Abstract
Gas turbine engines experience “rub” when the rotating blades come in contact with a static abradable coating. This results in extreme strain rates and dynamics inside a high-temperature/high-pressure environment. Current rub models are phenomenological and do not reflect the underlying microstructures, thus limiting their prediction accuracy. In this work, a microstructure-informed, reduced order modeling framework is introduced for simulating abradable coating “rub" behavior. This framework comprises a microscale model constructed based on digitized abradable microstructure and explicitly simulates the mechanical behavior of each constituent phases and their interactions. After calibration and validation with experiment data, the calibrated microscale model is used to generate data across a vast range of applied strain rates and temperature with various load paths. Then, the virtually generated data are used to fit the macroscopic-reduced order model, which enables fast component scale rub simulation without compromising the integrity of the complex material behavior. The proposed effort will address the technical challenge of predicting abradable material behavior during rub through the application of multiscale modeling from microstructure to engines behavior, effectively reducing the development costs and time of new abradable material for better “rub” properties.
Original language | English |
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Pages (from-to) | 4934-4947 |
Number of pages | 14 |
Journal | Journal of Materials Science |
Volume | 59 |
Issue number | 12 |
DOIs | |
State | Published - Mar 2024 |
Funding
This research was sponsored by the Oak Ridge National Laboratory (ORNL). ORNL is managed by UT-Battelle, LLC for the U.S. Department of Energy under Contract DE-AC05 00OR22725. This work was funded by the HPC4materials program supported by U.S. Department of Energy Office of Fossil Energy in collaboration with Raytheon Technologies Corporation—Pratt & Whitney Division. Computing support by the Compute and Data Environment for Science (CADES) at Oak Ridge National Laboratory is gratefully acknowledged.