Engineering topics

Kovach, Yao E.

Publications and source records attributed to Kovach, Yao E..

4 records

DOE OSTI2021

Particle emission with identification from an atmospheric pressure plasma liquid interface

A conspicuous emission phenomenon of luminous particles from the liquid anode surface of an atmospheric pressure DC glow discharge is reported. The emission has been shown to occur when the plasma forms a pattern on the surface of the liquid electrolyte. Here, the spatial-temporal evolutions of the trajectories of emitted particles were studied using a high-speed imaging system. Particles were sampled in flight using a witness plate for analysis. Critical particle characteristics were examined using electron microscope technologies. The morphology of the resulting splats and compositions suggest that they are molten droplets with a great deal of structure, including evidence of nanoprecipitation. A theoretical model was employed to estimate the particle size from the measured splats, which provides a radius range within a hundred micrometers. This experiment leads to the postulation of a mechanism in which particle emission at the plasma liquid interface is likely driven by the Taylor cone effect.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

The variation in self-organized anode plasma pattern structure with solution electrolyte type in 1 atm DC glow discharge

Abstract Plasma self-organization on anode surfaces in 1 atm DC glow discharges remains poorly understood. This effort aims to elucidate the nature of self-organization through the experimental study of resulting patterns on liquid anode surfaces with 13 different electrolytes and thus improves our understanding of the underlying physical processes that give rise to self-organization by investigating electrolyte sensitivity. Self-organization pattern formation and behavior were studied as a function of discharge current, solution ionic strength, and their chemical property evaluation. The response of the patterns to variation in these parameters was measured using an imaging camera and optical emission spectroscopy. Observed pattern characteristic length scales for all of the electrolytes were ranged from 2 to 13 mm and typically increased with current over the investigated range of 20–80 mA. Complex self-organized pattern structures not reported to date were also observed. The parameters associated with pattern formation and morphology complexity are discussed and summarized.

Physics↗

DOE OSTI2020

Self-organization in 1 atm DC glows with liquid anodes: current understanding and potential applications

Self-organization refers to the spontaneous generation of spatially or temporally organized patterns in an otherwise disordered system. Self-organization is ubiquitous in plasma physics particularly in the low-pressure regime as observed in astrophysical jets or plasma loaded flux loops that form on the surface of the Sun. In recent times, self-organization in atmospheric pressure plasmas has captured the attention of researchers. Its occurrence has been observed in DBD discharges as well as DC 1 atm glows with liquid electrodes. The mechanism of pattern formation is still not well understood. In this work, we briefly review the current understanding of pattern formation in DC glows with liquid anode, surveying past work, application areas, theories on mechanisms of formation from the context of reaction diffusion systems, current experimental work and computational progress towards predicting pattern formation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Microparticle cloud imaging and tracking for data-driven plasma science

Oceans of image and particle track data encountered in plasma interactions with microparticle clouds motivate development and applications of machine-learning (ML) algorithms. A local-constant-velocity tracker, a Kohonen neural network or self-organizing map, the feature tracking kit, and U-Net are described and compared with each other for microparticle cloud datasets generated from exploding wires, dusty plasmas, and atmospheric plasmas. Particle density and the signal-to-noise ratio have been identified as two important factors that affect the tracking accuracy. Fast Fourier transform is used to reveal how U-Net, a deep convolutional neural network developed for non-plasma applications, achieves the improvements for noisy scenes. Viscous effects are revealed in the ballistic motions of the particles from the exploding wires and atmospheric plasmas. Subdiffusion of microparticles satisfying Δr 2 ∝t k (k=0.84±0.02) is obtained from the dusty plasma datasets. Microparticle cloud imaging and tracking, when enhanced with data and ML models, present new possibilities for plasma physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗