Effects of Secondary Electron Emission on the Plasma Sheath and Local Electron Energy Distribution with Application to Hall Thrusters.

The nature of plasma transport across the magnetic field in crossed-field (CF) devices such as Hall effect thrusters (HETs) remains largely an unsolved problem. This can be further complicated by the presence of secondary electrons derived from the thrusters channel wall due to the impact of photons and electrons. The role of these secondary electrons in the operation of HETs has been a subject of investigation in recent years. Under normal operating conditions of a HET, several physical phenomena occur simultaneously and the interaction of the plasma with the channel walls of the thruster play an important role in its effective operation. These plasma wall interactions produce secondary electrons that have a non-linear coupling effect with the bulk plasma and affect the performance of crossed field devices by changing the sheath potential as well as the electron energy distribution. This influence is not yet fully understood in the community and thus the computational models are based on assumptions that are not highly accurate. Experimentally, there is little available data on the SEE yield in plasma and its effects to environments similar to that of a Hall thruster, which could be used to validate existing numerical models. A test-bed apparatus is needed to understand these effects that could serve as a tool to validate and improve existing numerical models by providing the appropriate boundary conditions, secondary yield coefficients and variation of plasma parameters to aid the future design of HETs. In this work, a bench-top apparatus is developed to elucidate the role that secondary electrons play in regards to crossed field transport and energy flow to the walls. An electron beam which simulates energetic electrons in Hall channel is used to generate a secondary electron plume at the surface of various targets (Cu, C, BN) which simulates channel wall. The response of the plasma to these secondary electrons is assessed by measuring changes to the potential distribution in the sheath of the irradiated target and the measured electron energy distribution. An attempt is made to relate phenomena and trends observed in this work with those in Hall thrusters.PhDNuclear Engineering and Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111614/1/sawlanik_1.pd

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final Section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary Section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required.</p

2022 Review of Data-Driven Plasma Science

International audienceData-driven science and technology offer transformative tools and methods to science. This review article highlightsthe latest development and progress in the interdisciplinary fieldof data-driven plasma science (DDPS), i.e., plasma science whoseprogress is driven strongly by data and data analyses. Plasma isconsidered to be the most ubiquitous form of observable matterin the universe. Data associated with plasmas can, therefore,cover extremely large spatial and temporal scales, and oftenprovide essential information for other scientific disciplines.Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a largeamount of data that can no longer be analyzed or interpretedmanually. This trend now necessitates a highly sophisticateduse of high-performance computers for data analyses, makingartificial intelligence and machine learning vital components ofDDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overviewof fundamental data-driven science, five other sections coverwidely studied topics of plasma science and technologies, i.e.,basic plasma physics and laboratory experiments, magneticconfinement fusion, inertial confinement fusion and high-energydensity physics, space and astronomical plasmas, and plasmatechnologies for industrial and other applications. The finalsection before the summary discusses plasma-related databasesthat could significantly contribute to DDPS. Each primary sectionstarts with a brief introduction to the topic, discusses the stateof-the-art developments in the use of data and/or data-scientificapproaches, and presents the summary and outlook. Despite therecent impressive signs of progress, the DDPS is still in its infancy.This article attempts to offer a broad perspective on the development of this field and identify where further innovations arerequired

2022 Review of Data-Driven Plasma Science

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future.Comment: 112 pages (including 700+ references), 44 figures, submitted to IEEE Transactions on Plasma Science as a part of the IEEE Golden Anniversary Special Issu