2019
DOI: 10.1088/1361-6463/ab06cd
|View full text |Cite
|
Sign up to set email alerts
|

Numerical simulation of the effect of water admixtures on the evolution of a helium/dry air discharge

Abstract: In this study, a one-dimensional plasma fluid model is used to shed light into the evolution of a He/dry air (500 ppm, 79% N2 and 21% O2) dielectric barrier discharge (DBD) under different levels of water admixtures (20 to 2000 ppm). The model considers the analytical chemistry between helium, nitrogen, oxygen and water species and it is verified with experimental results to ensure its correctness. The simulation results show that water admixtures highly affect the discharge characteristics and the dominant io… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

1
27
0

Year Published

2019
2019
2022
2022

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 19 publications
(28 citation statements)
references
References 103 publications
1
27
0
Order By: Relevance
“…where ρ represents the density of the mixture, ω n , n , and S n represent the mass fraction, the diffusive flux vector and the source term of the corresponding nth heavy particle, respectively. The mobility and diffusion coefficients of heavy particles considered in our model are taken from [29]- [31]. Q represents the number of total species of heavy particles.…”
Section: Plasma Model Descriptionmentioning
confidence: 99%
See 2 more Smart Citations
“…where ρ represents the density of the mixture, ω n , n , and S n represent the mass fraction, the diffusive flux vector and the source term of the corresponding nth heavy particle, respectively. The mobility and diffusion coefficients of heavy particles considered in our model are taken from [29]- [31]. Q represents the number of total species of heavy particles.…”
Section: Plasma Model Descriptionmentioning
confidence: 99%
“…They proposed that due to the strong electronegativity of O 2 , anions are formed and the attachment of electrons would reduce the effective ionization, thereby influencing the ignition of the discharge and other discharge characteristics. In addition, taking the air impurities (both N 2 and O 2 ) into account, Lazarou et al found that the main cations are O + 2 and O + 4 when the air content reaches 500 ppm, and Penning ionization of O 2 with metastable states of helium is the main pathway for the electron generation in this situation [29]. Therefore, according to the basic physical understanding and the abovementioned reports, we should be more prudential to deal with the role of oxygen in atmospheric helium HDBDs with air impurities.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…In the 1-D fluid model, electron properties, including the electron density and energy density, are governed by the Boltzmann equation under drift-diffusion approximation [23][24][25], as given by Equation (1-4); Equation (5) and Equation (6) are multi-component transport equations [26,27] determining the flux of heavy species. Besides, in order to determine the electric field distribution, Poisson's equation is firmly coupled.…”
Section: Model Descriptionmentioning
confidence: 99%
“…A self‐consistent fluid model is developed within the plasma module of COMSOL® Multiphysics software. The detailed model equations have been thoroughly described in many previous studies, so here just some brief introductions are presented. In the model, the governing equations of electron properties are derived from the Boltzmann equation under the drift‐diffusion approximation, as given in Equations –, and the transport property of heavy species is solved by multicomponent transport equations, as given in Equations and : nnormalet+ bold-italicΓnormale=Snormale, nnormalεt+ bold-italicΓbold-italicε+Ebold-italicΓbold-italice=Sen, bold-italicΓnormale=μnormalennormaleEDnormale nnormale, bold-italicΓbold-italicε=53μnormalennormalεE53Dnormale nnormalε, ρωkt= bold-italicJk+Sk, bold-italicJk=ρωktrue(Dkωkωk+DkMnMnzkμkE<...>…”
Section: Model Descriptionmentioning
confidence: 99%