A Deterioration Predictor Model of Performance Parameters of Turbine Modules in Aeroengines

Abstract

Abstract. Aeroengines inevitably deteriorate over their service life due to the accumulation of flight hours under various routes, ambient conditions, and thrust demands. Current methods estimate the health parameters, such as flow capacity and efficiency, for different aeroengine modules and perform their statistical evaluation. Although degradation is embedded within their results, these methods do not model individually the physical mechanisms causing the deterioration of specific modules. To address this gap, we propose a deterioration predictor model to calculate degraded performance parameters over the flight hours of a turbine section. Initially, we developed degradation functions to correlate deterioration mechanisms with their effects using information available in the open literature. We then developed and validated an off-design performance calculator to predict the degradation's impact on axial turbine performance, choosing proven loss models capable of incorporating the effects of deterioration. Finally, we predicted the degraded performance map of a turbine by combining the effects of geometry variation, surface roughness, and tip clearance. Our results indicate that geometry variation is the primary driver for changes in turbine performance. This deterioration predictor model demonstrates how the performance of a turbine evolves through flight hours, providing valuable information to enhance the predictive ability of aeroengine degradation.