A measure-correlate-predict model based on neural networks and frozen flow hypothesis for wind resource assessment
文献类型：期刊论文
| 作者 | Chen, Danyang1,2; Zhou, Zhideng1,2; Yang, Xiaolei1,2 ; Yang XL(杨晓雷)
|
| 刊名 | PHYSICS OF FLUIDS
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 34期号:4页码:19 |
| ISSN号 | 1070-6631 |
| DOI | 10.1063/5.0086354 |
| 通讯作者 | Yang, Xiaolei(xyang@imech.ac.cn) |
| 英文摘要 | In this paper, a measure-correlate-predict (MCP) model based on neural networks (NN) and frozen flow hypothesis, which is abbreviated as the MCPNN-frozen model, is proposed for wind resource assessment and tested using turbulent channel flows with three different surface roughness lengths, i.e., k 0 = 0.001, 0.01, and 0.1m. The predictions from the MCPNN-frozen model are compared with the real data for different separations (s) between the reference point and the target point. The results show that the correlation coefficients C.C. between the model predictions and real data are roughly higher than 0.5 for small separations s / delta <= 3 (where delta is the boundary layer thickness), and the coefficients of determination (R-2) are approximately higher than 0.3 when s / delta <= 2. The generalization capacity of the MCPNN-frozen model is tested for different roughness lengths and different velocity components. Further analyses show that, even though C.C. and R-2 decrease when increasing s, the large-scale variations of velocity fluctuations are well captured by the MCPNN-frozen model especially for the one trained using the data filtered in time. Furthermore, it is found that the model trained using the filtered data without a spanwise offset can well predict the large-scale variations at the target point when the spanwise offsets between the target point and the reference point are small (e.g., 0.1 delta and 0.2 delta). The proposed model leverages the power of neural networks and physical understanding. Further development of the model for complex scenarios will be carried out in the future work. |
| WOS关键词 | LARGE-EDDY SIMULATION ; LIDAR |
| WOS研究方向 | Mechanics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000788837600012 |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/89045]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 通讯作者 | Yang, Xiaolei |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Danyang,Zhou, Zhideng,Yang, Xiaolei,et al. A measure-correlate-predict model based on neural networks and frozen flow hypothesis for wind resource assessment[J]. PHYSICS OF FLUIDS,2022,34(4):19. |
| APA | Chen, Danyang,Zhou, Zhideng,Yang, Xiaolei,&杨晓雷.(2022).A measure-correlate-predict model based on neural networks and frozen flow hypothesis for wind resource assessment.PHYSICS OF FLUIDS,34(4),19. |
| MLA | Chen, Danyang,et al."A measure-correlate-predict model based on neural networks and frozen flow hypothesis for wind resource assessment".PHYSICS OF FLUIDS 34.4(2022):19. |
入库方式： OAI收割
来源：力学研究所
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