Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA 90089, United States of America.
Bioinspir Biomim. 2018 Feb 16;13(2):025003. doi: 10.1088/1748-3190/aaa787.
Unsteady flows contain information about the objects creating them. Aquatic organisms offer intriguing paradigms for extracting flow information using local sensory measurements. In contrast, classical methods for flow analysis require global knowledge of the flow field. Here, we train neural networks to classify flow patterns using local vorticity measurements. Specifically, we consider vortex wakes behind an oscillating airfoil and we evaluate the accuracy of the network in distinguishing between three wake types, 2S, 2P + 2S and 2P + 4S. The network uncovers the salient features of each wake type.
非定常流包含了产生它们的物体的信息。水生生物为利用局部感应测量提取流信息提供了有趣的范例。相比之下，经典的流分析方法需要对流场有全局的了解。在这里，我们使用局部涡度测量来训练神经网络对流型进行分类。具体来说，我们考虑了振荡翼型后的涡尾流，并评估了网络在区分三种尾流类型 2S、2P+2S 和 2P+4S 方面的准确性。该网络揭示了每种尾流类型的显著特征。
