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A Hybrid Transport-Diffusion Algorithm for Monte Carlo Radiation-Transport Simulations on Adaptive-Refinement Meshes in XY Geometry

机译:XY几何中自适应精化网格的蒙特卡罗辐射传输模拟的混合传输扩散算法。

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Discrete Diffusion Monte Carlo (DDMC) is a technique for increasing the efficiency of Monte Carlo simulations in diffusive media. If standard Monte Carlo is employed in such a regime, particle histories will consist of many small steps, a situation that results in a computationally inefficient calculation. In DDMC, particles take discrete steps between spatial cells according to a discretized diffusion equation. Each discrete step replaces many smaller Monte Carlo steps, thus increasing the efficiency of the simulation. In addition, because DDMC is based on the diffusion approximation, it should yield accurate solutions if used judiciously. In this paper, we present a new DDMC method for linear, steady-state radiation transport on adaptive-refinement meshes in two-dimensional Cartesian geometry. Adaptive-refinement meshes are characterized by local refinement such that a spatial cell may have multiple neighboring cells across each face. We specifically examine the cases of (a) a regular mesh structure without refinement, (b) a refined mesh structure where neighboring cells differ in refinement, and (c) a boundary mesh structure representing the interface between a diffusive region (where DDMC is used) and a nondiffusive region (where standard Monte Carlo is employed). With numerical examples, we demonstrate that our new DDMC technique is accurate and can provide efficiency gains of two orders of magnitude over standard Monte Carlo.
机译:离散扩散Monte Carlo(DDMC)是一种用于在扩散介质中提高Monte Carlo模拟效率的技术。如果在这种情况下使用标准的蒙特卡洛,粒子历史将包含许多小步骤,这种情况会导致计算效率低下。在DDMC中,粒子根据离散扩散方程在空间像元之间采取离散步骤。每个离散步骤替换许多较小的蒙特卡洛步骤,从而提高了仿真效率。另外,由于DDMC基于扩散近似,因此,如果谨慎使用,它应该会产生准确的解。在本文中,我们为二维笛卡尔几何中的自适应细化网格上的线性稳态辐射传输提供了一种新的DDMC方法。自适应细化网格的特征在于局部细化,使得空间像元可以在每个面上具有多个相邻像元。我们专门研究以下情况:(a)没有细化的规则网格结构;(b)相邻单元在细化上不同的细化网格结构;(c)代表扩散区域(其中使用DDMC)之间的界面的边界网格结构)和非扩散区(采用标准的蒙特卡洛方法)。通过数值示例,我们证明了我们的新DDMC技术是准确的,并且可以提供比标准Monte Carlo效率高两个数量级的效率。

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