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The aim of the current work was to illustrate the effect of the fibre area correction factor on the results of modelling natural fibre-reinforced composites. A mesoscopic approach is adopted to represent the stochastic heterogeneity of the composite, i.e. a meso-structural numerical model was prototyped using the finite element method including quasi-unidirectional discrete fibre elements embedded in a matrix. The model was verified by the experimental results from previous work on jute fibres but is extendable to every natural fibre with cross-sectional non-uniformity. A correction factor was suggested to fine-tune both the analytical and numerical models. Moreover, a model updating technique for considering the size-effect of fibres is introduced and its implementation was automated by means of FORTRAN subroutines and Python scripts. It was shown that correcting and updating the fibre strength is critical to obtain accurate macroscopic response of the composite when discrete modelling of fibres is intended. Based on the current study, it is found that consideration of the effect of flaws on the strength of natural fibres and inclusion of the fibre area correction factor are crucial to obtain realistic results.
Herein, the general constitutive equation of bi-phasic materials equipped with orientation tensor is presented in direct notation. The formulation is refined by some correction factors specific to natural fibre-reinforced composites; then, a planar case is derived. The necessity of local information is emphasised through the introduction of auxiliary maps, which included volume fraction and orientation data. A semi-analytical homogenisation method is introduced through finite element analysis. Auxiliary maps are shown to be a better alternative to the overall orientation of fibres. Global calculations are insensitive to local variations whilst appropriate auxiliary maps offer refined results. Considering the multidisciplinary application of orientation tensors, the proposed scheme can be used in all areas where local information cannot be disregarded.