Fatigue Damage Modeling Approach Based on Evolutionary Power Spectrum Density

2019-01-0524

04/02/2019

Event
WCX SAE World Congress Experience
Authors Abstract
Content
Fatigue damage prediction approaches in both time and frequency domains have been developed to simulate the operational life of mechanical structures under random loads. Fatigue assessment of mechanical structures and components subjected to those random loads is increasingly being addressed by frequency domain approaches because of time and cost savings. Current frequency-based fatigue prediction methods focus on stationary random loadings (stationary Power Spectral Density), but many machine components, such as jet engines, rotating machines, and tracked vehicles are subjected to non-stationary PSD conditions under real service loadings. This paper describes a new fatigue damage modeling approach capable of predicting fatigue damage for structures exposed to non-stationary (evolutionary) PSD loading conditions where the PSD frequency content is time-varying. The underlying concept of the proposed approach is that the evolutionary response PSD function of a structure can be decomposed into a finite number of narrow frequency bands which can be associated with Rayleigh distributions. Fatigue damage is estimated by summing up damages for each individual band on the basis of an appropriate damage accumulation rule. The proposed modeling approach is numerically validated by a finite element method using three simplified structures made of 5052-H32 aluminum alloy. The modeling approach is more efficient, better simulates real environmental random loadings, provides more accurate fatigue life predictions, and can easily be adapted to optimize the design of engineering structural components.
Meta TagsDetails
DOI
https://doi.org/10.4271/2019-01-0524
Pages
10
Citation
Li, Z., Ince, A., and Lacombe, J., "Fatigue Damage Modeling Approach Based on Evolutionary Power Spectrum Density," SAE Technical Paper 2019-01-0524, 2019, https://doi.org/10.4271/2019-01-0524.
Additional Details
Publisher
Published
Apr 2, 2019
Product Code
2019-01-0524
Content Type
Technical Paper
Language
English