five

Incompressible Multiphase Flows: Physical Formulation and Numerical Algorithm

收藏
DataCite Commons2025-12-18 更新2025-04-16 收录
下载链接:
https://purr.purdue.edu/publications/1840/1
下载链接
链接失效反馈
官方服务:
资源简介:
<p>We present a family of physical formulations, and a  numerical algorithm, based on a class of general order parameters for simulating the motion of a mixture of N (N >= 2) immiscible incompressible fluids with given densities, dynamic viscosities, and pairwise surface tensions. The N-phase formulations stem from a phase field model we developed in a recent work based on the conservations of mass/momentum, and the second law of thermodynamics. The introduction of general order parameters leads to an extremely strongly-coupled system of (N-1) phase field equations. On the other hand, the general form enables one to compute the N-phase mixing energy density coefficients in an explicit fashion in terms of the pairwise surface tensions. We show that the increased complexity in the form of the phase field equations associated with general order parameters in actuality does not cause essential computational difficulties. Our numerical algorithm reformulates the (N-1) strongly-coupled phase field equations for general order parameters into 2(N-1) Helmholtz-type equations that are completely de-coupled from one another. This leads to a computational complexity comparable to that for the simplified phase field equations associated with certain special choice of the order parameters. We demonstrate the capabilities of the method developed herein using several test problems involving multiple fluid phases and large contrasts in densities and viscosities among the multitude of fluids. In particular, by comparing simulation results with the Langmuir-de Gennes theory of floating liquid lenses we show that the method using general order parameters produces physically accurate results for multiple fluid phases. </p>
提供机构:
Purdue University Research Repository
创建时间:
2015-04-07
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作