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您的检索式：作者名="XiaoZhe Wan"
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| 1 | Integrated optimization on aerodynamics-structure coupling and flight stability of a large airplane in preliminary design显示文摘The preliminary phase is significant during the whole design process of a large airplane because of its enormous potential in enhancing the overall performance. However, classical sequential designs can hardly adapt to modern airplanes, due to their repeated iterations, long periods, and massive computational burdens. Multidisciplinary analysis and optimization demonstrates the capability to tackle such complex design issues. In this paper, an integrated optimization method for the preliminary design of a large airplane is proposed, accounting for aerodynamics, structure, and stability. Aeroelastic responses are computed by a rapid three-dimensional flight load analysis method combining the high-order panel method and the structural elasticity correction. The flow field is determined by the viscous/inviscid iteration method, and the cruise stability is evaluated by the linear small-disturbance theory. Parametric optimization is carried out using genetic algorithm to seek the minimal weight of a simplified plate-beam wing structure in the cruise trim condition subject to aeroelastic, aerodynamic, and stability constraints, and the optimal wing geometry shape, front/rear spar positions, and structural sizes are obtained simultaneously. To reduce the computational burden of the static aeroelasticity analysis in the optimization process, the Kriging method is employed to predict aerodynamic influence coefficient matrices of different aerodynamic shapes. The multidisciplinary analyses guarantee computational accuracy and efficiency, and the integrated optimization considers the coupling effect sufficiently between different disciplines to improve the overall performance, avoiding the limitations of sequential approaches utilized currently. | Xiaozhe WANG Zhiqiang WAN Zhu LIU Chao YANG | 2018 | Chinese Journal of Aeronautics2018，31，6： | 2 |
| 2 | Drug target inference by mining transcriptional data using a novel graph convolutional network framework显示文摘A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or offtarget effects.Recently,the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology.Here,we propose a novel Siamese spectral-based graph convolutional network(SSGCN)model for inferring the protein targets of chemical compounds from gene transcriptional profiles.Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets,and the biological networks under different experiment conditions further complicate the situation,the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles.On a benchmark set and a large time-split validation dataset,the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map.Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound,or reversely,in finding novel inhibitors of a given target of interest. | Feisheng Zhong Xiaolong Wu Ruirui Yang Xutong Li Dingyan Wang Zunyun Fu Xiaohong Liu XiaoZhe Wan Tianbiao Yang Zisheng Fan Yinghui Zhang Xiaomin Luo Kaixian Chen Sulin Zhang Hualiang Jiang Mingyue Zheng | 2022 | Protein & Cell2022，13，4： | 1 |
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