Random models versus periodic models for fibre reinforced composites

Trias, D.; Costa, J.; Mayugo, J.A.; Hurtado, Jorge E.

doi:10.1016/j.commatsci.2006.03.005

Cited by 162 publications

(91 citation statements)

References 29 publications

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“…Trias et al [3] compared the stress and strain distributions resulting from a transverse tensile load applied to both a random model and a periodic model of a carbon fibre composite. It was concluded that the use of periodic models could lead to an underestimation of matrix cracking and damage initiation and so the use of random models are needed when modelling failure and damage.…”

Section: Introductionmentioning

confidence: 99%

A combined experimental–numerical approach for generating statistically equivalent fibre distributions for high strength laminated composite materials

Vaughan

McCarthy

2010

Composites Science and Technology

203

140

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Section: Introductionmentioning

confidence: 99%

A combined experimental–numerical approach for generating statistically equivalent fibre distributions for high strength laminated composite materials

Vaughan

McCarthy

2010

Composites Science and Technology

203

140

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“…These results shall be compared with the results obtained by Wongsto and Li [214] who carried out numerical analyses of unidirectionally fiber-reinforced composites for both random and regular packed fibers in the context of linear elasticity. Trias et al [317] provided a detailed comparison on random and periodic models for fiber-reinforced composites and reported that the periodic models could lead to underestimation of matrix failure initiation. Segurado and Llorca [318] conducted finite element analyses to determine the influence of particle clustering in cubic RVE reinforced with stiff spherical elastic particles.…”

Section: 22mentioning

confidence: 99%

Aspects of Computational Homogenization at Finite Deformations: A Unifying Review From Reuss' to Voigt's Bound

Saeb

Steinmann

Javili

2016

Applied Mechanics Reviews

190

138

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The objective of this contribution is to present a unifying review on strain-driven computational homogenization at finite strains, thereby elaborating on computational aspects of the finite element method. The underlying assumption of computational homogenization is separation of length scales, and hence, computing the material response at the macroscopic scale from averaging the microscopic behavior. In doing so, the energetic equivalence between the two scales, the Hill-Mandel condition, is guaranteed via imposing proper boundary conditions such as linear displacement, periodic displacement and antiperiodic traction, and constant traction boundary conditions. Focus is given on the finite element implementation of these boundary conditions and their influence on the overall response of the material. Computational frameworks for all canonical boundary conditions are briefly formulated in order to demonstrate similarities and differences among the various boundary conditions. Furthermore, we detail on the computational aspects of the classical Reuss' and Voigt's bounds and their extensions to finite strains. A concise and clear formulation for computing the macroscopic tangent necessary for FE 2 calculations is presented. The performances of the proposed schemes are illustrated via a series of two-and three-dimensional numerical examples. The numerical examples provide enough details to serve as benchmarks.

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“…The nearest distance fibre distances distribution is an important feature of composite microstructure models and the initial damage laminates generally occurs where fibres are much close to each other [16] . PDF (Probability Density Function) is defined by the minimum distance value of the target fibre with its neighbour fibre and it can provide information on the short-range interactions between fibres.…”

Section: Nearest Neighbour Fibre Distances Distribution Functionmentioning

confidence: 99%

“…Ripley's K function, also known as the second-order intensity function, is mainly used to describe the spatial distribution of information [16] . Ripley's K function K(h) is calculated in formula (2):…”

Section: Ripley's K Functionmentioning

confidence: 99%

“…However, when the fibre volume content was reached, the fibre may not be extended to the corner of RVE and it was easy to appear in the middle or somewhere centralized. The NAA [16] used the nearest distance distribution function to define the distance of different fibres in RVE. Its advantage included short execution time, basically consistent with the statistical distribution and reasonable simulating of the fibres microscopic characteristics.…”

Section: The Random Sequence Generation Algorithm (Rsga)mentioning

confidence: 99%

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The mechanical property prediction of fibre-reinforced composite materials based on a new generating random fibre distributions method

Huang¹,

Zhao

Dai³

et al. 2015

Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering

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This paper focuses on the issues of the fibre-reinforced composite material microstructure and presents a new numerical method to generate random fibre distributions and predict the macroscopic mechanical properties of composite materials based on the represent volume element (RVE). The method, named the random sequence generation algorithm (RSGA), obtains fibre radius distributions and positional relationships by image processing to generate a statistical equivalence RVE effectively. On this basis, the composite material microstructure finite element model is established to predict its macroscopic elastic properties by Python language in ABAQUS. Comparative analysis shows the algorithm is in good agreement with experimental results and has the statistical equivalence herein. The research of this paper on the algorithm for composite materials of fibre distributions provides a useful alternative to generate random numerical models that can be used in micromechanical analysis of composite materials.

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Random models versus periodic models for fibre reinforced composites

Cited by 162 publications

References 29 publications

A combined experimental–numerical approach for generating statistically equivalent fibre distributions for high strength laminated composite materials

A combined experimental–numerical approach for generating statistically equivalent fibre distributions for high strength laminated composite materials

Aspects of Computational Homogenization at Finite Deformations: A Unifying Review From Reuss' to Voigt's Bound

The mechanical property prediction of fibre-reinforced composite materials based on a new generating random fibre distributions method

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