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Research Papers: Gas Turbines: Structures and Dynamics

A Probabilistic Framework for Gas Turbine Engine Materials With Multiple Types of Anomalies

[+] Author and Article Information
Michael P. Enright

 Southwest Research Institute, 6220 Culebra Road, San Antonio, TX 78238menright@swri.org

R. Craig McClung

 Southwest Research Institute, 6220 Culebra Road, San Antonio, TX 78238craig.mcclung@swri.org

J. Eng. Gas Turbines Power 133(8), 082502 (Apr 05, 2011) (10 pages) doi:10.1115/1.4002675 History: Received May 04, 2010; Revised May 19, 2010; Published April 05, 2011; Online April 05, 2011

Some rotor-grade gas turbine engine materials may contain multiple types of anomalies such as voids and inclusions that can be introduced during the manufacturing process. The number and size of anomalies can be very different for the various anomaly types, each of which may lead to premature fracture. The probability of failure of a component with multiple anomaly types can be predicted using established system reliability methods provided that the failure probabilities associated with individual anomaly types are known. Unfortunately, these failure probabilities are often difficult to obtain in practice. In this paper, an approach is presented that provides treatment for engine materials with multiple anomalies of multiple types. It is based on a previous work that has been extended to address the overlap among anomaly type failure modes using the method of Kaplan–Meier and is illustrated for risk prediction of a nickel-based superalloy. The results can be used to predict the risk of general materials with multiple types of anomalies.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

PDFs of components containing single anomalies can be transformed to predict PDFs of components containing multiple anomalies of one or more types using established system reliability methods

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Figure 2

The conditional probability of fracture failure P(F∣d) of multiple anomaly materials is dependent on the number of anomalies j at a specified location in the component

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Figure 3

For multiple anomaly materials, both the probability of fracture given an anomaly P(Fi∣d1) and the anomaly occurrence probability P(d1) are dependent on the number of anomalies present

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Figure 4

For components with multiple anomalies of multiple types at multiple locations, the probability of failure can be modeled using several nested series systems to represent the various failure events

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Figure 5

Analytical probability of failure values associated with single and multiple anomalies of a single type for the example component containing multiple anomaly types

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Figure 6

Analytical and simulated probability of failure of failure values for the example component containing two anomaly types were in close agreement as expected

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Figure 7

CDF values for individual anomaly types estimated from censored failure data were significantly different from the analytical values

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Figure 8

CDF values for individual anomaly types estimated using the combined Kaplan–Meier/Meeker–Escobar approach were in close agreement with the analytical values

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Figure 9

The transformed CDF values based on the Kaplan–Meier/Meeker–Escobar approach were in close agreement with the analytical parent distributions

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Figure 10

The marginal CDFs for each anomaly type were obtained from the Kaplan–Meier/Meeker–Escobar fit of the failure data

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Figure 11

The parent CDFs were determined by transforming the marginal CDF values for each anomaly type

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Figure 12

The influence of volume to surface area ratio on probability of failure and associated contribution of (a) surface NMPs, (b) subsurface NMPs, and (c) surface pores

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