论文标题
粒子粒子粒子树代码用于行星系统形成,采用单个截止方法:gplum
Particle-particle Particle-tree Code for Planetary System Formation with Individual Cut-off Method: GPLUM
论文作者
论文摘要
在我们太阳系中行星形成的标准理论中,陆地行星和气体巨人岩心是通过在原行星磁盘中积聚了公里大小的物体(行星)的。由众多行星组成的磁盘系统的引力$ n $ - 体体模拟是研究积聚过程的最直接方法。但是,由于可用数量的模拟运行和粒子,使用$ n $ body模拟限于理想化的模型(例如,完美的积聚)和/或狭窄的空间范围。我们已经开发了配备有粒子粒子粒子树($ {\ rm p^3t} $)的新的$ n $体体模拟代码,用于研究行星系统形成过程:gplum。对于每个粒子,Gplum使用四阶Hermite方案来计算与截止半径内的颗粒和截止半径外的颗粒中的颗粒相互作用。在现有的实现中,$ {\ rm p^3t} $方案对所有粒子都使用相同的截止半径,当行星群体的质量范围更宽时,模拟会变得越来越慢。我们通过允许每个粒子具有适当的截止半径,具体取决于其质量,距中心恒星的距离以及行星标语的局部速度分散,解决了这个问题。除了实现重大的加速外,在$ n = 10^6 $的情况下,我们还提高了代码的可扩展性,达到良好的强级性能。 https://github.com/yotaishigaki/gplum可以免费获得Gplum。
In a standard theory of the formation of the planets in our Solar System, terrestrial planets and cores of gas giants are formed through accretion of kilometer-sized objects (planetesimals) in a protoplanetary disk. Gravitational $N$-body simulations of a disk system made up of numerous planetesimals are the most direct way to study the accretion process. However, the use of $N$-body simulations has been limited to idealized models (e.g. perfect accretion) and/or narrow spatial ranges in the radial direction, due to the limited number of simulation runs and particles available. We have developed new $N$-body simulation code equipped with a particle-particle particle-tree (${\rm P^3T}$) scheme for studying the planetary system formation process: GPLUM. For each particle, GPLUM uses the fourth-order Hermite scheme to calculate gravitational interactions with particles within cut-off radii and the Barnes-Hut tree scheme for particles outside the cut-off radii. In existing implementations, ${\rm P^3T}$ schemes use the same cut-off radius for all particles, making a simulation become slower when the mass range of the planetesimal population becomes wider. We have solved this problem by allowing each particle to have an appropriate cut-off radius depending on its mass, its distance from the central star, and the local velocity dispersion of planetesimals. In addition to achieving a significant speed-up, we have also improved the scalability of the code to reach a good strong-scaling performance up to 1024 cores in the case of $N=10^6$. GPLUM is freely available from https://github.com/YotaIshigaki/GPLUM with MIT license.