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您的检索式：作者名="Chengkun REN"
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| 1 | Synthesis of Alumina-Coated Natural Graphite for Highly Cycling Stability and Safety of Li-Ion Batteries显示文摘The natural graphite(NG)urdformly coated with alumina ceramics(Al2O3)was successfully synthesized through sol-gel method.The aluminum plastic film soft-packed battery prepared using Al2O3-coated NG as anode material exhibits excellent cycle performance and safety performance.The cycling retention of AN-1 is 84.95% after 200 cycles at a rate of 1 C in a potential window ranging from 3.0 to 4.35 V,which is much greater than 75.07% of NG under the same test conditions.The result of the nail penetration tests shows that the successful nail penetration rate of the NG used as anode is 0%,while that of Al2O3 coated samples AN-1 is 100%.The test results show that the Al2O3 coating could act as a solid electrolyte to suppress side reactions,improve cycle stability,and prevent a thermal runaway under mechanical abuse. | Tao Xu Chengkun Zhou Haihui Zhou Zekun Wang Jianguo Ren | 2019 | Chinese Journal of Chemistry2019，37，4： | 13 |
| 2 | A point cloud deep neural network metamodel method for aerodynamic prediction显示文摘Aiming to reduce the high expense of 3-Dimensional(3D)aerodynamics numerical sim-ulations and overcome the limitations of the traditional parametric learning methods,a point cloud deep learning non-parametric metamodel method is proposed in this paper.The 3D geometric data,corresponding to the object boundaries,are chosen as point clouds and a deep learning neural net-work metamodel fed by the point clouds is further established based on the PointNet architecture.This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities.With the proposed aerodynamic metamodel approach,the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain,which can maintain the boundary smoothness and allow the network to detect small changes between geometries.Moreover,the point clouds are easily accessible from 3D sensors.The point cloud deep learning neural network,which employs re-sampling technique,the spatial transformer network and the fully connected layer,is developed to predict the aerodynamic char-acteristics of 3D geometry.The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing.The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network. | Fenfen XIONG Li ZHANG Xiao HU Chengkun REN | 2023 | Chinese Journal of Aeronautics2023，36，4： | 0 |
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