A physics-based framework for online surface roughness assessment for high-pressure turbines  

作　　者：Jie LIU Zelig LI Houman HANACHI 

机构地区：[1]National Research Base of Intelligent Manufacturing Service,Chongqing Technology and Business University,Chongqing 400067,China [2]Department of Mechanical and Aerospace Engineering,Carleton University,Ottawa,ON K1S 5B6,Canada [3]Algonquin College,Ottawa,ON,K2G 1V8,Canada

出　　处：《Chinese Journal of Aeronautics》2021年第7期135-156,共22页中国航空学报（英文版）

基　　金：This project was supported by the Life Prediction Technologies Inc.(LPTi)and Natural Sciences and Engineering Research Council of Canada.

摘　　要：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.

关 键 词：Fault inference Gas turbine Health monitoring Operational data Performance deterioration Surface roughness TURBINE 

分 类 号：TK478[动力工程及工程热物理—动力机械及工程]