dc.contributor.author | Marskar, Robert | |
dc.date.accessioned | 2024-03-11T13:12:38Z | |
dc.date.available | 2024-03-11T13:12:38Z | |
dc.date.created | 2024-02-20T07:53:56Z | |
dc.date.issued | 2024 | |
dc.identifier.issn | 0021-9991 | |
dc.identifier.uri | https://hdl.handle.net/11250/3121830 | |
dc.description.abstract | This paper contains the foundation for a new Particle-In-Cell model for gas discharges, based on Îto diffusion and Kinetic Monte Carlo (KMC). In the new model the electrons are described with a microscopic drift-diffusion model rather than a macroscopic one. We discuss the connection of the Îto-KMC model to the equations of fluctuating hydrodynamics and the advection-diffusion-reaction equation which is conventionally used for simulating streamer discharges. The new model is coupled to a particle description of photoionization, providing a non-kinetic all-particle method with several attractive properties, such as: 1) Taking the same input as a fluid model, e.g. mobility coefficients, diffusion coefficients, and reaction rates. 2) Guaranteed non-negative densities. 3) Intrinsic support for reactive and diffusive fluctuations. 4) Exceptional stability properties. The model is implemented as a particle-mesh model on cut-cell grids with Cartesian adaptive mesh refinement. Positive streamer discharges in atmospheric air are considered as the primary application example, and we demonstrate that we can self-consistently simulate large discharge trees. | en_US |
dc.description.abstract | Stochastic and self-consistent 3D modeling of streamer discharge trees with Kinetic Monte Carlo | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Stochastic and self-consistent 3D modeling of streamer discharge trees with Kinetic Monte Carlo | en_US |
dc.title.alternative | Stochastic and self-consistent 3D modeling of streamer discharge trees with Kinetic Monte Carlo | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | The Author | en_US |
dc.source.volume | 504 | en_US |
dc.source.journal | Journal of Computational Physics | en_US |
dc.identifier.doi | 10.1016/j.jcp.2024.112858 | |
dc.identifier.cristin | 2247810 | |
dc.relation.project | Norges forskningsråd: 311449 | en_US |
dc.relation.project | Norges forskningsråd: 319930 | en_US |
dc.relation.project | Sigma2: NN9887K | en_US |
dc.source.articlenumber | 112858 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |