Machine Learning to Classify Vortex Wakes of Energy Harvesting Oscillating Foils
Machine Learning to Classify Vortex Wakes of Energy Harvesting Oscillating Foils
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DOI：
10.2514/1.j062091
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发表时间：
2022-05
期刊：
影响因子：
2.5
通讯作者：
B. L. R. Ribeiro;Jennifer A. Franck
中科院分区：
文献类型：
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作者：
B. L. R. Ribeiro;Jennifer A. Franck
开发了一个机器学习模型来建立振动翼后的尾迹模式，用于能量收集。尾迹结构的作用对于摆动翼片的阵列展开特别重要，因为非定常尾迹对下游翼片的性能有很大影响。本文研究了46个摆动翼面的运动学，目的是根据输入的运动学变量对尾迹进行参数化，并通过涡量场的图像分析对涡迹进行分组。将卷积神经网络与长短期记忆单元相结合，将尾迹分为三类。为了充分验证翼片运动学之间的物理尾迹差异，使用卷积自动编码器结合[Formula：See Text]-Means++聚类通过非监督方法揭示了四种尾迹模式。未来的工作可以使用这些模式来预测放置在尾流中的翼片的性能，并建立用于潮汐能量收集的最佳翼片布置。
A machine learning model is developed to establish wake patterns behind oscillating foils for energy harvesting. The role of the wake structure is particularly important for array deployments of oscillating foils since the unsteady wake highly influences the performance of downstream foils. This work explores 46 oscillating foil kinematics, with the goal of parameterizing the wake based on the input kinematic variables and grouping vortex wakes through image analysis of vorticity fields. A combination of a convolutional neural network with long short-term memory units is developed to classify the wakes into three classes. To fully verify the physical wake differences among foil kinematics, a convolutional autoencoder combined with [Formula: see text]-means++ clustering is used to reveal four wake patterns via an unsupervised method. Future work can use these patterns to predict the performance of foils placed in the wake and build optimal foil arrangements for tidal energy harvesting.