Enhanced Life Prediction Methodologies for Engine Rotor Life Extension, 18-R9414Printer 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 over the next eight to 10 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. SwRI is leading a team with unique capabilities to enhance, as well as integrate, several of the above-mentioned 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 project are to develop and demonstrate:
Accomplishments - The project 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. The effectiveness of the FaNG model has been 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, 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 modeling using Bayesian updating has also been implemented and applied to the assessment of the potential benefits of on-board health 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 U.S. Air Force Research Laboratory.