De-Zhi Wei
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
De-Zhi Wei has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
Decheng Wan
1 shared publication- Decheng Wan
Computational Marine Hydrodynamics Lab (CMHL), School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
1 shared publication - Ni-Na Wang
Key Laboratory of Far-shore Wind Power Technology of Zhejiang Province, Power China Huadong Engineering Corporation Limited, Hangzhou 310014, China
1 shared publication Nina Wang
1 shared publication- Sergei Vladimirovich Strijhak
Scientific Center for Information Technology and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sirius, Russia
1 shared publication
Latest Publications
- 26 January 2023
- Published by Elsevier in Ocean Engineering
- Vol. 271
Parametric study of the effectiveness of active yaw control based on large eddy simulation
- 26 January 2023
- Published by Elsevier in Ocean Engineering
- Vol. 271
Parametric study of the effectiveness of active yaw control based on large eddy simulation
- Open Access
Modelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model
Yaw-based wake steering control is a potential way to improve wind plant overall performance. For its engineering application, it is crucial to accurately predict the turbine wakes under various yawed conditions within a short time. In this work, a two-dimensional analytical model is proposed for far wake modeling under yawed conditions by taking the self-similarity assumption for the streamwise velocity deficit and skewness angle at hub height. The proposed model can be applied to predict the wake center trajectory, streamwise velocity, and transverse velocity in the far-wake region downstream of a yawed turbine. For validation purposes, predictions by the newly proposed model are compared to wind tunnel measurements and large-eddy simulation data. The results show that the proposed model has significantly high accuracy and outperforms other common wake models. More importantly, the equations of the new proposed model are simple, the wake growth rate is the only parameter to be specified, which makes the model easy to be used in practice.
Open AccessModelling Yawed Wind Turbine Wakes: Extension of a Gaussian-Based Wake Model
Yaw-based wake steering control is a potential way to improve wind plant overall performance. For its engineering application, it is crucial to accurately predict the turbine wakes under various yawed conditions within a short time. In this work, a two-dimensional analytical model is proposed for far wake modeling under yawed conditions by taking the self-similarity assumption for the streamwise velocity deficit and skewness angle at hub height. The proposed model can be applied to predict the wake center trajectory, streamwise velocity, and transverse velocity in the far-wake region downstream of a yawed turbine. For validation purposes, predictions by the newly proposed model are compared to wind tunnel measurements and large-eddy simulation data. The results show that the proposed model has significantly high accuracy and outperforms other common wake models. More importantly, the equations of the new proposed model are simple, the wake growth rate is the only parameter to be specified, which makes the model easy to be used in practice.