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主动和被动驱动下仿生柔性结构的流固耦合研究 学位论文
博士论文,北京: 中国科学院大学, 2019
Authors:  张翔
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流固耦合  浸入边界方法  主动和被动驱动  仿生推进  流致振动  
Passive hovering of a flexible Lambda-flyer in a vertically oscillating airflow 期刊论文
Journal of Fluid Mechanics, 2019, 卷号: 878, 页码: 113-146
Authors:  Zhang X(张翔);  He GW(何国威);  Wang SZ(王士召);  Zhang X(张星)
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Flow–structure Interactions  Swimming/flying  Vortex Shedding  
Estimating lift from wake velocity data in flapping flight 期刊论文
JOURNAL OF FLUID MECHANICS, 2019, 卷号: 868, 页码: 501-537
Authors:  Wang SZ(王士召);  He GW(何国威);  Liu TS(刘天舒)
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swimming/flying  
External-Compression Supersonic Inlet Free from Violent Buzz 期刊论文
AIAA JOURNAL, 2019, 卷号: 57, 期号: 6, 页码: 2513-2523
Authors:  Chen H;  Tan H;  Liu YZ;  Zhang QF(张启帆)
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流动干扰对颤振的影响研究 学位论文
硕士论文,北京: 中国科学院大学, 2019
Authors:  姜倩
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颤振  干扰流动  CFD/CSD耦合  复杂结构  
Numerical study of chemically reacting flow in a shock tube using a high-order point-implicit scheme 期刊论文
COMPUTERS & FLUIDS, 2019, 卷号: 184, 页码: 107-118
Authors:  Chen S;  Sun QH(孙泉华);  Klioutchnikov I;  Olivier H
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Shock wave/boundary-layer interactions  Real gas effects  Chemical nonequilibrium  High-order scheme  
Wall-modeling for large-eddy simulation of flows around an axisymmetric body using the diffuse-interface immersed boundary method 期刊论文
APPLIED MATHEMATICS AND MECHANICS-ENGLISH EDITION, 2019, 卷号: 40, 期号: 3, 页码: 305-320
Authors:  Shi BJ(时北极);  Yang XL(杨晓雷);  Jin GD(晋国栋);  He GW(何国威);  Wang SZ(王士召)
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wall model  large-eddy simulation (LES)  immersed boundary (IB) method  diffuse-interface  
Effects of Bogies on the Wake Flow of a High-Speed Train 期刊论文
APPLIED SCIENCES-BASEL, 2019, 卷号: 9, 期号: 4, 页码: AR759
Authors:  Liu W(刘雯);  Guo DL(郭迪龙);  Zhang ZJ;  Chen DW;  Yang GW(杨国伟)
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high-speed train  unsteady wake  bogie  DMD  vortex dynamics  
Spectral measurements of hypervelocity flow in an expansion tunnel 期刊论文
ACTA MECHANICA SINICA, 2019, 卷号: 35, 期号: 1, 页码: 24-31
Authors:  Yuan CK(苑朝凯);  Zhou K(周凯);  Liu YF(刘云峰);  Hu ZM(胡宗民);  Jiang ZL(姜宗林)
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Spectral measurement  Hypervelocity flow  Radiation  Expansion tunnel  
Application of deep learning method to Reynolds stress models of channel flow based on reduced-order modeling of DNS data 期刊论文
JOURNAL OF HYDRODYNAMICS, 2019, 卷号: 31, 期号: 1, 页码: 58-65
Authors:  Zhang Z(张珍);  Song XD;  Ye SR;  Wang YW(王一伟);  Huang CG(黄晨光);  An YR;  Chen YS
Adobe PDF(1492Kb)  |  Favorite  |  View/Download:48/3  |  Submit date:2019/04/11
Deep neural network  channel flow  turbulence model  Reynolds stress