27th International Conference on Mathematical and Computer Simulations in Mechanics of Solids and Structures - Fundamentals of Static and Dynamic Fracture (MCM), Saint Peter, Guernsey And Alderney, 25 - 27 September 2017, vol.6, pp.168-173
Fibrous networks are ubiquitous structures for many natural materials, such as bones and bacterial cellulose, and artificial ones (e.g. polymer-based nonwovens). Mechanical behaviour of these networks are of interest to researchers since it deviates significantly from that of traditional materials treated usually within the framework of continuum mechanics. The main reason for this difference is a discontinuous character of networks with randomly distributed fibres (that can be also curved) resulting in complex scenarios of fibre-to-fibre interactions in the process of their deformation. This also affects a character of load transfer, characterised by spatial non-uniformity and localisation. A discontinuous nature of fibrous networks results in their non-trivial failure character and, more specifically, evolution of failure caused by notches. In order to investigate these mechanisms, various notches are introduced both into real-life specimens used in experimentation and discontinuous finite-element (FE) models specially developed (Farukh et al., 2014a; Hou et al., 2009, 2011a, Sabuncuoglu et al, 2013) to mimic the microstructure of fibrous networks. The specimens were tested under tensile loading in one of the principal directions, with FE-based simulations emulating this regime. The effect of notch shape on damage mechanisms, effective material toughness and damage patterns was investigated using the obtained experimental and numerical methods. The developed discontinuous model with direct introduction of microstructural features of fibrous networks allowed assessment of strain distribution over selected paths in them in order to obtain strain profiles in the vicinity of notch tips. Additionally, evolution of damage calculated in advanced numerical simulations demonstrated a good agreement with images from experiments. Copyright (C) 2017 The Authors. Published by Elsevier B.V.