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Research Papers: Gas Turbines: Manufacturing, Materials, and Metallurgy

Probabilistic Treatment of Crack Nucleation and Growth for Gas Turbine Engine Materials

[+] Author and Article Information
M. P. Enright, R. C. McClung, S. J. Hudak, W. L. Francis

 Southwest Research Institute, San Antonio, TX 78238

J. Eng. Gas Turbines Power 132(8), 082106 (May 26, 2010) (8 pages) doi:10.1115/1.4000289 History: Received May 14, 2009; Revised June 05, 2009; Published May 26, 2010; Online May 26, 2010

The empirical models commonly used for probabilistic life prediction do not provide adequate treatment of the physical parameters that characterize fatigue damage development. For these models, probabilistic treatment is limited to statistical analysis of strain-life regression fit parameters. In this paper, a model is proposed for life prediction that is based on separate nucleation and growth phases of total fatigue life. The model was calibrated using existing smooth specimen strain-life data, and it has been validated for other geometries. Crack nucleation scatter is estimated based on the variability associated with smooth specimen and fatigue crack growth data, including the influences of correlation among crack nucleation and growth phases. The influences of crack nucleation and growth variability on life and probability of fracture are illustrated for a representative gas turbine engine disk geometry.

Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 1

A proposed FaNG model addresses the different phases of fatigue damage development

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Figure 2

The FaNG model predicts the nucleation life portion of smooth specimen life associated with a specific initial crack size

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Figure 3

A probabilistic framework has been developed to predict the fracture risk associated with crack nucleation and growth (19)

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Figure 4

Predicted nucleation life for a smooth specimen at various definitions of the crack nucleation size

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Figure 5

Predicted nucleation life fraction for a smooth specimen at various definitions of the crack nucleation size

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Figure 6

Comparison of FaNG model (for different crack nucleation sizes) and normalized SWT predictions with R=0.1 notch fatigue test data

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Figure 7

Comparison of FaNG model predictions (crack nucleation size=0.003 in.) with notch fatigue test data for three different stress ratios

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Figure 8

Predicted crack nucleation life fractions based on the FaNG model for notch fatigue specimens at three different stress ratios

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Figure 9

Crack initiation lives and associated nonlinear regression equation for Ti–6Al–4V smooth specimen data (24)

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Figure 10

Ti–6Al–4V crack growth rate data (24-25) and associated probability densities illustrate the dependence of da/dN variability on ΔK

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Figure 11

Crack growth lives based on data from the AGARD study (24-25) were used to estimate Ti–6Al–4V crack growth life variability: (a) crack growth life results based on individual specimens and (b) crack growth life probability density

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Figure 12

Influence of correlation among crack nucleation and growth portions of crack initiation life on crack nucleation life confidence intervals: (a) R=0.1 and (b) R=0.5

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Figure 13

The FaNG model was applied to risk assessment of an aircraft gas turbine engine disk

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Figure 14

For the gas turbine engine disk, predicted life values based on the FaNG model are generally higher than the smooth-specimen-based SWT model, particularly at relatively high stress range values: (a) R=0.1 and (b) R=0.5

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Figure 15

A comparison of fracture probability values associated with FaNG and smooth-specimen-based SWT models indicates that FaNG predictions may be under- or overconservative depending on the stress range value and on the relationship among nucleation and growth lives

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