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Journal Articles Image Analysis & Stereology Year : 2021

Simulation of Large Aggregate Particles Systems with a New Morphological Model

Giulia Ferri
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Severine Humbert
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Mathieu Digne
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Jean-Marc Schweitzer
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For the development of a new porous material such as catalytic carrier, the control of the textural properties is of fundamental importance. In order to move towards rational synthesis, it is necessary to better understand the physical phenomena that generate a defined solid structure. A contribute to this purpose can be achieved by studying the aggregation process inside colloidal suspensions, leading to porosity generation: this phenomenon can be described with a Brownian dynamics model that, for any set of chemical parameters, gives access to the mass distribution and the fractal dimension of colloidal aggregates. However, this model cannot be used for the simulation of large colloidal systems, due to its high computational time, limiting comparison with analytical methods, which probe the whole multi-scale system. This problem is solved by developing a new aggregation morphological model, wherein the fractal dimension is tuned with two compactness parameters. An efficient simulation algorithm is proposed in case of spheres, for which the fractal dimension of the generated aggregates varies between 1.2 and 3. Brownian dynamics results are used to parametrize this purely geometric model, in order to constrain the size and the morphology of the aggregates created. The large numerical solid will be representative of the textural properties of a real solid and will give more information on the porous network. It could be used, for example, to simulate diffusive transport coupled with chemical reaction and to study the impact of the geometry of the porous system on the catalytic performance.
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Dates and versions

hal-03352752 , version 1 (23-09-2021)


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Giulia Ferri, Severine Humbert, Mathieu Digne, Jean-Marc Schweitzer, Maxime Moreaud. Simulation of Large Aggregate Particles Systems with a New Morphological Model. Image Analysis & Stereology, 2021, 40, pp.71-84. ⟨10.5566/ias.2488⟩. ⟨hal-03352752⟩
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