Three‐dimensional morphological modelling of concrete using multiscale Poisson polyhedra

Escoda, Julie; Jeulin, Dominique; Willot, François; Toulemonde, Charles

doi:10.1111/jmi.12213

Cited by 24 publications

(11 citation statements)

References 45 publications

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“…Numerical simulations showed that the realistic shape of cement particles influences the (simulated) rate of hydration, percolation threshold, early age E modulus and rheology properties [11]. Implementing the realistic cement particle shape [35] (similarly as it is done for aggregates in concrete [9,10,36]), and therefore realistic heterogeneity of the cement paste in micromechanical and microstructure models, is very important for any kind of study on cement paste.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, a step further was made with particle shape models for concrete and mortar. In such models, the irregular shape of aggregate particles is taken into account, resulting in a more realistic material structure [9,10]. Including realistic shapes of cement particles in hydration models is, however, non-trivial, as the particle shape influences the cement hydration [11].…”

Section: Introductionsmentioning

confidence: 99%

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Combined experimental and numerical study of fracture behaviour of cement paste at the microlevel

Luković

Schlangen

2015

Cement and Concrete Research

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Section: Resultsmentioning

confidence: 99%

Section: Introductionsmentioning

confidence: 99%

Combined experimental and numerical study of fracture behaviour of cement paste at the microlevel

Luković

Schlangen

2015

Cement and Concrete Research

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“…In Moreaud et al (2012), a hardcore deposit model is proposed to simulate a boehmite material. However, the simulation of microstructures using hardcore models becomes difficult when approaching the close-packing limit (Escoda et al, 2015). To overcome this difficulty, random walk stochastic procedures have been developed (Altendorf and Jeulin, 2011) to generate fibrous microstructures, as an improvement on random sequential adsorption models (Feder, 1980).…”

Section: Introductionmentioning

confidence: 99%

A Mixed Boolean and Deposit Model for the Modeling of Metal Pigments in Paint Layers

2015

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Pigments made of metal particles of around 10 µm or 20 µm produce sparkling effects in paints, due to the specular reflection that occurs at this scale. Overall, the optical aspect of paints depend on the density and distribution in space of the particles. In this work, we model the dispersion of metal particles of size up to 50 µm, visible to the eyes, in a paint layer. Making use of optical and scanning electron microscopy (SEM) images, we estimate the dispersion of particles in terms of correlation functions. Particles tend to aggregate into clusters, as shown by the presence of oscillations in the correlation functions. Furthermore, the volume fraction of particles is non-uniform in space. It is highest in the middle of the layer and lowest near the surfaces of the layer. To model this microstructure, we explore two models. The first one is a deposit model where particles fall onto a surface. It is unable to reproduce the observed measurements. We then introduce a "stack" model where clusters are first modeled by a 2D Poisson point process, and a bi-directional deposit model is used to implant particles in each cluster. Good agreement is found with respect to SEM images in terms of correlation functions and density of particles along the layer height.

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“…As touching particles are frequently observed in our samples, we use a variant of the algorithm discussed in Escoda et al . () for modelling particles in concrete. The model is obtained in the following way: For

n = 0

, start with an empty window W and insert a random polyhedron p 0 at a random location. Increase n by 1 and suggest to insert a random polyhedron

p_{n}

at a random location in W .In case

p_{n}

intersects any previously placed polyhedron

p_{m}

0 \leq m < n

, move

p_{n}

in a random direction.…”

Section: Modelmentioning

confidence: 99%

“…The method is easy to implement and was also used in Zhang et al (2014) for selecting the random position of reinforcement particles in an MMC. As touching particles are frequently observed in our samples, we use a variant of the algorithm discussed in Escoda et al (2015) for modelling particles in concrete. The model is obtained in the following way:…”

Section: Fitting Of Particle Volume and Elongationmentioning

confidence: 99%

A stochastic microstructure model for particle reinforced aluminium matrix composites

Losch

Schuff

Balle

et al. 2018

Journal of Microscopy

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Summary Metal matrix composites are complex materials consisting of various phases which can display largely different mechanical properties. The deformation behaviour of these composites cannot be sufficiently modelled by averages or simple particle shapes due to the local stresses that occur on the particle edges. Therefore, a sophisticated model of the microstructure is needed. We introduce a method for stochastic modelling of a silicon carbide (SiC) particle reinforced aluminium matrix composite. The SiC particles are modelled by Laguerre polyhedra generated by densely packed spheres. The shape factors of the polyhedra have been fitted to the particle shapes observed in three‐dimensional images. Particle elongation in extrusion direction and the observed log‐normal volume distribution of the particles are included in the model by suitable scaling. An outlook is presented on how to model the grains of the polycrystalline aluminium matrix and intermetallic precipitates, which result from the strengthening mechanism of the matrix. Lay Description Metal matrix composites are complex materials consisting of different phases which can display largely different mechanical properties. The deformation behaviour of these composites cannot be sufficiently modelled by averages or simple particle shapes due to the local stresses that occur on the particle edges. Therefore, a sophisticated model of the microstructure is needed. We introduce a method of stochastic modelling of a silicon carbide (SiC) particle reinforced aluminium matrix composite. The SiC particles are modelled by Laguerre polyhedra generated by densely packed spheres. The shape factors of the polyhedra have been fitted to the SiC shapes observed in three‐dimensional images. Additionally, the polyhedra are scaled anisotropically to account for orientation anisotropy and to obtain a log‐normal volume distribution. An outlook is presented on how to model the aluminium phase's grains and intermetallic precipitates, which result from the strengthening mechanism of the aluminium matrix alloy.

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Three‐dimensional morphological modelling of concrete using multiscale Poisson polyhedra

Cited by 24 publications

References 45 publications

Combined experimental and numerical study of fracture behaviour of cement paste at the microlevel

Combined experimental and numerical study of fracture behaviour of cement paste at the microlevel

A Mixed Boolean and Deposit Model for the Modeling of Metal Pigments in Paint Layers

A stochastic microstructure model for particle reinforced aluminium matrix composites

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