论文标题
一种机器学习方法，以分类能量收获振荡箔的涡流唤醒
A Machine Learning Approach to Classify Vortex Wakes of Energy Harvesting Oscillating Foils
论文作者
论文摘要
开发机器学习模型是为了在振荡箔背后建立尾流模式，其运动学在能量收集方案范围内。尾流结构的作用对于振荡箔的阵列部署尤其重要，因为不稳定的尾流很大程度上影响下游箔的性能。这项工作探讨了46个振荡的箔运动学，其目的是根据输入运动学变量参数唤醒，并通过涡流场的图像分析将涡流唤醒分组。开发了卷积神经网络（CNN）与长期记忆（LSTM）单元的组合，以将唤醒分为三组。为了充分验证箔运动学之间的物理唤醒差异，使用了卷积自动编码器与K-均值++聚类相结合的卷积自动编码器，发现了四种不同的唤醒模式。通过分类模型，这些模式与一系列箔运动学相关联。未来的工作可以利用这些相关性来预测尾流层的箔的性能，并为潮汐能量收获建立最佳的箔布置。
A machine learning model is developed to establish wake patterns behind oscillating foils whose kinematics are within the energy harvesting regime. The role of wake structure is particularly important for array deployments of oscillating foils, since the unsteady wake highly influences 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 (CNN) with long short-term memory (LSTM) units is developed to classify the wakes into three groups. To fully verify the physical wake differences among foil kinematics, a convolutional autoencoder combined with k-means++ clustering is utilized and four different wake patterns are found. With the classification model, these patterns are associated with a range of foil kinematics. Future work can use these correlations to predict the performance of foils placed in the wake and build optimal foil arrangements for tidal energy harvesting.
