Compressible multiphase particle-in-cell method (CMP-PIC) for full pattern flows of gas-particle system

Tian, Buning; Zeng, Jiang; Meng, Baoqing; Chen, Qian; Guo, Xiaohu; Xue, Kun

doi:10.1016/j.jcp.2020.109602

Cited by 23 publications

(18 citation statements)

References 57 publications

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“…2018; Tian et al. 2020). The CCFD-DPM approach tracks and accounts for parcel–parcel contact interactions.…”

Section: Methodsmentioning

confidence: 99%

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Explosive dispersal of granular media

Xue

Miu

et al. 2023

J. Fluid Mech.

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Explosive dispersal of granular media widely occurs in nature across various length scales, also enabling engineering applications ranging from commercial or military explosive systems to the loss prevention industry. However, the complex particle–flow coupling makes the explosive dispersal behaviour of particles difficult to control or even characterize. Here, we study the central explosion-driven dispersal of dense particle layers using the coarse-grained computational fluid dynamics–discrete element method and present a comprehensive investigation of both macroscale dispersal behaviours and particle-scale pattern formation. Employing three independent dimensionless parameters that characterize the efficiency, homogeneity and completeness of explosive dispersal, we categorize the dispersal behaviours into ideal, partial, retarded and failed modes, and propose the corresponding thresholds. As the mass ratio of granular materials to central pressurized gases (M/C) spans four orders of magnitude, the dispersal mode transitions from ideal to partial, then to retarded and finally to failed mode. The transitions of dispersal modes correspond to the particle–flow coupling regime crossovers, which change from decoupling to weak, medium and finally to strong coupling as the dispersal mode undergoes corresponding transitions. We proceed to develop continuum models accounting for the shock compaction and the ensuing pulsation of the particle ring that are capable of identifying the ideal dispersal mode from various dispersal systems. We also provide insights into the origins of diverse particle-scale patterns that are strongly correlated with macroscale dispersal modes and critical for the accurate prediction of dispersal modes.

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“…2018; Tian et al. 2020). The CCFD-DPM approach tracks and accounts for parcel–parcel contact interactions.…”

Section: Methodsmentioning

confidence: 99%

“…2020; Tian et al. 2020). Thus, we conducted four-way coupled two-dimensional (2-D) Euler‒Lagrange simulations of the explosive dispersal of the particle ring wherein the denotation of the cylindrical burster was simulated by the sudden release of pressurized gases in the central gas pocket.…”

mentioning

confidence: 99%

Explosive dispersal of granular media

Xue

Miu

et al. 2023

J. Fluid Mech.

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“…Bilinear/trilinear interpolation functions were adopted to calculate the particle volume fraction and source terms on the Eulerian grids, as well as the fluid variables on Lagrangian parcels. Numerical details with regard to CCFD-DPM can be found in our previous works (Meng et al 2019; Tian et al 2020; Xue et al 2020). The present CCFD-DPM framework has been validated against Rogue's experiments involving shock waves propagating through particle curtains (Tian et al 2020), shock tube experiments wherein particle columns are impinged head-on by incident shocks (Tian et al 2020) and shock dispersion of particle rings (Xue et al 2020).…”

Section: Methodsmentioning

confidence: 99%

“…The present work aims to gain insights into both the perturbation growth law and the underlying physics of SDGIs in a more general scenario, wherein the coupling between shock-induced flows and particles is persistent throughout. A series of numerical shock tube experiments involving a range of structural parameters are carried out via the coarse-grained compressible computational fluid dynamics–discrete parcel method (CCFD-DPM), which incorporates four-way interphase coupling and can resolve particle-scale flow details (Sundaresan, Ozel & Kolehmainen 2018; Tian et al 2020). The results reveal a three-stage growth law of the SDGI for granular media with finite length.…”

Section: Introductionmentioning

confidence: 99%

Shock-induced interfacial instabilities of granular media

Xue

Zeng

et al. 2021

J. Fluid Mech.

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This paper investigates the shock-induced instability of the interfaces between gases and dense granular media with finite length via the coarse-grained compressible computational fluid dynamics–discrete parcel method. Despite generating a typical spike-bubble structure reminiscent of the Richtmyer–Meshkov instability (RMI), the shock-driven granular instability (SDGI) is governed by fundamentally different mechanisms. Unlike the RMI arising from baroclinic vorticity deposition on the interface, the SDGI is closely associated with the interfacial and bulk granular dynamics, which evolve with the transient coupling between particles and gases. Consequently, the SDGI follows a growth law distinctly different from that of the RMI, namely a semilinear slow regime followed by an exponentially expedited regime and a quadratic asymptotic regime. We further establish the instability criteria of the SDGI for granular media with infinite and finite lengths, which do not exist in the RMI. A scaling growth law of the SDGI for dense granular media with finite length is derived by normalizing the time with the rarefaction propagation time, which successfully collapses the data from cases with varying shock strength, particle column length and particle volume fraction and ought to hold for granular media with varying particle parameters. The effect of the initial perturbation magnitude can be properly considered in the scaling growth law by incorporating it into the length normalization.

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“…Multiphase particle in a cell (MP-PIC) [18], also referred to as Computational Particle Fluid Dynamics (CPFD) [19], is a novel computational technique developed for accelerating the simulation of particle-laden gas flows. MP-PIC lumps particles into computational parcels [20], and the interaction between the solid and the gas phases is calculated as the gradient of the stress tensor between the phases [21]. In the MP-PIC method, a Liouville equation is used to solve for the distribution function for the positions, velocities and momenta of the computational parcels.…”

Section: Introductionmentioning

confidence: 99%

Multiphase Particle in Cell Simulations of Fluidized Beds: Studies on Bubble Rise Velocity and Minimum Fluidization Velocity

Pal

Theuerkauf

2021

Chemie Ingenieur Technik

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Ing. Matthias Kraume on the occasion of his 65th birthday Multiphase particle in a cell simulation of fluidized beds for different Geldart group particles (A, B and D) were investigated. The minimum fluidization velocities predicted by the Barracuda Virtual Reactor were compared and contrasted by theoretical predictions with the Wen-Yu model. The optimal drag force coefficients based on the Wen-Yu model for MP-PIC simulations was further evaluated for different particle sizes and densities for Group A catalysts. A neural network model was constructed for the optimal model constants as a function of the explanatory variables for group A catalysts. This work illustrates the importance of choosing the correct value for the drag force coefficients to obtain realistic simulation results for fluidized beds. This work clearly demonstrates that the Barracuda simulation results are very sensitive to the value of the Wen-Yu parameter. Using multiphase particle in cell simulations in conjunction with a neural network model can be directly used in further studies as a rapid tool to get the correct values for realistic drag force coefficients based on the Wen-Yu model.

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Compressible multiphase particle-in-cell method (CMP-PIC) for full pattern flows of gas-particle system

Cited by 23 publications

References 57 publications

Explosive dispersal of granular media

Explosive dispersal of granular media

Shock-induced interfacial instabilities of granular media

Multiphase Particle in Cell Simulations of Fluidized Beds: Studies on Bubble Rise Velocity and Minimum Fluidization Velocity

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