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Explicit finite difference schemes for particulate flows.

机译:颗粒流量的显式有限差分方案。

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摘要

Many implicit schemes have been proposed for direct numerical simulation of particles in fluids, but they can be quite complicated and require a lot of memory when the concentration of particles is high. Explicit finite difference schemes on a regular grid are one way around these issues that has not yet been proposed. The fluid velocity and density can be marched in time, and the particles can be moved according to Newton's laws. Compressible flows can be exactly simulated, and the scheme is easily parallelized. Enforcing the no-slip condition on the spherical particle's surfaces as they move on a Cartesian grid is not so easy, however, so we have proposed a spectral expansion method that exactly satisfies no-slip to deal with the problem, improving on prior methods that required matrix inversions and iteration. This method allows a grid as coarse as ten grid spacings per particle diameter to give smooth forces and accurately resolved pressure distributions on the particle surface. The method has been validated by comparison to a finite element particle solver, and by direct comparison to experiment. Up to 1035 particles have been simulated on a PC with four cores, and the effective viscosity of a sheared particulate suspension has been calculated up to a particle concentration of 25%.
机译:已经提出了许多隐式方案来对流体中的粒子进行直接数值模拟,但是当粒子的浓度很高时,它们可能会非常复杂并且需要大量的存储空间。规则网格上的显式有限差分方案是解决尚未提出的这些问题的一种方法。流体速度和密度可以及时进行,粒子可以根据牛顿定律移动。可以精确地模拟可压缩流,并且该方案很容易并行化。当球形粒子在笛卡尔网格上移动时,要在其表面上施加防滑条件并不是一件容易的事,因此,我们提出了一种能精确满足防滑要求的光谱扩展方法,以解决现有问题。所需的矩阵求逆和迭代。该方法允许每个颗粒直径的网格粗到十个网格间距,以提供平稳的力并在颗粒表面上精确解析压力分布。通过与有限元粒子求解器进行比较,并通过直接与实验进行比较,验证了该方法的有效性。在具有四个核心的PC上模拟了多达1035个颗粒,并且计算出剪切的颗粒悬浮液的有效粘度,直至颗粒浓度达到25%。

著录项

  • 作者

    Perrin, Andrew.;

  • 作者单位

    University of Pennsylvania.;

  • 授予单位 University of Pennsylvania.;
  • 学科 Engineering Mechanical.;Physics Fluid and Plasma.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 164 p.
  • 总页数 164
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;等离子体物理学;
  • 关键词

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