University of Texas at San Antonio Comparison of Continual On-Board Inspections to a Single Mid-Life Inspection for Gas Turbine Engine Disks Brian D. Shook, Harry R. Millwater Department of Mechanical Engineering University of Texas at San Antonio Steve J. Hudak, Michael P. Enright, and William L. Francis Southwest Research Institute AIAA/ASME/ASCE/AHS/ASC Structural Dynamics & Materials Conference
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University of Texas at San Antonio
Comparison of Continual On-Board Inspections to a Single Mid-Life Inspection for Gas Turbine
Engine Disks
Brian D. Shook, Harry R. MillwaterDepartment of Mechanical Engineering
University of Texas at San Antonio
Steve J. Hudak, Michael P. Enright, and William L. FrancisSouthwest Research Institute
AIAA/ASME/ASCE/AHS/ASC Structural Dynamics & Materials Conference
University of Texas at San Antonio
Introduction
Current maintenance requirements are that engine disks are removed after a certain number of usage hours.
On-board engine health monitoring will facilitate continual
inspection of engine disks (once per flight).
On-board inspection could revolutionize the cost associated with gas turbine engine maintenance (retirement for cause).
University of Texas at San Antonio
Key Issue
Which has a Lower Probability-of-Fracture?
• Continual monitoring (once per flight) with a low-precision inspection
or• A single high-precision inspection at the mid-life of the disk
Is it Possible to Improve Safety and Decrease Maintenance Costs?
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MeasuringProbability-of-Fracture (POF)
SamplesTotal
DisksFailedPOF
_
_#
Monte Carlo Sampling
Yes
Yes
No
No
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DARWIN® Software Package
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Probability of Detection (POD) Curve
Gives the Probability of Detection as a Function of Crack size
Lognormal Distribution for Ultrasonic Sensors
0
1
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Inspection Simulation
Failure
Minimum Detectable Crack Size (MDCS)
Sample Value
0
1
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Inspection Simulation Cont.
Sample Value
Crack is detected for sensor realization j because POD(aj)<POD(a(Ni))
*
*
ji
ji
aPODNaPOD
aNa
POD((Ni))
POD(a*j)
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Differences in On-Board Inspection
Depot Inspection• Different Inspectors• Different Equipment• Human Error
On-Board Inspection• No Inspectors• Identical Equipment• No Human Error
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DependenceMDCS(j) MDCS(j+1)
k
iikk aPODkaCPODaPOD
1
))(1(1),()(
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Numerical Examples
2 Mission Types with 8000 Flight Cycles Air-to-Ground Functional-Check-Flight
Stress from Flight-Data-Recorder RPM Data
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Specific Cases Studied
3 Median POD Values 200 Mil 400 Mil 600 Mil
32% Coefficient of Variation
30 Mil POD32% Coefficient of
Variation
5000 Monte Carlo Simulations for each Inspection Type
Continual Inspections Depot Inspection
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Computational Methodology
Large Numbers of Inspections Lead to Long Computational Times 12 – 24 Hours
Condor Software Used to Pool Lab Resources Distributes Input Files (Jobs) to
Available Machines Manages Jobs Returns Results on Completion
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Condor Network
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Air-to-Ground Results
No Inspection
Single Mid-Life Inspection
Continual Inspection
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Air-to-Ground Results
No Inspection
Single Mid-Life Inspection
Continual Inspection
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Air-to-Ground Results
Continual Inspection Single Mid-Life Inspection
No Inspection
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Functional-Check-Flight Results
No Inspection
Single Mid-Life Inspection
Continual Inspection
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Functional-Check-Flight Results
No Inspection
Single Mid-Life Inspection
Continual Inspection
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Functional-Check-Flight Results
Continual InspectionSingle Mid-Life
Inspection
No Inspection
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Conclusions / Future Work
Conclusions Continual Inspection is Significantly Better Than a Single Depot
Inspection if the Sensor is Sufficiently Accurate. Conservative Simulations can be Used to Assist Sensor Designers
in Determining the Required Accuracy of an On-Board System.
Future Work Investigate Other Mission Types
Instruments and NavigationLive FireTarget TowEtc.
Evaluate Anticipated POF when Experimental Data is Available.
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Questions
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