上海理工大学 a. 能源与动力工程学院， b. 上海市动力工程多相流动与传热重点实验室，上海 200093.
凡凤仙，许玥. 颗粒声凝并微观动力学模型及数值模拟［J］. 中国粉体技术，2026，32（1）：1-11.
FAN Fengxian， XU Yue. Microkinetic model and numerical simulations of particulate acoustic agglomeration［J］. China Powder Science and Technology，2026，32（1）：1−11.
DOI：10.13732/j.issn.1008-5548.2026.01.004
收稿日期：2025-03-18，修回日期：2025-08-27，上线日期：2025-10-30。
基金项目：国家自然科学基金项目，编号：52476157。
第一作者：凡凤仙（1982—），女，博士，教授，博士生导师，研究方向为多相流动与传热。E-mail： fanfengxian@usst. edu. cn。
摘要：【目的】为了弥补现有声凝并微观动力学模型的不足，构建在多机制耦合作用下的改进的颗粒声凝并动力学离散元模型，提高颗粒微观动力学行为预测的准确性。【方法】采用离散元方法研究驻波声场中颗粒间相互作用的过程；对在声尾流与互散射效应耦合作用下的气相速度进行修正，建立改进的颗粒声凝并微观动力学离散元模型；将基于传统模型和改进模型得到的模拟结果与实验结果进行对比，验证所提出的改进模型的准确性，进而探究颗粒初始位置对2个颗粒声凝并时间的影响。【结果】改进模型不仅能够准确预测颗粒碰撞时间，还能完整再现实验中观测到颗粒相互靠近直至声凝并形成稳定颗粒团并继续运动的全过程；对于初始位置在2个相邻波节点之间的颗粒，粒径较大的颗粒发生声凝并的初始位置区间更大，声凝并时间更短；颗粒的声凝并时间随初始位置的变化呈对称性，颗粒初始位置越靠近波腹点，声凝并时间越短。【结论】与传统模型相比，改进的颗粒声凝并微观动力学离散元模型具有更高的精度。
关键词：颗粒动力学；声凝并；离散元方法；气固两相流
Objective Existing studies on acoustic particle agglomeration have predominantly focused on several research areas. Kinetic models for single particles were developed, and the particle entrainment velocity by acoustic waves was measured. The acoustic wake theory has been systematically elaborated, and particle interaction simulations under the acoustic wake effect were conducted. Studies have also simulated the microkinetics of particle agglomeration under the coupled influence of acoustic wake and mutual scattering effects. Significant attention has been given to the discrete element method (DEM) for acoustic agglomeration characterization that incorporates particle contact processes and related numerical simulations. Although these studies have provided insights into the microkinetic behaviors of acoustic particle agglomeration, the particulate acoustic agglomeration process cannot be fully characterized. This limitation primarily arises from the accuracy constraints of the current modelling approaches. To address the deficiency in existing microkinetic models for acoustic agglomeration regarding the coupled effects of acoustic wake and mutual scattering, an improved microkinetic model for particulate acoustic agglomeration under multiple coupling mechanisms was constructed, greatly enhancing the accuracy in predicting the microkinetic behaviors of particles.
Methods The DEM was used to investigate the interaction between two particles of identical size in a standing wave acoustic field. The gas-phase velocity was modified under the coupled effects of acoustic wake and mutual scattering to ensure that the flow field at the particle surface satisfied the no-slip velocity boundary condition. Previous models superimposed the acoustic wave fluctuation velocity with the perturbation velocity induced by acoustic wake and mutual scattering to reproduce the multi-mechanism coupling effects. A comparative analysis was conducted between the experimental and the numerical simulation results obtained from both previous models and the improved particle acoustic agglomeration model under multiple coupling mechanisms. This validation confirmed the accuracy of the proposed model. Based on these findings, the influence of the particles’initial positions on their acoustic agglomeration kinetic behaviors was further explored.
Results and Discussion The improved microkinetic model for particulate acoustic agglomeration under multiple coupling mechanisms enabled the prediction of particle collision time. It also fully reproduced the entire process of particle interactions observed in experiments within the acoustic field, involving particle approach, collision, acoustic agglomeration, and the subsequent movement of the formed particle aggregates. In contrast, previous models exhibited unreliable predictions regarding the post-collision kinetic behaviors of particles. For particles initially located between two adjacent nodes in a standing-wave acoustic field, the acoustic agglomeration time of particles varied symmetrically with their initial positions due to the symmetry of the acoustic wave fluctuation equation. The closer the particles’initial positions were to the antinodes, the stronger the attractive effect of the acoustic wake, and consequently, the shorter the acoustic agglomeration time. Particles with larger diameters had a broader range of initial positions from which acoustic agglomeration could occur and required shorter acoustic agglomeration time, indicating that larger particles were more prone to acoustic agglomeration. When the particles’initial positions were close to the nodes, the weak acoustic wake effect could not overcome the repulsive force caused by mutual scattering, thereby preventing particle collision and acoustic agglomeration.
Conclusions In comparison with previous models, the improved DEM-based microkinetic model for particulate acoustic agglomeration under multiple coupling mechanisms demonstrates superior accuracy in predicting kinetic behavior of particle acoustic agglomeration. This enhanced model plays a significant role in elucidating the microkinetic behaviors and underlying mechanisms of acoustic agglomeration processes.
Keywords：particle kinetics; acoustic agglomeration; discrete element method; gas-solid two-phase flow
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