Gas path deterioration assessment for turbofan engines based on stochastic dynamics responses in the thermodynamic cycle

Abstract

Aiming at the adverse effects of strong nonlinearity and high uncertainty existing in the gas path performance deterioration assessment of turbofan engines, in contrast to previously reported techniques on dealing with nonlinearity and uncertainty that this work proposes a new method for assessing the gas path performance based on the stochastic dynamics responses, which fully utilizes the synergy of nonlinearity and uncertainty to accurately identify health status. The nonlinear expressions of turbofan engines are derived from thermodynamic equations and component characteristics. The gas path deterioration assessment method is further established based on the stochastic dynamics, and the thrust and specific fuel consumption integrating the nonlinearity and uncertainty are correspondingly designed to quantify the performance deterioration degree. The influences of nonlinearity and uncertainty on the assessment results are then discussed, which proves the necessity of considering nonlinearity and uncertainty in the performance assessment. Finally, the effectiveness and superiority of the proposed method are verified by comparing with the traditional method under two performance deterioration cases. The results indicate that the proposed method enhances early weak deterioration features through the optimal matching among fuel flow rate, nonlinearity and uncertainty to accurately assess the gas path performance. The statistical accuracy of the proposed method is 94%, which describes performance evolution trends and provides a novel and effective way to synthetically deal with the nonlinearity and uncertainty in the performance assessment of turbofan engines.