Surface roughness is a critical health parameter of a turbine blade due to its implications on blade surface heat transfer and structural integrity.This paper proposes a physics-based online framework for Gas Turbine ...Surface roughness is a critical health parameter of a turbine blade due to its implications on blade surface heat transfer and structural integrity.This paper proposes a physics-based online framework for Gas Turbine Engines(GTE),in order to assess the blade surface roughness in a highpressure turbine without engine shutdown.The framework consolidates Gas Path Analysis(GPA)based performance monitoring models and meanline turbomachinery analysis,using a novel GPAmeanline matching process.This extracts meaningful performance deviation trends from GPA,while addressing the uncertainties associated with the measurements and modelling.To relate efficiency loss to surface roughness severity,a meanline-based system-identification process has been developed to establish the meanline representation of the turbine stage,and to incorporate the empirical surface roughness loss correlations.The roughness loss correlations have been evaluated against recent transonic test data in the literature.A modification to the compressibility correction factor has been made according to the evaluation outcome,which improved loss predictions compared to the experimental measurements.The framework was tested on the three-year operational data of a cogeneration GTE,and the results verified the framework’s potential for online surface roughness monitoring.The predicted surface roughness showed agreement in both trend and the magnitude-level with the measurements reported in the literature.展开更多
基金This project was supported by the Life Prediction Technologies Inc.(LPTi)and Natural Sciences and Engineering Research Council of Canada.
文摘Surface roughness is a critical health parameter of a turbine blade due to its implications on blade surface heat transfer and structural integrity.This paper proposes a physics-based online framework for Gas Turbine Engines(GTE),in order to assess the blade surface roughness in a highpressure turbine without engine shutdown.The framework consolidates Gas Path Analysis(GPA)based performance monitoring models and meanline turbomachinery analysis,using a novel GPAmeanline matching process.This extracts meaningful performance deviation trends from GPA,while addressing the uncertainties associated with the measurements and modelling.To relate efficiency loss to surface roughness severity,a meanline-based system-identification process has been developed to establish the meanline representation of the turbine stage,and to incorporate the empirical surface roughness loss correlations.The roughness loss correlations have been evaluated against recent transonic test data in the literature.A modification to the compressibility correction factor has been made according to the evaluation outcome,which improved loss predictions compared to the experimental measurements.The framework was tested on the three-year operational data of a cogeneration GTE,and the results verified the framework’s potential for online surface roughness monitoring.The predicted surface roughness showed agreement in both trend and the magnitude-level with the measurements reported in the literature.
