• English
  • Deutsch
  • Log In
    Password Login
    or
  • Research Outputs
  • Projects
  • Researchers
  • Institutes
  • Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Scopus
  4. Probabilistic Approach for the Fatigue Strength Prediction of Polymers
 
  • Details
  • Full
Options
2022
Journal Article
Titel

Probabilistic Approach for the Fatigue Strength Prediction of Polymers

Abstract
One of the dominating factors in the fatigue of structures made from fiber-reinforced polymers, for example, wind turbine blades, is the polymer matrix. Traditionally, experimental stress–life data of polymers are approximated via a linear double-log Basquin model. Recently, the nonlinear stress–life formulation by Stüssi was found to provide a better fit of the experimental data with a substantially reduced standard deviation. Moreover, a nonlinear constant– life formulation, as proposed by Boerstra, can enhance the representation of the mean stress effect compared with state-of-the-art linear models given by the modified Goodman relation. To this end, Stüssi’s model was incorporated into the Boerstra relation to take account of the mean stress effects of an epoxy. This stress–life formulation was then enhanced with the Weibull probability function. The probabilistic–stress–life model provided a good approximation of the fatigue performance as a function of the stress ratio on the basis of an experimental data set. Finally, a stepwise engineering approach was suggested to derive the permissible stress–life with a view to practical design purposes. The procedure increased the reliability of the fatigue design evaluation compared with the state-of-the-art methodologies.
Author(s)
Rosemeier, Malo
Fraunhofer-Institut für Windenergiesysteme IWES
Antoniou, Alexandros
Fraunhofer-Institut für Windenergiesysteme IWES
Zeitschrift
AIAA journal
Thumbnail Image
DOI
10.2514/1.J060444
Language
English
google-scholar
Fraunhofer-Institut für Windenergiesysteme IWES
  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Send Feedback
© 2022