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Your search keyword '"Shu, Bowen"' showing total 48 results
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48 results on '"Shu, Bowen"'

3. Sensitivity Analysis of Infrared Radiation from Tailless Aircraft Nozzle Caused by Incoming Conditions

Author

He, Chengjun, Shu, Bowen, Huang, Jiangtao, Chen, Cheng, Chen, Xian, Zheng, Haibo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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4. Air Inlet/Airframe Integration Optimization Based on the Discrete Adjoint Method

Author

Shu, Bowen, He, Chengjun, Huang, Jiangtao, Gao, Zhenghong, Chen, Xian, Deng, Jun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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7. Hierarchical optimization method of supersonic low sonic boom configuration

Author

MA Chuang, HUANG Jiangtao, SHU Bowen, LIU Gang, and ZHONG Shidong

Subjects
supersonic civil aircraft, sonic boom suppression, decision tree, configuration design, evolutionary algorithms, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

Sonic boom suppression is a key technology that must be broken through in the development of a new generation of supersonic civil aircraft. The reasonable design of configuration parameters can make the aircraft have good sonic boom characteristics. In order to break through the bottleneck of evolutionary algorithm in optimizing large-scale design variables, a hierarchical optimization method based on data mining is proposed, and the decision tree (DT) algorithm based data mining is used to extract the design knowledge, obtain the hierarchical information of design variables, and guide the configuration optimization of low sonic boom aircraft. For a low boom supersonic aircraft, five configuration parameters, including sweep angle, aspect ratio, taper ratio, dihedral angle and fuselage slenderness ratio, are selected as design variables to carry out the numerical experiments of hierarchical optimization, and the method is performed with comparison verification with the integrated optimization method. The results show that the hierarchical optimization method can obtain the optimal solution consistent with the integrated optimization method, and the convergence speed of hierarchical optimization method is significantly faster than that of integrated optimization method, and the performance of different optimization processes is more robust.

Published
2023
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9. On the Correction of k-ω SST Turbulence Model to Three-Dimensional Shock Separated Flow

Author

Du, Yiming, Shu, Bowen, Gao, Zhenghong, Nie, Shengyang, Ma, Rui, Chinese Society of Aeronautics and Astronautics, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, and Zhang, Junjie James, Series Editor

Published
2022
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16. Airfoil Design Optimization of Blended Wing Body for Various Aerodynamic and Stealth Stations.

Author

Zhang, Wei, Zhou, Lin, Zhao, Ke, Zhang, Ruibin, Gao, Zhenghong, and Shu, Bowen

Subjects
DRAG shows, DESIGN exhibitions, AEROFOILS, AERODYNAMICS, CONTRADICTION
Abstract

The airfoil is the foundation of an aircraft, and its characteristics have a significant impact on those of the aircraft. Conventional airfoil design mainly focuses on improving aerodynamic performance, while flying wing airfoil designs should also consider layout stability and stealth performance. The design requirements for an airfoil vary with its position on the flying wing layout aircraft based on corresponding spanwise flow field characteristics. By analyzing the spanwise flow characteristics of the flying wing, partition design models for flying wing airfoils were established in this study, and a series of flying wing airfoil designs that consider aerodynamics and aerodynamic/stealth were implemented. Then, the designed airfoils were configured on a three-dimensional X-47B layout for testing and verification. The results showed that the aerodynamic design and the aerodynamic/stealth design exhibited significant improvements in terms for aerodynamic and longitudinal trimming characteristics. However, the cruise drag performance of the aerodynamic/stealth design was slightly worse than that of the aerodynamic design, although the longitudinal moment trimming characteristics were basically the same. The stealth characteristics of the aerodynamic/stealth design had significant advantages, indicating that there were weak contradictions between the aerodynamic, stealth, and trimming requirements in the design of the flying wing. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Aggregation‐Induced Emission‐Based Macrophage‐Like Nanoparticles for Targeted Photothermal Therapy and Virus Transmission Blockage in Monkeypox

Author

Li, Bin, primary, Wang, Wei, additional, Zhao, Lu, additional, Li, Mengjun, additional, Yan, Dingyuan, additional, Li, Xiaoxue, additional, Zhang, Jie, additional, Gao, Qiuxia, additional, Feng, Yi, additional, Zheng, Judun, additional, Shu, Bowen, additional, Yan, Yan, additional, Wang, Jiamei, additional, Wang, Huanhuan, additional, He, Lingjie, additional, Wu, Yunxia, additional, Zhou, Sitong, additional, Qin, Xinchi, additional, Chen, Wentao, additional, Qiu, Kaizhen, additional, Shen, Chenguang, additional, Wang, Dong, additional, Tang, Ben Zhong, additional, and Liao, Yuhui, additional

Published
2023
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25. Emergency Treatment and Photoacoustic Assessment of Spinal Cord Injury Using Reversible Dual‐Signal Transform‐Based Selenium Antioxidant (Small 35/2023)

Author

Ji, Zhisheng, primary, Zheng, Judun, additional, Ma, Yanming, additional, Lei, Hongyi, additional, Lin, Weiqiang, additional, Huang, Jialin, additional, Yang, Hua, additional, Zhang, Guowei, additional, Li, Bin, additional, Shu, Bowen, additional, Du, Xianjin, additional, Zhang, Jian, additional, Lin, Hongsheng, additional, and Liao, Yuhui, additional

Published
2023
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27. NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing.

Author

Lin, Yusen, Zhang, Yongjun, Sun, Hang, Jiang, Hang, Zhao, Xing, Teng, Xiaojuan, Lin, Jingxia, Shu, Bowen, Sun, Hao, Liao, Yuhui, and Zhou, Jiajian

Subjects
DEEP learning, CONVOLUTIONAL neural networks, MICROBIAL genomes, BACTERIAL genomes, SHOTGUN sequencing, DNA sequencing, HUMAN origins
Abstract

Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequencing. We present a deep learning framework, NanoDeep, to overcome these limitations by incorporating convolutional neural network and squeeze and excitation. We first showed that the raw squiggle derived from native DNA sequences determines the origin of microbial and human genomes. Then, we demonstrated that NanoDeep successfully classified bacterial reads from the pooled library with human sequence and showed enrichment for bacterial sequence compared with routine nanopore sequencing setting. Further, we showed that NanoDeep improves the sequencing efficiency and preserves the fidelity of bacterial genomes in the mock sample. In addition, NanoDeep performs well in the enrichment of metagenome sequences of gut samples, showing its potential applications in the enrichment of unknown microbiota. Our toolkit is available at https://github.com/lysovosyl/NanoDeep. [ABSTRACT FROM AUTHOR]

Published
2024
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