发表机构
Gothenburg University（哥德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对湍流中导航的局部感知局限，对比局部策略与预先计算轨迹引导策略，提出利用群体中表现优异成员作为目标的集体策略，可保留大部分引导优势。
AI 中文摘要
在湍流中导航颇具挑战性，因为局部流动测量所能提供的、超出流动相关尺度的有利路径信息十分有限。我们研究了均匀各向同性湍流中游泳者的垂直导航问题，对比了利用局部流动信息的策略与由高性能轨迹引导的策略。与单纯向上游泳相比，局部策略使平均垂直速度提升了约均方根流速的10%，而由预先计算的轨迹引导的策略则实现了约40%的提升。这种提升受拦截有利轨迹所需时间的限制，对慢速游泳者而言尤为明显。随后我们证明，预先计算的轨迹并非必需：游泳者可动态识别群体中表现优异的成员并将其作为目标。对于足够快速的游泳者，这种集体策略仅利用群体的瞬时状态就能保留预先计算引导的大部分优势。我们的结果表明，分布式轨迹信息可用于克服湍流导航中局部感知的局限性。
英文摘要
Navigation in turbulence is challenging because local flow measurements provide limited information about favorable paths beyond the flow correlation scales. We investigate vertical navigation by swimmers in homogeneous isotropic turbulence and compare strategies using local flow information with strategies guided by high-performing trajectories. Compared with naive upward swimming, local strategies enhance the mean vertical velocity by about 10% of the root-mean-square flow velocity, while guidance by precalculated trajectories reaches enhancements of about 40%. This gain is limited by the time required to intercept favorable trajectories, particularly for slow swimmers. We then show that precalculated trajectories are unnecessary: swimmers can dynamically identify high-performing members of a swarm and use them as targets. For sufficiently fast swimmers, this collective strategy keeps much of the benefit of precalculated guidance using only the instantaneous swarm state. Our results demonstrate how distributed trajectory information can be used to overcome limitations of local sensing for navigation in turbulence.
Comments7 pages, 5 figures
