Explicit integrators for nonlocal equations: The case of the Maxey-Riley-Gatignol equation

Pub Date : 2024-03-25 DOI:10.1090/qam/1693
Divya Jaganathan, Rama Govindarajan, V. Vasan
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Abstract

The Maxey-Riley-Gatignol (MRG) equation, which describes the dynamics of an inertial particle in nonuniform and unsteady flow, is an integro-differential equation with a memory term and its solution lacks a well-defined Taylor series at t = 0 t=0 . In particulate flows, one often seeks trajectories of millions of particles simultaneously, and the numerical solution to the MRG equation for each particle becomes prohibitively expensive due to its ever-rising memory costs. In this paper, we present an explicit numerical integrator for the MRG equation that inherits the benefits of standard time-integrators, namely a constant memory storage cost, a linear growth of operational effort with simulation time, and the ability to restart a simulation with the final state as the new initial condition. The integrator is based on a Markovian embedding of the MRG equation. The integrator and the embedding are consequences of a spectral representation of the solution to the linear MRG equation. We exploit these to extend the work of Cox and Matthews [J. Comput. Phys. 176 (2002), 430–455] and derive Runge-Kutta type iterative schemes of differing orders for the MRG equation. Our approach may be generalized to a large class of systems with memory effects.
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非局部方程的显式积分器:Maxey-Riley-Gatignol 方程的情况
Maxey-Riley-Gatignol (MRG)方程描述了惯性粒子在非均匀和非稳定流中的动力学特性,它是一个带有记忆项的积分微分方程,其解在 t = 0 t=0 时缺乏一个定义明确的泰勒级数。在微粒流中,人们经常要同时寻找数百万个粒子的轨迹,而每个粒子的 MRG 方程的数值解由于内存成本不断增加而变得过于昂贵。在本文中,我们提出了一种用于 MRG 方程的显式数值积分器,它继承了标准时间积分器的优点,即内存存储成本不变、操作工作量随模拟时间呈线性增长,以及能够以最终状态作为新的初始条件重新开始模拟。积分器基于 MRG 方程的马尔可夫嵌入。积分器和嵌入是线性 MRG 方程解的频谱表示的结果。我们利用它们扩展了 Cox 和 Matthews [J. Comput. Phys.我们的方法可以推广到一大类具有记忆效应的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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