求解稳态和瞬态扩散复合问题的球上随机游走算法

IF 0.8 Q3 STATISTICS & PROBABILITY Monte Carlo Methods and Applications Pub Date : 2021-11-04 DOI:10.1515/mcma-2021-2099
I. Shalimova, K. Sabelfeld
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引用次数: 1

摘要

摘要在本文中,我们进一步发展了我们在最近的文章[K.]中提出的求解耦合扩散-重组方程系统的随机漫步球(RWS)随机算法。《扩散-反应方程耦合系统的蒙特卡罗算法》,中国科学院学报。数学。科学通报,2019,(1):1 - 4。球体上的随机游走过程模拟了两种粒子的各向同性扩散,这两种粒子可以相互重组。我们的动机来自于自由和束缚激子复合的输运问题。该算法基于精确跟踪扩散粒子的轨迹,该轨迹完全符合由球和球的相关格林函数的显式表示导出的概率分布。因此,该方法在空间和时间上都是无网格的。本文实现了求解稳态和瞬态扩散复合问题的RWS算法。将模拟结果与精确解进行了比较。我们还展示了如何应用RWS算法来计算提供电子束感应电流的边界激子通量,幸存激子的浓度和阴极发光强度,这些都是扩散-重组问题解的积分特征。
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Random walk on spheres algorithm for solving steady-state and transient diffusion-recombination problems
Abstract We further develop in this study the Random Walk on Spheres (RWS) stochastic algorithm for solving systems of coupled diffusion-recombination equations first suggested in our recent article [K. Sabelfeld, First passage Monte Carlo algorithms for solving coupled systems of diffusion–reaction equations, Appl. Math. Lett. 88 2019, 141–148]. The random walk on spheres process mimics the isotropic diffusion of two types of particles which may recombine to each other. Our motivation comes from the transport problems of free and bound exciton recombination. The algorithm is based on tracking the trajectories of the diffusing particles exactly in accordance with the probabilistic distributions derived from the explicit representation of the relevant Green functions for balls and spheres. Therefore, the method is mesh free both in space and time. In this paper we implement the RWS algorithm for solving the diffusion-recombination problems both in a steady-state and transient settings. Simulations are compared against the exact solutions. We show also how the RWS algorithm can be applied to calculate exciton flux to the boundary which provides the electron beam-induced current, the concentration of the survived excitons, and the cathodoluminescence intensity which are all integral characteristics of the solution to diffusion-recombination problem.
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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
期刊最新文献
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