葛铭纬

葛铭纬,教授,博士生导师,国家级青年人才,华北电力大学新能源学院副院长。长期从事风力机和风电场空气动力学研究工作。担任2020年国家重点研发计划“可再生能源与氢能技术”重点专项指南专家、中国可再生能源学会青委会副秘书长、全国能源名词审定委员会委员、《IET Renewable Power Generation》《电力建设》期刊编委、《Frontiers in Energy Research》期刊客座编辑等学术职务。

提出了大型风电叶片高效低载气动设计方法,设计了2.0MW、3.0MW、5.5/7.0MW等8款大型风电叶片气动外形,其中76.6米海上风电叶片获评“2018全球最佳叶片”,叶片应用于明阳MYSE5.5-155机型,入选国际权威杂志同类机型年度Top10,获评中国风能“2018年度最佳机型”等奖项。提出了高精度的尾流模型和风电场模型,发明了增效降载的风电场微观选址方法,提出了风电场智能运行控制方法,研究成果在三峡新能源、华能集团等多家大型企业应用。

主持国家自然科学基金项目青年基金,面上项目(2项),国家重点研发计划子课题等国家级项目,获北京市杰青资助。以第一或唯一通信作者在JFM,Applied Energy,Energy等国际权威期刊发表SCI论文近40篇(SCI一区论文22篇),以第一发明人申请发明专利20项,授权10项,登记软件著作权1项,出版学术专著2部。获国家级教学成果二等奖,河北省科技进步一等奖、吴仲华优秀青年学者奖等奖励。


主要研究方向:风电叶片气动设计、风电场微观选址和风电场尾流调控等。

联系电话:010-61771725

E-mail:gemingwei@ncepu.edu.cn


教学奖励

[1] 国家级教学成果二等奖,2023年

[2] 北京市教育教学成果一等奖,2022年;

[3] 北京市课程思政示范课、教学名师和团队(普通本科教育),2022年;

[4] 北京市课程思政示范课、教学名师和团队(研究生教育),2022年;

[5] 北京高校“优质本科教案”,2023年;

[6] 北京普通高校毕业设计(论文)优秀指导教师,2021年;

[7] 北京高校青年教师教学基本功比赛三等奖,2019年;

[8] 华北电力大学教育教学成果特等奖,2021年;

[9] 华北电力大学教育教学成果一等奖,2021年;

[10] 华北电力大学百篇优秀毕业设计指导教师,2021年;

[11] 华北电力大学优秀硕士论文指导教师,2019,2020和2022年;

[12] 协和新能源育才奖,2020年;

[13] 华北电力大学教学名师培育计划,2017年;

[14] 华北电力大学教学优秀奖,2016年;


学术奖励和荣誉

[1] 国家级青年人才,2023年

[2] 北京市杰青,2022年;

[3] 吴仲华优秀青年学者奖,2023年

[4] 首都前沿学术成果奖,2022年;

[5] 河北省科学技术进步一等奖,2018年;

[6]北京地区广受关注学术论文奖,2020年;

[7] “创新中山”科技进步一等奖,2022年;

[8] 全国流体力学学术年会优秀青年论文奖,2020年。


近五年代表性论文(2019年至今):

[1]Li L, WangB, Ge M W*, et al. A novel superposition method for streamwise turbulence intensity of wind-turbine wakes[J]. Energy, 2023, 276: 127491. (IF: 8.857, Q1)

[2]Li B, He J, Ge M W*, et al. Study of three wake control strategies for power maximization of offshore wind farms with different layouts[J]. Energy Conversion and Management, 2022, 268: 116059. (IF: 11.533, Q1)

[3]Cao L, Ge M W*, Gao X, et al. Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines[J]. Applied Energy, 2022, 323: 119599. (IF: 11.446, Q1)

[4]Du B, Ge M W*, Liu Y. A physical wind-turbine wake growth model under different stratified atmospheric conditions[J]. Wind Energy, 2022. (IF: 3.710, Q2,国际风能顶刊)

[5]Yang H, Ge M W*, Abkar M, et al. Large-eddy simulation study of wind turbine array above swell sea[J]. Energy, 2022: 124674. (IF: 8.857, Q1)

[6]Yang H, Ge M W*, Gu B, Du B, Liu Y. The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine[J]. Energy, 2022, 244(13): 123200. (IF: 8.857, Q1)

[7]Yang H, Lang B, Du B, JinZ, Li B, GeMW*. Effects of the steepness on the evolution of turbine wakes above continuous hilly terrain[J]. IET Renewable Power Generation, 2022, 16(6): 1170-1179. (IF: 3.034, Q2)

[8]Zhang S, Du B, Ge M W*, Zuo Y. Study on the operation of small rooftop wind turbines and its effect on the wind environment in blocks[J]. Renewable Energy, 2022, 183: 708-718. (IF: 8.634, Q1)

[9]Ma H, Ge M W*, Wu G, Du B, Liu Y. Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production[J]. Applied Energy, 2021, 303(12): 117691.(IF: 11.446, Q1)

[10]Li L, Huang Z, Ge M W*, Zhang Q. A novel three-dimensional analytical model of the added streamwise turbulence intensity for wind-turbine wakes[J]. Energy, 2021, 238(1): 121806.(IF: 8.857, Q1)

[11]Gu B*, Meng H, Ge M W, Zhang H, Liu X. Cooperative Multiagent Optimization Method for Wind Farm Power Delivery Maximization[J]. Energy, 2021, 233(3): 121076. (IF: 8.857, Q1)

[12]Fan X, Ge M W, Tan W, Li Q*. Impacts of coexisting buildings and trees on the performance of rooftop wind turbines: An idealized numerical study[J]. Renewable Energy, 2021, 177: 164-180. (IF: 8.634, Q1)

[13]Du B, Ge M W*, Zeng C, Cui G, Liu Y. Influence of atmospheric stability on wind-turbine wakes with a certain hub-height turbulence intensity[J]. Physics of Fluids, 2021, 33(5): 055111.(IF: 4.980, Q1)

[14]Ge M W*, Yang H, Zhang H, Zuo Y. A prediction model for vertical turbulence momentum flux above infinite wind farms[J]. Physics of Fluids, 2021, 33(5): 055108.(IF: 4.980, Q1)

[15]Zhang H, Ge M W*, Liu Y, Yang X. A new coupled model for the equivalent roughness heights of wind farms[J]. Renewable Energy, 2021, 171: 34-46. (IF: 8.634, Q1)

[16]Yang X*, Ge M W. Revisiting Raupach’s Flow-Sheltering Paradigm[J]. Boundary-Layer Meteorology, 2021, 179: 313-323.(IF:3.471,Q3)

[17]Ge M W*, Gayme D, Meneveau C. Large-eddy simulation of wind turbines immersed in the wake of a cube-shaped building[J]. Renewable Energy, 2021, 163: 1063-1077. (IF: 8.634, Q1)

[18]Ge M W*, Zhang S, Meng H, Ma H. Study on interaction between the wind-turbine wake and the urban district model by large eddy simulation[J]. Renewable Energy, 2020, 157: 941-950. (IF: 8.634, Q1)

[19]Yang X, Xu H, Huang X, Ge M W*. Drag forces on sparsely packed cube arrays[J]. Journal of Fluid Mechanics, 2019, 880: 992-1019. (IF: 4.245, Q1, 国际流体力学顶刊)

[20]Ge M W*, Wu Y, Liu Y, Yang X. A two-dimensional Jensen model with a Gaussian-shaped velocity deficit[J]. Renewable Energy, 2019, 141: 46-56. (IF: 8.634, Q1)

[21]Ge M W*, Zhang H, Wu Y, Li Y. Effects of leading edge defects on aerodynamic performance of the S809 airfoil[J]. Energy Conversion and Management, 2019, 195: 466-479. (IF: 11.533, Q1)

[22]Wang C, Ge M W*. Applying resolved-scale linearly forced isotropic turbulence in rational subgrid-scale modeling[J]. Acta MechanicaSinica, 2019, 35(3): 486-494. (IF: 2.910, Q2)

[23]Ge M W*, Ke W, Chen H. Pitch control strategy before the rated power for variable speed wind turbines at high altitudes[J]. Journal of Hydrodynamics, 2019, 31(2): 379-388. (IF: 2.983, Q2)

[24]Ge M W, Yang X*, Marusic I. Velocity probability distribution scaling in wall-bounded flows at high Reynolds numbers[J]. Physical Review Fluids, 2019, 4(3): 034101. (IF: 2.895, Q2)

[25]Ge M W*, Wu Y, Liu Y*, Li Q. A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes [J]. Applied Energy, 2019, 233-234: 975-984.(IF: 11.446, Q1)


专著:

[1]葛铭纬,李莉,孟航.海上风电机组技术[M].北京:电子工业出版社,2022. (国家出版基金项目)

[2]葛铭纬.风电场多尺度流动模拟和数学模型[M].北京:科学出版社,2021.