拉格朗日随机方法计算三维非结构化网格中粒子轨迹的时间步长鲁棒算法

IF 0.8 Q3 STATISTICS & PROBABILITY
Guilhem Balvet, J. Minier, C. Henry, Y. Roustan, M. Ferrand
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引用次数: 2

摘要

摘要:本文的目的是在粒子/网格拉格朗日随机方法中提出一种时间步长鲁棒的三维非结构化网格中粒子轨迹的胞间积分方法。其主要思想是动态更新时间积分中使用的平均场,方法是将每个粒子的时间步分成子步,这样每个子步对应于粒子单元的停留时间。这减少了空间离散误差。考虑到模型的随机性质,一个关键方面是推导出不预测维纳过程未来的停留时间的估计。为了达到这个效果,新的算法依赖于一个虚拟粒子,附着在每个随机粒子上,它的平均条件行为提供了无统计偏差的停留时间预测。通过一致性检验,在统计均匀流中的粒子弥散和非均匀流中的粒子动力学两个典型测试案例上对该算法进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A time-step-robust algorithm to compute particle trajectories in 3-D unstructured meshes for Lagrangian stochastic methods
Abstract The purpose of this paper is to propose a time-step-robust cell-to-cell integration of particle trajectories in 3-D unstructured meshes in particle/mesh Lagrangian stochastic methods. The main idea is to dynamically update the mean fields used in the time integration by splitting, for each particle, the time step into sub-steps such that each of these sub-steps corresponds to particle cell residence times. This reduces the spatial discretization error. Given the stochastic nature of the models, a key aspect is to derive estimations of the residence times that do not anticipate the future of the Wiener process. To that effect, the new algorithm relies on a virtual particle, attached to each stochastic one, whose mean conditional behavior provides free-of-statistical-bias predictions of residence times. After consistency checks, this new algorithm is validated on two representative test cases: particle dispersion in a statistically uniform flow and particle dynamics in a non-uniform flow.
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来源期刊
Monte Carlo Methods and Applications
Monte Carlo Methods and Applications STATISTICS & PROBABILITY-
CiteScore
1.20
自引率
22.20%
发文量
31
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