Title

Methods Of Extrapolating Low Cycle Fatigue Data To High Stress Amplitudes

Abstract

Characterizing the low cycle fatigue (LCF) properties of each alloy used in a gas turbine is expensive and time consuming. As such, each material is typically characterized over only the necessary cyclic range to insure safe, reliable component design. If it is desired to understand properties outside of that range, the accepted procedure is to acquire additional LCF test data. The objective of this project is to test the validity of several extrapolation methods versus acquiring additional test data for a particular alloy. Several models are developed by the following techniques: a monotonic test data anchor point, a strain-compatibility derived anchor point, and temperature independence of the Coffin-Manson relation. Predictions are made using a base data set of Ni-base superalloy IN738 LC at several test temperatures. High stress amplitude data points are acquired, and incorporated into the base data set to form an augmented data set. The extrapolation methods are compared to the results from the augmented data set to test validity. The results show that the strain compatibility anchor point model has marginal improvement over extrapolation using the base data set, and the other two models had reduced accuracy relative to extrapolation using the base data set. Anomalies were found in the base test data set due to serrated yielding and oxidation. Two models offer improved fit to test data when used with data sets that contain anomalies. If this is a general trend, it offers potential that the models could be used to indicate anomalous behavior. It is not known whether the results of this study can be applied to other materials. Copyright © 2008 Siemens Power Generation, Inc.

Publication Date

12-1-2008

Publication Title

Proceedings of the ASME Turbo Expo

Volume

5

Issue

PART A

Number of Pages

159-168

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1115/GT2008-50365

Socpus ID

69949167194 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/69949167194

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