Analysis, modelling and simulation of the fragmentation of agglomerates

Wachem, Berend van; Thalberg, Kyrre; Nguyen, Duy; Juan, Luis Martín de; Remmelgas, Johan; Niklasson-Björn, Ingela

doi:10.1016/j.ces.2020.115944

Cited by 21 publications

(16 citation statements)

References 28 publications

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“…van Wachem et al. (2020) proposed a discrete fragmentation model (DFM) to simulate the breakup behaviour due to aggregate–aggregate and aggregate–wall collisions without tracking each individual primary particle. Unlike these studies, the present work provides a framework that isolates the effect of turbulence on particle breakup using a simple phenomenological model.…”

Section: Phenomenological Model Of Deagglomerationmentioning

confidence: 99%

Deagglomeration of cohesive particles by turbulence

Yao

Capecelatro

2021

J. Fluid Mech.

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Section: Phenomenological Model Of Deagglomerationmentioning

confidence: 99%

Deagglomeration of cohesive particles by turbulence

Yao

Capecelatro

2021

J. Fluid Mech.

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“…A long‐standing point of debate in cohesive‐particle systems is whether a force‐based or energy‐based description of particle interactions is more appropriate. Estimates of agglomerate size, 19,55–57 constitutive relations for source terms in populations balances, 24,58–63 and regime maps 15,26,62,64–69 are a few prominent examples in which both force‐ and energy‐based descriptions have appeared in the literature, without justification as to why one is more appropriate than the other. Here we aim to decipher the force versus energy conundrum.…”

Section: Discussionmentioning

confidence: 99%

“…Regime maps for cohesive‐particle flows have been a mainstay in industry for the prediction of various unit operations 10,13–15,26,62,64–70 . The generality (or lack thereof) of a given regime map depends on what quantities are being plotted.…”

Section: Discussionmentioning

confidence: 99%

Toward general regime maps for cohesive‐particle flows: Force versus energy‐based descriptions and relevant dimensionless groups

et al. 2021

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Much confusion exists on whether force‐ or energy‐based descriptions of cohesive‐particle interactions are more appropriate. We hypothesize a force‐based description is appropriate when enduring‐contacts dominate and an energy‐based description when contacts are brief in nature. Specifically, momentum is transferred through force‐chains when enduring‐contacts dominate and particles need to overcome a cohesive force to induce relative motion, whereas particles experiencing brief contacts transfer momentum through collisions and must overcome cohesion‐enhanced energy losses to avoid agglomeration. This hypothesis is tested via an attempt to collapse the dimensionless, dependent variable characterizing a given system against two dimensionless numbers: A generalized bond number, BoG–ratio of maximum cohesive force to the force driving flow, and a new Agglomerate number, Ag–ratio of critical cohesive energy to the granular energy. A gamut of experimental and simulation systems (fluidized bed, hopper, etc.), and cohesion sources (van der Waals, humidity, etc.), are considered. For enduring‐contact systems, collapse occurs with BoG but not Ag, and vice versa for brief‐contact systems, thereby providing support for the hypothesis. An apparent discrepancy with past work is resolved, and new insight into Geldart's classification is gleaned.

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“…Dizaji et al [ 63 ] examined the collision of two agglomerates in a shear flow and indicated that depending on the value of the adhesion parameter and the ratio of offset distance to agglomerate radius of gyration, the collision could result in an agglomerate merger, bouncing and fragmentation. A study by van Wachem et al [ 39 ] proposed a new dimensionless parameter to capture the properties of the agglomerate and the collision, leading to a satisfactory fit of the micro-scale collision modelling results.…”

Section: Review Of Up-to-date Dpi Modelling Studiesmentioning

confidence: 99%

“…By integrating all three mechanisms, it was shown that the simulation model was able to predict the FPF of different formulations, devices and flow rates, with overall good agreement with experiments. Van Wachem et al [ 39 ] recently carried out further simulations to account for the fragmentation of agglomerates due to agglomerate–agglomerate and agglomerate–wall impactions. The resolved micro-scale modelling results were used to construct a macro-scale discrete fragmentation model.…”

Section: Review Of Up-to-date Dpi Modelling Studiesmentioning

confidence: 99%

Flow and Particle Modelling of Dry Powder Inhalers: Methodologies, Recent Development and Emerging Applications

2021

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Dry powder inhaler (DPI) is a device used to deliver a drug in dry powder form to the lungs. A wide range of DPI products is currently available, with the choice of DPI device largely depending on the dose, dosing frequency and powder properties of formulations. Computational fluid dynamics (CFD), together with various particle motion modelling tools, such as discrete particle methods (DPM) and discrete element methods (DEM), have been increasingly used to optimise DPI design by revealing the details of flow patterns, particle trajectories, de-agglomerations and depositions within the device and the delivery paths. This review article focuses on the development of the modelling methodologies of flow and particle behaviours in DPI devices and their applications to device design in several emerging fields. Various modelling methods, including the most recent multi-scale approaches, are covered and the latest simulation studies of different devices are summarised and critically assessed. The potential and effectiveness of the modelling tools in optimising designs of emerging DPI devices are specifically discussed, such as those with the features of high-dose, pediatric patient compatibility and independency of patients’ inhalation manoeuvres. Lastly, we summarise the challenges that remain to be addressed in DPI-related fluid and particle modelling and provide our thoughts on future research direction in this field.

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Analysis, modelling and simulation of the fragmentation of agglomerates

Cited by 21 publications

References 28 publications

Deagglomeration of cohesive particles by turbulence

Deagglomeration of cohesive particles by turbulence

Toward general regime maps for cohesive‐particle flows: Force versus energy‐based descriptions and relevant dimensionless groups

Flow and Particle Modelling of Dry Powder Inhalers: Methodologies, Recent Development and Emerging Applications

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