A measure-correlate-predict model based on neural networks and frozen flow hypothesis for wind resource assessment

doi:10.1063/5.0086354

CORC > 力学研究所 > 中国科学院力学研究所 > 非线性力学国家重点实验室

	A measure-correlate-predict model based on neural networks and frozen flow hypothesis for wind resource assessment
	Chen, Danyang 1,2; Zhou, Zhideng 1,2; Yang, Xiaolei 1,2
刊名	PHYSICS OF FLUIDS
	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
内容类型	期刊论文
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. 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.(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.