Probabilistic Modeling Geometric Tolerance and Low Cycle Fatigue Life of Gas Turbine Compressor Blade

Abstract This paper presents a probabilistic low cycle fatigue (LCF) assessment for geometric tolerances on high load contact surfaces of gas turbine compressor blades. The typical patterns of the geometric deviations for the root contact flank of the compressor blades are identified and characterized according to coordinate measurement machine (CMM) measurements of the blade root. These typical patterns are closely related to the root form manufacture tools and process. Finite element (FE) models for the typical geometric deviation patterns are created based on nodal coordinates transformation of the surface nodes on the blade root contact flanks and radius. An optimized blade root profile tolerance is established, which enables significant cost saving. The elastic-plastic FE analysis with nonlinear contact model, material strain-life test, response surface, and constrained Monte Carlo simulation are used to create a probabilistic LCF life model for the optimized tolerance. The model quantifies the effect of the geometric deviations, blade mass, and material property on the blade LCF life. The result shows that with the optimized tolerance, the probability of blade LCF failure is very low and acceptable. It is also shown that the strain life material property is the most critical factor for the LCF failure. The root profile tolerance and blade mass are seen to have a much weaker effect on the blade LCF life.

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Publication Details

Journal
Journal of Engineering for Gas Turbines and Power
Published
2024-09-28
DOI
https://doi.org/10.1115/1.4066714
Citations
1
Primary Topic
Turbomachinery Performance and Optimization
Type
article
Field-Weighted Citation Impact
1.30
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article

Probabilistic Modeling Geometric Tolerance and Low Cycle Fatigue Life of Gas Turbine Compressor Blade

Zhiqiang Meng, Björn Sjödin, R. M. Bluck
1 citations
Journal of Engineering for Gas Turbines and Power
Turbomachinery Performance and Optimization
1.30
article

Probabilistic Modeling Geometric Tolerance and Low Cycle Fatigue Life of Gas Turbine Compressor Blade

Zhiqiang Meng, Björn Sjödin, R. M. Bluck
article en
1 citations

Abstract

Abstract This paper presents a probabilistic low cycle fatigue (LCF) assessment for geometric tolerances on high load contact surfaces of gas turbine compressor blades. The typical patterns of the geometric deviations for the root contact flank of the compressor blades are identified and characterized according to coordinate measurement machine (CMM) measurements of the blade root. These typical patterns are closely related to the root form manufacture tools and process. Finite element (FE) models for the typical geometric deviation patterns are created based on nodal coordinates transformation of the surface nodes on the blade root contact flanks and radius. An optimized blade root profile tolerance is established, which enables significant cost saving. The elastic-plastic FE analysis with nonlinear contact model, material strain-life test, response surface, and constrained Monte Carlo simulation are used to create a probabilistic LCF life model for the optimized tolerance. The model quantifies the effect of the geometric deviations, blade mass, and material property on the blade LCF life. The result shows that with the optimized tolerance, the probability of blade LCF failure is very low and acceptable. It is also shown that the strain life material property is the most critical factor for the LCF failure. The root profile tolerance and blade mass are seen to have a much weaker effect on the blade LCF life.

Journal of Engineering for Gas Turbines and PowerVol. 147(3)
Siemens (United Kingdom) (GB), Siemens (Sweden) (SE)
Responsible consumption and production
Openalex Percentile: Top 13%
Turbomachinery Performance and Optimization
1.30
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Probabilistic Modeling Geometric Tolerance and Low Cycle Fatigue Life of Gas Turbine Compressor Blade — Zhiqiang Meng, Björn Sjödin, et al. · Journal of Engineering for Gas Turbines and Power (2024) | TGRS Research Map | TGRS