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浏览/检索结果: 共5条，第1-5条 帮助 限定条件 发表日期：2019     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Computational cavitating viscous liquid flows in a pump as turbine and Reynolds number effects 期刊论文 PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING, 2019, 卷号: 233, 期号: 3, 页码: 536-550 作者:  Li, Wen-Guang;  Zhang, Yu-Liang 收藏  |  浏览/下载：0/0  |  提交时间：2019/11/15 Cavitation  pump as turbine  Reynolds number  net positive suction head  computational fluid dynamics  viscous liquid   Influence of Cantilever Ratio of Rotor on Hydraulic Vibration of Nuclear Main Pump 期刊论文 Yuanzineng Kexue Jishu/Atomic Energy Science and Technology, 2019, 卷号: 53, 期号: 4, 页码: 673-681 作者:  Cheng, Xiaorui;  Lyu, Boru;  Ji, Chenying;  Wang, Xiaoquan;  Zhang, Shuyan 收藏  |  浏览/下载：4/0  |  提交时间：2020/11/14 Impellers  Natural frequencies  Navier Stokes equations  Numerical methods  Occupational risks  Pumps  Rhenium compounds  Turbulence models  Cantilever ratio  Fluid solid interaction  Fluid-solid coupling  Full three-dimensional  Multiple-reference frames  Reduced scale models  Rotor dynamic  Stress and strain   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 作者:  Zhang Z(张珍);  Song XD;  Ye SR;  Wang YW(王一伟);  Huang CG(黄晨光) 收藏  |  浏览/下载：50/0  |  提交时间：2019/04/11 Deep neural network  channel flow  turbulence model  Reynolds stress   Effect of RANS Method on the Stall Onset Prediction by an Eigenvalue Approach 期刊论文 JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME, 2019, 卷号: 141 作者:  Xie, Zhe;  Liu, Yangwei;  Liu, Xiaohua;  Lu, Lipeng;  Sun, Xiaofeng 收藏  |  浏览/下载：6/0  |  提交时间：2019/12/30 Compressors  Computational fluid dynamics  Flow fields  Forecasting  NASA  Navier Stokes equations  Shear flow  Shear stress  Supersonic aircraft  Vortex flow  Wall function  Eigenvalue calculations  Flux difference splitting  Reynolds-averaged navier-stokes simulations  S-a turbulence models  Shear-stress transport  Spatial discretization schemes  Three dimensional flow field  Total variation diminishing  Eigenvalues and eigenfunctions   Reconstruction of RANS model and cross-validation of flow field based on tensor basis neural network 会议论文 San Francisco, CA, United states, July 28, 2019 - August 1, 2019 作者:  Song XD;  Zhang Z(张珍);  Wang YW(王一伟);  Ye SR(叶舒然);  Huang CG(黄晨光) 收藏  |  浏览/下载：4/0  |  提交时间：2020/11/20 Cross-validation  Multi-layer neural network  Reynolds stress  Turbulence model