristics of 

an engine. Meeting NASA’s aggressive goals remains difficult, but low-FPR, geared, UHB turbofans 

appear to satisfy the requirements of minimizing fuel and minimizing noise. 

The Pareto-optimal solution is shown graphically in Figure 4. All feasible solutions encountered 

during the run of NSGA-II are shown in the chart on the left. The dominated points are retained to 

illustrate how the optimizer considered, but discarded, direct-drive fan architectures (gray triangles) in 

favor of geared fans (black diamonds). No direct-drive engines survive the optimizer’s selection process 

that, in this case, favors low-FPR designs. A detailed view of the small, nondominated Pareto front is 

shown in the chart on the right. Note the change in scale. Propulsion systems with “high OPRs” are 

shown using open symbols, while systems with “low OPRs” are shown using closed symbols. All of these 

low-FPR engines along the frontier are geared. No differentiation between “low” or “high” compression 

work split designs is noticeable after 167 generations, although eventually a preference for one or the 

other may be expected. The low-OPR engine designs are slightly quieter (the core noise model used is 

sensitive to compressor exit pressure and temperature), but being less thermally efficient, they consume 

more fuel. With the clustering in objective space around very low FPRs, the range in block fuel values 

along the small frontier is less than 1300 lb for the high OPR designs.
NASA/TM—2012-217428 14

Figure 4.—Pareto-optimal solutions for minimum block fuel and noise. Left: all feasible solutions, showing 

geared engines (black diamonds) and direct-drive engines (gray triangles); Right: A detailed view of the 

small, nondominated front, showing high-OPR engines (open symbols) and low-OPR engines (solid 

symbols). The global block fuel minimum is marked by the arrows.

Note that the minimum block fuel found here (30,500 lb) is not as low as the single-objective 

minimum found in Section 4.2 (29,800 lb; marked by the arrows in both charts of Fig. 4). An examination 

of the lowest block fuel nondominated frontier point revealed that it had not yet converged on optimum 

wing and engine sizes. It is uncertain if the minimum block fuel design of 29,800 lb discovered in 

Section 4.2 would have been found by the evolutionary algorithm. 

The minimum noise margin found by the evolutionary optimizer is –30.5 EPNdB. It is achieved with 

a very low-FPR, low-OPR, geared fan design. 

4.4 Minimum Ramp Weight and Noise Solution (Two Objectives) 

A much broader Pareto front may be expected for this solution, since the FPR for the global minimum 

ramp weight (1.70) is far removed from the FPR preferred for minimum noise (1.35). The objectives for 

this problem are defined as

1 = ramp Wf (lb) 150,000 (EPNdB) 25 f 2 = NM Cum (7) 

As before, the evolutionary algorithm is used with a population size of 48, and the independent design 

variables, xi, and the inequality constraints, gi, are the same as Equations (2) through (4).

The analysis was interrupted after 198 generations with 9504 designs analyzed (2628 of them 

feasible). The Pareto-optimal solution is shown graphically in Figure 5. Once again, all feasible solutions 

encountered during the run of NSGA-II are shown in the chart on the left, while a more detailed view of 

the nondominated Pareto front is shown on the right. Note the change in scale. 

Once again, the minimum noise margin appears to be approximately –30.5 EPNdB with an engine 

design similar to that found above in Section 4.3. The minimum ramp weight of 152,000 lb shown in the 

Pareto front is achieved with a gear-driven fan system with a FPR of 1.48. This is somewhat unexpected, 

since, in Section 4.2, the global minimum ramp weight is deterministically found via single-objective 

optimization to be a direct-drive fan design at 150,800 lb (marked by the downward-pointing arrow in 

Fig. 5). Several competitive direct-drive engine designs (marked by the gray triangles) can be seen in 

Figure 5 near 153,000 lb, but they are all dominated by geared engine designs (at least after 198 

-35

-30

-25

-20

-15

-10

-5

0

28 30 32 34 36 38 40 42

Block Fuel, klb

Stage 4 Cumulative Margin, EPNdB

-32

-31

-30

-29

-28

-27

-26

29 30 31 32 33 34 35

Block Fuel, klb 
NASA/TM—2012-217428 15

Figure 5.—Pareto-optimal solutions for minimum ramp weight and noise. Left: all feasible solutions, 

showing geared engines (black diamonds) and direct-drive engines (gray triangles); Right: A detailed 

view of the nondominated front, showing high-OPR engines (open symbols) and low-OPR engines 

(solid symbols). The global ramp weight minimum is marked by the downward arrow; the “best 

compromise” design is marked by the upward arrows.

generations). It is uncertain if the minimum ramp weight, direct-drive design of 150,800 lb discovered in 

Section 4.2 would have been found by the evolutionary optimizer. For this problem, a multiple-objective 

algorithm using a scalarization method to optimize a composite objective with weighting factors may be 

better suited to finding this part of the frontier. 

As it is, however, this evolutionary optimization comes remarkably close to the best “compromise 

solution” engine design described in Section 4.2 (its ramp weight is 151,200 lb; marked by the upwardpointing arrows in Fig. 5). That design, found deterministically via single-objective optimization, also has 

a nearly-identical, gear-driven fan design with a FPR of 1.48. 

Note the low-noise designs in Figure 5 with ramp weights heavier than 190,000 lb. These are 

solutions having direct-drive engines with very low FPRs. Although they are feasible solutions, the 

engines are, of course, very long and heavy, with many LPT stages. The evolutionary optimizer likely 

discovered these solutions using its crowded comparison operator feature while attempting to extend the 

frontier towards lower-noise designs. In general, however, most unreasonable engine designs are 

effectively avoided by the optimizer.

This two-objective optimization clearly shows the trade between ramp weight and noise. Given 

constant technology levels, improvements in one objective cannot be made without punishing the other. 

This illustrates the difficulty in designing extremely quiet aircraft, such as those called for by NASA’s 

Subsonic Fixed Wing goals. While it perhaps can be done, it is often uneconomical to do so. 

4.5 Minimum Ramp Weight and NOX Solution (Two Objectives)

Cycles with higher ADP FPRs have higher OPRs at sea level static conditions due to engine specific 

thrust and thrust lapse characteristics, even if the OPRs at the ADP are designed to be identical. At sea 

level static conditions (where the LTO NOX metrics are measured), cycles having higher ADP FPRs have 

higher combustor entrance temperatures and pressures and therefore have higher levels of LTO NOX. 

Thus, a Pareto front would be expected in a multi-objective optimal solution of ramp weight and LTO 

NOX. 

This behavior is in contrast to the optimal solution of block fuel and LTO NOX, where minima for 

both of those objectives would tend to cluster around low-FPR engine designs. Since a case of nearly 

non-conflicting objectives has already been shown (i.e., the minimum block fuel and noise solution in 

Section 4.3), a minimum block fuel and LTO NOX problem is not presented here. 

-32

-30

-28

-26

-24

-22

-20

-18

150 155 160 165 170

Ramp Weight, klb

-35

-30

-25

-20

-15

-10

-5

0

150 160 170 180 190 200

Ramp Weight, klb

Stage 4 Cumulative Margin, EPNdB

 
NASA/TM—2012-217428 16

Figure 6.—Pareto-optimal solutions for minimum ramp 

weight and LTO NOX. All feasible solutions, showing 

geared engines (black diamonds) and direct-drive 

engines (gray triangles).

Once again, we note that LTO NOX is an engine-only metric and is entirely independent of any 

airplane characteristic. It is linked here to ramp weight only by way of our multi-objective system 

optimization.

The objectives for the minimum ramp weight and LTO NOX problem are defined as 

1 = ramp Wf (lb) 150,000 f 2 = LTO NOX (g/kN) 20 (8) 

The same method, design variables, and constraint vector as before are used. The analysis was 

interrupted after 143 generations with 6864 designs analyzed (2773 of them feasible). The Pareto-optimal 

solution is shown graphically in Figure 6. All feasible designs are shown, with the geared engines plotted 

using black diamonds and the direct-drive engines using gray triangles. 

Once again, the optimizer did not discover the global minimum ramp weight represented by a highFPR, high-OPR, direct-drive engine design (marked by the downward-pointing arrow in the figure), but it 

did come very close to the compromise design represented by a geared fan design (marked by the 

upward-pointing arrow). The lower boundaries of the objective space are horizontal and flat because the 

optimizer found the NOX minimum at the limiting 1.35 FPR boundary. As it is, the minimum LTO NOX

solution is nearly identical to the minimum noise margin solution found in the previous two sections. 

In Figure 6, there are two noticeable groupings, or regimes of engine designs. The high- and low-NOX

regimes are caused by high- and low-OPR engine cycles, respectively. For combustors of equivalent 

emissions technology and effectiveness, low-OPR cycles should result in lower LTO NOX. There is 

noticeable symmetry between the low- and high-OPR regimes. That is, one regime has the same general 

shape as the other, and they are offset in the objective space by the same amounts. Of course, this is an 

artifact of OPR being represented as a logical design variable; if OPR were a continuous-real variable, 

two regimes would not be visible and the frontier would not be nonconvex. In any case, the distinct 

regime behavior permits a generalization: if an engine is designed with a “high” OPR of 42, it should 

produce approximately 15% higher LTO NOX and have a 1.5% lower ramp weight than one designed 

with a “low” OPR of 32, if the engines are otherwise similarly designed. An alternate, and perhaps more 

appropriate, way to interpret the data is that the high-OPR “best compromise” design (the diamond 

marked by the upward-pointing arrow) has 36% higher NOX than the lightest, low-OPR, low-NOX design 

(158 klb, 16.2 g/kN). 

14

16

18

20

22

24

26

28

30

150 160 170 180 190 200

Ramp Weight, klb

LTO NOX, g/kN

 
NASA/TM—2012-217428 17

4.6 Engine Design Considerations

The chief objective in engine design is minimum airplane life cycle cost, while achieving acceptable 

levels of operational safety, risk, and environmental impact. Vehicle cost is indirectly addressed in this 

study by predicting its classical surrogate indicator: ramp weight. The block fuel weights predicted are 

important in finding the minimum cash-direct operating cost design. Safety and risk are much more 

complex to assess, but they are indirectly recognized here by selecting only technologies mature enough 

for consideration. 

Every attempt has been made in this analysis to provide an unbiased, independent, accurate 

assessment of aircraft powered by UHB turbofans. Thermodynamic cycle and aeromechanical engine 

design methods, and even the analytical tools used (NPSS, for example, is a U.S. industry standard) 

follow engineering state-of-the-practice. 

However, the caveat must be stated that the optimal engine designs described here have been found 

for our analytical model, and not necessarily for the true problem. The engine design approach and 

technology assumptions used for this study are not exclusive. There are a number of possible variations in 

the design approach; such as different choices for the ADP (particularly the cruise Mach number), thrust 

sizing conditions, different cooling philosophies, or a different choice of extraction ratio. Furthermore, 

our assumptions of future technology levels are speculative. Changes in the technology assumptions and 

design approach can affect the absolute engine performance and weight, as well as the relative differences 

among the engine types. All of this together makes our analytical model potentially inexact. The results of 

this study should be viewed, therefore, in light of the assumptions and approach used. With that reader 

caution stated, the following engine design recommendations are made.

Higher engine OPR and the resulting thermal efficiency benefits always appear justified for ramp 

weight and block fuel metrics, despite increases in turbomachinery weight, engine length, and cooling air 

temperature. The maximum OPR (42) is set in this study by a reasonable compressor exit annular duct 

height constraint. However, LTO NOX emissions are higher for high-OPR engine designs (for given 

combustor technology levels) due to higher combustor entrance temperature and pressure. There is also a 

slight increase in core noise for high-OPR engine designs. In advanced UHB engines, core noise may 

become significant—even at higher throttle settings—since jet and fan noise are lower due to increased 

bypass ratio, low fan tip speeds, and modern, more effective noise reduction technologies. Accurate core 

noise modeling for UHB engines is essential. A need is foreseen for improved engine core noise modeling 

methods at NASA that reflect the high overall pressure ratios of modern engines.

At sufficiently low values of FPR, turbofan engine cycles require some type of variable geometry to 

avoid fan surge margin problems near sea level and to ensure proper operation throughout the flight 

envelope. Although it is not the only option, the variable-geometry bypass nozzle appears to be a practical 

means to enable low-FPR, UHB turbofans. 

A summary of each of the optimum solutions is presented in Table 1. The minimum ramp weight and 

the minimum block fuel weight solutions are the global minima found by the single-objective optimizer in 

Sections 4.1 and 4.2, respectively. The ramp weight and block fuel “compromise solution” is the design 

noted in Section 4.1. The minimum noise and the minimum LTO NOX solutions are simply designs 

selected from among the multi-objective cases discussed in Sections 4.3 and 4.5 and may not necessarily 

be global minima. 

TABLE 1.—SUMMARY OF OPTIMUM SOLUTIONS

Description FPR Drive OPR FN,SLS SW Wramp Wblock fuel NMCum LTO NOX

(lb) (ft2) (lb) (lb) (EPNdB) (g/kN)

Minimum ramp weight 1.70 Direct 42 22,650 1330 150,800 31,250 –8.4 25.6

Minimum block fuel 1.36 Geared 42 25,000 1450 154,900 29,800 –27.8 19.2

Ramp weight and fuel compromise 1.48 Geared 42 23,150 1340 151,200 30,400 –21.1 22.1

Minimum noise 1.35 Geared 32 25,100 1620 164,200 33,900 –30.5 16.5

Minimum LTO NOX 1.35 Geared 32 25,550 1430 158,700 31,900 –30.3 16.2
NASA/TM—2012-217428 18

One of the most important design options considered in this study is the fan drive system. This study 

is not intended to be an endorsement—or an indictment—of either gear-driven or directly-driven fan 

designs. Arguments for both engine architectures are given below.

4.6.1 The Case for Gear-Driven Fans

The benefits of fuel efficiency that accompany geared turbofans are very attractive. A fan gearbox 

effectively solves the classical low-spool shaft speed mismatch problem corresponding to low-FPR 

engine designs. With low FPRs made possible by a geared drive, more enthalpy is available from the LPT 

that may be used to increase the bypass ratio to ultrahigh levels. A gearbox is thus an enabling technology 

to dramatic gains in propulsive efficiency.

A gearbox also provides greater freedom in designing the low-pressure compressor, particularly for

engines with low-pressure compressors shouldering a greater portion of the overall pressure ratio. Since it 

rotates rapidly, the low-pressure compressor may have a high pressure ratio with a practical number of 

stages.

Furthermore, a gearbox appears to justify its additional weight when FPR is sufficiently low. With a 

gearbox, the LPT need not have a great number of stages when the FPR becomes small, and the fuel 

efficiency benefits that are inherent in low-FPR, high-bypass-ratio engine cycles pay off dramatically in 

reduced fuel weight. The best compromise engine design and the minimum block fuel engine design 

described in Sections 4.1 and 4.2, respectively, are geared engines. 

Engines having very low FPRs—geared, typically—are also very quiet, since the bypass ratio 

increases as FPR decreases. If the bypass ratio is high enough, jet noise becomes a minor contributor to 

community noise (see Ref. 7). Fan noise is also greatly reduced, particularly if the FPR is low enough to 

justify subsonic fan tip speeds at takeoff and all shock-related fan noise sources vanish. And as it has been 

shown in Section 4.5, low-FPR engine cycles can also be low in LTO NOX emissions (for combustors of 

equivalent emissions technology and effectiveness).

The gearbox extends the range of viable FPRs to lower levels and effectively widens the engine cycle 

design space. If engine size and integration issues can be overcome, geared turbofans have the potential to 

extend the bypass ratio into the UHB range.

4.6.2 The Case for Directly-Driven Fans

Despite the apparent advantages of geared turbofans, direct-drive turbofans remain a good design 

choice. Direct-drive turbofans result in the lowest engine weight, vehicle ramp weight, and operating 

empty weight, and, by inference, the lowest vehicle cost. 

Engines having higher FPRs—directly-driven, typically—can have relatively small, compact nacelles 

with less wetted area and drag than higher-diameter geared engines. They also simplify landing gear 

design. Although the effects of landing gear length and weight are accounted for in this study, the impacts 

of gear integration, retraction, and internal wing packaging are not. Likewise, the aerodynamic effects of 

high-diameter engines are accounted for here, but only to the first-order. A higher-fidelity modeling of 

high-diameter geared engine issues is called for, such as nacelle-wing interference drag effects and 

engine-out drag and its impact on tail sizing. Moreover, no gearbox maintenance is necessary for directdrive turbofans.

Furthermore, a large portion of the direct-drive turbofan design space remains unexplored in this 

study. Constant design fan loading is assumed here—that is, as FPR is reduced, fan tip speed is reduced 

as well. The classical low-spool shaft speed mismatch problems associated with lower-FPR, direct-drive 

engines can be alleviated somewhat by using lightly-loaded fans. More fuel-efficient, direct-drive engines 

having moderately lower FPRs and higher bypass ratios may have been discovered in this assessment 

if fan loading was treated as a design variable. Also, and perhaps counterintuitively, lower burner 

temperatures may lead to better direct-drive engines. Lowering the gas temperature lowers the speed of 

sound in the hot section and can mitigate the shaft speed mismatch by effectively increasing the LPT tip 

Mach numbers. Turbine cooling and hot section material benefits may exist as well. Attractive, lowertemperature, direct-drive engine designs with lightly-loaded fans may exist throughout the design space. 
NASA/TM—2012-217428 19

5.0 Conclusions

Single- and multi-objective optimized solutions are presented for the multidisciplinary design of 

ultrahigh bypass ratio engines applied to an advanced, notional, single-aisle airplane. NASA’s Subsonic 

Fixed Wing Project goals serve as optimization objectives. This study is intended to provide independent 

information to NASA program management to help guide its technology development efforts.

Identifying a “best” engine design depends entirely on the metric(s) of interest. The engine design for 

minimum ramp weight—a traditional aircraft optimization objective—is found to be a high-FPR, highOPR, direct-drive turbofan. Although its turbomachinery, material selection, cooling, and construction 

technologies are assumed to be advanced, it may yet be said to be of a conventional architecture. Block 

fuel, however, is minimized by a strikingly different engine design: a low-FPR, high-OPR, geared UHB 

turbofan with a variable-geometry bypass nozzle. And between these two extremes, an excellent 

“compromise” engine design exists—a moderate-FPR, high-OPR, geared turbofan—that nicely balances 

the ramp weight and block fuel metrics. This engine also has relatively low community noise and NOX

emissions.

An interesting finding is how, when multiple objectives are considered, some metrics may be 

improved upon simultaneously. It is widely believed at NASA that meeting our aggressive noise and fuel 

burn goals simultaneously is not possible. However, block fuel and noise appear to be minimized together 

by selecting similar engine cycle design characteristics (i.e., low-FPR, geared, UHB turbofans). NOX

exhaust emissions are minimized by low-FPR, geared designs as well, although NOX is minimized more 

dramatically by lowering OPR. Other metrics, when taken together as multiple objectives, form classical 

Pareto frontiers, where one metric cannot be improved without punishing another. Optimization has the 

potential to discover many engine designs that acceptably satisfy multiple objectives. The choice of 

optimization algorithm, the problem analysis fidelity and formulation are key considerations when 

performing this type of analysis.

Ultimately the primary metric is life cycle cost, while achieving acceptable levels of environmental 

impact and achieving operational safety. Historically, ramp weight has been used as a surrogate indicator 

for life cycle cost in aircraft design and optimization. However, recent increases in fuel cost have made 

fuel consumption a more important factor in the calculation of life cycle cost. It may no longer be valid to 

assume the lowest ramp weight configuration has the lowest life cycle cost. 

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Concept Study for an Advanced Single-Aisle Transport,” NASA TM-2009-215784, August 2009. 

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of Turbofan Design Options for an Advanced Single-Aisle Transport Aircraft,” AIAA Paper 2009-

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Wing Project Noise Goal,” AIAA Paper 2009-3144, May 2009. 
NASA/TM—2012-217428 20

8. Claus, R. W., Evans, A. L., Lytle, J. K., and Nichols, L. D., “Numerical Propulsion System 

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Multi-Criterion Optimization, March 2001, pp. 284-298. 
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14. ABSTRACT

Considerable interest surrounds the design of the next generation of single-aisle commercial transports in the Boeing 737 and Airbus A320 

class. Aircraft designers will depend on advanced, next-generation turbofan engines to power these airplanes. The focus of this study is to 

apply single- and multi-objective optimization algorithms to the conceptual design of ultrahigh bypass turbofan engines for this class of 

aircraft, using NASA’s Subsonic Fixed Wing Project metrics as multidisciplinary objectives for optimization. The independent design 

variables investigated include three continuous variables: sea level static thrust, wing reference area, and aerodynamic design point fan 

pressure ratio, and four discrete variables: overall pressure ratio, fan drive system architecture (i.e., direct- or gear-driven), bypass nozzle 

architecture (i.e., fixed- or variable geometry), and the high- and low-pressure compressor work split. Ramp weight, fuel burn, noise, and 

emissions are the parameters treated as dependent objective functions. These optimized solutions provide insight to the ultrahigh bypass 

engine design process and provide information to NASA program management to help guide its technology development efforts.

15. SUBJECT TERMS

Aircraft engines; Aircraft design; Aircraft noise; Exhaust emission; Multidisciplinary design optimization 

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