Achieving the Potential of Materials Prognosis for Turbine Engines

DARPA Bidders Conferenceon Materials Prognosis

26 September 2002

J.M. Larsen, S.M. Russ,A.H. Rosenberger, R. John,

T. Fecke*, and B. RasmussenMaterials and Manufacturing Directorate

* Propulsion DirectorateAir Force Research Laboratory

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2

Overview

• The need for Materials Prognosis of turbine engines

• Science and technology outline for Prognosis of Turbine Engine Materials

• Future applications and opportunities for technology transition

3

Many Aircraft Systems Many Aircraft Systems Now In A Second LifeNow In A Second Life

100

Initial design life

Planned useful life

(as of 1994)

Average age as of 1998

7970 66

52

38 33

KC-135

2040

B-52

2030

C-130

2030

C-5A

2021

F-15

2020

F-16

2020

Fleet's age

(years)

0

50

Year of retirement

Source: USAF SAB, TR-94-01

4

USAF Propulsion Product GroupEngine Inventory

∼ 22,500 Engines / $33.3B Value

Airlift/Tanker/Trainer13,400 (60%)

Bomber1,400 (6%)

Fighter7,700 (34%)

Source: Mr. Timothy Dues, Manager, USAF Propulsion Product Group

5

Field Maintenance

Reliability Improvements Are Reliability Improvements Are Riding on the Backs of our Riding on the Backs of our Maintainers with Additional Maintainers with Additional

Borescope and Engine Borescope and Engine InspectionsInspections

Source: Mr. Timothy Dues, SESManager, USAF Propulsion Product Group

6

Those of Us in 72Those of Us in 72°°F F ClimateClimate--Controlled Offices Controlled Offices Have a Responsibility to Have a Responsibility to

the Maintainers Who the Maintainers Who Often Have to Do Their Often Have to Do Their Jobs in 120Jobs in 120°°F Heat or F Heat or

-- 4040°°F Cold…F Cold…

Source: Mr. Timothy Dues, SESUSAF Propulsion Product Group

Field Maintenance

7

Development

* FY00 USAF Budget+Sources

$2.2B

$559M$631M

AcquisitionS&T$95M

Sustainment (63%)

USAF Gas Turbine Propulsion Budget

Engine Sustainment Burden

Condemned Disks

8

ProblemTurbine engine disks must not fail

Condemned

Engine Disks

9

0 50,000

Usage (Duty Cycles)Log normal distribution viewed on a linear scale~ one order of magnitude assumed for ±3σMedian at 24,000 cycles, - 3σ at 8,000 cycles

We currently throw away 1000 componentsto remove the unknown one

that is theoretically predicted to be in a “failed state”

The Problem / Opportunity

999 of 1000 components

still have useable life

999 of 1000 components

still have useable life

Current8000 TACLife Limit

Probability of “failure”

Goal: Recover wasted life without increasing riskGoal: Recover wasted life without increasing risk

10

Life PredictionPotential for Improvements

Cycles

ImprovedNDE

ImprovedLifePrediction

EngineMonitoring

Cra

ck L

engt

h

Current Predicted Life

+++

(Cra

ck L

engt

h)

Cycles

Predicted Actual

Grow Ni Grow Np

Ni Np

0 24,000 48,000 72,000 96,000

50%increasein Mean

current8000 TAC

life limit

Cycles

Failu

re O

ccur

renc

e

11

High-Payoff Demonstration Problem:Turbine Engines

• Turbine engines are the primary powerplant for all DoD Services• Turbine engines represent a crucial, high technology system, that

often controls asset readiness• Turbine engines contain a wide variety of components, and pose a

range of levels of difficulty for Materials Damage Prognosis:– Disks, blades, vanes, cases, bearings, shafts– Range of materials, temperatures, damage modes, and usage and

state-awareness sensors– Hot flow path is a particularly aggressive environment, requiring

improved tools for health assessment and prediction• Development of Materials Prognosis science and technology offers a major

payoff for both the military and commercial sectors:– Safety– Readiness– Asset management– Reliability– Life extension– Reduce maintenance burden

12

DARPA - Materials Prognosis

Physically-BasedProbabilistic

Life-Prediction Models

FE

Life Gauge

Engine Health Monitoring

Robotic Inspection “Worms”

Materials Prognosis:• Physics of failure• Interrogation

techniques• Feature extraction• Mission Needs

EngineHealth

• Life prediction will analyze real-time data, learn, & calculate remaining life

• Sensors will evaluate the state of assets, and mission trends

• Future-mission needs and life calculations will dictate asset allocation

Real-time Usage Data

13

Vision: Materials Damage Prognosisfor Turbine Engines

VISION: Develop tools for a reliable, robust, quantitative, integrated life assessment and management system using physics-based models enhanced by information from state-awareness sensors

Key science and technology disciplines! Coupled physics-based models of materials damage and

behavior• Interaction of multiple damage/failure mechanisms• Multi-scale, mechanism-based• Microstructurally-based stochastic behavior• Integrated information from state-awareness tools

! Interrogation of damage-state• Intelligently exploit existing sensors• Feature extraction from global sensors• Materials-damage-state interrogation techniques and recorders

! Data management and fusion• Component history and pedigree• Component usage data• Capability matched to mission

14

Benefits of Prognosis for Turbine Engines

2002 2008

Log Life (e.g. Cycles)

(Near Actual Life)

Initi

atio

n of

!/32

in. C

rack

Log Life (e.g. Cycles)

Initi

atio

n of

!/32

in. C

rack

8000(Design Life)

Log Life (e.g. Cycles)

Cra

ck S

ize

ai

ac

Cra

ck S

ize

Log Life (e.g. Cycles)

ai

ac

as

Adjusted Mission LifeNominal Life

15

Benefits of Prognosis for Turbine Engines

Adjusted Mission LifeNominal Life

Cra

ck S

ize

log Life (e.g. Cycles)

ai

ac

as

16

InfraredCamera

Physics of Failure

Crystal Plasticity Models Scalable Computational Models and Algorithms

Auxiliary ModelsModel Development

Model Validation Life Prediction

Crack Initiation andGrowth Prediction

IDDSInteractive Analysis

and Experiment

Deformation Field Mapping

OIM

Conventional Testing

Probabilistic Material Characterization

• Explicit FEA• Adaptive Meshing• Multi-scale Models

• Subscale Processes• Subscale Properties• Defect Distributions• Microstructural Data• Residual Stresses

300

400

500

600

10 -1 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8

σm

ax (M

Pa)

N (Cycles)

3

4

5

6

7

8

9

10

0 100 200 300 400 500 600 700 800

∆K th

(MPa

¦m)

Temperature (°C)

10 -10

10 -9

10 -8

10 -7

10 -6

10 -5

6 7 8 9 10 20 30

da/d

N (m

/cyc

le)

∆K (MPa¦m)

1

2

3

4

5

0 1 10 6 2 10 6 3 10 6 4 10 6

RT600°C800°C

Nor

mal

ized

CM

OD

Cycles

K5-DuplexR=0.1

Crack Detected

Failure

State-awareness sensorsAnalytical Predictions

300

400

500

600

10 -1 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 7 10 8

σm

ax (M

Pa)

N (Cycles)

Physically BasedLife Prediction Model

17

Interrogation Techniques

Crack Detection

Holistic Monitoring Engine Parameter Trending

VibrationMonitors

Eddy Current orCapacitance Sensors

ElectrostaticDebris Monitors

OilAnalysis

Off-line InspectionsWhole-field Imaging

In-situ Monitoring

0

50

100

150

200

250

300

350

0 5000 10000 15000 20000 25000 30000 35000

Cycle Number

Cra

ck P

hase

- D

egre

es

0.000.200.400.600.801.001.201.401.601.80

0 5000 10000 15000 20000 25000 30000 3

Cycle Number

Cra

ck A

mpl

itude

- m

ils

State-AwarenessInterrogation

Trending Tolerance

Mul

ti Pa

ram

eter

Vec

tor S

pace

Time

• Metal Temperatures

• Rotational Speeds

• Pressures• Other ...

18

Component Damage Assessmentand Prediction

Component demonstrations

Component analysis

Laboratory specimen experiment & analysis

Effect of mission loading

0

20

40

60

80

100

Time

% M

ax S

tres

s

Physics-basedLife Prediction Models

(incorporating state-awarenessinput & probabilistic tools)

Crack Detected

Failure

State-awareness sensors

Field experience

19

Engine Health ManagementTime-Phased Descriptors

2002SOA

2007 2010VAATE I

2017VAATE II

Incr

easi

ng T

ime

on W

ing

Intelligent Engine/Active Management

Current Systems

Proactive HealthManagement

Reactive HealthMonitoring

20

Turbine Engine Science and Technology PlanTri-Service/NASA/Industry Coordinated

TOD

AY

Integrated High Performance Turbine EngineTechnology (IHPTET)…Constant Life (F119)

• 2X Propulsion Capability+100% Engine Thrust/Weight-40% Fuel Burn-35% Production & Maintenance Cost

• National HCF S&T Program

• 10X Propulsion Affordability (Capability / Cost) National Durability Program Maintenance Friendly Versatile Core Ultra-Intelligent Adaptive Engine

• Environmental Efficiencies• Dev/Prod/Maint Cost Reduction Focus

Versatile, Affordable, Advanced Turbine Engines

1987 2002 2005 2017

21

Engine Life Management

22

Transition to Service

“The Materials Prognosis Program has huge potential for us in the propulsion community, both today with our legacy engines, and for the future engines like JSF, as well as those which will be derived from the AFRL VAATE initiative.”

Mr. Timothy Dues, SESManager, Propulsion Product GroupU.S. Air Force17 September 2002

TMS Symposiumon Materials Prognosis

24

“the two most important things we do: flying and fixing airplanes.

That doesn’t mean that you’re not important if you’re not pulling on a pole in the cockpit or turning a wrench on the flightline. It means that the importance of the rest of us is how we contribute to flying and fixing airplanes.”Source Air Combat Command News Service:“Jumper looks back, looks ahead”Released: Aug. 30, 2001

General John Jumper Chief of Staff USAF

General John JumperChief of Staff, USAF

25

Notional Lifing Scenario

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0

Hot Time Fraction

TAC

S/H

r

Fighter-4000hrs

Tanker/Transport-12000hrs

Commercial-30000hrs

Bomber-6000hrs

Long RangeStrike-4000hrs

FatigueFailure Region

Creep

Durability Failure ModesModes are Mission Dependent

Need a method to compare multi-application life requirements, while maintaining common parts