Metaball-Imaging discrete element lattice Boltzmann method for fluid–particle system of complex morphologies with case studies

Zhao, Yifeng; Zhang, Pei; Lei, Liang; Kong, Lingwei; Galindo‐Torres, S. A.; Li, Stan Z.

doi:10.1063/5.0135834

Cited by 5 publications

(1 citation statement)

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“…( 1) is however very challenging to determine because it depends on many parameters including the particle Reynolds number and particle shape. 10 Herein, the particle Reynolds number is defined as , with being the kinematic viscosity. Except at sufficiently small , where an analytical solution exists for spheres based on Stokes' law, in which is inversely proportional to , no general solution can be found for determining the drag coefficient of particles of any shape.…”

Section: Introductionmentioning

confidence: 99%

A numerical study of the settling of non-spherical particles in quiescent water

Cheng,

Cao,

et al. 2023

Physics of Fluids

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Settling of non-spherical particles is poorly understood with previous studies having focused mainly on spherical particles. Here, a series of particle-resolved direct numerical simulations are conducted using FLOW-3D (commercial computational fluid dynamics software) for spheres and five regular, non-spherical shapes of sediment particles, i.e., prolate spheroid, oblate spheroid, cylinder, disk, and cube. The Galileo number varies from 0.248 to 360, and the particle Reynolds number Rep ranges from 0.002 77 to 562. The results show that a non-spherical particle may experience larger drag and, consequently, attain a lower terminal velocity than an equivalent sphere. If Rep is sufficiently small, the terminal velocity is less affected by particle shape as characterized by the particle aspect ratio. For relatively large Rep, the shape effect (represented by the Corey shape factor) becomes more significant. Empirical correlations are derived for the dimensionless characteristic time t95∗ and displacement s95∗ of particle settling, which show that t95∗ remains constant in the Stokes regime (Rep < 1) and decreases with increasing Rep in the intermediate regime (1 ≤ Rep < 103), whereas s95∗ increases progressively with increasing Rep over the simulated range. It is also found that in the Stokes regime, particle orientation remains essentially unchanged during settling, and so the terminal velocity is governed by the initial orientation. In the intermediate regime, a particle provisionally settling at an unstable orientation self-readjusts to a stable equilibrium state, such that the effect of initial orientation on the terminal velocity is negligible. Moreover, an unstable initial orientation can enhance the vertical displacement and may promote vortex shedding.

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Section: Introductionmentioning

confidence: 99%

A numerical study of the settling of non-spherical particles in quiescent water

Cheng,

Cao,

et al. 2023

Physics of Fluids

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Machine learning and numerical investigation on drag coefficient of arbitrary polygonal particles

Xiang,

Cheng,

Zhang

et al. 2024

Int Journal of Mech Sys Dyn

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The drag coefficient, as the most important parameter that characterizes particle dynamics in flows, has been the focus of a large number of investigations. Although good predictability is achieved for simple shapes, it is still challenging to accurately predict drag coefficient of complex‐shaped particles even under moderate Reynolds number (Re). The problem is that the small‐scale shape details of particles can still have considerable impact on the drag coefficient, but these geometrical details cannot be described by single shape factor. To address this challenge, we leverage modern deep‐learning method's ability for pattern recognition, take multiple shape factors as input to better characterize particle‐shape details, and use the drag coefficient as output. To obtain a high‐precision data set, the discrete element method coupled with an improved velocity interpolation scheme of the lattice Boltzmann method is used to simulate and analyze the sedimentation dynamics of polygonal particles. Four different machine‐learning models for predicting the drag coefficient are developed and compared. The results show that our model can well predict the drag coefficient with an average error of less than 5% for particles. These findings suggest that data‐driven models can be an attractive option for the drag‐coefficient prediction for particles with complex shapes.

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