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Defining kerogen maturity from orbital hybridization by machine learning 期刊论文
FUEL, 2022, 卷号: 310, 页码: 10
作者:  Ma J(马俊);  Kang DL(康东亮);  Wang XH(王晓荷);  Zhao YP(赵亚溥)
Adobe PDF(4071Kb)  |  收藏  |  浏览/下载：276/56  |  提交时间：2021/11/29
Kerogen maturity  Orbital hybridization  Machine learning  Quantum chemistry  
Wall-model integrated computational framework for large-eddy simulations of wall-bounded flows 期刊论文
PHYSICS OF FLUIDS, 2021, 卷号: 33, 期号: 12, 页码: 10
作者:  Lv Y(吕钰);  Huang XLD;  Yang XL(杨晓雷);  Yang XIA
Adobe PDF(3695Kb)  |  收藏  |  浏览/下载：190/51  |  提交时间：2022/01/24
Analysis and prediction of high-speed train wheel wear based on SIMPACK and backpropagation neural networks 期刊论文
EXPERT SYSTEMS, 2021, 卷号: 38, 期号: 7, 页码: 11
作者:  Wang, Shuwen;  Yan, Hao;  Liu, Caixia;  Fan, Ning;  Liu XM(刘小明);  Wang, Chengguo
Adobe PDF(1092Kb)  |  收藏  |  浏览/下载：210/50  |  提交时间：2021/11/01
BP neural networks  high-speed train  SIMPACK  wheel wear  
Reynolds number effect on statistics of turbulent flows over periodic hills 期刊论文
PHYSICS OF FLUIDS, 2021, 卷号: 33, 期号: 10, 页码: 28
作者:  Zhou ZD(周志登);  Wu T(吴霆);  Yang XL(杨晓雷)
Adobe PDF(16361Kb)  |  收藏  |  浏览/下载：128/26  |  提交时间：2022/01/13
Predicting the components and types of kerogen in shale by combining machine learning with NMR spectra 期刊论文
FUEL, 2021, 卷号: 290, 页码: 10
作者:  Kang DL(康东亮);  Wang XH(王晓荷);  Zheng XJ(郑晓娇);  Zhao YP(赵亚溥)
Adobe PDF(5174Kb)  |  收藏  |  浏览/下载：370/132  |  提交时间：2021/03/30
Machine learning  Kerogen and shale  Molecular structure  High-throughput prediction  NMR spectra datasets  
A Simple Model for Predicting the Two-Phase Heavy Crude Oil Horizontal Flow with Low Gas Fraction 期刊论文
CHEMICAL ENGINEERING COMMUNICATIONS, 2016, 卷号: 203, 期号: 9, 页码: 1131-1138
作者:  Chen XP(陈小平);  Xu JY(许晶禹);  Zhang J(张健);  Xu, JY (reprint author), Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China.
浏览  |  Adobe PDF(698Kb)  |  收藏  |  浏览/下载：526/88  |  提交时间：2016/06/02
Drift-flux Model  Gas-liquid Flow  Heavy Crude Oil  Pressure Gradient  Viscosity  Void Fraction