Enhanced Life Prediction Methodologies for Engine Rotor Life Extension, 18-R9414

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Principal Investigators
Stephen J. Hudak, Jr.
Michael P. Enright
R. Craig McClung

Inclusive Dates:  07/16/03 – 03/31/08

Background - The U.S. Air Force is facing a potentially large wave of turbine engine disc replacement costs over the next eight to ten years that are inconsistent with anticipated budgets. Consequently, the Engine Rotor Life Extension (ERLE) program was conceived by the Air Force Research Laboratory (AFRL) as a sound science and technology investment that offers the potential for significant cost avoidance by extending the life of certain life-limiting components. The concept is to extend the life of these components by recovering the conservatism believed to exist in current design and life management practices, without increasing risk, by systematically improving and more effectively integrating a number of life management technologies — life prediction, nondestructive inspection, engine health monitoring, maintenance and repair. Enhancements in engine life management technology would also be applicable to developmental and future military engines, commercial engines and land-based combustion turbines where safety, reliability, and cost of ownership are of paramount importance. Southwest Research Institute led a team with unique capabilities to enhance, as well as integrate, several of the above life-management technologies. Other team members include Smiths Aerospace (now GE Aviation Systems), The University of Texas at San Antonio and Mustard Seed Software.

Approach - The approach and technical objectives of this program are to develop and demonstrate: 1) a new family of physically based, deterministic life prediction models for treating total fatigue life including crack nucleation, microcrack growth and large crack growth; 2) an efficient probabilistic life prediction methodology based on the stochastic nature of each of the above phases of fatigue life; and 3) a methodology for enhanced engine life management based on hybridization of state-of-the-art probabilistic life prediction and classical engine health monitoring.

Accomplishments - The program has developing and validated a new deterministic fatigue crack nucleation and growth model (FaNG). Model predictions were found to be in good agreement with measured fatigue lives for notched Ti-6Al-4V specimens over a wide range of loading conditions. The effectiveness of the FaNG model was also demonstrated by predicting the beneficial effect of residual stresses introduced by shot peening and low plasticity burnishing on enhancing fatigue life. The FaNG model has been incorporated into SwRI's DARWIN® probabilistic fracture mechanics software. Methods have also been developed and implemented in DARWIN® to perform probabilistic sensitivity analyses to rank the impact of key random variables on component reliability. Probabilistic methods are being developed to automatically identify missions from engine usage data from aircraft flight data recorders (Figure 1), as well as use this information in combination with the newly developed models to forecast future damage resulting from changes in the mission planning. Probabilistic modelling using Bayesian updating was also implemented and applied to the assessment of the potential benefits of on-board health monitoring of turbine engine discs. This analysis fused data from probabilistic FaNG model predictions and continual input from a crack detection sensor to forecast current and future probability of failure. This project resulted in the award of a Dual Use Science and Technology Program from the Air Force Research Laboratory, as well as a number of publications (one of which received a "Best Paper" award from ASME). This project also helped to establish SwRI's DARWIN® as the software of choice within the AFRL for probabilistic analysis of fatigue critical components in military turbine engines.

Figure 1. Two-dimensional probability distributions characterizing the variability in turbine engine stresses resulting from "Live Fire" missions; stresses are represented as a contour plot (left) and net plot (right) derived from a kernel-density estimation method.

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