Stimulated Raman scattering(SRS)is one of the main instabilities affecting success of fusion ignition.Here,we study the relationship between Raman growth and Landau damping with various distribution functions combinin...Stimulated Raman scattering(SRS)is one of the main instabilities affecting success of fusion ignition.Here,we study the relationship between Raman growth and Landau damping with various distribution functions combining the analytic formulas and Vlasov simulations.The Landau damping obtained by Vlasov-Poisson simulation and Raman growth rate obtained by Vlasov-Maxwell simulation are anti-correlated,which is consistent with our theoretical analysis quantitatively.Maxwellian distribution,flattened distribution,and bi-Maxwellian distribution are studied in detail,which represent three typical stages of SRS.We also demonstrate the effects of plateau width,hot-electron fraction,hot-to-cold electron temperature ratio,and collisional damping on the Landau damping and growth rate.They gives us a deep understanding of SRS and possible ways to mitigate SRS through manipulating distribution functions to a high Landau damping regime.展开更多
Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters,so monitoring is required.Data collected by structural health monitoring(SHM)systems are easily affected by many fa...Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters,so monitoring is required.Data collected by structural health monitoring(SHM)systems are easily affected by many factors,such as temperature,sensor fluctuation,sensor failure,which can introduce a lot of noise,increasing the difficulty of structural anomaly identification.To address this problem,this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder(CIDAE),a denoising autoencoder-based deep learning model for SHM of civil infrastructure.As a case study,the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation.Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted.It is concluded that CIDAE is superior to traditional methods.展开更多
基金supported by the National Key Research and Development Program of China(2018YFB2101003)the National Natural Science Foundation of China(51991395,51991391,71901011,and U1811463)。
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA25050700)the National Natural Science Foundation of China(Grant Nos.11805062,11875091 and 11975059)+1 种基金the Science Challenge Project(Grant No.TZ2016005)the Natural Science Foundation of Hunan Province,China(Grant No.2020JJ5029)。
文摘Stimulated Raman scattering(SRS)is one of the main instabilities affecting success of fusion ignition.Here,we study the relationship between Raman growth and Landau damping with various distribution functions combining the analytic formulas and Vlasov simulations.The Landau damping obtained by Vlasov-Poisson simulation and Raman growth rate obtained by Vlasov-Maxwell simulation are anti-correlated,which is consistent with our theoretical analysis quantitatively.Maxwellian distribution,flattened distribution,and bi-Maxwellian distribution are studied in detail,which represent three typical stages of SRS.We also demonstrate the effects of plateau width,hot-electron fraction,hot-to-cold electron temperature ratio,and collisional damping on the Landau damping and growth rate.They gives us a deep understanding of SRS and possible ways to mitigate SRS through manipulating distribution functions to a high Landau damping regime.
基金supported by the National Natural Science Foundation of China(Grant Nos.51991395,51991391,and U1811463)the S&T Program of Hebei,China(No.225A0802D).
文摘Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters,so monitoring is required.Data collected by structural health monitoring(SHM)systems are easily affected by many factors,such as temperature,sensor fluctuation,sensor failure,which can introduce a lot of noise,increasing the difficulty of structural anomaly identification.To address this problem,this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder(CIDAE),a denoising autoencoder-based deep learning model for SHM of civil infrastructure.As a case study,the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation.Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted.It is concluded that CIDAE is superior to traditional methods.
