Wuhan University of Technology

About: Wuhan University of Technology is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Microstructure & Catalysis. The organization has 40384 authors who have published 36724 publications receiving 575695 citations. The organization is also known as: WUT.

Nanoflake‐Assembled Hierarchical Na3V2(PO4)3/C Microflowers: Superior Li Storage Performance and Insertion/Extraction Mechanism

Abstract: Na3V2(PO4)3 (NVP) has excellent electrochemical stability and fast ion diffusion coefficient due to the 3D Na+ ion superionic conductor framework, which make it an attractive cathode material for lithium ion batteries (LIBs). However, the electrochemical performance of NVP needs to be further improved for applications in electric vehicles and hybrid electric vehicles. Here, nanoflake-assembled hierarchical NVP/C microflowers are synthesized using a facile method. The structure of as-synthesized materials enhances the electrochemical performance by improving the electron conductivity, increasing electrode–electrolyte contact area, and shortening the diffusion distance. The as-synthesized material exhibits a high capacity (230 mAh g−1), excellent cycling stability (83.6% of the initial capacity is retained after 5000 cycles), and remarkable rate performance (91 C) in hybrid LIBs. Meanwhile, the hybrid LIBs with the structure of NVP || 1 m LiPF6/EC (ethylene carbonate) + DMC (dimethyl carbonate) || NVP and Li4Ti5O12 || 1 m LiPF6/EC + DMC || NVP are assembled and display capacities of 79 and 73 mAh g−1, respectively. The insertion/extraction mechanism of NVP is systematically investigated, based on in situ X-ray diffraction. The superior electrochemical performance, the design of hybrid LIBs, and the insertion/extraction mechanism investigation will have profound implications for developing safe and stable, high-energy, and high-power LIBs.

Monthly runoff forecasting based on LSTM–ALO model

Abstract: Accurate runoff forecasting plays an important role in management and utilization of water resources. This paper investigates the accuracy of hybrid long short-term memory neural network and ant lion optimizer model (LSTM–ALO) in prediction of monthly runoff. As the parameters of long short-term memory neural network (LSTM) have influence on the prediction performance, the parameters of the LSTM are calibrated by using ant lion optimizer. Then the selection of suitable input variables of the LSTM–ALO is discussed for monthly runoff forecasting. Finally, we decompose root mean square error into three parts, which can help us better understanding the origin of differences between the observed and predicted runoff. To test the merits of the LSTM–ALO for monthly runoff forecasting, other models are employed to compare with the LSTM–ALO. The scatter-plots and box-plots are adopted for evaluating the performance of all models. In the case study, simulation results with the historical monthly runoff of the Astor River Basin show that the LSTM–ALO model has higher accuracy than that of other models. Therefore, the proposed LSTM–ALO provides an effective method for monthly runoff forecasting.

Field monitoring and numerical analysis of Tsing Ma suspension bridge temperature behavior

Abstract: SUMMARY It is important to take into account the effect of temperature in assessing the structural condition of bridges. However, very few quantitative studies have examined the temperature behavior of large-scale bridges because of their large size and complicated configuration. This paper, for the first time, investigates the temperature distribution and associated responses of a long-span suspension bridge—the 2132-m-long Tsing Ma Bridge—through a combination of numerical analysis and field monitoring. With appropriate assumptions, fine finite element models of a deck plate, section frame, and bridge tower are constructed to facilitate thermal analysis. With ambient temperature measurements and a solar radiation model, the time-dependent temperature distribution within each of these components is calculated through transient heat transfer analysis. The numerical results are verified by comparing them with field monitoring data on temperature distribution and variation at different times and in different seasons. The temperature data are then input into the structural model of the whole bridge to obtain the displacement and strain responses of various bridge components, with a good level of agreement being achieved between the bridge responses and the monitoring data. This exercise verifies both the accuracy of the analytical method employed and the effectiveness of the monitoring system installed on the bridge. The study shows that integrating numerical analysis with field monitoring data provides for a thorough understanding of the temperature behavior of long-span bridges. Copyright © 2012 John Wiley & Sons, Ltd.