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Fast lagrangian particle tracking in unstructured ocean model grids
Ocean Dynamics ( IF 2.2 ) Pub Date : 2021-02-22 , DOI: 10.1007/s10236-020-01436-7
Ross Vennell , Max Scheel , Simon Weppe , Ben Knight , Malcolm Smeaton

Lagrangian particle tracking, based on currents derived from hydrodynamic models, is an important tool in quantifying bio-physical transports in the ocean. Particle tracking in the unstructured grids typically used in coastal regions is computationally slow, limiting the number of particles and ranges of behaviours that can be modeled. Techniques used in a new offline particle tracker “OceanTracker” are shown to be two orders of magnitude faster than those used in an existing ocean particle tracker for unstructured grids when run on a single computer core. More significantly, its computational speed can exceed that achieved when particle tracking on a regular grid. The techniques for unstructured grids make it possible to routinely calculate the trajectories of millions of particles. This large number of particles allows much better estimates of dispersion and transport statistics, particularly when the probability of connection is low but the consequences are significant, e.g. the spread of invasive species. It also enables wider exploration of parameter sensitivity and particles’ bio-physical behaviours to provide more robust results. The speed increases result largely from exploiting history and reuse within the spatial interpolation of the hydrodynamic model’s output. Using multiple computer cores further increased the speed to track a given number of particles.



中文翻译:

非结构化海洋模型网格中的快速拉格朗日粒子跟踪

基于流体动力学模型得出的电流的拉格朗日粒子跟踪是量化海洋中生物物理传输的重要工具。通常在沿海地区使用的非结构化网格中的粒子跟踪计算速度较慢,从而限制了可以建模的粒子数量和行为范围。当在单个计算机内核上运行时,新的脱机粒子跟踪器“ OceanTracker”中使用的技术比现有的非结构化网格海洋粒子跟踪器中使用的技术快两个数量级。更重要的是,其计算速度可以超过在常规网格上进行粒子跟踪时所达到的速度。非结构化网格的技术使常规计算数百万个粒子的轨迹成为可能。如此大量的粒子可以更好地估计扩散和传输统计数据,特别是在连接可能性低但后果显着(例如入侵物种扩散)的情况下。它还可以更广泛地探索参数敏感性和粒子的生物物理行为,以提供更可靠的结果。速度提高的主要原因是在流体动力学模型输出的空间插值中利用历史和重复使用。使用多个计算机内核进一步提高了跟踪给定数量粒子的速度。它还可以更广泛地探索参数敏感性和粒子的生物物理行为,以提供更可靠的结果。速度提高的主要原因是在流体动力学模型输出的空间插值中利用历史和重复使用。使用多个计算机内核进一步提高了跟踪给定数量粒子的速度。它还可以更广泛地探索参数敏感性和粒子的生物物理行为,以提供更可靠的结果。速度提高的主要原因是在流体动力学模型输出的空间插值中利用历史和重复使用。使用多个计算机内核进一步提高了跟踪给定数量粒子的速度。

更新日期:2021-02-22
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