Wyniki wyszukiwania - BazTech

1

Effects of B content on wear and corrosion resistance of laser-cladded Co-based alloy coatings

Xia Rong, Zhang Kun, Shu Fengyuan, Zhang Xin, Yan Liying, Li Cheng

Materials Science Poland

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2023

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Vol. 41, No. 4

13--23

EN

To improve the surface properties of Ti alloy, (Co34Fe8Cr29Ni8Si7)100–x Bx alloy, coatings were prepared by laser cladding. The coatings—mainly composed of TiCr, Fe0.1Ti0.18V0.72, CoTi, Ti2Ni, and TiB—and amorphous phases were investigated in terms of microstructure, wear resistance, and corrosion resistance. The results showed that the microhardness of the Co-based coatings first increased and then decreased with the increase of B content. When the B content was 6%, the microhardness of the coating increased up to 1210 HV0.2 which was 3.4 times that of TC4 alloy substrate. The coatings exhibited diverse wear mechanisms that gradually transitioned from severe fatigue spalling and oxidative wear to slightly abrasive wear. The corrosion current density of Co-based coatings in 3.5 wt% NaCl solution first increased and then decreased as B contents increased. Coatings with 4% B content, however, exhibited the best corrosion resistance, which was most suitable for improving the corrosion resistance of Ti alloy.

2

Fillet effect on the bending crashworthiness of thin-walled square tubes

Xie Zhongyou, Zhao Zhixian, Zhou Xiuneng, Li Cheng

Journal of Theoretical and Applied Mechanics

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2022

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Vol. 60 nr 1

103--111

EN

Filleting four corners of square tubes is suggested to reduce the peak force and improve energy absorbing performance. Three-point bending tests are conducted to investigate fillet radius effects employing an ABAQUS explicit code. Three cases characterized by the ratio of width to thickness are considered. Fillet greatly reduces the maximum forces compared with square cross-sections, and the normalized maximum forces decrease with increasing wall thickness when the fillet radius is larger. Additionally, the fillet dramatically improves SEA (Specific Energy Absorption). The normalized CFE (Crash Load Efficiency) significantly exceeds that of the square ones, and the normalized CLEs are almost identical with the increasing fillet radius.

3

Collapse modes of concrete reinforced square bridge piers under vehicle collision

Zhou Xiuneng, Xie Zhongyou, Zhao Zhixian, Li Cheng

Journal of Theoretical and Applied Mechanics

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2022

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Vol. 60 nr 3

375--384

EN

In the field of engineering protection, there is a structural disaster named heavy vehicles impacting column structures. When a heavy truck collides with a reinforced concrete (RC) column at a high velocity, a large impact force generated makes perhaps the column fail and even collapse. Therefore, it is necessary to study the dynamic characteristics during such a disaster, which can provide some reference for structural design, optimization and protection. The RC column impacted by a vehicle could be simplified as a beam fixed at the bottom loaded by a concentrated force, whose deformation is controlled by shearing and bending. In the present work, the ultimate static forces corresponding to shearing and bending collapse are proposed based on theoretical analyses. The model validation is performed using the finite element approach and the theoretical analytical results are in good agreement with the finite element simulation results, which validates the present analytical model. Three cases are simulated by utilizing finite element code ABAQUS, which reveals that the approximate plateau collapse force keeps a long stage beyond the peak failure one. In addition, three collapse modes are observed based on the static force and deformation analysis, validating the present framework which can be used for routine pier design. The work can be extended to estimate collapse modes of building columns under a vehicle collision.

4

Prediction of PM2.5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR

Fang Zhiyuan, Yang Hao, Li Cheng, Cheng Liangliang, Zhao Ming, Xie Chenbo

Archives of Environmental Protection

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2021

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Vol. 47, no. 3

98--107

EN

The prediction of PM2.5 is important for environmental forecasting and air pollution control. In this study, four machine learning methods, ground-based LiDAR data and meteorological data were used to predict the ground-level PM2.5 concentrations in Beijing. Among the four methods, the random forest (RF) method was the most effective in predicting ground-level PM2.5 concentrations. Compared with BP neural network, support vector machine (SVM), and various linear fitting methods, the accuracy of the RF method was superior by 10%. The method can describe the spatial and temporal variation in PM2.5 concentrations under different meteorological conditions, with low root mean square error (RMSE) and mean square deviation (MD), and the consistency index (IA) reached 99.69%. Under different weather conditions, the hourly variation in PM2.5 concentrations has a good descriptive ability. In this paper, we analyzed the weights of input variables in the RF method, constructed a pollution case to correspond to the relationship between input variables and PM2.5, and analyzed the sources of pollutants via HYSPLIT backward trajectory. This method can study the interaction between PM2.5 and air pollution variables, and provide new ideas for preventing and forecasting air pollution.