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Robust optimisation of the streamlined shape of a high-speed train in crosswind conditions 期刊论文
ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS, 2023, 卷号: 17, 期号: 1, 页码: 2234012
Authors:  Wang YT(王业腾);  Sun ZX(孙振旭);  Ju SJ(鞠胜军);  Guo DL(郭迪龙);  Yang GW(杨国伟)
Adobe PDF(3802Kb)  |  Favorite  |  View/Download：118/9  |  Submit date：2023/09/05
Railway aerodynamic  High-speed train  Crosswind  Robust optimisation  Flow structures  >  
Aerodynamic-Aeroacoustic Optimization of a Baseline Wing and Flap Configuration 期刊论文
APPLIED SCIENCES-BASEL, 2022, 卷号: 12, 期号: 3, 页码: 19
Authors:  Ju SJ(鞠胜军);  Sun ZX(孙振旭);  Guo DL(郭迪龙);  Yang GW(杨国伟);  Wang YT(王业腾);  Yan C(闫畅)
Adobe PDF(12043Kb)  |  Favorite  |  View/Download：304/44  |  Submit date：2022/03/28
high-lift devices  computational fluid dynamics  computational aeroacoustics  Kriging surrogate model  optimization design  
Aerodynamic shape optimization of an urban maglev train 期刊论文
ACTA MECHANICA SINICA, 2021, 页码: 16
Authors:  Sun ZX(孙振旭);  Wang MY(王梦莹);  Wei LY(魏涟漪);  Kong FB;  Yang GW(杨国伟)
Adobe PDF(2376Kb)  |  Favorite  |  View/Download：339/93  |  Submit date：2021/06/15
Urban maglev train  Aerodynamic characteristics  Cross validation  Kriging model  Genetic algorithm  
高速列车气动外形优化研究进展 期刊论文
力学学报, 2021, 卷号: 53, 期号: 01, 页码: 51-74
Authors:  孙振旭;  姚永芳;  郭迪龙;  杨国伟;  姚拴宝;  张业;  陈大伟;  李桂波;  尚克明;  贾玲
Adobe PDF(18412Kb)  |  Favorite  |  View/Download：303/53  |  Submit date：2021/04/19
气动外形  流场机理  优化算法  参数化  替代模型  
Multi-objective design optimization of the combinational configuration of the upstream energy deposition and opposing jet for drag reduction in supersonic flows 期刊论文
AEROSPACE SCIENCE AND TECHNOLOGY, 2020, 卷号: 105, 页码: 10
Authors:  Ju SJ(鞠胜军);  Sun ZX(孙振旭);  Yang GW(杨国伟);  Prapamonthon P;  Zhang JY
Adobe PDF(3818Kb)  |  Favorite  |  View/Download：459/119  |  Submit date：2020/11/30
Optimization design  Drag reduction  Supersonic flow  Energy deposition  Opposing jet  
高速列车受电弓气动噪声特性分析 期刊论文
北京大学学报(自然科学版), 2020, 卷号: 56, 期号: 03, 页码: 385-398
Authors:  姚永芳;  孙振旭;  刘文;  杨国伟
View  |  Adobe PDF(4738Kb)  |  Favorite  |  View/Download：239/46  |  Submit date：2020/07/15
高速列车  受电弓  气动噪声  脱体涡模拟  声学有限元  
A novel CACOR-SVR multi-objective optimization approach and its application in aerodynamic shape optimization of high-speed train 期刊论文
SOFT COMPUTING, 2019, 卷号: 23, 期号: 13, 页码: 5035-5051
Authors:  Zhang Y;  Guo DL(郭迪龙);  Sun ZX(孙振旭);  Chen DW
View  |  Adobe PDF(2843Kb)  |  Favorite  |  View/Download：430/105  |  Submit date：2019/09/09
Chaos ant colony optimization  Support vector machine  Multi-objective optimization  Vehicle modeling function  High-speed trains  
Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains 期刊论文
APPLIED SCIENCES-BASEL, 2017, 卷号: 7, 期号: 2
Authors:  Sun ZX(孙振旭);  Zhang Y;  Yang GW(杨国伟);  Sun, ZX (reprint author), Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China.
View  |  Adobe PDF(6165Kb)  |  Favorite  |  View/Download：650/332  |  Submit date：2017/07/24
Aerodynamic Noise  Nlas  Kriging Model  Cross Validation  Equivalent Continuous Spl  
PARAMETRIZATION OF HIGH-SPEED TRAIN STREAMLINE SHAPE 会议论文
ASME Fluids Engineering Division Summer Meeting, Waikoloa, HI, JUL 30-AUG 03, 2017
Authors:  Sun ZX(孙振旭);  Zhang Y;  Yang GW(杨国伟);  Sun, ZX (reprint author), Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing, Peoples R China.
View  |  Adobe PDF(1180Kb)  |  Favorite  |  View/Download：243/88  |  Submit date：2018/01/16
Multiobjective Optimization  Head  
A study of the optimization design method for a high speed train 会议论文
THE THIRD INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE, Cagliari, Sardinia, Italy , 5-8 April 2016
Authors:  Zhao GL;  Sun ZX(孙振旭);  Yao SB(姚拴宝);  Guo DL(郭迪龙);  Yang GW(杨国伟)
Favorite  |  View/Download：192/0  |  Submit date：2018/11/08
Aerodynamic drag  Genetic algorithms  Lift  Railroad cars  Railroads  Aerodynamic lift  Ga  grnn  High speed train (HST)  Kriging model  Optimization design  Optimization method  Surrogate model