Department of Mathematics, University of Wisconsin, Madison, Wisconsin 53706, USA.
Chaos. 2010 Mar;20(1):017516. doi: 10.1063/1.3262494.
In many applications, the two-dimensional trajectories of fluid particles are available, but little is known about the underlying flow. Oceanic floats are a clear example. To extract quantitative information from such data, one can measure single-particle dispersion coefficients, but this only uses one trajectory at a time, so much of the information on relative motion is lost. In some circumstances the trajectories happen to remain close long enough to measure finite-time Lyapunov exponents, but this is rare. We propose to use tools from braid theory and the topology of surface mappings to approximate the topological entropy of the underlying flow. The procedure uses all the trajectory data and is inherently global. The topological entropy is a measure of the entanglement of the trajectories, and converges to zero if they are not entangled in a complex manner (for instance, if the trajectories are all in a large vortex). We illustrate the techniques on some simple dynamical systems and on float data from the Labrador Sea. The method could eventually be used to identify Lagrangian coherent structures present in the flow.
在许多应用中,可获得流体质点的二维轨迹,但对潜在流场却知之甚少。海洋浮标就是一个明显的例子。若要从这类数据中提取定量信息,可以测量单个粒子的扩散系数,但这种方法一次仅使用一条轨迹,因此会丢失大量关于相对运动的信息。在某些情况下,轨迹碰巧保持足够长的近距离以测量有限时间李雅普诺夫指数,但这种情况很少见。我们建议使用辫理论和曲面映射拓扑学的工具来逼近潜在流场的拓扑熵。该过程使用所有轨迹数据,且本质上是全局的。拓扑熵是轨迹缠结程度的一种度量,如果轨迹没有以复杂的方式缠结(例如,如果轨迹都在一个大漩涡中),则拓扑熵会趋近于零。我们用一些简单的动力系统和拉布拉多海的浮标数据来说明这些技术。该方法最终可用于识别流场中存在的拉格朗日相干结构。