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A hierarchy of particle-size segregation models: From polydisperse mixtures to depth-averaged theories

机译:粒度分离模型的层次结构:从多分散混合到深度平均理论

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摘要

Particle size segregation in granular avalanches occurs due to inter-particle percolation and squeeze expulsion. The general theory for a polydisperse mixture yields a segregation equation for each grain size class. For a three constituent mixture of large, small and medium sized particles there are three segregation equations, one of which can be eliminated, since the concentrations of all the species necessarily sums to unity. The remaining two coupled parabolic equations can be solved using a standard Galerkin finite element method. Numerical solutions show that small particles percolate to the base of the avalanche, large particles are squeezed to the surface and the medium sized grains are sandwiched in between. For certain choices of the segregation parameters it is possible to generate instabilities that lead to saw-tooth segregation in the three-phase mixture, but these die off as the grains separate into bi-disperse sub-mixtures. In general, all the grains contribute to the segregation process and develop an inversely graded particle size distribution, that coarsens upwards. This is known as reverse distribution grading. Sometimes, however, the fine particles may not segregate readily, leading to reverse coarse tail grading. For a bi-disperse mixture, the general theory yields one independent segregation equation, which always seeks to drive the particles into an inversely graded state. However, when the bulk flow shears small particles over the top of large, a breaking size-segregation wave is created. Such waves are important close to flow fronts, because they allow large particles that are over-run to rise up to the surface again and be recirculated. Computing the evolving particle-size distribution in a three-dimensional flow is still a challenge. However, it is possible to obtain a simplified representation by integrating the segregation equation through the avalanche depth. This fits naturally into the depth-averaged framework of avalanche models and opens the door to fully couple calculations to study levee formation and segregation induced flow fingering.
机译:颗粒雪崩中的颗粒尺寸分离是由于颗粒间的渗滤和挤压排出而发生的。对于多分散混合物的一般理论,得出了每个晶粒尺寸类别的偏析方程。对于大,小和中型颗粒的三成分混合物,存在三个分离方程,其中一个可以消除,因为所有物质的浓度必须加一。剩下的两个耦合的抛物线方程可以使用标准的Galerkin有限元方法求解。数值解表明,小颗粒渗入雪崩的底部,大颗粒被挤到雪崩的表面,中等大小的颗粒夹在中间。对于偏析参数的某些选择,可能会产生不稳定性,从而导致三相混合物中的锯齿偏析,但随着晶粒分离成双分散子混合物,这些不稳定性会消失。通常,所有晶粒都有助于偏析过程,并形成反向分级的粒度分布,并向上粗化。这称为反向分布分级。但是,有时细颗粒可能不容易分离,导致反向粗尾定级。对于双分散混合物,一般理论得出一个独立的偏析方程,该方程始终试图将颗粒驱动为逆梯度状态。但是,当大流量在大颗粒顶部剪切小颗粒时,会产生破裂的尺寸分离波。这样的波在靠近流动前沿很重要,因为它们使溢出的大颗粒再次上升到表面并被再循环。计算三维流中不断变化的粒度分布仍然是一个挑战。但是,可以通过将整个雪崩深度的偏析方程积分来获得简化表示。这自然适合于雪崩模型的深度平均框架,并为全面耦合计算以研究堤坝形成和偏析引起的流动指法打开了大门。

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    Gray, J MNT;

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  • 年度 2013
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