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Hierarchical learning method for array flow field prediction integrated with deep neural network
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Shanxun SUN1, Zijiang XU1, Zhuoheng WANG1, Shuangshuang CUI2, Ting HE1, Yang CAI1. Hierarchical learning method for array flow field prediction integrated with deep neural network[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="Hierarchical learning method for array flow field prediction integrated with deep neural network",
author="Shanxun SUN1, Zijiang XU1, Zhuoheng WANG1, Shuangshuang CUI2, Ting HE1, Yang CAI1",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2500344"
}
%0 Journal Article
%T Hierarchical learning method for array flow field prediction integrated with deep neural network
%A Shanxun SUN1
%A Zijiang XU1
%A Zhuoheng WANG1
%A Shuangshuang CUI2
%A Ting HE1
%A Yang CAI1
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
%P
%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2500344
TY - JOUR
T1 - Hierarchical learning method for array flow field prediction integrated with deep neural network
A1 - Shanxun SUN1
A1 - Zijiang XU1
A1 - Zhuoheng WANG1
A1 - Shuangshuang CUI2
A1 - Ting HE1
A1 - Yang CAI1
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
SP -
EP -
%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A2500344
Abstract: Real-time and accurate dynamic wake information is essential for wind resource assessment and the optimization of wind farm operations. To further understand the wake characteristics of wind turbines, we propose a hierarchical learning approach integrated with a deep neural network-based prediction method. The integrated framework combines physical and mathematical models, enabling three-dimensional spatiotemporal wind field predictions with minimal measured data requirements. Evaluation and validation results demonstrate that the proposed method achieves accurate short-term wake predictions across the entire domain with minimal prediction errors. Compared with conventional methods, the proposed hierarchical learning framework markedly lowers the training-data requirements of physics-informed neural networks for large-scale flow-field prediction while maintaining high accuracy. In addition, it demonstrates superior performance in both local and global wake forecasts, offering practical insights for efficient turbine operation and wake analysis.
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