A new relation of drag force for high Stokes number monodisperse spheres by direct numerical simulation

Zaidi, Ali A.; Tsuji, Tomohiro; Tanaka, Toshitsugu

doi:10.1016/j.apt.2014.07.019

Cited by 60 publications

(16 citation statements)

References 45 publications

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“…The regularities of the gravitational sedimentation of a particle assembly are generally studied by analytical or numerical modeling [35,[37][38][39][40][41]. The verification of the theoretical models used requires experimental data on basic characteristics of the motion of a consolidated particle system.…”

Section: Particle Motion Modes In the Gravitational Fieldmentioning

confidence: 99%

Symmetry in Aerosol Mechanics: Review

2022

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The present review is concerned with the motion of aerosol particles, including that under the exposure to external fields, with special focus being put on the problems related to the similarity theory and invariants that manifest themselves as symmetry in physics. Research on the mechanics of aerosols is extremely important for managing environmental practices. Ultrasonic and electrostatic effects are used in technological processes for cleaning industrial aerosol emissions. In addition, aerosol systems are commonly used to prevent emergency situations (fire extinguishing, fog deposition). Understanding these processes requires knowledge of aerosol mechanics. At the same time, fundamental laws of particulate matter behavior have not been established until now, especially in the presence of external fields. In this paper, we consider the main similarity criteria that are applied for aerosol description. The motion of aerosol particles in the gravitational, electric, and ultrasonic fields is described. The results from studies into acoustic and electrostatic aerosol coagulations are presented herein.

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Section: Particle Motion Modes In the Gravitational Fieldmentioning

confidence: 99%

Symmetry in Aerosol Mechanics: Review

2022

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“…Particle movement inside droplets represents the mixing rate in the flow. Motion of a spherical particle in fluid medium with low Reynolds number flow is formulated as (Zaidi et al 2012); Additionally, to determine the mixing performance in microchannels, different parameters were used. First one was the dispersion length which is denoted in the following equation (Bruss 2008):…”

Section: Theory and Modelingmentioning

confidence: 99%

Numerical analysis of mixing performance in sinusoidal microchannels based on particle motion in droplets

Özkan

Erdem

2015

Microfluid Nanofluid

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This numerical study was conducted to analyze and understand the parameters that affect the mixing performance of droplet-based flow in sinusoidal microfluidic channels. Finite element analysis was used for modeling fluid flow and droplet formation inside the microchannels via tracking interface between the two heterogeneous fluids along with multiple particle trajectories inside a droplet. The solutions of multiphase fluid flow and particle trajectories were coupled with each other so that drag on every single particle changed in every time step. To solve fluid motion in multiphase flow, level set method was used. Parametric study was repeated for different channel dimensions and different sinusoidal channel profiles. These results were compared with mixing in droplets inside a straight microchannel. Additionally, tracking of multiple particles inside a droplet was performed to simulate the circulating flow profile inside the droplets. Based on the calculation of the dispersion length, particle trajectories, and velocities inside droplets, it is concluded that having smaller channel geometries increases the mixing performance inside the droplet. This also shows that droplet-based fluid flow in microchannels is very suitable for performing chemical reactions inside droplets as it will occur faster. Moreover, narrower and sinusoidal microchannels showed better dispersion length difference compared to straight and wider microchannels. © 2015, Springer-Verlag Berlin Heidelberg

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“…Analytical expressions of each component are available for an isolated particle in the low Reynolds number limit, which are then empirically corrected for finite values of Reynolds number (Re) and particle volume fraction (φ). Correlations of quasi-steady drag that are dependent on Re and φ are available in the literature [8][9][10][11][12] (we will refer to these as (Re, φ)-dependent correlation). Similar analytical expressions of the added-mass coefficient as a function of φ have also been obtained [13].…”

Section: Introductionmentioning

confidence: 99%

“…Results from PR-DNS of flow through a random array of particles were used in developing the (Re, φ)-dependent quasi-steady drag relations [8][9][10][11][12], where a simple curve fit through the quasi-steady drag as a function of (Re, φ) was sought. In our quest towards the perfect closure model, here we are interested in using the PR-DNS data to develop a more complex force model that systematically includes the effect of neighbors.…”

Section: Introductionmentioning

confidence: 99%

Towards Particle-Resolved Accuracy in Euler-Lagrange Simulations of Multiphase Flow Using Machine Learning and Pairwise Interaction Extended Point-particle (PIEP) Approximation

Balachandar,

Moore,

Akiki

et al. 2020

Preprint

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This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations is used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle (PIEP) model cannot be developed by either physics or machine learning alone.

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A new relation of drag force for high Stokes number monodisperse spheres by direct numerical simulation

Cited by 60 publications

References 45 publications

Symmetry in Aerosol Mechanics: Review

Symmetry in Aerosol Mechanics: Review

Numerical analysis of mixing performance in sinusoidal microchannels based on particle motion in droplets

Towards Particle-Resolved Accuracy in Euler-Lagrange Simulations of Multiphase Flow Using Machine Learning and Pairwise Interaction Extended Point-particle (PIEP) Approximation

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