1. 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

2. 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).

3. 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?

4. University of Texas at San Antonio  Measuring Probability-of-Fracture (POF)  Monte Carlo Sampling Yes No

5. University of Texas at San Antonio DARWIN ® Software Package

6. University of Texas at San Antonio Probability of Detection (POD) Curve  Gives the Probability of Detection as a Function of Crack size  Lognormal Distribution for Ultrasonic Sensors 0 1

7. University of Texas at San Antonio Inspection Simulation Failure Minimum Detectable Crack Size (MDCS) Sample Value 0 1

8. University of Texas at San Antonio Inspection Simulation Cont. Sample Value Crack is detected for sensor realization j because POD(a j )<POD(a(N i )) POD(  (N i )) POD(a * j )

9. University of Texas at San Antonio Differences in On-Board Inspection  Depot Inspection Different Inspectors Different Equipment Human Error  On-Board Inspection No Inspectors Identical Equipment No Human Error

10. University of Texas at San Antonio  Dependence  MDCS(j)   MDCS(j+1)

11. University of Texas at San Antonio Numerical Examples  2 Mission Types with 8000 Flight Cycles  Air-to-Ground  Functional-Check-Flight  Stress from Flight-Data- Recorder RPM Data

12. University of Texas at San Antonio Specific Cases Studied  3 Median POD Values  200 Mil  400 Mil  600 Mil  32% Coefficient of Variation  30 Mil POD  32% Coefficient of Variation 5000 Monte Carlo Simulations for each Inspection Type Continual Inspections Depot Inspection

13. University of Texas at San Antonio 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

14. University of Texas at San Antonio Condor Network

15. University of Texas at San Antonio Air-to-Ground Results No Inspection Single Mid-Life Inspection Continual Inspection

16. University of Texas at San Antonio Air-to-Ground Results No Inspection Single Mid-Life Inspection Continual Inspection

17. University of Texas at San Antonio Air-to-Ground Results Continual Inspection Single Mid-Life Inspection No Inspection

18. University of Texas at San Antonio Functional-Check-Flight Results No Inspection Single Mid-Life Inspection Continual Inspection

19. University of Texas at San Antonio Functional-Check-Flight Results No Inspection Single Mid-Life Inspection Continual Inspection

20. University of Texas at San Antonio Functional-Check-Flight Results Continual Inspection Single Mid-Life Inspection No Inspection

21. University of Texas at San Antonio 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 Navigation  Live Fire  Target Tow  Etc.  Evaluate Anticipated POF when Experimental Data is Available.

22. University of Texas at San Antonio  Questions