effects of boundary layer ingesting (bli) propulsion...

American Institute of Aeronautics and Astronautics

1

Effects of Boundary Layer Ingesting (BLI) Propulsion

Systems on Engine Cycle Selection and HWB Vehicle Sizing

Jonathan C. Gladin1, Jonathan S. Sands

2, Brian K. Kestner

3, and Dimitri N. Mavris

4

Aerospace Systems Design Laboratory

Georgia Institute of Technology

Atlanta, GA 30332

A methodology for analyzing the boundary layer ingestion technology on a hybrid wing

body aircraft has been developed using a simplified boundary layer analysis based on

computation fluid dynamic results. With certain assumptions, a method for calculating the

boundary layer velocity profiles across the flight envelope was shown using a log-wake

velocity profile. This boundary layer profile was integrated over the surface of an assumed

“D-shape” inlet to produce the inlet total pressure, temperature, and the ratio of the area

averaged Mach number to the free-stream Mach number. The resulting curves were used

within the EDS multi-disciplinary environment to analyze an HWB aircraft with BLI and

other N+2 technologies over a range of cycle design parameters and for varying inlet aspect

ratios. Aerothermodynamic engine cycle design explorations are performed that show that

the candidate engine cycle selection that minimizes design mission fuel burn depends greatly

on the assumed negative impacts BLI has on the engine performance.

Nomenclature

a = Speed of sound

α = Integral constant

β = Second Integral constant

= Additive constant for skin friction

Cf = skin friction coefficient

c = Chord

Dingested = Friction drag ingested by the engine

Dprofile = Total profile drag of the vehicle

δ = Boundary layer thickness

= Normalized boundary layer height

eRam = Inlet total pressure loss

Fnet = Net thrust

FRAM = Ram drag

GTF = Geared Turbofan

h = Inlet height

κ = Pre-logarithmic coefficient

= Constant for skin friction

= Total engine mass flow

= Total engine mass flow

= Mach number

Me = Boundary layer edge Mach number

M∞ = Free-Stream Mach number

1 PhD Student, Graduate Research Assistant, School of Aerospace Engineering, Member, AIAA.

2 PhD Student, Graduate Research Assistant, School of Aerospace Engineering, Member, AIAA.

3 Research Engineer II, School of Aerospace Engineering, Member, AIAA.

4 Boeing Professor of Advanced Aerospace Systems Analysis, School of Aerospace Engineering, Director, ASDL,

Senior Member, AIAA.

50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition09 - 12 January 2012, Nashville, Tennessee

AIAA 2012-0837

Copyright © 2012 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.


2

Figure 1. NASA ERA Goals for N+1, N+2, and N+3 Time Frames

ηp = Propulsive efficiency

= Density

pexit = Nozzle exit pressure

patm = Atmospheric static pressure

= Total pressure

Π = Wake constant

Re = Reynolds number

Reθ = Momentum thickness Reynolds number

Sw = Wetted area

Tt = Total tempature

Ue = Boundary layer edge velocity

u = Flow axial velocity

u*

= Shear velocity

= Nozzle jet velocity

= Free-Stream velocity

I. Introduction

N response to rising concerns about fuel prices, greenhouse pollutants, and noise pollution due to increased air

traffic volume, the NASA Subsonic Fixed Wing project has set very ambitious goals for the next generations of

aircraft1. NASA’s Environmentally Responsible Aviation (ERA) project was created to conduct research at a system

level on promising new concepts and technologies, especially focusing on subsonic transport technologies and their

integration into advanced vehicle concepts that simultaneously meet the project metrics for noise, emissions, and

fuel burn shown in Figure 1. In the

context of ERA, the Georgia

Institute of Technology’s

Aerospace Systems Design

Laboratory (ASDL) has created the

environmental design space (EDS),

a vehicle modeling and simulation

environment, which incorporates

various NASA tools to size

commercial aircraft of various

classes. As part of the EDS

environment aircraft library, a

Hybrid Wing Body (HWB) model

has been constructed to represent

the revolutionary aircraft configuration which may lead to improvements with regard to the ERA metrics of interest.

Coupled with the HWB architecture, boundary layer ingesting (BLI) inlets have the potential to assist in meeting

the ERA metric goals. This technology aims to increase propulsive efficiency as well as decrease overall vehicle

drag. Many studies have been done previously to ascertain the potential benefits of BLI, but the complexities of the

design problem coupled with a lack of enabling technologies have kept the concept from commercial

implementation. The HWB design provides a new level of synergy with BLI inlets such that much of the difficulty

in the design problem may be overcome by the gains in the BLI inlet and emerging technologies such as active inlet

flow control and advanced fan designs.1 However, because of the engine location, the HWB airframe aerodynamics

and propulsion system are tightly coupled than traditional “tube and wing” configurations with nacelles mounted on

pylons6. This coupling necessitates the use of the multi-disciplinary techniques such as those used in the

Environmental Design Space (EDS) environment developed by ASDL to fully evaluate the effects of BLI inlets on

system level metrics such as fuel burn, noise, and emissions.

In order to provide an initial estimate of the effects of BLI, this study attempts to develop capabilities to address

two critical steps in the design process of a vehicle with BLI technology. First, it is necessary to estimate the

boundary layer profile which will be ingested by the propulsion system. This problem has previously been addressed

using CFD methods. However, the use of full CFD methods is impractical in the current context of EDS due to the

increased computational run time and the difficulty of parameterization. This study utilizes methodologies from

previous HWB studies to estimate the incoming boundary layer profile at the engine inlet location on the HWB

I


3

Figure 2. Control Volume Momentum Balance

baseline model using simplified turbulent boundary layer methods for both “on-design” and “off-design” flight

conditions.

In conjunction with the new BLI modeling capability, an aircraft system level trade study is conducted using the

EDS environment. The study goes beyond simply capturing the physical effects of the design variables on BLI

performance and instead focuses on the system level design space as a whole. The effect of cycle design variables

on vehicle performance will be presented. The results will show the impact of the design variables on vehicle

sizing and the optimal cycle variable selection. Additionally, sensitivity studies are performed to quantify the

effects of uncertainties due to the modeling assumptions made.

II. Effects of Boundary Layer Ingestion

The primary benefit of boundary layer ingestion (BLI) is to decrease the aircraft’s lift independent pressure drag

and the ram drag exerted on the propulsion system and thus the fuel burn. This reduces the net thrust required from

the propulsion system. For a given inlet size and local Mach number at the inlet capture area, the average velocity of

the incoming flow is reduced due to the presence of the lower momentum boundary layer. The ram drag term, the

negative momentum flux term in the net thrust equation shown in Eq. (1) is reduced, which in turn reduces the gross

thrust required from the engine in order to meet the net thrust required by the vehicle.

( ) ( ) (1)

Another way of conceptualizing the physical effects of BLI is to consider that the inlet ingests a portion of the

wake produced by the fuselage interacting with the fluid. Through frictional and compressibility effects, the relative

velocity of the fluid with respect to the vehicle is decreased, creating a decreased momentum wake behind the

aircraft. A control volume momentum balance, visualized in Figure 2, shows the loss of momentum of the flow

traveling around a body.

This momentum loss contributes to the

lift independent drag of the vehicle. Eq.

(2) relates this momentum loss to the

thrust required to overcome it. By

mounting the engines’ inlets flush with

the surface of the fuselage, a portion of

the wake can be ingested into the

propulsion system and reenergized. By

doing so, it is possible to reduce the total

momentum loss of the free stream flow

traveling around the aircraft, decreasing

the overall lift independent vehicle drag.

Net Momentum Flux through

Control Volume ∬(

) Thrust Force required to

Overcome Momentum Loss (2)

As with most new technologies, BLI has some potential negative impacts which must be considered when doing

conceptual design studies. The ingestion of a non-uniform boundary layer flow into the vehicle inlet means that

there will be flow non-uniformities existing at the throat intake, which propagate downstream to the fan face.8 This

could possibly lead to additional stagnation pressure losses in the inlet duct as well as losses due to the presence of

fan distortion and flow non-uniformities which propagate past the fan face. These effects essentially act to decrease

the thermal efficiency of the cycle, although they can be minimized by properly designing the components to

tolerate these effects. However, if the magnitude of these losses is large enough to offset the gains from the

propulsive efficiency, then it is possible to see little or no benefit from the BLI technology in such a case. It is

therefore imperative to have the capability of modeling these detriments and to ascertain the sensitivity of

performance metrics to these impacts.

III. Previous Work

The history of the boundary layer problem extends many decades, most notably to Smith and later to the Douglas

Aircraft Company.6 The primary conclusion of many of these early studies was that the potential benefits of BLI


4

Figure 3. Data Flow Diagram for the Environmental Design Space

were apparent, but the magnitude was subject to the constraints and assumptions of the studies involved.

Furthermore, the known risks associated with inlet distortion and the lack of flow control technology has hindered

BLI from being applied to commercial system use. BLI has, however, been implemented on some military and non-

conventional applications.

Some work has been done to understand the flow field of the hybrid wing body aircraft, especially with regard to

the boundary layer. Rodriguez performed a multi-disciplinary CFD analysis to determine the drag forces and inlet

conditions, while implementing a non-linear solver to optimize the aircraft configuration for fuel burn.6 Another

study performed by NASA presented CFD generated flow characteristics in the boundary layer for a Blended Wing

Body (N2A-EXTE) configuration at different chord-wise locations.5 The flow profiles were used to model the inlet

conditions of the engine and to perform system level analysis of the aircraft in question.

Another study done by the Boeing Company highlighted the effect of inlet configuration on the performance of

the BLI inlets for a specific HWB configuration.1 This work highlights an interesting new degree of freedom with

regard to the system level studies in the form of inlet shape configuration. Another important result of the Boeing

study was the conclusion that the flow around the nacelles can potentially cause huge negative impacts in terms of

drag. The study also showed the impact of the wetted area reductions obtained by flush mounting the engine, which

turned out to be significant.

Several studies have been

conducted to model the impact of

the ingested boundary layer on

the fan component performance.

A NASA and Pratt and Whitney

study recently showed that an

efficiency penalty of 2% for the

fan could be achieved and

perhaps as low as 1%, if the inlet

is optimized to reduce

distortion.15

An additional study

performed by Plas et. al. showed

a modeling approach for the

interaction between the fan and

the inlet configuration and

showed the sensitivity of the

system to inlet pressure losses at

lower fan pressure ratios.8

IV. EDS Environment and the HWB Baseline Model

EDS is a tool developed for the U.S. Federal Aviation Administration's Office of Environment and Energy

(FAA-OEE) as part of a comprehensive suite of software tools that allows for the thorough assessment of the

environmental effects of aviation. EDS provides an integrated analysis of aircraft performance, source noise and

exhaust emissions at the aircraft level for potential future aircraft designs under different policy and technological

scenarios. EDS is a physics-based, integrated, multidisciplinary modeling and simulation environment which

seamlessly combines core EDS modules originally developed by NASA, coupled with design rules and logic along

with user defined engine and airframe design parameters to create aircraft designs. The basic flow of information

during the execution of EDS for a single aircraft is shown in Figure 3.

As part of the EDS aircraft library, an HWB 300 passenger aircraft model has been created. The modeling of the

HWB was done within FLOPS using built-in algorithms for structural weight estimates and aerodynamic

performance11

. As no HWB aircraft currently exist in the fleet, it was necessary to generate a “baseline” HWB

aircraft with podded engines in the N+2 timeframe so as to perform a proper comparison with the flush mounted

inlets. The reference for this baseline HWB was in part based on the Boeing N2A HWB configuration12

using the

same GE90 class engines as the reference large twin aisle T&W.11

The vehicle study conducted herein additionally assumes that the HWB vehicle will exist within the N+2

timeframe and therefore the technology level will have increased relative to the baseline vehicle. The engine model

is representative of the ultra-high bypass geared turbofan model. The cycle model uses a multi-design point

approach with the design points being: top of climb, cruise, hot day take-off, sea level static, and sea level static- hot


5

Figure 4. HWB Flow Field Diagram for Cruise Mach of 0.8 at 35000 ft

altitude.

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0

0.2

0.4

0.6

0.8

1

1.2

0.55 0.65 0.75 0.85 0.95 1.05

Cp

Mac

h

x/c

MachCp

day. The description of the current N+2 vehicle is shown in Table 1, which is treated as the baseline for the current

study.

V. Modeling BLI on the HWB A. HWB Flow Field

In a study previously performed by NASA,5 CFD analysis was performed on a vehicle geometry based on the

N3X HWB. The NASA vehicle geometry is similar to the 300 passenger HWB baseline model used for this study,

which is based on the Boeing N2A. Additionally, the cruise Mach number and altitude at which the CFD data was

collected are the same as the HWB baseline model. The flow field from the CFD results is reproduced in Figure 4 in

the form of Mach number and pressure coefficient plotted vs. percent of fuselage length along the vehicle centerline.

In this analysis, it is necessary to assume that the boundary layer is attached at the engine inlet. Furthermore, it is

necessary to have some rational method for determining the local Mach number at flight conditions for which the

data is not available. To accomplish this, the pressure coefficient distribution will be corrected for compressibility

using the simple Prandtl-Glauert rule and the corrected Cp will be used to compute the new Mach number at the

engine inlet location. This has the effect of making the local inlet boundary layer edge Mach number roughly

proportional to the freestream Mach number.

It is worth noting that the assumption of similar pressure distribution is incorrect if the presence of a shock wave

is found on the upper surface or

if the flight angle of attack is

different than at the condition

for which the data was gathered.

In such a case, the engine inlet

Mach number will be different

than calculated using the above

described method. Further

computational work in this area

is necessary to understand how

the upper surface flow field, and

therefore engine performance

vary as the angle of attack

changes through the flight

envelope.

B. Boundary Layer Profile

In the study previously performed by NASA,5 CFD analysis was performed on a vehicle geometry based on the

N3X HWB and inlet flow Mach number profiles at cruise were found within the boundary layer at different points

along the vehicle centerline. These curves are reproduced in Figure 5 to show the general boundary layer shape at

each longitudinal position along the HWB centerline. As expected, the turbulent boundary layer grows along the

upper surface of the HWB, decreasing the flow’s average velocity relative to the vehicle as it travels further aft. In

concordance with previous HWB with BLI studies, the longitudinal location of the inlet from the nose of the HWB

was set at 85% of the total aircraft length.

Table 1. 300 Passenger HWB Baseline Design

Parameter Value

TOGW 427,680 lbm

Design Mission Fuel Burn 144,636 lbm

Thrust-to-Weight Ratio 0.29

Wing Loading 68 psf

Design FPR 1.40

Design OPR 55.00

Design BPR 15.67

Fan Diameter 112.8 in


6

Figure 5. Mach Number Profiles throughout the Boundary Layer

at Various Positions along the HWB Centerline.5

One of the issues that has arisen in

previous studies is the fact that

typically the CFD data is not gathered

at each flight condition and so the

curves that arise from the integration

of the various properties are assumed

to be constant at the flight condition or

are interpolate between a few data

points. The following methodology

will show how an approximation of

the boundary layer properties can be

made at various flight conditions

while maintaining the original curves

from the cruise condition. The effect

of this variation on engine

performance through the engine deck

will be shown through the flight

envelope.

From the above profiles in Figure 5, the boundary layer thickness δ99 can be computed. The boundary layer

thickness was found to scale approximately with Re-.26

. This is close to the standard flat plate, zero pressure

gradient calculation where δ scales roughly as Re-0.2

. Therefore, the boundary layer thickness will be computed at

different flight conditions by scaling the original cruise boundary layer thickness by the local Reynolds number at

the engine inlet location.

Generally, a turbulent boundary on a flat plate can be represented by the typical “log-wake” formulation.

However, recent studies have shown that some corrections can be made to improve the correlation between the log-

wake formulation and experimental data. Eq. (4) correlates well with turbulent zero-pressure gradient boundary

layers9, where the variable is the normalized y coordinate and the constants Π are typically 0.4 and 0.7577

respectively. Another study showed that the Eq. (4) can also be extended to adverse pressure gradient flows by

assuming that the constant Π is a function of the so called Clauser pressure parameter β given by Eq(7). Eq (8)

shows an empirical correlation for a flat plate adverse pressure gradient flow of the wake constant vs. β. Eq. (6) can

be shown to be the proper correlation of the skin friction coefficient9, and is used to calculate the friction velocity

required to define the profile.

(4)

; √

(5)

√

( )

( √

) (6)

(7)

(8)

Eq. (6) was used to determine the shear velocity u* as described by Qian13

, for the various curves in Figure 5,

and the velocity profiles can be determined from the knowledge of the boundary layer thicknesses and the Clauser

pressure parameter correlation computed from the pressure gradient in the CFD data. Figure 6 depicts the resulting

normalized curves in comparison with the CFD data. This shows that this methodology is relatively robust at

estimating the shear velocity u* and that the modified log-wake boundary layer assumption is reasonable at

approximating the boundary layer distributions predicted by the CFD.


7

Figure 6. Boundary Layer Mach Number Profile for various center line

locations with Simplified Log-Wake Model compared to CFD Data

0

5

10

15

20

25

30

0 0.2 0.4 0.6 0.8 1

y (i

n.)

MN

Log-Wake (0.6)

CFD (0.6)

Log-Wake (0.7)

CFD (0.7)

Log-Wake (0.8)

CFD (0.8)

Log-Wake (0.85)

CFD (0.85)

At other flight

conditions, it is necessary

to apply the assumption of

similarity between the

boundary layer profiles in

Figure 6. This is

tantamount to assuming

that the Clauser parameter

β, and therefore the wake

constant П, are the same at

a given location along the

center line at different

flight conditions. By

making this assumption,

and with the previously

described method for

approximating the

boundary layer given the

local Reynolds number, it

enables the calculation of

the shear friction velocity and skin friction coefficient from the solution of Eq. (7).

C. Inlet Geometry and Thermodynamic Property Averaging

There are many different types of inlet configurations and shapes which have been considered in previous work.

Most notably, Boeing and NASA performed a study of a few types of inlets with differing aspect ratios and the

performance differences between varying geometries.1 One shape considered in the study was the flush mounted

“D-inlet,” which is characterized by an elliptical shape on the upper surface and a flat lower surface which is flush

with the fuselage upper surface. This is the general shape that will be assumed for this study for the purposes of

thermodynamic property averaging. The calculation of the inlet capture area is shown in Eq. (9). For inlet sizing,

the inlet height will be varied to match the mass flow capture area required by the engine at cruise.

(

)

(9)


8

Figure 7. Inflow Mach number Ratio vs. Inlet height at various Design

conditions

15

25

35

45

55

65

75

85

95

105

0.8 0.85 0.9 0.95 1

He

igh

t (i

n.)

MNratio

TOC

ADP

Takeoff

Figure 8. Inflow Mach number Ratio vs. Inlet height at various Design

conditions

0

10

20

30

40

50

60

70

80

90

100

0.88 0.9 0.92 0.94 0.96 0.98 1

Hei

ght

(in

.)

Ptratio

ADP

Takeoff

TOC

The definition of the

inlet geometry as a

function of inlet height

allows for the integration

of the velocity profiles,

also a function of the inlet

height, entering the inlet.

Inlet boundary conditions

required by the engine

cycle model consist of

stagnation pressure,

temperature, and the

average Mach number.

With the velocity profiles

and definition of the inlet

geometry, the average

inflow Mach number of the

inlet was computed

according to the calculated

ratio of local Mach number to the free stream value. The average Mach number was then used within the NPSS

model to compute the average flow velocity into the inlet and subsequently size the inlet height in order to supply

the required amount of air flow to the engine and also to compute the engine ram drag. The average stagnation

temperature and pressure normalized by the free stream values are then determined. Although other methods exist

which conserve various physical parameters, the mass averaged approach to the determination of temperature and

pressure used in previous studies5 is the preferred method for this study. Figure 7 visualizes the variation of the

average inflow Mach number ratio with inlet height at various flight conditions. Figure 8 shows variation of the ratio

of local total pressure to free stream with inlet height. Note that the effect of adding the boundary layer scaling

methodology is to produce somewhat higher local to freestream Mach number and Pt ratios at the takeoff conditions,

meaning that the effect of BLI at takeoff will be in less proportion than at the cruise condition.

D. Drag Reduction

With the reduced average inflow velocity calculated, the ram drag exerted on each engine can be calculated using

Eq. (10).

(10)

For the initial estimation of the vehicle profile drag ingested by each engine, it is assumed that the ratio of drag

ingested by each engine to the total vehicle profile drag is proportional to the ratio of the wetted stream tube area

projected on the fuselage to the total wetted area of the vehicle, as shown in Eq. (11).

( )

( ) (11)

where the stream tube wetted area is estimated using the engine fan diameter, fuselage length, and longitudinal

location of the inlet, as shown in Eq. (12). A scalar multiplier was applied to the equation to account for the non-

rectangular shape of the stream tube area as well as nacelle wetted area reduction due to embedding the engines. In

reality, the stream tube area is a function of detailed vehicle and engine geometry, flight conditions, and throttle

setting. However, without detailed geometry, the actual stream tube areas and impacts of BLI on vehicle profile drag

cannot be determined. Therefore, a reduction in profile drag was assumed to be 5% for the baseline vehicle. A scalar

multiplier of 0.4 achieved this assumed value of profile drag reduction.

(12) Figure 7

The internally calculated profile drag was scaled using a factor, FCDO. The value of this factor was calculated

using Eq. (13).


9

Figure 9. Propulsive Efficiency as a function of Design FPR at Various Conditions.

0.6

0.62

0.64

0.66

0.68

0.7

0.72

0.74

0.76

1.15 1.25 1.35 1.45 1.55

Pro

pu

lsiv

e E

ffic

ien

cy

Design FPR

ADP Podded

TOC Podded

ADP BLI

TOC BLI

(

) (13)

Figure 8

One additional source of drag is the interference pressure drag which can result as a consequence of embedding

the nacelle on the upper surface of the aircraft. Kawai, et. al. showed that, in fact, the area around a flush mounted

nacelle which intersects the fuselage can produce locally supersonic or separated flow, and indeed this is a strong

function of the inlet lip design, aspect ratios, and nacelle diameters.1 Additionally, they showed that this drag can be

significant and may perhaps even override the viscous drag reduction to the decreased wetted area of the

configuration leading to an overall drag rise. Inlet installation drag is modeled within EDS and generally

encapsulates the typical trends of spillage drag with engine mass flow ratio. Initial sensitivity studies of the aircraft

system model showed that without capturing the effects of the interference drag on the installation drag, and using

the current method for predicting wetted area reduction, the model gave somewhat optimistic numbers relative to

similar studies that have been done on BLI concepts. A significant assumption was made, that 50% of the ram drag

reduction calculated at each point was diminished to account for the installation drag until improved system level

analyses can performed to determine the magnitude of the installation drag.

E. Inlet Duct and Fan

In addition to the calculation of the inlet total flow properties, it is necessary to model the negative impacts of

ingesting the degraded flow into the inlet duct and its propagation to the fan face and further downstream.

Accurately modeling the intricate flow field occurring within these components with high fidelity tools is both

difficult and time consuming. In order to provide estimates of the engines cycle’s thermal efficiency losses with

BLI, assumptions are made on the fan efficiency and inlet pressure recovery changes due to the technology. Kawai

applies an inlet pressure drop of approximately 1% to inlets with aspect ratios of 2 and 0.86.1 In reality, this value is

a function of the detailed inlet geometry as well as the inflow quality. However, in practice this number is difficult

to tether to the geometric parameters used here, and therefore two nominal values of 1% and 2% inlet pressure loss

will be assumed for two separate cycle studies. This will yield the sensitivity of the system level parameters to these

parameters which are, in general, hard to quantify without true physics based system models.

Additionally, the distortion of the cross-sectional flow at the fan face will cause degraded fan performance.

Kim, et. al.4 suggests that the fan adiabatic efficiency loss will be on the order of 1%-3% . Therefore, a fan

efficiency penalty of 1% and 3% will be assessed as nominal values for the current study. In order to properly

understand the impact of these assumptions on the system model, the study which follows will be presented with

two different settings of assumptions: one with the larger set of assumptions, and one with the less conservative set.

This also allows for the investigation into which component improvements will maximize the performance of a

propulsion system with boundary layer ingestion and how these improvements affect the cycle optimization process.

VI. Results and Discussion A. Effects of BLI

on Engine

Performance

As stated in the

discussion on the

theoretical benefits

of BLI, the essence

of the concept

hinges upon the

tradeoff between

propulsive

efficiency gains

and thermal

efficiency losses

due to the lower

pressure recoveries

and component

performance


10

Figure 11. Plot of TSFC vs. FPR for the fixed OPR, variable OPR, and podded cases.

0.42

0.44

0.46

0.48

0.5

0.52

0.54

0.56

1.15 1.25 1.35 1.45 1.55

TSFC

Design FPR

ADP BLI (Variable OPR)

ADP BLI (Fixed OPR)

ADP Podded

TOC BLI (Variable OPR)

TOC BLI (Fixed OPR)

TOC Podded

detriments. Figure 9 shows the trend of propulsive efficiency vs. fan pressure ratio for the ERA work-plan baseline

case and with a fixed booster pressure ratio (variable OPR) with no additional inlet or fan losses. As is typical of

many previous BLI system studies, the propulsive efficiency tends to improve as the fan pressure ratio decreases

because of the lower bypass nozzle gross thrusts and improved ram drag. Figure 9 also shows that BLI, without

additional thermal losses, gives significant propulsive efficiency increase relative to the podded case and that the

benefit gets larger at lower fan pressure ratios. For the case of the cruise point, the FPR reduction continues to

improve propulsive efficiency to a design FPR of 1.2. For the top of climb point, which is at a slightly higher Mach

number and is sized to a higher thrust requirement, the propulsive efficiency begins to degrade at a slightly higher

FPR of 1.23. Below this point, the fan efficiency degrades enough to offset further gains in decreasing the FPR at

the top of climb point. These trends are consistent for both the constant OPR and variable OPR case.

Figure 10 shows

a similar plot of

thermal efficiency

comparing the case

of fixed OPR,

meaning the booster

pressure ratio is

adjusted as the FPR

is changed to keep

the same OPR given

a fixed high pressure

compressor ratio of

29.4. Since the OPR

is fixed, this means

that the booster is

compensating for the

reduction in fan

pressure ratio and

the thermal

efficiency actually

increases at low fan pressure ratios for the cruise condition. This implies that it is possible, with more highly loaded

boosters to decrease the optimal FPR for minimum TSFC(which includes the ram drag effect), which is generally a

function of overall efficiency. A plot of TSFC vs. FPR for the cruise and top of climb conditions are shown in

Figure 11. Depending on the FPR chosen, BLI by itself will give 2.4-6% improvement in TSFC for this fixed set of

assumptions and with only nominal thrust targets. However, if the vehicle drag effects are considered, then the

Figure 10. Plot of thermal efficiency vs. FPR for the fixed OPR and variable OPR cases

0.52

0.54

0.56

0.58

0.6

0.62

1.15 1.25 1.35 1.45 1.55

The

rmal

Eff

icie

ncy

Design FPR

ADP BLI (Variable OPR)

TOC BLI (Variable OPR)

TKO BLI (Variable OPR)

ADP BLI (Fixed OPR)

TOC BLI (Fixed OPR)

TKO BLI (Fixed OPR)


11

Table 2. BLI Negative Impact Assumption Sets for Engine Cycle Design

Explorations

BLI Impact Assumptions Setting 1 Assumptions Setting 2

Fan Efficiency Delta -3% -1%

Inlet Total Pressure Delta -2% -1%

Inlet Flow Control Required 1% of Total Airflow 0% of Total Airflow

Inlet Aspect Ratio 3 1

Table 3. Engine Cycle Parameter Ranges for Cycle Design

Exploration

Minimum Maximum

FPR @ ADP 1.2 1.8

LPCPR @ ADP 1.2 2.2

Extraction Ratio @ ADP 1.0 1.5

Gear Ratio 1.0 4.0

HPCPR @ ADP Fixed @ 29.4187

Max T4 @ TKO Fixed @ 3450 degR

resulting thrust targets for the engine would yield more gains in fuel burn than the 2.4-6% since the engine will gain

in propulsive efficiency due to the lower thrust sizing targets.

The previous results were shown for the cases with no additional inlet or fan losses and without the addition of

the vehicle sizing logic and drag reductions discussed previously. To understand how changes in these assumptions

affect the trends presented in Figure 12, the same fan pressure ratio trade study was performed on the engine model

for two separate settings. The first setting asserts an additional inlet pressure drop of 1% and 1% fan efficiency hit,

as well as a 1% bleed penalty for inlet flow control. The second set assumes an additional 1% for the inlet pressure

drop and a total of 3% for the fan efficiency hit. Figure 12 shows the trend of TSFC with FPR for the podded, no

loss, and both loss settings. This plot indicates that a 1 percent loss in inlet pressure and fan efficiency adds about 3-

4% to TSFC depending on the fan pressure. The ADP trend for the case of no losses shows that the fan pressure

ratio should be minimized as far as 1.2 to minimize TSFC. However, for the case with the worst losses, there is a

“bucket” trend where the TSFC actually minimizes at a FPR of about 1.26. This highlights the particular sensitivity

of a given design to the inlet pressure drop, but also that the cycle design space can shift depending on the assumed

values of the thermal efficiency losses in the system.

B. Parametric Vehicle Study

Due to the highly coupled nature of a HWB aircraft employing boundary layer ingestion, it is vital to perform

vehicle-level

analyses in order to

predict the total

impacts of BLI on

the HWB system as

well as to select a

well-suited

candidate engine

cycle for the aircraft. Using the HWB with BLI

model within EDS, engine cycle design

explorations were performed for two discrete

sets of BLI negative impact assumptions, listed

in Table 2. Setting 1 is the more severe of the

two groups of assumptions, where Setting 2 is

the more optimistic of the two. The goal of this

vehicle study is to show how the candidate

aerothermodynamic engine cycle that

minimizes mission fuel burn changes as the BLI

impacts change. The explorations will also

Figure 12. Plot of TSFC vs. FPR showing the affects of loss assumptions on the

trends

0.44

0.45

0.46

0.47

0.48

0.49

0.5

0.51

0.52

0.53

1.15 1.25 1.35 1.45 1.55

TSFC

Design FPR

ADP BLI (Setting 1)

ADP BLI (Setting 2)

ADP Podded

ADP BLI (no losses)


12

Figure 12. Fuel Burn Trends with Design Fan Pressure Ratio - T3

Constrained

120,000

130,000

140,000

150,000

160,000

170,000

180,000

1.2 1.3 1.4 1.5 1.6 1.7 1.8

De

sign

Mis

sio

n F

ue

l Bu

rn (

lbm

)

FPR at Aero Design Point

Setting 1

Setting 2

Baseline

show fuel burn trends with varying cycle parameters and how these trends change with different assumptions made

for the impacts that BLI has on the engine.

For each fixed set of impact assumptions, the engine cycle design exploration was performed by varying engine

cycle parameters at the Aero Design Point applying uniform distributions within the parameters ranges listed in

Table 3. The Aero Design Point (ADP) is one of the five design points used in the Multi-Design Point methodology

used within EDS. The high pressure compressor’s design pressure ratio at ADP and maximum T4 @ takeoff were

fixed at their respective values from the NASA N+2 ERA Workplan cycle for this study and their respective values

are noted in Table 3.

When comparing the

clouds of data and from the two BLI negative impact settings and the baseline cycle shown in Figure 13, it looks as

if the design fan pressure ratio at which the mission fuel burn is minimized does not change between the ERA

Workplan baseline cycle and the two assumptions settings. However, after eliminating points that violate a T3

constraint of 1400 degF

(1860 degR) as in Figure

14, it is clear that the

design FPR that

minimizes fuel burn for

each group of data

changes with respect to

the assumptions made.

The group of data

with the more severe

negative impacts, Setting

1, causes the design FPR

that minimizes fuel burn

to increase at or above

the design FPR of the

NASA N+2 baseline

vehicle. Contrasting this,

the best design FPR for

the group of negative

impacts that are more

optimistic is less than

that of the baseline cycle.

Figure 11. Fuel Burn Trends with Design Fan Pressure Ratio

120,000

130,000

140,000

150,000

160,000

170,000

180,000

1.2 1.3 1.4 1.5 1.6 1.7 1.8

De

sign

Mis

sio

n F

ue

l Bu

rn (

lbm

)

FPR at Aero Design Point

Setting 1

Setting 2

Baseline


13

Table 4. Baseline and Selected Cycles and Corresponding Performance

NASA N+2 ERA

Workplan

Setting 1 Best

Fuel Burn Cycle

Setting 2 Best

Fuel Burn Cycle

FPR at ADP 1.40 1.46 1.39

LPCPR at ADP 1.36 1.35 1.54

HPCPR at ADP 29.42 29.42 29.42

OPR at ADP 55.00 56.94 62.00

Extraction Ratio at ADP 1.09 1.41 1.34

BPR at ADP 15.67 14.12 16.88

Gear Ratio 2.5 2.2 2.9

Design Mission Fuel Burn (lbm) 144,636 136,599 127,435

Design TOGW (lbm) 427,680 418,562 405,778

SLS Thrust (lbf) 62,123 60,965 58,944

Fan Diameter (in) 112.8 112.2 115.7

T3 max (degR) 1,797 1,824 1,850

In retrospect, in Figure 13 it does look as if comparing the Setting 1 data to the Setting 2 data, that the cloud shifts

down and to lower fan pressure ratios when the assumed negative impacts are not as severe.

Table 5 shows the selected candidate engine cycle parameter settings and the corresponding vehicle performance

metrics compared to the baseline NASA N+2 ERA Workplan vehicle. From purely an engine thermodynamic

perspective, in general a lower design fan pressure ratio seems to allow for the greatest efficiency. However, from a

vehicle fuel burn perspective, a nominal fan pressure ratio should be employed for this class of vehicle and engine

architecture. Low fan pressure ratios and corresponding high bypass ratios have large fan diameters, increasing the

amount of ram drag exerted on the engines, in addition to compressibility losses incurred on the fan blade tips at

these large diameters.

VII. Conclusion

A discussion was presented on the relevance of the boundary layer ingestion technology to the hybrid wing body

concept and its potential for simultaneously improving fuel burn, noise, and emissions. Previous computational

results for the baseline hybrid wing body boundary layer profiles were compared with a simplified log-wake

approximation, and a method for scaling that boundary layer based on the assumption of similarity was presented.

These profiles were used to compute the average thermodynamic properties at the engine inlet capture plane, which

were passed to the NPSS cycle model for performance calculations.

An engine cycle performance analysis was conducted using the large twin aisle HWB geared turbofan engine

model in the EDS environment at cruise and top of climb flight conditions which are used to design the engine. The

propulsive efficiency was found to increase as the fan pressure decreased, although this trend proved to be extremely

sensitive to the assumptions involved, especially the fan efficiency penalty and the inlet pressure recovery. It was

shown that a 1% drop in both fan efficiency and inlet pressure recovery can incur a 3-4% increase in TSFC

depending on the design FPR. It was also shown that the optimal FPR shifts to higher values as the loss penalties

increase.

Engine cycle design explorations were performed on two sets of possible engine penalties due to ingesting the

vehicle wake. It was shown that the best candidate engine cycle, in this case the cycle that minimizes design mission

fuel burn, greatly depends on these assumed negative impacts. With severe negative impacts, the engine with a

higher design fan pressure ratio and lower design bypass ratio with respect to the baseline tends to minimize fuel

burn. On the other hand, with less severe penalties, the engine cycle which minimizes mission fuel burn tends to

have a lower design fan pressure ratio and higher design bypass ratio. The cycle design explorations show that it is

vital to accurately model and predict the incurred negative impacts on the engine due to BLI. Otherwise, the

candidate engine cycle that is selected for the vehicle could cause the mission fuel burn to be greater than otherwise

could be achieved.

Finally, the primary deficiency in a system level study such as this is that detailed analysis tools which are

computationally expensive are necessary to determine the parameters which are somewhat uncertain. These include,

but are not limited to drag effects, inlet duct and fan interactions, and the details of the unsteady turbulent flow field

entering the inlet capture area. Additionally, this study does not consider performance during crosswind, take-off


14

rotation, engine-out cases, foreign object ingestion and others that can affect the viability of the system as a whole,

regardless of the steady state performance benefits which are uncertain as well. A true system model with higher

fidelity tools at different conditions would be required to ascertain the actual viability of BLI for the HWB aircraft

and to also find optimal engine designs which are operable, robust and simultaneously achieve the desired fuel

savings to make the investment in the technology worthwhile.

Acknowledgments

The authors would like to thank the NASA ERA program team for all of their much-appreciated support,

specifically thanking Fay Collier, Tony Washburn, and Craig Nickol. The authors would also like to thank ASDL’s

NASA ERA team for all of their tireless effort in the development of the HWB and geared turbofan models in EDS.

In addition, the authors would like to acknowledge NASA’s Systems Engineering and Integration team and all of the

subject matter experts that have provided input and feedback during the technology and vehicle modeling

development and assessment that has taken place over the last two years.

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