Next Generation Of Electron Cyclotron Emission Imaging Ecei Instrument For The J Text Tokamak And Data Processing And Visualization

Next Generation of Electron Cyclotron Emission Imaging (ECEI) Instrument for the J-TEXT Tokamak and Data Processing and Visualization

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.

The Next Generation of Electron Cyclotron Emission Imaging (ECEI) Diagnostics

Electron Cyclotron Emission Imaging and Applications in Magnetic Fusion Energy

Energy production through the burning of fossil fuels is an unsustainable practice. Exponentially increasing energy consumption and dwindling natural resources ensure that coal and gas fueled power plants will someday be a thing of the past. However, even before fuel reserves are depleted, our planet may well succumb to disastrous side effects, namely the build up of carbon emissions in the environment, thereby triggering world-wide climate change and the countless industrial spills of pollutants that continue to this day. Many alternatives are currently being developed, but none has so much promise as fusion nuclear energy, the energy of the sun. The confinement of hot plasma at temperatures in excess of 100 million Kelvin by a carefully arranged magnetic field for the realization of a self-sustaining fusion power plant requires new technologies and improved understanding of fundamental physical phenomena. Millimeter wave imaging of electron cyclotron radiation lends insight into the spatial and temporal behavior of electron temperature fluctuations and instabilities, providing a powerful diagnostic for investigations into basic plasma physics and nuclear fusion reactor operation. This dissertation presents the design and implementation of a new generation of Electron Cyclotron Emission Imaging (ECEI) diagnostics on toroidal magnetic fusion confinement devices, such as tokamaks and stellarators, around the world. The underlying physics of cyclotron radiation in fusion plasmas is reviewed, and a thorough discussion of millimeter wave imaging techniques and heterodyne radiometry in ECEI follows. The imaging of turbulence and fluid flows has evolved over half a millennium since Leonardo da Vinci's first sketches of cascading water, and applications for ECEI in fusion research are broad ranging. Two areas of physical investigation are discussed in this dissertation: the identification of poloidal shearing in Alfvén eigenmode structures predicted by hybrid gyrofluid-magnetohydrodynamic (gyrofluid-MHD) modeling, and magnetic field line displacement during precursor oscillations associated with the sawtooth crash, a disruptive instability observed both in tokamak plasmas with high core current and in the magnetized plasmas of solar flares and other interstellar plasmas. Understanding both of these phenomena is essential for the future of magnetic fusion energy, and important new observations described herein underscore the advantages of imaging techniques in experimental physics.