发表机构
Max-Planck-Institut für Astronomie（马克斯·普朗克天文学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GameDev提出GPU加速的尘埃演化模型，耦合三维尘埃动力学与蒙特卡洛碰撞演化，利用最近邻碰撞采样和自适应冻结邻域实现高效并行，支持CUDA与ROCm，为原行星盘尘埃研究提供准确工具。
AI 中文摘要
在原行星盘中，尘埃碰撞导致的颗粒生长与碎裂与尘埃动力学强耦合。这是因为尘埃的空间和速度分布决定了碰撞频率和结果，而尘埃颗粒大小则决定了与气体的气动耦合。然而，现有的尘埃演化代码通常依赖于预设的尘埃速度，且大多局限于一维或二维空间。在本工作中，我们提出了GameDev，一种由图形处理单元（GPU）加速的尘埃演化模型，该模型将独立演化的三维尘埃动力学与尘埃的蒙特卡洛碰撞演化耦合起来。在GameDev中，我们将尘埃建模为预设气体盘中的拉格朗日代表性粒子。我们采用交错半解析方法积分粒子轨迹，该方法对紧密耦合和弱耦合的尘埃颗粒均保持准确。我们根据每个粒子的最近邻而非网格来估计碰撞率，从而避免物理上靠近的粒子因跨越网格边界而被排除在碰撞采样之外。最重要的是，我们在自适应时间间隔内保持冻结的邻域中进行碰撞事件采样，从而实现跨粒子的大规模并行化。这些实现使GameDev成为研究原行星盘中尘埃演化的准确且高效的工具。GameDev支持NVIDIA CUDA和AMD ROCm平台，提供较小规模的独立欧拉尘埃流体模型，并在GitHub上公开可用。
英文摘要
Grain growth and fragmentation through dust collisions in protoplanetary disks are strongly coupled with dust dynamics. This is because the spatial and velocity distributions of dust set the collision rates and outcomes, while dust grain sizes set the aerodynamic coupling with gas. However, existing dust evolution codes often rely on prescribed dust velocities and are mostly restricted to one or two spatial dimensions. In this work, we present GameDev, a dust evolution model accelerated by graphics processing units (GPUs) that couples independently evolved three-dimensional dust dynamics with Monte Carlo collisional evolution of dust. In GameDev, we model dust as Lagrangian representative particles in a prescribed gas disk. We integrate particle trajectories with the staggered semi-analytic method, which remains accurate for both tightly and weakly coupled dust grains. We estimate collision rates from each particle's nearest neighbors rather than on a grid, preventing physically close particles from being excluded from collision sampling because they lie across a grid boundary. Most importantly, we sample collision events against neighborhoods that remain frozen over adaptive intervals, enabling massive parallelization across particles. These implementations make GameDev an accurate and efficient tool for studying dust evolution in protoplanetary disks. GameDev supports both NVIDIA CUDA and AMD ROCm platforms, provides a smaller-scale standalone Eulerian dust fluid model, and is publicly available on GitHub.
Comments23 pages, 13 figures, submitted to A&A. Comments and requests are welcome. The code is available at https://github.com/bijiaqing/GameDev
