To predict stall and surge in advance that make the aero-engine compressor operatesafely,a stall prediction model based on deep learning theory is established in the current study.The Long Short-Term Memory(LSTM)origi...To predict stall and surge in advance that make the aero-engine compressor operatesafely,a stall prediction model based on deep learning theory is established in the current study.The Long Short-Term Memory(LSTM)originating from the recurrent neural network is used,and a set of measured dynamic pressure datasets including the stall process is used to learn whatdetermines the weight of neural network nodes.Subsequently,the structure and function hyperpa-rameters in the model are deeply optimized,and a set of measured pressure data is used to verify theprediction effects of the model.On this basis of the above good predictive capability,stall in low-and high-speed compressor are predicted by using the established model.When a period of non-stallpressure data is used as input in the model,the model can quickly complete the prediction of sub-sequent time series data through the self-learning and prediction mechanism.Comparison with thereal-time measured pressure data demonstrates that the starting point of the predicted stall is basi-cally the same as that of the measured stall,and the stall can be predicted more than 1 s in advanceso that the occurrence of stall can be avoided.The model of stall prediction in the current study canmake up for the uncertainty of threshold selection of the existing stall warning methods based onmeasured data signal processing.It has a great application potential to predict the stall occurrenceof aero-engine compressor in advance and avoid the accidents.展开更多
[Objectives]This study was conducted to investigate the differences in antimony tolerance among different maize varieties.[Methods]The effects of antimony stress(1000 mg/L)on seed germination and seedling growth of 26...[Objectives]This study was conducted to investigate the differences in antimony tolerance among different maize varieties.[Methods]The effects of antimony stress(1000 mg/L)on seed germination and seedling growth of 26 maize varieties cultivated in Hunan Province were studied.[Results]The antimony stress had little effect on germination rate and germination index of maize seeds,but a significant effect on seed vigor index and root growth;and the antimony stress inhibited shoots less than roots.The tolerance of different maize varieties to antimony stress was quite different,and cluster analysis could divide the tested varieties into three types:susceptible type,intermediate type and tolerant type.Among them,Zhaoyu 999,Huayu 130,Changyu 1,Qingqingyu 800,Qiandan 12 and Huangdan 008 had strong antimony tolerance.[Conclusions]This study is of great significance to the screening of antimony-tolerant maize varieties for breeding research and the planting and application in antimony-contaminated areas around mining areas.展开更多
基金funded by the National Natural Science Foundation of China(No.52376039 and U24A20138)the Beijing Natural Science Foundation of China(No.JQ24017)+1 种基金the National Science and Technology Major Project of China(Nos.J2019-II-0005-0025 and Y2022-Ⅱ-0002-0005)the Special Fund for the Member of Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2018173)。
文摘To predict stall and surge in advance that make the aero-engine compressor operatesafely,a stall prediction model based on deep learning theory is established in the current study.The Long Short-Term Memory(LSTM)originating from the recurrent neural network is used,and a set of measured dynamic pressure datasets including the stall process is used to learn whatdetermines the weight of neural network nodes.Subsequently,the structure and function hyperpa-rameters in the model are deeply optimized,and a set of measured pressure data is used to verify theprediction effects of the model.On this basis of the above good predictive capability,stall in low-and high-speed compressor are predicted by using the established model.When a period of non-stallpressure data is used as input in the model,the model can quickly complete the prediction of sub-sequent time series data through the self-learning and prediction mechanism.Comparison with thereal-time measured pressure data demonstrates that the starting point of the predicted stall is basi-cally the same as that of the measured stall,and the stall can be predicted more than 1 s in advanceso that the occurrence of stall can be avoided.The model of stall prediction in the current study canmake up for the uncertainty of threshold selection of the existing stall warning methods based onmeasured data signal processing.It has a great application potential to predict the stall occurrenceof aero-engine compressor in advance and avoid the accidents.
基金Supported by Hunan Provincial Postgraduate Education Innovation Project and Professional Ability Improvement Project(CX20201200,CX20211220)Scientific Research Project of the Department of Education of Hunan Province(20A278).
文摘[Objectives]This study was conducted to investigate the differences in antimony tolerance among different maize varieties.[Methods]The effects of antimony stress(1000 mg/L)on seed germination and seedling growth of 26 maize varieties cultivated in Hunan Province were studied.[Results]The antimony stress had little effect on germination rate and germination index of maize seeds,but a significant effect on seed vigor index and root growth;and the antimony stress inhibited shoots less than roots.The tolerance of different maize varieties to antimony stress was quite different,and cluster analysis could divide the tested varieties into three types:susceptible type,intermediate type and tolerant type.Among them,Zhaoyu 999,Huayu 130,Changyu 1,Qingqingyu 800,Qiandan 12 and Huangdan 008 had strong antimony tolerance.[Conclusions]This study is of great significance to the screening of antimony-tolerant maize varieties for breeding research and the planting and application in antimony-contaminated areas around mining areas.
