Estimation of Random Sonic Fatigue Life Based on Peak Probability Density of Von Mises Stress

Yun Dong Sha; Zhen Zhang; Feng Tong Zhao; Jing Wei

doi:10.4028/www.scientific.net/AMR.199-200.913

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 199-200Estimation of Random Sonic Fatigue Life Based on...

Estimation of Random Sonic Fatigue Life Based on Peak Probability Density of Von Mises Stress

Abstract:

As to the random sonic fatigue problem of thin walled structure of aerospace flight vehicle, estimation method for fatigue life based on Von Mises stress peak probability density is investigated. Assuming that Von Mises stress process satisfies three-parameter Weibull distribution, the peak probability density function of Von Mises stress is derived through the threshold crossing analysis and the peak distribution analysis of stationary random process. According to the Miner linear cumulative damage theory, the method for estimating the fatigue life in the frequency domain is established with the Von Mises stress peak probability density function applied. As an example, an aero-engine Combustor liner is considered, using coupled Finite Element Method (FEM) and Boundary Element Method (BEM) method, the structure vibration response to limited bandwidth Gaussian white noise is calculated. Based on the results, fatigue life of the structure is estimated by using the proposed method. Further more, the influences of the probability density function which is characterized by three-parameter Weibull distribution and two-parameter Weibull distribution respectivily for Von Mises stress response of the Combustor liner structure subjected to random accoustic loadings are discussed.

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Periodical:

Advanced Materials Research (Volumes 199-200)

Pages:

913-921

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.199-200.913

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Online since:

February 2011

Authors:

Yun Dong Sha, Zhen Zhang, Feng Tong Zhao, Jing Wei

Keywords:

Combustor Liner, Fatigue Life, Peak Probability Density, Sonic Fatigue, Von Mises Stress

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