Fatigue Life Prediction of Turbine Blades Based on QAR Data

Authors

Pi Jun, 1. Civil Aviation University of China, Sino-European Institute of Aviation Engineering, Tianjin 300300, China;;
Shuwei Gao, 2. Civil Aviation University of China, Institute of Aviation Engineering, Tianjin 300300, China;
Jiangbo Huang, 2. Civil Aviation University of China, Institute of Aviation Engineering, Tianjin 300300, China;
Huang Lei, 2. Civil Aviation University of China, Institute of Aviation Engineering, Tianjin 300300, China;
Ma Long, 1. Civil Aviation University of China, Sino-European Institute of Aviation Engineering, Tianjin 300300, China;;

Abstract

Abstract: Based on the characteristics of the high-pressure turbine blade, a numerical simulation method for the actual load spectrum of the high-pressure turbine blade of the civil aircraft engine based on the QAR data is proposed. The actual service load spectrum of the engine recorded during aircraft flight is extracted from the QAR data; the time history of centrifugal force, aerodynamic force and thermal stress of the turbine blade in actual use is obtained by means of finite element simulation; and with life prediction model, the actual service life of the blade is estimated. This method provides a reliable engineering method for evaluating the remaining life and obtaining the actual service load of the high-pressure turbine blade, which is valuable for studying the reliability and load characteristics of the high-pressure turbine blade.

Recommended Citation

Jun, Pi; Gao, Shuwei; Huang, Jiangbo; Lei, Huang; and Long, Ma (2019) "Fatigue Life Prediction of Turbine Blades Based on QAR Data," Journal of System Simulation: Vol. 31: Iss. 6, Article 17.
DOI: 10.16182/j.issn1004731x.joss.17-0202
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss6/17

First Page

1165

Revised Date

2017-09-17

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.17-0202

Last Page

1171

CLC

V232.4

Recommended Citation

Pi Jun, Gao Shuwei, Huang Jiangbo, Huang Lei, Ma Long. Fatigue Life Prediction of Turbine Blades Based on QAR Data[J]. Journal of System Simulation, 2019, 31(6): 1165-1171.

DOI

10.16182/j.issn1004731x.joss.17-0202