Download or read book Next Generation of Electron Cyclotron Emission Imaging (ECEI) Instrument for the J-TEXT Tokamak and Data Processing and Visualization written by Jinhua Cao and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation work, an Electron Cyclotron Emission Imaging (ECEI) instrument has been developed for electron temperature fluctuation visualization measurements in the experimental fusion plasma device on the J-TEXT (located at Huazhong University of Science and Technology in Wuhan, China). The ECEI instrument provides centimeter level spatial resolution and microsecond level temporal resolution. Two observation windows (128 pixel-channel) are used to image separate radial depths in the plasma. The 256-channel system was successfully installed on J-TEXT in 2019. An intelligent control module has been developed and applied on one million frame per second (SPS) imaging system. The multiple radial zoom options are used to measure large radial coherent structures and fine structures with high resolution, and which are able to switch flexibly. Signal levels are optimized by the feedback control to match the dynamic measurement range facing different plasma scenarios. A system configuration logfile can be saved. In addition, the preset and manual training options are available for operator to calibrate the system before each experiment. A large package of raw data (36 GB daily) will be generated by the high spatial and temporal resolution ECEI diagnostic system. To address this issue, a general graphical analysis process routine has been developed for 2D temperature fluctuation profiles for ECEI. The ECEI analysis program has been developed and released by UC Davis for the J-TEXT ECEI system. Both narrowband and broadband MHD instabilities are clearly presented in the ECEI frequency spectra. The characteristics of MHD evolution are clearly described by 2D electron temperature animations. Artificial intelligence technology has been applied to automatically detect and separate each of the MHD modes. The primary algorithms used are OpenCV Canny Edge Detection and Depth-first search (DFS). The machine learning algorithm of Random Forest has been applied to classify the so-called edge localized modes.