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• W4387397465 abstract "Abstract Swelling, that is, volumetric expansion, of particles is ubiquitous in many industrial systems containing both swellable particles and non‐swellable particles. However, the interactions between these two types of particles and the internal stress distribution within the binary particle systems are not yet fully understood. The dynamic behavior of binary mixtures with both swellable and non‐swellable particles was explored for the first time in this study by employing a microscopic diffusion swelling model that considers the change in microstructure of swelling particles implemented in the discrete element method coupled with computational fluid dynamics (DEM‐CFD). The expansion of particle bed consists of super absorbent polymers (SAPs) particles, that is, the swellable particles, and polyformaldehyde (POM) particles, that is, the non‐swellable particles, in a rectangle container is then analyzed experimentally and numerically. The results show that a tapered pattern of SAP particles and an arch pattern of POM particles are formed during the swelling process. The Lacey index is used to quantify the mixing degree of the binary particle system, which increases with time until the POM particle layer is fully encompassed by SAP particles. The mean stress distribution within the particle system is also examined, revealing that the swollen SAP particles migrate upward through a “low‐stress channel,” pushing the POM particles in the central area outward. Moreover, it is found that, for case with a relative low friction, SAP particles penetrate the POM particle layer along the container walls, while for the cases with relative high friction, SAP particles penetrate the POM particle layers through the central regions of the container." @default.
• W4387397465 created "2023-10-07" @default.
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• W4387397465 date "2023-10-06" @default.
• W4387397465 modified "2023-10-09" @default.
• W4387397465 title "<scp>DEM‐CFD</scp> analysis of swelling behaviors of binary particle systems with a microscopic diffusion model" @default.
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• W4387397465 doi "https://doi.org/10.1002/aic.18248" @default.
• W4387397465 hasPublicationYear "2023" @default.
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