Analysis of gas turbine blade low-cycle fatigue life and reliability

To ensure the safe and reliable operation of gas turbines, an analysis was conducted on a specific turbine blade to obtain the stress and strain distribution under rated conditions. A high-precision surrogate model was built using a deep fully-connected neural network to assess the impact of multiple sources of uncertainty on the low-cycle fatigue life and reliability of the turbine blades. The results indicate that the maximum stress in the turbine blade is 1 024.91 MPa, located in the middle of the suction side of the blade root platform. The mean life of the blade under material and manufacturing stochastic factors is 1.237 × 10<sup>4</sup> cycles, while under operational stochastic factors, it is 1.146 × 10<sup>4</sup> cycles. The design life of the blade was <?A3B2 tlsb=.02w?>set at 8.0 × 10 <sup>3</sup> cycles, with a reliability of 0.945 2 under material and manufacturing influences, and 0.936 8 <?A3B2 tlsb?>under operational influences. The findings demonstrate the effectiveness of the proposed method in the reliability analysis of turbine blades, providing reference data for the design optimization, performance enhancement, and life management of gas turbine turbine blades.

Authors

Publication Details

Journal
Ranqi wolun shiyan yu yanjiu.
Published
2024-12-01
DOI
https://doi.org/10.3724/j.gter.20240048
Citations
1
Primary Topic
Fatigue and fracture mechanics
Type
article
Field-Weighted Citation Impact
0.27
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article

Analysis of gas turbine blade low-cycle fatigue life and reliability

Yonghui Xie, J. B. Jiao, Di Zhang, Jinxing Li
1 citations
Ranqi wolun shiyan yu yanjiu.
Fatigue and fracture mechanics
0.27
article

Analysis of gas turbine blade low-cycle fatigue life and reliability

Yonghui Xie, J. B. Jiao, Di Zhang, Jinxing Li
article en
1 citations

Abstract

To ensure the safe and reliable operation of gas turbines, an analysis was conducted on a specific turbine blade to obtain the stress and strain distribution under rated conditions. A high-precision surrogate model was built using a deep fully-connected neural network to assess the impact of multiple sources of uncertainty on the low-cycle fatigue life and reliability of the turbine blades. The results indicate that the maximum stress in the turbine blade is 1 024.91 MPa, located in the middle of the suction side of the blade root platform. The mean life of the blade under material and manufacturing stochastic factors is 1.237 × 10<sup>4</sup> cycles, while under operational stochastic factors, it is 1.146 × 10<sup>4</sup> cycles. The design life of the blade was <?A3B2 tlsb=.02w?>set at 8.0 × 10 <sup>3</sup> cycles, with a reliability of 0.945 2 under material and manufacturing influences, and 0.936 8 <?A3B2 tlsb?>under operational influences. The findings demonstrate the effectiveness of the proposed method in the reliability analysis of turbine blades, providing reference data for the design optimization, performance enhancement, and life management of gas turbine turbine blades.

Ranqi wolun shiyan yu yanjiu.Vol. 37(6)
Responsible consumption and production
Openalex Percentile: Top 46%
Fatigue and fracture mechanics
0.27
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Analysis of gas turbine blade low-cycle fatigue life and reliability — Yonghui Xie, J. B. Jiao, et al. · Ranqi wolun shiyan yu yanjiu. (2024) | TGRS Research Map | TGRS