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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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1. REPORT DATE 2002
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4. TITLE AND SUBTITLE Achieving the Potential of Materials Prognosis for Turbine Engines
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6. AUTHOR(S) 5d. PROJECT NUMBER
5e. TASK NUMBER
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7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Materials and Manufacturing Directorate Propulsion Directorate AirForce Research Laboratory
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13. SUPPLEMENTARY NOTES The original document contains color images.
14. ABSTRACT
15. SUBJECT TERMS
16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT
UU
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25
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Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18
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