| A framework of data assimilation for wind flow fields by physics-informed neural networks | |
Yan C(闫畅)1,2,3; Xu SF(许盛峰)1,2; Sun ZX(孙振旭)1 ; Lutz, Thorsten3; Guo DL(郭迪龙)1,2; Yang GW(杨国伟)1,2
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| Corresponding Author | Sun, Zhenxu([email protected]) |
| Source Publication | APPLIED ENERGY
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| 2024-10-01 | |
| Volume | 371Pages:18 |
| ISSN | 0306-2619 |
| Abstract | Various types of measurement techniques, such as Light Detection and Ranging (LiDAR) devices, anemometers, and wind vanes, are extensively utilized in wind energy to characterize the inflow. However, these methods typically gather data at limited points within local wind fields, capturing only a fraction of the wind field's characteristics at wind turbine sites, thus hindering detailed wind field analysis. This study introduces a framework using Physics-informed Neural Networks (PINNs) to assimilate diverse sensor data types. This includes line-of-sight (LoS) wind speed, velocity magnitude and direction, velocity components, and pressure. Moreover, the parameterized Navier-Stokes (N-S) equations are integrated as physical constraints, ensuring that the neural networks accurately represent atmospheric flow dynamics. The framework accounts for the turbulent nature of atmospheric boundary layer flow by including artificial eddy viscosity in the network outputs, enhancing the model's ability to learn and accurately depict large-scale flow structures. The reconstructed flow field and the effective wind speed are in good agreement with the actual data. Furthermore, a transfer learning strategy is employed for the online deployment of pre-trained PINN, which requires less time than that of the actual physical flow. This capability allows the framework to reconstruct wind flow fields in real time based on live data. In the demo cases, the maximum error between the effective wind speed reconstructed online and the actual value at the wind turbine site is only 3.7%. The proposed data assimilation framework provides a universal tool for reconstructing spatiotemporal wind flow fields using various measurement data. Additionally, it presents a viable approach for the online assimilation of real-time measurements. To facilitate the utilization of wind energy, our framework's source code is openly accessible. |
| Keyword | Data assimilation Wind field reconstruction Physics-informed deep learning |
| DOI | 10.1016/j.apenergy.2024.123719 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001339518700001 |
| WOS Keyword | TURBINES |
| WOS Research Area | Energy & Fuels ; Engineering |
| WOS Subject | Energy & Fuels ; Engineering, Chemical |
| Funding Project | National Key Research and Development Program of China[2022YFB2603400] ; China National Railway Group Science and Technology Program[K2023J047] ; Chinese Academy of Sciences (CAS)[91842771] ; German Academic Exchange Service (DAAD)[91842771] |
| Funding Organization | National Key Research and Development Program of China ; China National Railway Group Science and Technology Program ; Chinese Academy of Sciences (CAS) ; German Academic Exchange Service (DAAD) |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Sun, Zhenxu |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/97113 |
| Collection | 流固耦合系统力学重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.Univ Stuttgart, Inst Aerodynam & Gas Dynam, D-70569 Stuttgart, Germany |
| Recommended Citation GB/T 7714 | Yan C,Xu SF,Sun ZX,et al. A framework of data assimilation for wind flow fields by physics-informed neural networks[J]. APPLIED ENERGY,2024,371:18.Rp_Au:Sun, Zhenxu |
| APA | 闫畅,许盛峰,孙振旭,Lutz, Thorsten,郭迪龙,&杨国伟.(2024).A framework of data assimilation for wind flow fields by physics-informed neural networks.APPLIED ENERGY,371,18. |
| MLA | 闫畅,et al."A framework of data assimilation for wind flow fields by physics-informed neural networks".APPLIED ENERGY 371(2024):18. |
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