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contributor authorCelli, Dino
contributor authorShen, M.-H. Herman
contributor authorScott-Emuakpor, Onome
contributor authorHolycross, Casey
contributor authorGeorge, Tommy
date accessioned2022-02-04T14:33:04Z
date available2022-02-04T14:33:04Z
date copyright2020/02/03/
date issued2020
identifier issn0742-4795
identifier othergtp_142_03_031017.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4273892
description abstractThe aim of this paper is to provide a novel stochastic life prediction approach capable of predicting the total fatigue life of applied uniaxial stress states from a reduced dataset reliably and efficiently. A previously developed strain energy-based fatigue life prediction method is integrated with the stochastic state space approach for prediction of total cycles to failure. The approach under consideration for this study is the Monte Carlo method (MCM) where input is randomly generated to approximate the output of highly complex systems. The strain energy fatigue life prediction method is used to first approximate SN behavior from a set of two SN data points. This process is repeated with another unique set of SN data points to evaluate and approximate distribution of cycles to failure at a given stress amplitude. Uniform, normal, log-normal, and Weibull distributions are investigated. From the MCM, fatigue data are sampled from the approximated distribution and an SN curve is generated to predict high cycle fatigue (HCF) behavior from low cycle fatigue (LCF) data.
publisherThe American Society of Mechanical Engineers (ASME)
titleStochastic Fatigue Life Prediction Based on a Reduced Data Set
typeJournal Paper
journal volume142
journal issue3
journal titleJournal of Engineering for Gas Turbines and Power
identifier doi10.1115/1.4045065
page31017
treeJournal of Engineering for Gas Turbines and Power:;2020:;volume( 142 ):;issue: 003
contenttypeFulltext


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