数值噪声在粒子细胞跟踪中的传播
Propagation of numerical noise in particle-in-cell tracking
摘要：
粒子池（PIC）是最常用的算法来执行强流带电粒子束的自洽跟踪。它是基于在网格上沉积宏观粒子，并随后求解泊松方程。众所周知，PIC算法具有固有的局限性，因为它们引入了数值噪声。虽然这对于短期跟踪并不重要，但这在数百万圈的圆形机器的模拟中变得重要，因为它可能会引起光束的人工扩散。在这项工作中，我们提出了一个模型的数值噪声诱导PIC算法，并讨论其对粒子动力学的影响。粒子跟踪和PIC算法产生的噪声的组合效应导致相关或去相关的数值噪声。对于去相关的数值噪声，我们推导出模拟参数的比例律，允许人工发射度增长的估计。最后，讨论了相关数值噪声的影响，并提出了一种抑制策略。
英文摘要：
Particle-in-cell (PIC) is the most used algorithm to perform self-consistent tracking of intense charged particle beams. It is based on depositing macro-particles on a grid, and subsequently solving on it the Poisson equation. It is well known that PIC algorithms occupy intrinsic limitations as they introduce numerical noise. Although not significant for short-term tracking, this becomes important in simulations for circular machines over millions of turns as it may induce artificial diffusion of the beam. In this work, we present a modeling of numerical noise induced by PIC algorithms, and discuss its influence on particle dynamics. The combined effect of particle tracking and noise created by PIC algorithms leads to correlated or decorrelated numerical noise. For decorrelated numerical noise we derive a scaling law for the simulation parameters, allowing an estimate of artificial emittance growth. Lastly, the effect of correlated numerical noise is discussed, and a mitigation strategy is proposed.
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