Non-transferred Arc Torch Simulation by a Non-equilibrium Plasma Laminar-to-Turbulent Flow Model

Modirkhazeni, S. Mahnaz; Trelles, Juan Pablo

doi:10.1007/s11666-018-0765-4

Cited by 18 publications

(11 citation statements)

References 63 publications

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“…Heavy particle temperature. The spatiotemporal evolution law of T h can be obtained by substituting the heavy particle number density into equation (7), as shown in figure 10. T h in the arc burning stage is distributed in a gradient along the radial direction and decays smoothly from the center of the arc.…”

Section: Temperaturementioning

confidence: 99%

“…Theory: electron collisions that originally play a leading role in reactions, such as excitation and ionization, are weakened because of the low arc current. The temperature of electrons differs from that of the heavier particles in the arc, and the plasma will be in a nonlocal thermodynamic equilibrium (NLTE) state [7]. The NLTE state includes two different chemical equilibrium states: local chemical equilibrium (LCE) and nonlocal chemical equilibrium (NLCE).…”

Section: Introductionmentioning

confidence: 99%

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Measurement of medium-voltage AC air arc temperature and particle number density based on dual-wavelength Moiré deflection technology

Zhou,

Yang,

Yuan

et al. 2024

J. Phys. D: Appl. Phys.

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AC air arcs are generated in medium-voltage (MV) power systems under the effect of harsh weather conditions, equipment aging, and high penetration of distributed generation, threatening equipment and public safety. The arc current and temperature are low due to the wide application of arc suppression devices. In this scenario, the MV AC air arc does not satisfy the local thermodynamic equilibrium (LTE) condition. In addition, the repeated arcing and extinguishing processes further complicate the arc discharge mechanism, which bring challenges in the modeling and detection of MV AC air arcs. Experimental methods are a direct and efficient approach to determine the properties of arc plasmas. In this study, a dual-wavelength moiré deflection diagnostic system was established to determine the time evolution of the particle density and radial distribution of the temperature in an MV AC air arc without relying on the LTE assumption. The electron number density and heavy particle number density change transiently during the arc discharge process and change gradient along the radial direction. The heavy particle temperature and electron temperature were then calculated based on the measured particle number density. During the arcing stage, the temperature of the electrons exceeded that of the heavy particles significantly, and the arc deviated from LTE. Finally, the limitations of the traditional single-wavelength moiré deflection method are analyzed. The classic single-wavelength moiré deflection method, while capable of estimating heavy particle temperature in plasma, exhibits a significant error in electron density estimation compared to the dual-wavelength moiré deflection method.

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Section: Temperaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement of medium-voltage AC air arc temperature and particle number density based on dual-wavelength Moiré deflection technology

Zhou,

Yang,

Yuan

et al. 2024

J. Phys. D: Appl. Phys.

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“…The intricacies of the modeling of turbulence in thermal plasma flows is discussed in the review article by Shigeta [78]. The state-of-the-science in the simulation of turbulence in thermal plasma flows is demonstrated by the work of Shigeta [79, [80] of a turbulent jet for nanopowder production, and by the work of Modir Khazeni and Trelles [55] of the flow in a non-transferred arc plasmas torch. Those simulations rely on coarse-grained approximations, such as the use of so-called sub-grid models, to account for the effects of flow features of smaller extent than the discretization used [92].…”

Section: Instability Chaos and Turbulencementioning

confidence: 99%

Nonequilibrium Phenomena in (Quasi-)thermal Plasma Flows

Trelles

2019

Plasma Chem Plasma Process

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Thermal plasmas are utilized in diverse applications that require high power densities or throughputs, such as metal cutting, welding, spraying, metallurgy, and materials synthesis. Thermal plasma applications involve interactions between the highly energetic plasma and working gas streams, confining devices, or processing materials. Whereas thermal plasma implies a state of equilibrium (i.e. Local Thermodynamic Equilibrium, LTE), due to the above interactions, thermal plasma flows depict nonequilibrium phenomena of two types: kinetic and dissipative. Kinetic nonequilibrium manifests microscopically and is caused by localized imbalances between particles and fields interactions. Its occurrence is evidenced, for example, as deviations from thermal equilibrium between heavy-species and electrons or from mass-action laws. In contrast, dissipative nonequilibrium reveals macroscopically and is produced by external driving forces that incite distributed responses, such as the growth of instabilities, the occurrence of self-organization, or the establishment of turbulence. Although kinetic nonequilibrium has been increasingly incorporated in thermal plasma flow models (e.g. finite-rate chemistry, two-temperature models), it is the great advances in numerical computing that is enabling the exploration of dissipative nonequilibrium (e.g. pattern formation, small-scale turbulent features). Both types of nonequilibrium are reviewed, including their estimation and incidence, within the context of computational models and their relevance to thermal plasma sources and processes.

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“…In order to improve the quality and productivity of functional coatings, many studies have been carried out over the past few decades. Some studies have focused on the suppression of arc instabilities [14], the extension of the lifetime of the electrodes [15][16][17], the highprecision control on the plasma jet [18], the optimization of the plasma torch design for higher energy fluxes [19,20], the interaction between the fed particles and the plasma jet [21,22], and the combination of plasma spray process with machine learning algorithms [23,24], just to mention a few of the topics.…”

Section: Introductionmentioning

confidence: 99%

Effect of a Spatially Fluctuating Heating of Particles in a Plasma Spray Process

Zhu

Baeva

Testrich

et al. 2022

Plasma Chem Plasma Process

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The work is concerned with the effect of a spatially fluctuating heating of Al2O3 particles with diameters of 5–120 μm during a plasma spray process. A plasma jet is generated in a mixture of Ar (40 NLPM) and H2 (14 NLPM) and in pure Ar at an electric current of 600 A. The tracing of the injected particles in the plume region of the plasma jets is considered in the framework of a three-dimensional model taking into account a turbulent fluid flow. It is shown that the heat source for the injected particles exhibits a well pronounced spatially fluctuating structure due to the enhancement of the thermal conductivity resulting from dissociation and ionization of the molecular gas in the temperature range of 2500–4000 K and 13,000–14,000 K, respectively. During their travel towards the substrate, the particles are therefore repeatedly heated in the gas mixture in contrast to the case of pure argon. Particles injected in the gas mixture reach the substrate with a higher average temperature and velocity.

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Non-transferred Arc Torch Simulation by a Non-equilibrium Plasma Laminar-to-Turbulent Flow Model

Cited by 18 publications

References 63 publications

Measurement of medium-voltage AC air arc temperature and particle number density based on dual-wavelength Moiré deflection technology

Measurement of medium-voltage AC air arc temperature and particle number density based on dual-wavelength Moiré deflection technology

Nonequilibrium Phenomena in (Quasi-)thermal Plasma Flows

Effect of a Spatially Fluctuating Heating of Particles in a Plasma Spray Process

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