|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Stall flutter prediction based on multi-layer GRU neural network显示文摘The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model(ROM) framework for predicting the aerodynamic forces of an airfoil undergoing large-amplitude pitching oscillation at various velocities is presented in this work. First, the dynamic stall aerodynamics is calculated by solving RANS equations and the transitional SST-γ model. Afterwards, the stall flutter bifurcation behavior is calculated by the above CFD solver coupled with structural dynamic equation. The critical flutter speed and limit-cycle oscillation amplitudes are consistent with those obtained by experiments. A newly multi-layer Gated Recurrent Unit(GRU) neural network based ROM is constructed to accelerate the calculation of aerodynamic forces. The training and validation process are carried out upon the unsteady aerodynamic data obtained by the proposed CFD method. The well-trained ROM is then coupled with the structure equation at a specific velocity, the Limit-Cycle Oscillation(LCO) of stall flutter under this flow condition is predicted precisely and more quickly. In order to predict both the critical flutter velocity and LCO amplitudes after bifurcation at different velocities, a new ROM with GRU neural network considering the variation of flow velocities is developed. The stall flutter results predicted by ROM agree well with the CFD ones at different velocities. Finally, a brief sensitivity analysis of two structural parameters of ROM is carried out. It infers the potential of the presented modeling method to depict the nonlinearity of dynamic stall and stall flutter phenomenon. | Yuting DAI Haoran RONG You WU Chao YANG Yuntao XU | 2023 | Chinese Journal of Aeronautics2023，36，1： | 1 |
| 2 | Temporal and Spatial Distribution of SARS-CoV-2 Aerosols in a Large-Scale Fangcang Shelter Hospital in Shanghai,China显示文摘The coronavirus disease 2019(COVID-19)pandemic caused by frequently mutating severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)has had a worldwide impact.However,detailed data on the potential aerosol transmission of SARS-CoV-2 in real-world and controlled laboratory settings remain sparse.During the COVID-19 pandemic in Shanghai,China in 2022,samples were collected in a Fangcang shelter hospital,a large-scale temporary hospital rapidly built by converting the existing National Exhibition and Convention Center(Shanghai)into a health care facility.Aerosol samples at different sites and intervals around patients and in public areas,surface samples,and pharyngeal swab samples from corresponding patients were included.Samples were tested for SARS-CoV-2 using real-time quantitative polymerase chain reaction(RT-qPCR)assays,followed by sequencing if the cycle threshold(Ct)value was<30.The positivity rate for SARS-CoV-2 in aerosol samples was high in contaminated zones(37.5%,104/277),especially around the bed(41.2%,68/165)and near ventilation inlets(45.2%,14/31).The prevalence of SARS-CoV-2 around the bed,public areas,and air inlets of exhaust vents fluctuated and was closely related to the positivity rate among patients at corresponding sampling sites.Some surface samples of different personal protective equipment from medical staff had high positivity rates.Sixty sequences of joined ORF1ab and spike genes obtained from sixty samples represented two main clusters of Omicron SARS-CoV-2.There was consistency in virus sequences from the same patient and their environment,and the detected virus sequences matched those of virus strains in circulation during the collection periods,which indicated a high likelihood of cross-contamination in the Fangcang shelter hospital.In summary,the results provide a quantitative and real landscape of the aerosol transmission of SARS-CoV-2 and a patient-centered view of contamination in large and enclosed spaces and offer a useful guide for taking targeted measures to avoid nosocomial infections during the management of SARS-CoV-2 or other respiratory virus diseases in a Fangcang shelter hospital. | Jiafu Jiang Zhe Yin Jing Li Leili Jia Rulin He Wenhui Yang Jihu Yang Hang Fan Sen Zhang Yunfei Wang Zengming Zhao Haoran Peng Lizhong Li Yi Yang Shi-Yong Fan Rong Xiang Jianshu Guo Jinjin Wang Juanning Wei Fengling Zhou Ding Liu Ping Zhao Yujun Cui Yunxi Liu Dongsheng Zhou Gang Dong | 2023 | Engineering2023，，9： | 0 |
| 3 | Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning显示文摘Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method. | Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng | 2023 | Biophysics Reports2023，9，2： | 0 |
| 4 | FT-ICR mass spectrometry for molecular characterization of water-insoluble organic compounds in winter atmospheric fine particulate matters显示文摘Water-insoluble organic compounds(WIOCs)are an important fraction of atmospheric fine particulate matters(PM_(2.5)),which could affect the climate system and threaten human health potentially.In this study,molecular characterization of WIOCs in PM_(2.5) were investigated by 15 T Fourier transform ion cyclotron resonance mass spectrometry(FT-ICR MS)with atmospheric pressure photoionization(APPI)source in positive ion mode.A total of 2573 and1875 molecular formulas were identified in WIOCs extracted by dichloromethane,which were collected in hazy and normal days,respectively.The identified molecular formulas were further classified into four major subgroups,including CH,CHN,CHO and CHNO compounds.CHO compounds predominated in WIOCs,accounting for more than 60%in both samples.CHNO compounds(26.6%)and CH compounds(16.1%)were the second highest subgroups in WIOCs from the hazy days and normal days,respectively.The relative abundance and number of nitro-substituted aromatic compounds were significantly higher in hazy days than in normal days.The molecular composition of WIOCs was more complex in hazy days while more aromatic compounds were identified in normal days. | Cuiping Ning Yuan Gao Haoran Yu Haijun Zhang Ningbo Geng Rong Cao Jiping Chen | 2022 | Journal of Environmental Sciences2022，34，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员