格子Boltzmann方法
物理
离散元法
机械
阻力
复杂流体
椭球体
经典力学
颗粒流
粒子系统
粒子(生态学)
流体力学
统计物理学
流量(数学)
两相流
计算机科学
海洋学
天文
地质学
操作系统
作者
Yifeng Zhao,Pei Zhang,Liang Lei,Lingwei Kong,S. A. Galindo‐Torres,Stan Z. Li
出处
期刊:Physics of Fluids
[American Institute of Physics]
日期:2023-01-18
卷期号:35 (2)
被引量:8
摘要
Fluid–particle systems are highly sensitive to particle morphologies. While many attempts have been made on shape descriptors and coupling schemes, how to simulate particle–particle and particle–fluid interactions with a balance between accuracy and efficiency is still a challenge, especially when complex-shaped particles are considered. This study presents a Metaball-Imaging (MI) based Discrete Element Lattice Boltzmann Method (DELBM) for fluid simulations with irregular shaped particles. The major innovation is the MI algorithm to capture the real grain shape for DELBM simulations, where the Metaball function is utilized as the mathematical representation due to its versatile and efficient expressiveness of complex shapes. The contact detection is tackled robustly by gradient calculation of the closest point with a Newton–Raphson based scheme. The coupling with LBM is accomplished by a classic sharp-interface scheme. As for refiling, a local refiling algorithm based on the bounce back rule is implemented. Validations on the Jeffery orbit of ellipsoidal particles and three settling experiments of irregular-shaped natural cobblestones indicate the proposed model to be effective and powerful in probing micromechanics of irregular-shaped granular media immersed in fluid systems. The potential of this model on studies of shape-induced physical processes is further investigated with numerical examples that consider the drag and lift forces experienced by realistic particles, as well as the “drafting, kissing and tumbling” process of pairs of non-spherical particles.
科研通智能强力驱动
Strongly Powered by AbleSci AI