Estimation of extreme hydrodynamic environments of Puti Island wind farm in Bohai Sea | |
Zhou JF(周济福)1,2; Liu JL3 | |
会议录名称 | IUTAM Symposium on Storm Surge Modelling and Forecasting, 2016 |
2017 | |
页码 | 74-81 |
会议名称 | IUTAM Symposium on Storm Surge Modelling and Forecasting, 2016 |
会议日期 | October 18, 2017 - October 19, 2017 |
会议地点 | Shianghai, China |
摘要 | In order to manifest the extreme hydrodynamic environments for Puti Island wind farm in Bohai Sea, flow and wave climates in the area induced by cold waves are investigated. According to the historical meteorology statistics and taking the global climate change into account, three typical paths (NNE, ENE and NNW) with four high wind speeds for cold waves in Bohai Sea are presumed. After calibrated with field data, the Princeton Ocean Model (POM) and the third-generation wave model, SWAN, are employed to delineate possible extreme hydrodynamic conditions in the area of Puti Island wind farm. The dependence of the extreme current and water wave parameters to the wind speed and wind direction is investigated. The distribution of the extreme current and water wave parameters in the wind farm is discussed. © 2017 The Author(s). |
关键词 | Climate change Electric utilities Floods Hydrodynamics Storms Water waves Weather forecasting Wind Wind power Bohai Global climate changes High wind speed Hydrodynamic conditions Princeton ocean model Third generation Wind directions Wind farm |
收录类别 | EI |
语种 | 英语 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://dspace.imech.ac.cn/handle/311007/78019 |
专题 | 流固耦合系统力学重点实验室 |
作者单位 | 1.Key Laboratory for Mechanics in Fluid Solid Coupling Systems, Institute of Mechanics, Chinese Academy of Sciences, Beijing; 100190, China; 2.School of Engineering Sciences, University of Chinese Academy of Sciences, Beijing; 100049, China; 3.China National Offshore Oil Corp Research Institute, Beijing; 100028, China |
推荐引用方式 GB/T 7714 | Zhou JF,Liu JL. Estimation of extreme hydrodynamic environments of Puti Island wind farm in Bohai Sea[C]IUTAM Symposium on Storm Surge Modelling and Forecasting, 2016,2017:74-81. |
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