Kinetic shear-stress of particles in the particle-laden flow simulated using classic and second-order moment of kinetic theory of granular flow

IF 4.3 2区材料科学 Q2 ENGINEERING, CHEMICAL

Particuology Pub Date : 2025-12-01 Epub Date: 2025-10-25 DOI:10.1016/j.partic.2025.10.009

Dan Sun

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Abstract

Kinetic viscosity of particles in the kinetic theory of granular flow (KTGF) was derived from the turbulence viscosity of the gas phase based on the kinetic theory of gas, with the effects of the dense phase of granular materials. KTGF is prominent in predicting the dense particle flow, being the primary numerical method for the gas-particle flow in fluidization, predominantly in the large-scale simulations as a Eulerian method. Recent studies presented that the second-order moment (SOM) of KTGF is superior to the classic KTGF in the particle-flow prediction. The difference between classic KTGF and SOM KTGF exists in the numerical model of the kinetic stresses of particles, which is calculated by the pseudo kinetic viscosity of particles in classic KTGF and by the SOM of the fluctuating velocity of particles by using the partial differential equations in SOM-KTGF. In this study, the gas-particle flow was simulated using SOM-KTGF and the stress tensors of particles predicted by the two methods were compared. It was demonstrated that the normal components of the kinetic stress tensor predicted by the two methods were close in value. However, the kinetic shear-stress was over-predicted by the classic KTGF in the dilute phase of particles in the gas-particle flow of fluidization, when the volume fraction of particles was less than 0.01. Therefore, SOM-KTGF is superior to the classic KTGF, particularly when the particle flow is dominated by the interstitial gas phase, as the particle-laden flow occurs in the lower volume fraction of particles in the dense regime, and further in the dilute and median regimes, when the volume fraction of particles less than 0.01. This superiority is caused by the high-fidelity prediction of the kinetic shear stress in SOM-KTGF rather than the prediction by classic KTGF. In addition, SOM-KTGF extended the application of KTGF from dense flows of particles in fluidization to median-dilute flows of particles in pneumatic conveying, when the volume fraction is less than 0.001.

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利用颗粒流动力学理论的经典矩和二阶矩模拟了颗粒流中颗粒的运动剪切应力

颗粒流动动力学理论（KTGF）中颗粒的运动粘度是在气体动力学理论的基础上，结合颗粒物料致密相的影响，推导出气相的湍流粘度。KTGF在预测致密颗粒流方面表现突出，是流化过程中气粒流的主要数值方法，在大规模模拟中以欧拉方法为主。近年来的研究表明，KTGF的二阶矩（SOM）在粒子流预测方面优于经典的KTGF。经典KTGF与SOM KTGF的区别在于粒子运动应力的数值模型，该模型是通过经典KTGF中粒子的伪运动粘度和SOM-KTGF中粒子波动速度的SOM来计算的。本研究使用SOM-KTGF模拟气-颗粒流动，比较两种方法预测的颗粒应力张量。结果表明，两种方法预测的运动应力张量法向分量值接近。然而，在流化气粒流中，当颗粒体积分数小于0.01时，经典KTGF对颗粒稀相的动力学剪切应力预测过高。因此，SOM-KTGF优于经典的KTGF，特别是当颗粒流以间隙气相为主时，颗粒载流发生在致密区颗粒体积分数较低的区域，当颗粒体积分数小于0.01时，颗粒载流进一步发生在稀区和中区。这一优势是由于SOM-KTGF对运动剪切应力的预测保真度高，优于经典KTGF的预测。此外，SOM-KTGF将KTGF的应用范围从流化颗粒的密集流扩展到体积分数小于0.001时的气力输送颗粒的中稀流。

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来源期刊

Particuology 工程技术-材料科学：综合

CiteScore

6.70

自引率

2.90%

发文量

1730

审稿时长

32 days

期刊介绍： The word ‘particuology’ was coined to parallel the discipline for the science and technology of particles. Particuology is an interdisciplinary journal that publishes frontier research articles and critical reviews on the discovery, formulation and engineering of particulate materials, processes and systems. It especially welcomes contributions utilising advanced theoretical, modelling and measurement methods to enable the discovery and creation of new particulate materials, and the manufacturing of functional particulate-based products, such as sensors. Papers are handled by Thematic Editors who oversee contributions from specific subject fields. These fields are classified into: Particle Synthesis and Modification; Particle Characterization and Measurement; Granular Systems and Bulk Solids Technology; Fluidization and Particle-Fluid Systems; Aerosols; and Applications of Particle Technology. Key topics concerning the creation and processing of particulates include: -Modelling and simulation of particle formation, collective behaviour of particles and systems for particle production over a broad spectrum of length scales -Mining of experimental data for particle synthesis and surface properties to facilitate the creation of new materials and processes -Particle design and preparation including controlled response and sensing functionalities in formation, delivery systems and biological systems, etc. -Experimental and computational methods for visualization and analysis of particulate system. These topics are broadly relevant to the production of materials, pharmaceuticals and food, and to the conversion of energy resources to fuels and protection of the environment.