Hygrothermal Fatigue of Structural Biocomposites: Pathways to Damage Analysis and Lifetime Prediction

Abstract (2023)

Authors

V.P. Perruchoud Bio-based Structures & Materials - CEG
Yasmine Mosleh Bio-based Structures & Materials - CEG
R.C. Alderliesten Structural Integrity & Composites - Aerospace Engineering
Research Group
Bio-based Structures & Materials (CEG) (TU Delft)
More Info
expand_more

Published Date

2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Bio-based Structures & Materials

Abstract

Biobased fibre reinforced polymer (FRP) composites, consisting of natural lignocellulosic fibres such as flax or hemp, are great alternatives to synthetic fibres with the potential of reducing the environmental impact, particularly regarding the recyclability of high-performance engineering structures. Natural fibres such as flax have damping and specific mechanical properties suitable to potentially replace glass fibres in FRP composites. However, the hygroscopicity in natural fibres raises durability questions for structures subjected to (diurnal and seasonal) environmental changes such as wind turbine blades. Existing research on flax FRPs describes on one hand damages related to hygrothermal ageing and on the other hand damage related to fatigue but the interaction of hygrothermal effect with mechanical ageing such as fatigue is not yet understood.
A concept is proposed to relate the hygrothermal fatigue behaviour of biobased FRP composites to their fatigue behaviour in standard laboratory air conditions, using damage analysis comprised of permanent strains and stiffness variations measurements, as well as visual macro and micro damage inspections, to enable the development of a mechanism based lifetime predictions model.