A Fast Algorithm for Simulating Multiphase Flows Through Periodic Geometries of Arbitrary Shape

数学 离散化 解算器 周期边界条件 计算 边界(拓扑) 算法 功能(生物学) 数学分析 几何学 边值问题 数学优化 进化生物学 生物
作者
Gary R. Marple,Alex H. Barnett,Adrianna Gillman,Shravan Veerapaneni
出处
期刊:SIAM Journal on Scientific Computing [Society for Industrial and Applied Mathematics]
卷期号:38 (5): B740-B772 被引量:43
标识
DOI:10.1137/15m1043066
摘要

This paper presents a new boundary integral equation (BIE) method for simulating particulate and multiphase flows through periodic channels of arbitrary smooth shape in two dimensions. The authors consider a particular system---multiple vesicles suspended in a periodic channel of arbitrary shape---to describe the numerical method and test its performance. Rather than relying on the periodic Green's function as classical BIE methods do, the method combines the free-space Green's function with a small auxiliary basis and imposes periodicity as an extra linear condition. As a result, we can exploit existing free-space solver libraries, quadratures, and fast algorithms and handle a large number of vesicles in a geometrically complex channel. Spectral accuracy in space is achieved using the periodic trapezoid rule and product quadratures, while a first-order semi-implicit scheme evolves particles by treating the vesicle-channel interactions explicitly. New constraint-correction formulas are introduced that preserve reduced areas of vesicles, independent of the number of time steps taken. By using two types of fast algorithms---(i) the fast multipole method for the computation of the vesicle-vesicle and the vesicle-channel hydrodynamic interaction, and (ii) a fast direct solver for the BIE on the fixed channel geometry---the computational cost is reduced to ${\cal O}(N)$ per time step, where $N$ is the spatial discretization size. Moreover, the direct solver inverts the wall BIE operator at $t = 0$, stores its compressed representation, and applies it at every time step to evolve the vesicle positions, leading to dramatic cost savings compared to classical approaches. Numerical experiments illustrate that a simulation with $N=128,000$ can be evolved in less than a minute per time step on a laptop.
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