Deflection of carbon dioxide laser and helium‐neon laser beams in a long‐pulse relativistic electron beam diode

Deflection of carbon dioxide and helium‐neon laser beams has been used to measure plasma and neutral density gradients during the operating mode and after the shorting time of a long‐pulse field‐emission electron beam diode. Plasma density gradients of (1014–1015) cm−4 were observed throughout the diode during the final microsecond of the 2–3 μs electron beam pulse. The neutral density gradient was less than 1×1018 cm−4 during the electron beam pulse. Upon diode shorting, neutral density gradients increased to (1018–1019) cm−4 over ∼1 μs, and decayed over many microseconds. Plasma density gradients of ∼1015 cm−4 were also observed after shorting. These experiments demonstrate the value of carbon‐dioxide laser and helium‐neon laser deflection for diagnosing plasma and neutral particles in long‐pulse electron beam diodes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70220/2/RSINAK-62-7-1776-1.pd

Effect of Electron Energy Distribution Function on Power Deposition and Plasma Density in an Inductively Coupled Discharge at Very Low Pressures

A self-consistent 1-D model was developed to study the effect of the electron energy distribution function (EEDF) on power deposition and plasma density profiles in a planar inductively coupled plasma (ICP) in the non-local regime (pressure < 10 mTorr). The model consisted of three modules: (1) an electron energy distribution function (EEDF) module to compute the non-Maxwellian EEDF, (2) a non-local electron kinetics module to predict the non-local electron conductivity, RF current, electric field and power deposition profiles in the non-uniform plasma, and (3) a heavy species transport module to solve for the ion density and velocity profiles as well as the metastable density. Results using the non-Maxwellian EEDF model were compared with predictions using a Maxwellian EEDF, under otherwise identical conditions. The RF electric field, current, and power deposition profiles were different, especially at 1mTorr, for which the electron effective mean free path was larger than the skin depth. The plasma density predicted by the Maxwellian EEDF was up to 93% larger for the conditions examined. Thus, the non-Maxwellian EEDF must be accounted for in modeling ICPs at very low pressures.Comment: 19 pages submitted to Plasma Sources Sci. Techno

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

QDB: A new database of plasma chemistries and reactions

One of the most challenging and recurring problems when modeling plasmas is the lack of data on the key atomic and molecular reactions that drive plasma processes. Even when there are data for some reactions, complete and validated datasets of chemistries are rarely available. This hinders research on plasma processes and curbs development of industrial applications. The QDB project aims to address this problem by providing a platform for provision, exchange, and validation of chemistry datasets. A new data model developed for QDB is presented. QDB collates published data on both electron scattering and heavy-particle reactions. These data are formed into reaction sets, which are then validated against experimental data where possible. This process produces both complete chemistry sets and identifies key reactions that are currently unreported in the literature. Gaps in the datasets can be filled using established theoretical methods. Initial validated chemistry sets for SF 6 /CF 4 /O 2 and SF 6 /CF 4 /N 2 /H 2 are presented as examples