TY - GEN
T1 - Research on Fatigue Strength Prediction Model of Aero-engine Blades Subjected to Foreign Object Damage
AU - Wang, Lingfeng
AU - Liu, Lulu
AU - Liu, Chao
AU - Pan, Xinlei
AU - Zhao, Zhenhua
AU - Zhou, Liucheng
N1 - Publisher Copyright:
© 2021 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021. All rights reserved.
PY - 2021
Y1 - 2021
N2 - The modification of average stress model based on notch types of foreign object damage was studied to improve the prediction accuracy on fatigue strength of damaged compressor blades. The 3-mm-diameter steel ball was impacted to the leading edge of the blade with the velocity of 200 m/s and angle of 0°. Four kinds of notches were induced, which are the semicircular notch, the tearing notch, the V notch and the scratch notch. The fatigue strength of the scratch notch is the highest, that of the semicircular notch is lower, and that of the tearing notch and the V notch are the lowest. The fatigue strength prediction results of damaged blades by the average stress model show low accuracy, and the errors are in the range of -20% and 50%. The prediction accuracy of the modified model is improved, and the errors are reduced to about -20% ～ 0.
AB - The modification of average stress model based on notch types of foreign object damage was studied to improve the prediction accuracy on fatigue strength of damaged compressor blades. The 3-mm-diameter steel ball was impacted to the leading edge of the blade with the velocity of 200 m/s and angle of 0°. Four kinds of notches were induced, which are the semicircular notch, the tearing notch, the V notch and the scratch notch. The fatigue strength of the scratch notch is the highest, that of the semicircular notch is lower, and that of the tearing notch and the V notch are the lowest. The fatigue strength prediction results of damaged blades by the average stress model show low accuracy, and the errors are in the range of -20% and 50%. The prediction accuracy of the modified model is improved, and the errors are reduced to about -20% ～ 0.
KW - Average stress model
KW - Fatigue prediction
KW - Foreign object damage
KW - High cycle fatigue
KW - Stress concentration factor
UR - https://www.scopus.com/pages/publications/85124475839
M3 - 会议稿件
AN - SCOPUS:85124475839
T3 - 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021
BT - 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021
PB - International Council of the Aeronautical Sciences
T2 - 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021
Y2 - 6 September 2021 through 10 September 2021
ER -