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arXiv 2409.08447physics.comp-ph
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层状介质中对流-扩散的随机模型

Stochastic models of advection-diffusion in layered media

Elliot J. Carr

AI总结:

针对确定性连续介质模型无法体现层状介质输运随机性的问题,推导了跨界面扩散与对流-扩散的等效随机游走模型,给出两种时间离散方案及步长约束,实验验证了模型一致性并开源了代码。

摘要:

对非均匀层状介质中粒子的扩散与输运过程进行数学建模,在计算物理、生物物理和医学物理的诸多应用中具有重要意义。尽管这类输运过程的确定性连续介质模型已较为成熟,但它们无法解释许多问题中固有的随机性,且仅在粒子数量庞大时成立。为解决这一问题,本文针对跨全透或半透界面的扩散与对流-扩散过程的几种标准连续介质(偏微分方程)模型,推导了一套等效的随机(离散时间离散空间随机游走)模型。我们的研究思路是对连续介质模型进行时空离散化,得到一个马尔可夫链,用于管控每个时间步内空间格点之间的转移概率。空间离散采用标准有限体积法实现,时间离散则考虑了两种方案:简单的前向欧拉离散可得到局部(最近邻)随机游走形式的随机模型,其转移概率具有简洁的解析表达式;而精确指数离散可得到非局部随机游走模型,其转移概率通过矩阵指数以数值方式定义。本文针对每种方案给出了空间步长和/或时间步长的约束条件,以确保转移概率非负。用于对比随机模型与连续介质模型的MATLAB代码已托管在GitHub(https://github.com/elliotcarr/Carr2024c),仿真结果表明两类模型在多个示例问题中具有良好的一致性。

英文摘要:

Mathematically modelling diffusive and advective transport of particles in heterogeneous layered media is important to many applications in computational, biological and medical physics. While deterministic continuum models of such transport processes are well established, they fail to account for randomness inherent in many problems and are valid only for a large number of particles. To address this, this paper derives a suite of equivalent stochastic (discrete-time discrete-space random walk) models for several standard continuum (partial differential equation) models of diffusion and advection-diffusion across a fully- or semi-permeable interface. Our approach involves discretising the continuum model in space and time to yield a Markov chain, which governs the transition probabilities between spatial lattice sites during each time step. Discretisation in space is carried out using a standard finite volume method while two options are considered for discretisation in time. A simple forward Euler discretisation yields a stochastic model taking the form of a local (nearest-neighbour) random walk with simple analytical expressions for the transition probabilities while an exact exponential discretisation yields a non-local random walk with transition probabilities defined numerically via a matrix exponential. Constraints on the size of the spatial and/or temporal steps are provided for each option to ensure the transition probabilities are non-negative. MATLAB code comparing the stochastic and continuum models is available on GitHub (https://github.com/elliotcarr/Carr2024c) with simulation results demonstrating good agreement for several example problems.

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