effects of boundary layer ingesting (bli) propulsion...
TRANSCRIPT
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.
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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
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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
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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
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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
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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.
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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)
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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).
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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
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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)
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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)
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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
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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
American Institute of Aeronautics and Astronautics
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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