论文标题
粘性流体中具有椭圆形颗粒的DNS的碰撞模型
A collision model for DNS with ellipsoidal particles in viscous fluid
论文作者
论文摘要
本文提出了一种算法，以建模粘性流体中任意椭圆形之间的碰撞。它由几个步骤组成，每个步骤都根据当前文献中采用的标准程序进行改进。首先，提出了有效的接触检测算法。然后，Tschisgale等人的半平滑浸入边界方法。 （2018）通过使用硬球方法的碰撞力增强了（使得模型在整个碰撞过程中都占流体力。此外，提出了一种新的润滑模型，该模型在未通过空间网格解析的区域中施加了恒定的润滑力。新的碰撞模型对粒子壁碰撞的基准测试案例进行了验证。此外，研究了椭圆形粒子与壁的碰撞。在椭圆形颗粒的情况下，正常的恢复系数并不仅仅取决于球形颗粒的stokes数，而是其形状的函数和碰撞之前的方向。使用新模型，研究了这些参数对篮板轨迹的影响。发现随着颗粒的平坦度的增加，最大正常恢复系数显着降低。同样，恢复的系数取决于粒子方向，趋势随粒子平整度增加。 >>>>>>>>>>>>>>>>>>>>>>>>>，论文中描述的距离算法是折叠的主题： https://www.sciendirect.com/science/article/pii/s030193222000313/pdffft?md5=1c1b8e790efdc6785c472972972999999906fe6fe6fe6fe6.pid6fe6 <<<<<<<<<<<<<<<<<<
The article proposes an algorithm to model the collision between arbitrary ellipsoids in viscous fluid. It is composed of several steps, each improving upon the standard procedure employed in the current literature. First, an efficient contact detection algorithm is presented. Then, the semi-implicit Immersed Boundary Method of Tschisgale et al. (2018) is enhanced by the collision forces using the hard-sphere approach, so that the resulting model accounts for fluid forces throughout the entire collision process. Additionally, a new lubrication model is proposed that applies a constant lubrication force in the region where hydrodynamic forces are not resolved by the spatial grid. The new collision model is validated against benchmark test cases of particle wall collisions with excellent agreement. Furthermore, the collision of an ellipsoidal particle with a wall is investigated. The normal restitution coefficient in the case of ellipsoidal particles does not solely depend upon the Stokes number as in the case of spherical particles but is also a function of its shape, and the orientation before the collision. Using the new model, the effect of these parameters on the rebound trajectory is studied. It is found that the maximum normal restitution coefficient decreases significantly as the flatness of the particle increases. Also, the coefficient of restitution depends on the particle orientation, a tendency increasing with particle flatness. >>>>>>>>>>>>>>> Note, that the distance algorithm described in the paper is subject of a Corrigendum: https://www.sciencedirect.com/science/article/pii/S0301932222000313/pdfft?md5=1c1b8e790efdc6785c47297d89906fe6&pid=1-s2.0-S0301932222000313-main.pdf <<<<<<<<<<<<<<<
