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Towards the development of a wake meandering model based on neural networks
Yang XL(杨晓雷)
Source PublicationJournal of Physics: Conference Series
Conference NameScience of Making Torque from Wind 2020
Conference DateSeptember 28, 2020 - October 2, 2020
Conference PlaceTU Delft

In this work, we develop a neural network model for predicting the instantaneous wake position, which is crucial for a wake meandering model. The data used for training are from the large-eddy simulation of a utility-scale wind turbine. A neural network of four hidden layers with 128 units for each layer is found to be effective when training the model. Effects of different input features on the accuracy of the trained model are systematically tested. It is found that the input features including the downwind and crosswind velocities at two locations upwind of the turbine and the thrust and torque acting on the turbine are enough to guarantee the accuracy of the trained model. Without using the thrust and torque as the input features, the accuracy of the model is significantly worse.

Indexed ByEI
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Document Type会议论文
AffiliationThe State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China. University of Chinese Academy of Sciences, Beijing 100049, China.
Recommended Citation
GB/T 7714
Yang XL. Towards the development of a wake meandering model based on neural networks[C]Journal of Physics: Conference Series,2020:062026.
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