Particle-based kinetic Monte Carlo simulations of neutral particles is one of the major computational bottlenecks in tokamak scrape-off layer simulations. This computational cost comes from the need to resolve individual collision events in high-collisional regimes. However, in such regimes, one can approximate the high-collisional kinetic dynamics with computationally cheaper diffusion. Asymptotic-preserving schemes make use of this limit to perform simulations in these regimes, without a blow-up in computational cost as incurred by standard kinetic approaches. One such scheme is Kinetic-diffusion Monte Carlo. In this paper, we present a first extension of this scheme to the two-dimensional setting and its implementation in the Eiron particle code. We then demonstrate that this implementation produces a significant speedup over kinetic simulations in high-collisional cases.

翻译：粒子基动力学蒙特卡罗模拟是托卡马克刮削层模拟的主要计算瓶颈之一。这一计算代价源于在高碰撞率区域中需要解析单个碰撞事件。然而在此类区域中，可以用计算成本更低的扩散过程近似高碰撞动力学。渐近保持格式利用这一极限在这些区域进行模拟，避免了标准动力学方法带来的计算成本激增。动力学-扩散蒙特卡罗正是其中一种格式。本文首次将该格式扩展到二维场景，并在Eiron粒子代码中实现。我们进一步证明，在强碰撞案例中，该实现相比动力学模拟能显著提升计算速度。