A reliable progressive fatigue damage model for life prediction of composite laminates incorporating an adaptive cyclic jump algorithm

Tao Zheng, Licheng Guo*, Zhenxin Wang, Rinze Benedictus, John Alan Pascoe

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
8 Downloads (Pure)


In this paper, a reliable progressive fatigue damage model (PFDM) for predicting the fatigue life of composite laminates is proposed by combining the normalized fatigue life model, nonlinear residual degradation models and fatigue-improved Puck criterion. To balance the accuracy of life predictions and computational efficiency, an adaptive cyclic jump algorithm is developed and implemented within the PFDM. The sensitivity of life prediction to cyclic jump parameter has been greatly reduced by correlating the cyclic jump with the increment time and viscous coefficient. Therefore, the cyclic jump parameter can be arbitrarily selected within a relatively large range to obtain convergent results. When incorporating the adaptive cyclic jump algorithm, there is no need to define a standard for determining the material failure in numerical calculations, which effectively eliminates an artificially induced uncertainty in life predictions. Two sets of experiments are conducted to validate the proposed PFDM. The numerical predictions including static failure strength and fatigue life correlate reasonably well with the available experimental data.

Original languageEnglish
Article number109587
JournalComposites Science and Technology
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • A. Laminate
  • A. polymer-matrix composites (PMCs)
  • B. Fatigue
  • C. Finite element analysis (FEA)
  • D. Life prediction

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