Piecewise stochastic rainflow counting for probabilistic linear and nonlinear damage accumulation considering loading and material uncertainties

Jie Chen, Anahita Imanian, Haoyang Wei, Nagaraja Iyyer, Yongming Liu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

A new framework is proposed for probabilistic fatigue life prediction considering randomness from both loadings and material properties. Piecewise stochastic rainflow counting (PSRC) is proposed to transform random loading spectrums to block loadings in a piecewise way with block randomization. The PSRC can be integrated with both linear and nonlinear damage accumulation rules. Mean stress corrected random fatigue limit model is proposed for uncertainty quantification of material fatigue properties under constant amplitude loadings. Probabilistic fatigue life prediction with both loading and material uncertainties is conducted using Monte Carlo simulation and is validated with experimental data from literature and in-house testing. Impact of different mean stress correction models on the fatigue life prediction under random loadings is investigated in detail. The influence of block piece length and block sequence randomization is discussed with respect to the mean and scatter behavior of the probabilistic life prediction results.

Original languageEnglish (US)
Article number105842
JournalInternational Journal of Fatigue
Volume140
DOIs
StatePublished - Nov 2020

Keywords

  • Fatigue life prediction
  • Probabilistic
  • Rainflow counting
  • Random loading
  • Uncertainty

ASJC Scopus subject areas

  • Modeling and Simulation
  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'Piecewise stochastic rainflow counting for probabilistic linear and nonlinear damage accumulation considering loading and material uncertainties'. Together they form a unique fingerprint.

Cite this