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Your search keyword '"Zhu, Xiaofei"' showing total 187 results
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187 results on '"Zhu, Xiaofei"'

22. HfSi2-HfB2-SiC coating prepared at low temperature to protect SiC-coated C/C composites against oxidation at 1473–1973 K.

Author

Zhu, Xiaofei, Ou, Chenyang, Li, Tao, Zhang, Yulei, Lv, Junshuai, and Chen, Ruicong

Subjects
*LOW temperatures, *SURFACE coatings, *OXIDATION, *CRACK propagation (Fracture mechanics), *THERMAL stability
Abstract

To improve the oxidation resistance of carbon/carbon (C/C) composites in 1473–1973 K, a HfSi 2 -HfB 2 -SiC coating was prepared on the surface of SiC-coated C/C composites by a combined method of slurry dipping and oxidation sintering at low temperature (below 1473 K). The Hf-Si-O multiphase layer generated by the oxidation of the coating acted as oxygen barriers at 1473–1973 K in air. The in-situ formed HfSiO 4 particles enhanced the thermal stability of the SiO 2 glass, terminated propagation of the crack, and relieved CTE mismatch between the inner and outer coating. Besides, B 2 O 3 filled pores in the early stage of oxidation and then SiO 2 took the place of the B 2 O 3 as a new filler with the increase time of oxidation. As a result, the coating can protect C/C composites for 516 h at 1473 K, 744 h at 1773 K, and 64 h at 1973 K with a mass gain of 0.63 wt%, 1.41 wt% and −1.91 wt%, respectively. [ABSTRACT FROM AUTHOR]

Published
2024
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33. A low-temperature prepared composite coating to protect SiC-coated C/C composites against oxidation in a wide temperature range for long-life service.

Author

Zhu, Xiaofei, Zhang, Yulei, Zhang, Jian, Li, Tao, Jung, In-Ho, Su, Yangyang, and Gai, Wenhan

Subjects
*COMPOSITE coating, *THERMAL stability, *LOW temperatures, *OXIDATION, *THERMOCYCLING
Abstract

To improve the oxidation resistance of carbon/carbon (C/C) composites in a wide temperature range (1173–1773 K), a composite coating containing rich B 2 O 3 glass was prepared on SiC-coated C/C composites by slurry dipping-densifying at low temperature. Borosilicate and SiO 2 glasses acted as oxygen barriers at low and medium-high temperatures, respectively. Besides, Hf-oxides (HfO 2 , HfSiO 4) ceramic particles improved the thermal stability of the glass and enhanced the crack resistance of glass layer. Therefore, the composite coating can effectively protect C/C composites against oxidation for 403 h at 1173 K, 723 h at 1473 K and 403 h at 1773 K with the mass gain of 3.77 g·m−2, 21.41 g·m−2 and 0.42 g·m−2, respectively. After 50 times thermal cycles between room temperature and 1773 K, the mass gain of the coated sample was 3.95 g·m−2 and the mass retention rate was up to 98.19 % during the thermos-gravimetric test from room temperature to 1773 K. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Information Retrieval meets Large Language Models: A strategic report from Chinese IR community

Author

Ai, Qingyao, Bai, Ting, Cao, Zhao, Chang, Yi, Chen, Jiawei, Chen, Zhumin, Cheng, Zhiyong, Dong, Shoubin, Dou, Zhicheng, Feng, Fuli, Gao, Shen, Guo, Jiafeng, He, Xiangnan, Lan, Yanyan, Li, Chenliang, Liu, Yiqun, Lyu, Ziyu, Ma, Weizhi, Ma, Jun, Ren, Zhaochun, Ren, Pengjie, Wang, Zhiqiang, Wang, Mingwen, Wen, Ji-Rong, Wu, Le, Xin, Xin, Xu, Jun, Yin, Dawei, Zhang, Peng, Zhang, Fan, Zhang, Weinan, Zhang, Min, and Zhu, Xiaofei

Published
2023
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48. Dynamic global structure enhanced multi-channel graph neural network for session-based recommendation.

Author

Zhu, Xiaofei, Tang, Gu, Wang, Pengfei, Li, Chenliang, Guo, Jiafeng, and Dietze, Stefan

Subjects
*DISTRIBUTION (Probability theory), *RECOMMENDER systems, *INFORMATION filtering
Abstract

Session-based recommendation is a challenging task, which aims at making recommendation for anonymous users based on in-session data, i.e. short-term interaction data. Most session-based recommendation methods only model user's preferences with the current session sequence, which ignore rich information from a global perspective. Meanwhile, previous works usually apply GNN to capture the transformation relationship between items, however the graph used in GNN is built through a static mode, which may introduce noise to the graph structure if user's preferences shift. In this paper, we propose a novel method called Dynamic Global Structure Enhanced Multi-channel Graph Neural Network (DGS-MGNN) to learn accurate representations of items from multiple perspectives. In DGS-MGNN, we propose a novel GNN model named Multi-channel Graph Neural Network to generate the local, global and consensus graphs dynamically and learn more informative representations of items based on the corresponding graph. Meanwhile, in order to reduce the noise information within sessions, we utilize the graph structure to assist the attention mechanism to filter noisy information within each session, so as to generate an accurate intention representation for the user. Finally, combined with a repeat and explore module, a more accurate prediction probability distribution is generated. We conduct extensive experiments on three widely used datasets, and the results demonstrate that DGS-MGNN is consistently superior to the state-of-the-art baseline models. [ABSTRACT FROM AUTHOR]

Published
2023
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