This paper presents a further study of the Manning and Darcy-Weisbach resistance coefficients, as they play a significant role in assessing the cross-sectional mean velocity, conveyance capacity and determining the la...This paper presents a further study of the Manning and Darcy-Weisbach resistance coefficients, as they play a significant role in assessing the cross-sectional mean velocity, conveyance capacity and determining the lateral distribution of depth mean velocity and local boundary shear stress in compound channels. The relationships between the local, zonal and overall resistance coefficients, and a wide range of geometries and different roughness between the main channel and the flood plain are established by analyzing a vast amount of experimental data from a British Science and Engineering Research Council Flood Channel Facility （SERC-FCF）. And the experimental results also show that the overall Darcy-Weisbach resistance coefficient for a compound channel is the function of Reynolds number, but the function relationship is different from that for a single channel. By comparing and analyzing the conventional methods with the experimental data to predict composite roughness in compound channels, it is found that these methods are not suitable for compound channels. Moreover, the reason why the conventional methods cannot assess correctly the conveyance capacity of compound channels is also analyzed in this paper.展开更多
An interactive web tool,PredPotS,has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions.The predictions are generated using deep learning models trained ...An interactive web tool,PredPotS,has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions.The predictions are generated using deep learning models trained and validated on a chemically diverse database comprising reduction potentials of approximately 8000 organic compounds.The reduction potentials of this database were computed using a composite computational protocol that combines the semiempirical quantum chemical method(GFN2-xTB)and awell-established DFT approach(M06-2X functional along with the SMD solvent model).While this computational approach is cost-effective,it is subject to certain limitations,which are nonetheless duly accounted for in the development of the database.The applied graph-based deep learning methods perform remarkably well in terms of the standard performance metrics.By entering or uploading the SMILES codes of the molecules,PredPotS provides fast and sensible predictions for one-electron standard reduction potentials for a diverse set of organic molecules also in the range compatible with the electrochemical stability of aqueous electrolytes.The PredPotS web tool is particularly well-suited for screening redox-active candidates for aqueous organic redox flow batteries,but it may also prove useful in a variety of other electrochemical applications.展开更多
基金The project supported by the National Natural Science Foundation of China(50279024)the National Key Basic Research and Development Program of China(973 Program)(2003CB415202)the Specialized Research Fund for the Doctoral Program of Higher Education(2
文摘This paper presents a further study of the Manning and Darcy-Weisbach resistance coefficients, as they play a significant role in assessing the cross-sectional mean velocity, conveyance capacity and determining the lateral distribution of depth mean velocity and local boundary shear stress in compound channels. The relationships between the local, zonal and overall resistance coefficients, and a wide range of geometries and different roughness between the main channel and the flood plain are established by analyzing a vast amount of experimental data from a British Science and Engineering Research Council Flood Channel Facility （SERC-FCF）. And the experimental results also show that the overall Darcy-Weisbach resistance coefficient for a compound channel is the function of Reynolds number, but the function relationship is different from that for a single channel. By comparing and analyzing the conventional methods with the experimental data to predict composite roughness in compound channels, it is found that these methods are not suitable for compound channels. Moreover, the reason why the conventional methods cannot assess correctly the conveyance capacity of compound channels is also analyzed in this paper.
基金funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875565 (CompBat project).
文摘An interactive web tool,PredPotS,has been developed for predicting one-electron standard reduction potentials of organic molecules in aqueous solutions.The predictions are generated using deep learning models trained and validated on a chemically diverse database comprising reduction potentials of approximately 8000 organic compounds.The reduction potentials of this database were computed using a composite computational protocol that combines the semiempirical quantum chemical method(GFN2-xTB)and awell-established DFT approach(M06-2X functional along with the SMD solvent model).While this computational approach is cost-effective,it is subject to certain limitations,which are nonetheless duly accounted for in the development of the database.The applied graph-based deep learning methods perform remarkably well in terms of the standard performance metrics.By entering or uploading the SMILES codes of the molecules,PredPotS provides fast and sensible predictions for one-electron standard reduction potentials for a diverse set of organic molecules also in the range compatible with the electrochemical stability of aqueous electrolytes.The PredPotS web tool is particularly well-suited for screening redox-active candidates for aqueous organic redox flow batteries,but it may also prove useful in a variety of other electrochemical applications.
