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Wang Yongkui,Huang Xiaochen,Zhou Jiafeng,Fang Jun,Gao Yan,Ge Jinlong,Miao Shu,Xie Zhuoming 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.2
In this work, pure W and W-0.5wt%HfC alloy (WHC05) were fabricated by sintering and hot-rolling following the same processing route. After exposing to a high flux deuterium plasma irradiation with the Dþ flux to three fluences of 6.00 1024, 2.70 1025 and 7.02 1025 D/m2, the evolution of surface morphology, deuterium retention and hardening behaviors in pureWand WHC05 has been studied. The SEM results show the formation of D blisters on the irradiated area, and with the increase of D implantation, the size of these blisters increases from 200 ~ 500 nm (2.70 1025 D/m2) to 1 ~ 2 mm (7.02 1025 D/m2) in WHC05 and from 1 ~ 2 mm (2.70 1025 D/m2) to > 3 mm (7.02 1025 D/m2) in pure W, respectively. A higher D retention and obvious hardening are observed in pure W than that of the WHC05 alloy, indicating an improve radiation resistance in WHC05 compared to pure W.
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A New Method for Separating Temperature Effect of Bridge Strain Monitoring
Lei Huang,Jingzhou Xin,Jiafeng Yang,Shuangjiang Li,Jianting Zhou 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.8
Temperature has a significant influence on bridge strain monitoring data. To improve the accuracy of temperature effect separation in strain monitoring data, this paper proposes a temperature effect separation method comprising variational nonlinear chirp mode decomposition (VNCMD), principal component analysis (PCA) and blind source separation. Firstly, VNCMD was used to decompose the monitoring data of strain and temperature, and the intrinsic mode functions (IMF) of strain and temperature signals were obtained. Secondly, PCA was used to reduce the dimension of IMF, and the false components were eliminated to select the optimal components. After reducing the dimension, the components were used as the input of fast independent component analysis model for blind source separation. Finally, the feasibility and accuracy of the method was verified via the signal-to-noise ratio (SNR) in the simulated signal, and the separation results were evaluated using the Pearson correlation coefficient between the strain component and the corresponding temperature component in real bridge monitoring data. The proposed method performed better than the empirical mode decomposition (EMD) method. The signal-to-noise ratio (SNR) of VNCMD improved 51.80% for daily temperature difference effect and 32.41% for annual temperature difference effect in the numerical study, respectively; the correlation coefficients of VNCMD improved 52.90% for daily temperature difference effect and 4.26% for annual temperature difference effect in practical verification, respectively.
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