Predicting residual strength of composites after cyclic loading

Predicting residual strength of composites after cyclic loading: A numerical, progressive damage analysis based approach

Mitrousias, Alexander (TU Delft Aerospace Engineering; TU Delft Aerospace Structures & Materials)

Benedictus, R. (graduation committee)
Teuwen, Julie J.E. (graduation committee)
Zarouchas, D. (mentor)
van Campen, J.M.J.F. (graduation committee)
Eleftheroglou, N. (graduation committee)

Delft University of Technology

Aerospace Engineering

2017-07-13

As the demand on new lightweight materials increases due to economic and environmental reasons, fiber reinforced plastics seem an interesting solution. In order to maximize their potential, ways have to be found to introduce damage tolerant designs. To this end, in the present exercise, the damage caused by cyclic loading is investigated.
Cyclic loading causes loss of stiffness and strength of composite structures. In the present work a progressive damage analysis model has been built. Damage is tracked by a macroscopic failure criterion. Failure occurring at levels below the macroscopic level are captured by a degradation theory which requires S-N curves as input. An attempt is made to use S-N curves derived from static tests only. Stress solutions are obtained using a finite element package. In order to bring the macroscopic damage, degradation and stress solution parts together, a cycle jumping algorithm has been developed which minimizes the amount of FEM stress solutions required and the required amount of function evaluations.
In its current form, the model is able to properly predict the trend of stiffness and strength behavior. Showing that a model like this has potential. For the proper predictions of mag- nitudes, additional failure modes should be modeled and the usage of S-N curves obtained from large specimens for applying on smaller geometries (elements) should be reconsidered.

Composites
Progressive Damage Analysis
Fatigue

http://resolver.tudelft.nl/uuid:2d9019cc-01f3-47ba-84d1-61625c5e5c96

Student theses

master thesis

© 2017 Alexander Mitrousias