A two-scale method for matrix cracking probability in fibre-reinforced composites based on a statistical representative volume element

Trias, D.; Costa, J.; Fiedler, Bodo; Hobbiebrunken, Thomas; Hurtado, Jorge E.

doi:10.1016/j.compscitech.2005.10.030

Cited by 32 publications

(23 citation statements)

References 24 publications

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“…Two-way coupling strategies were most commonly associated with CA models (Ghosh, Bai, & Raghavan, 2007;Gonz alez & Llorca, 2006), but they can be mixed with HA models as well (Trias, Costa, Fiedler, Hobbiebrunken, & Hurtado, 2006). Typically, the CA models use a single global analysis, which requires a hybrid mesh.…”

Section: Coupled Modelsmentioning

confidence: 98%

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From micro to macro

Sencu

Yang

Wang

2015

Structural Integrity and Durability of Advanced Composites

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Section: Coupled Modelsmentioning

confidence: 98%

“…An example of an MHCA model is the embedded cell approach (ECA). The ECA minimizes the boundary effects by using an ideally homogeneous buffer around the statistical computed window Li, McDowell, & Zhou 2013;Trias et al, 2006). In this way, the boundary effects may be decided before the scale transfer.…”

Section: Coupled Modelsmentioning

confidence: 99%

From micro to macro

Sencu

Yang

Wang

2015

Structural Integrity and Durability of Advanced Composites

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“…The notion and the determination of RVEs are well documented in the literature [18,15,12,6]. One of the basic definitions of the RVE is an image sub-region for which properties averaged values are similar to those of the bulk material.…”

Section: Representative Volume Elementmentioning

confidence: 99%

“…Drugan [4], found that the RVE size of the "overall" modulus term of reinforced composites, is at most twice the reinforcement diameter for any reinforcement concentration level. At the opposite, Trias [18] estimated the RVE size for different criteria and found the minimum size for a carbon fibre reinforced epoxy is 50 times the fibre radius. Many other RVE size suggestions can be found in the literature, but one have to keep in mind that it may be difficult to compare them since RVE size depends on the associated property, and on other parameters as the inclusions volume fraction, the ratio between the inclusion and matrix property, i.e the thermal contrast in our case, and the wanted relative precision for the estimation.…”

Section: Representative Volume Elementmentioning

confidence: 99%

Estimation of effective thermal conductivity tensor from composite microstructure images

Thomas

Boyard

Jarny

et al. 2008

J. Phys.: Conf. Ser.

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Abstract. The determination of the effective thermal properties of inhomogeneous materials is a long-standing problem of continuously interest. The impressive number of methods developed to measure or estimate the thermal properties of composite materials clearly exhibits the importance given to their knowledge. Homogenization models are a cheap way to determine or predict them. Many different approaches of homogenization were developed, but the last advances are credited to numerical methods. In this study, a new computational model is developed to estimate the 2D thermal conductivity tensor and the thermal main directions of a pure carbon/epoxy unidirectional composite. This tool is based on real composite microstructure.

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“…In Trilas's work [19], the random distribution of fibers was analyzed by means of optical microscopy and the corresponding images were used to generate realizations of statistical representative volume elements, then the probability density functions of the stress, strain and dilatational energy density were found. Blacklock and Rinaldi [20,21] generated virtual specimens of stochastic textile composite which possess the same statistical characteristics as specimens imaged by high resolution x-ray computed tomography.…”

Section: Introductionmentioning

confidence: 99%

Effect of Yarn Distortion on the Mechanical Properties of Fiber-Bar Composites Reinforced by Three-Dimensional Weaving

et al. 2015

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A meso-structure model of fiber-bar composites reinforced by three-dimensional weaving (FBCR3DW) is proposed. Optical microscopy images of the preform structure revealed that the fibers along the circumference of the yarn cross-weave were twisted randomly due to alternating yarn winding on either side of the fiber bars during the manufacturing process. Sections of the cross-woven yarn were divided into five regions based on the twist characteristics. Stochastic function theory was used to describe the twist characteristics and to calculate the compliance tensor for each twisted yarn region. The twist characteristics and compliance tensor of each region were then introduced into a finite element model to calculate the elastic properties of the twisted yarn and FBCR3DW; unidirectional tensile stress-strain curves were generated based on the Tsai-Wu failure criterion. Several FBCR3DW specimens with randomly twisted yarns inside the weave structure were used in experimental tests. Our numerical results were in good agreement with the experimental values. Yarn distortion had a significant effect on the elastic properties and axial tensile strength of the yarn; specifically, the influence of yarn distortion on the transverse elastic modulus and transverse shear modulus of FBCR3DW was severe, whereas only a slight effect occurred with regard to the other elastic Appl Compos Mater constants and unidirectional tensile properties. Thus, the proposed method provides an effective reference for modeling fiber composites with a weave structure.

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A two-scale method for matrix cracking probability in fibre-reinforced composites based on a statistical representative volume element

Cited by 32 publications

References 24 publications

From micro to macro

From micro to macro

Estimation of effective thermal conductivity tensor from composite microstructure images

Effect of Yarn Distortion on the Mechanical Properties of Fiber-Bar Composites Reinforced by Three-Dimensional Weaving

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