alejandra uranga gabilan assistant professor aerospace ...uranga.usc.edu › presentations ›...

Boundary Layer Ingestion

Benefit Quantification and Analysis Framework

Alejandra Uranga

Gabilan Assistant Professor

Aerospace & Mechanical Engineering

6th International Workshop on Aviation and Climate ChangeUniversity of Toronto Institute for Aerospace Studies

May 16–18, 2018

Outline

1 Introduction

2 D8 Aircraft: Conceptual Studies

3 Experimental Measurement of BLI Benefit

4 Analysis Framework: “Data Mining”

5 Other BLI-Related Topics

A. Uranga (USC) 1 / 37

Boundary Layer Ingestion (BLI)

wake, or “draft”

WastedKinetic Energy

Zero NetMomentum

combined wake and jet

propulsor jet

+

+

+

+

+

+

+

-

-

-

I BLI reduces wasted KE in combined jet+wake (mixing losses)

I Long known to have large potential, never realized for aircraft

I Ambiguous decomposition into drag and thrust(airframe) (propulsion system)

) use of power balance instead of force accounting


Summary

I Closer integration of propulsion system and airframe provides newopportunities to reduce fuel burn and emissions of commercial aircraft

I Boundary layer ingestion (BLI)

I Novel configurations

I System optimization (airframe, engine, operations)

I Flow power and dissipation in power balance frameworkprovide useful metrics for integrated configurations

I D8 wind tunnel testsI Quantification of aerodynamic BLI benefitI Proof-of-concept for use of BLI in transport aircraft

I Aerodynamic framework developed to analyze aircraft with BLI


Major Collaborators

MIT

Mark DrelaEdward Greitzer

UTRC

Scott OchsGreg Tillman

Pratt & Whitney

Wes Lord (retired)

University of Michigan

Joaquim Martins

NASA

Scott Anders (LaRC)Greg Gatlin (LaRC)Judith Hannon (LaRC)James Heidmann (GRC)Natari Madavan (ARC)Shishir Pandya (ARC)Sally Viken (LaRC)


Outline

1 Introduction






Phase 1: D8 Aircraft Concept2008-2010

MIT N+3 D8.2


I B737-800/A320 class

I 180 PAX, 3,000 nm range

I Double-bubble lifting fuselagewith pi-tail

I Two aft, flush-mounted enginesingest ⇠ 40% of fuselage BL

I Cruise Mach 0.72

�37% fuel with current tech(configuration)

�66% fuel with advanced tech(2025-2035)

No “magic bullet”

E. Greitzer et al. 2010, NASA CR 2010-216794A. Uranga et al. 2014, AIAA 2014-0906 NASA-MIT Cooperative Agreement NNX08AW63A


Photos NASA/George Homich

System Impact of BLI

BLI benefitsI

Aerodynamic (direct) benefitsI Reduced jet and wake dissipationI Reduced nacelle wetted area

ISystem-level (secondary) benefits

I Reduced engine weightI Reduced nacelle weightI Reduced vertical tail sizeI Compounding from reduced overall weight

“Morphing” sequence: B737-800 7! D8I Features of D8 introduced one at a time

I Sequence of conceptual designs, optimized at each step (TASOPT)


E. Greitzer et al. 2010, NASA CR 2010-216794

M. Drela 2011, AIAA 2011-3970

Morphing Sequence: B737-800 7! D8.2 7! D8.6

0

0.2

0.4

0.6

0.8

188 %

81 % 82 %

67 % 66 %

100 %op

timiz

ed73

7-80

0M

=0.

8,C

FM56

engi

ne

0

slow

toM

=0.

72

1

D8

fuse

lage

,pit

ail

2

rear

podd

eden

gine

s

3

inte

grat

eden

gine

s,B

LI

4

optim

ize

engi

neB

PR

,FP

R

5

2010

engi

nes

8

FuelBurn

63 %

D8.2

6 7 9 10

2035

engi

nes

2035

mat

eria

ls

win

gbo

t.N

LF

smar

tstru

ct

48 %

38 % 35 % 34 %

D8.6- 18 %

- 23 %

- 21 %


Phase 3: Trade-O↵s Summary2015-2017

Metric: Payload-Range Fuel Consumption =Fuel Energy Consumed

Payload Weight⇥Range

ID8 configuration benefit (20± 3)%relative to tube-and-wing at same cruise speed and technology

IN+3 technology benefit (45± 2)% relative to 1990s tech

I Tech advances benefit tube-and-wing more:D8 has lower structural/total weight and higher payload/total weight

ISlowing down from Mach 0.78 to 0.72 (5± 1.5)%

I Tube-and-wing benefits more from lower speed


NASA-MIT Cooperative Agreement NNX15AM91A

Outline

1 Introduction






Phase 2: Airframe-Engine Integration2010-2015

Quantification of D8 BLI benefit (experimental/computational)

I Direct back-to-back comparison of BLI vs non-BLI

I Wind tunnel tests of 1:11 scale (4m span) powered models


Non-BLI(Podded)

BLI(Integrated)

NASA-MIT Cooperative Agreement NNX11AB35A

BLI Benefit

BLI benefit (aerodynamic)

Savings in power required for given net stream-wise force

with BLI engines relative to non-BLI engines

Power metric

Mechanical flow power transmitted to the flow by the propulsors

PK =

I(po � po1)V · n̂ dS (incompressible)

BLI benefit ⌘ PKnon-BLI � PKBLIPK

non-BLI

��at given FX


Obtaining PK

Method 1: Integration of the flow on propulsor stream-tube

PK =

Z

exit

(po � po1)V · n̂ dS �Z

inlet

(po � po1)V · n̂ dS

exitinlet inlet exit

Method 2: Conversion of electrical power provided to the propulsor motors

PK|{z}mechanical

flow power

= ⌘f|{z}fan

e�ciency

PK/PS

⇥ ⌘m|{z}motor

e�ciency

PS/PE

⇥ PE|{z}electrical

power


Phase 2: Demonstrated Aerodynamic BLI Benefit2010-2015

0 0.02 0.04 0.06 0.08 0.1

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

CPK

CX

BLI

non-BLI

unpowered

net drag

net thrust

cruise

Direct Rec = 0.68⇥106 (FHP)Direct Rec = 0.57⇥106 (FHP)Direct Rec = 0.57⇥106 (rake)Indirect Rec = 0.68⇥106Indirect Rec = 0.57⇥106

0.04 0.045 0.05 0.055-4

-3

-2

-1

0

1

2

3

4⇥10�3

CPK

CX

BLI

non-BLI

net drag

net thrust

cruise

Indirect Rec = 0.57MIndirect Rec = 0.68MDirect Rec = 0.57M (rake)Direct Rec = 0.57M (FHP)Direct Rec = 0.68M (FHP)

8.6% Benefitat Cruise


NASA-MIT Cooperative Agreement NNX11AB35A

Outline

1 Introduction






BLI Benefit Sources

1 Lower propulsor jet dissipation and higher propulsive e�ciency:more useful power put into the flow

2 Lower surface dissipation (smaller nacelle size and surface velocities)

3 Lower wake dissipation (partial elimination of viscous wake)

4 Lower weight due to smaller nacelles and smaller engines,which in turn enables smaller wings, and thus even less weight

1 + 2 + 3 = aerodynamic benefit: less flight power requiredfor a given airframe operating at the same lift coe�cient

4 = system-level benefit after aircraft re-optimizations


Power Balance Method

Consider mechanical energy sources and sinks:

[ Net Force ] = [ Dissipation ] � [ Power In ]

FX|{z}“drag – thrust”

V1 = ( �surf + �wake + �vortex + �jet ) � ( PK|{z}mechanical

flow power

+ ⇢⇢PV|{z}p dVpower

)

PK

�wake

�wake �vortex

�vortex�surf

�surf

PK

�jet

�jet

Non-BLI Configuration

BLI Configuration


M. Drela 2009, AIAA Journal 47(7)

Airframe Dissipation (1/2)

I Conventional drag decomposition:

D

0V1 = �

0surf

+ �0wake| {z }

D0p V1

(profile)

+ �0vortex| {z }

D0i V1

(induced)

I Surface dissipation: �0surf

= (1�fwake

)D 0pV1

�surf

= (1�fwake

)D 0p V1 � ��surf

wheref

wake

⌘�0wake

�0surf

+ �0wake

��surf

⌘ �0surf

� �surf

> 0


��surf

Airframe Dissipation (2/2)

I Wake dissipation:

�0wake

= fwake

(�0surf

+ �0wake

) = fwake

D

0p V1

�wake

= (1�fBLI

) �0wake

= (1�fBLI

) fwake

D

0p V1

where fBLI

⌘ boundary layer ingestion fraction= fraction of total airframe viscous kinetic energy defect

ingested by propulsors

I Vortex dissipation: �vortex = �0vortex

= D 0i V1

assuming comparison is made with same airframe at fixed CL


(1� fBLI

)

Jet Dissipation

I Jet dissipation (with or without BLI):

�jet

=

ZZ

exit

1

2

(V�V1)2 dṁ

=1

2(V

jet

�V1)2 ṁ

for ṁ = Nprop

⇢jet

V

jet

A

jet

(total propulsor mass flow)

and assuming uniform velocity Vjet

across the jet

( jet pt non-uniformity ⌧ propulsor pt rise )


Mechanical Flow Power

PK ⌘ �ZZ

prop

⇥p1�p + 1

2

⇢�V

2

1�V 2�⇤

V · n̂ dS

=1

2

�V

2

jet

� V 21�ṁ

| {z }(exit)

� (�fBLI

�0surf

)| {z }

(inlet)

=1

2

�V

2

jet

�V 21�ṁ + (1�f

wake

) fBLI

D

0p V1| {z }

extra power


(1�fwake

) fBLI

D

0p V1| {z }

extra power

Parametric Expressions

Parametric expression for power required and stream-wise forcein terms of reference non-BLI configuration and propulsor operation

CPK = Nprop

"✓V

jet

V1

◆2

� 1#⇢jet

⇢1

V

jet

V1

A

jet

A

noz

A

noz

S

ref

+ (1�fwake

) fBLI

C

0Dp

CX = C0D � fBLIC 0Dp ��C�

surf

� 2Nprop

✓V

jet

V1� 1

◆⇢jet

⇢1

V

jet

V1

A

jet

A

noz

A

noz

S

ref

I Valid with and without BLI, depending on value of fBLI

I Jet properties (⇢jet

,Ajet

,Vjet

) with and without BLI may di↵er

I Quantify BLI benefit relative to known non-BLI configuration


Major Design Parameters for BLI Aircraft

(i) Ingested dissipation fBLI

C

0Dp

(ii) How “well-designed” the BLI engine installation is ) ��surf

(iii) Propulsor operating points for each of the configurations) respective propulsor jet velocities or mass flows


Data Mining: Application to D8 Wind Tunnel Tests

Use expressions for CPK and CX to fit experimental data (CL = C0L = 0.64)

0 0.02 0.04 0.06 0.08 0.1-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

CPK

CX

BLI

non-BLI

unpowered

8.2%BLI Benefit

(CI = ± 0.3%)

net drag

net thrust

cruise

Data Rec = 0.57⇥106

Power Balance Curve FitData Rec = 0.68⇥106


C

0D = 0.0370

�C�

surf

= 0.0012

f

wake

= 0.080

A

jet

A

noz

= 0.955

CX0

= 0.0357

CDairframe

= 0.0338

Propulsive E�ciency ⌘p ⌘PK � �jet

PK

0.74 0.75 0.76 0.77 0.78 0.79 0.80.88

0.9

0.92

0.94

0.96

0.98

1

1.02

⌘p

PKP 0K

non-BLI

4.0%lower airframe

dissipation

8.2%benefit

at equalA

nozzle

BLI

propulsive efficiency gain


63100

30100

Jet5.1%

Surface2.5%

Wake0.6%

7 100

Variable Nozzle Area: Di↵erent Propulsor Designs

0.74 0.75 0.76 0.77 0.78 0.79 0.80.88

0.9

0.92

0.94

0.96

0.98

1

1.02

⌘p

PKP 0K

non-BLI

BLI

Plug BPlug A

Plug C

8.5% 8.2% 7.6%


Bases for Comparison

0.4 0.6 0.8 1 1.20.88

0.9

0.92

0.94

0.96

0.98

1

1.02

PKP 0K

non-BLIBLI

Anozzle /A0

nozzle

equalVjet

equal⌘p

equalA

nozzle

equal�jet

equalPK

equalṁ



0.4 0.6 0.8 1 1.20.88

0.9

0.92

0.94

0.96

0.98

1

1.02

PKP 0K

non-BLIBLI

Anozzle /A0

nozzle

0.3

0.4 equalVjet

equal⌘p

equalA

nozzle

0.1

equal�jet

equalPK

equalṁ

0.2

fBLI = 0


Outline

1 Introduction






High-E�ciency, High-OPR, Small Cores (N+3 Phase 2, P&W)

Pratt & Whitney – Lord et al., AIAA 2015-0071 : reverse core engine arch.

SAE INTERNATIONAL

“Engine Architecture for High Efficiency at Small Core Size” Lord et al., AIAA 2015-0071 - Pratt & Whitney

42 A. Uranga (USC) 32 / 37

D8 Transonic Design (N+3 Phase 3, U.Michigan)

Transonic wing and engine integration MDO for Mach = 0.72, 0.78 :aero-structural optimization with loosely coupled engine model


Airframe-Propulsion Integration Challenges

Integration lines strongly dependent on optimization’s objective function

I Hard to identify MDO objective: need fully coupled engine model

I High sensitivity of fan face condition to di↵user shape


Airframe-Propulsion Integration Challenges


ReferencesDrela, M., “Power Balance in Aerodynamic Flows”, AIAA Journal, Vol. 47, No. 7,

2009, pp. 1761–1771. doi:10.2514/1.42409 [power balance method]

Uranga, A., Drela, M., Greitzer, E., Titchener, N., Lieu, M., Siu, N., Huang, A., Gatlin,G., and Hannon, J., “Preliminary Experimental Assessment of the Boundary LayerIngestion Benefit for the D8 Aircraft”, AIAA-2014-0906, 52nd AIAA AerospaceSciences Meeting, SciTech 2014, National Harbor, Maryland, 13–17 Jan. 2014.doi:10.2514/6.2014-0906 [preliminary wind tunnel test results, morphing chart]

Uranga, A., Drela, M., Greitzer, E. M., Hall, D. K., Titchener, N. A., Lieu, M. K., Siu,N. M., Casses, C., Huang, A. C., Gatlin, G. M., and Hannon, J. A., “Boundary LayerIngestion Benefit of the D8 Transport Aircraft”, AIAA Journal, Vol. 55, No. 11, pp.3693–3708, 2017. doi:10.2514/1.J055755 [wind tunnel tests]

Uranga, A., Drela, M., Hall, D. K., and Greitzer, E. M., “Analysis of the AerodynamicBenefit from Boundary Layer Ingestion for Transport Aircraft”, AIAA Journal, inpress [analysis, “data mining”]

Hall, D. K., Huang, A. C., Uranga, A., Greitzer, E. M., Drela, M., and Sato, S.,“Boundary Layer Ingestion Propulsion Benefit for Transport Aircraft”, Journal ofPropulsion and Power, Vol. 33, No. 5, pp. 1118–1129, 2017. doi:10.2514/1.B36321[analysis]


Contact

Alejandra Uranga, Ph.D.

Gabilan Assistant Professor

Aerospace & Mechanical Engineering Dept.University of Southern California

Email: [email protected]: (213) 821 - 0846Web: uranga.usc.edu


alejandra uranga gabilan assistant professor aerospace ...uranga.usc.edu › presentations ›...

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