Enhanced Life Prediction Methodologies for Engine Rotor Life Extension, 18-9414Printer Friendly Version
Inclusive Dates: 07/16/03 Current
Background - The U.S. Air Force is facing a potentially large wave of turbine engine disc replacement costs during 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 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 is leading a team with unique capabilities to enhance, as well as integrate, several of the above life management technologies. Other team members include Smiths Aerospace, 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 hybridisation of state-of-the-art probabilistic life prediction and classical engine health monitoring.
Accomplishments - The program has developed 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. Efforts are currently underway to incorporate the FaNG model 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 identify missions from recorded usage data from aircraft flight data recorders and use this information in combination with the newly developed models to forecast future damage based on changes in the mission planning. Probabilistic modelling using Bayesian updating is also being implemented and applied to the assessment of the potential benefits of on-board monitoring of turbine engine discs. This analysis fuses data from probabilistic FaNG model predictions and continual input from a crack-detection sensor to forecast current and future probability of failure. This project has resulted in the award of a Dual Use Science and Technology Program from the Air Force Research Laboratory.