Light field tomography,an optical combustion diagnostic technology,has recently attracted extensive attention due to its easy implementation and non-intrusion.However,the conventional iterative methods are high data t...Light field tomography,an optical combustion diagnostic technology,has recently attracted extensive attention due to its easy implementation and non-intrusion.However,the conventional iterative methods are high data throughput,low efficiency and time-consuming,and the existing machine learning models use the radiation spectrum information of the flame to realize the parameter field measurement at the current time.It is still an offline measurement and cannot realize the online prediction of the instantaneous structure of the actual turbulent combustion field.In this work,a novel online prediction model of flame temperature instantaneous structure based on deep convolutional neural network and long short-term memory(CNN-LSTM)is proposed.The method uses the characteristics of local perception,shared weight,and pooling of CNN to extract the threedimensional(3D)features of flame temperature and outgoing radiation images.Moreover,the LSTM is used to comprehensively utilize the ten historical time series information of high dynamic combustion flame to accurately predict 3D temperature at three future moments.A chaotic time-series dataset based on the flame radiation forward model is built to train and validate the performance of the proposed CNN-LSTM model.It is proven that the CNN-LSTM prediction model can successfully learn the evolution pattern of combustion flame and make accurate predictions.展开更多
The Space Advanced Technology demonstration satellite(SATech-01),a mission for low-cost space science and new technology experiments,organized by Chinese Academy of Sciences(CAS),was successfully launched into a Sun-s...The Space Advanced Technology demonstration satellite(SATech-01),a mission for low-cost space science and new technology experiments,organized by Chinese Academy of Sciences(CAS),was successfully launched into a Sun-synchronous orbit at an altitude of~500 km on July 27,2022,from the Jiuquan Satellite Launch Centre.Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit,SATech-01 is equipped with 16 experimental payloads,including the solar upper transition region imager(SUTRI),the lobster eye imager for astronomy(LEIA),the high energy burst searcher(HEBS),and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer(CPT).It also incorporates an imager with freeform optics,an integrated thermal imaging sensor,and a multi-functional integrated imager,etc.This paper provides an overview of SATech-01,including a technical description of the satellite and its scientific payloads,along with their on-orbit performance.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.51976044,and 52227813)the Foundation for Heilongjiang Touyan Innovation Team Program。
文摘Light field tomography,an optical combustion diagnostic technology,has recently attracted extensive attention due to its easy implementation and non-intrusion.However,the conventional iterative methods are high data throughput,low efficiency and time-consuming,and the existing machine learning models use the radiation spectrum information of the flame to realize the parameter field measurement at the current time.It is still an offline measurement and cannot realize the online prediction of the instantaneous structure of the actual turbulent combustion field.In this work,a novel online prediction model of flame temperature instantaneous structure based on deep convolutional neural network and long short-term memory(CNN-LSTM)is proposed.The method uses the characteristics of local perception,shared weight,and pooling of CNN to extract the threedimensional(3D)features of flame temperature and outgoing radiation images.Moreover,the LSTM is used to comprehensively utilize the ten historical time series information of high dynamic combustion flame to accurately predict 3D temperature at three future moments.A chaotic time-series dataset based on the flame radiation forward model is built to train and validate the performance of the proposed CNN-LSTM model.It is proven that the CNN-LSTM prediction model can successfully learn the evolution pattern of combustion flame and make accurate predictions.
基金supported by the Strategic Priority Program on Space Science,the Chinese Academy of Sciences。
文摘The Space Advanced Technology demonstration satellite(SATech-01),a mission for low-cost space science and new technology experiments,organized by Chinese Academy of Sciences(CAS),was successfully launched into a Sun-synchronous orbit at an altitude of~500 km on July 27,2022,from the Jiuquan Satellite Launch Centre.Serving as an experimental platform for space science exploration and the demonstration of advanced common technologies in orbit,SATech-01 is equipped with 16 experimental payloads,including the solar upper transition region imager(SUTRI),the lobster eye imager for astronomy(LEIA),the high energy burst searcher(HEBS),and a High Precision Magnetic Field Measurement System based on a CPT Magnetometer(CPT).It also incorporates an imager with freeform optics,an integrated thermal imaging sensor,and a multi-functional integrated imager,etc.This paper provides an overview of SATech-01,including a technical description of the satellite and its scientific payloads,along with their on-orbit performance.
