Spatially hybrid computations for streamer discharges with generic features of pulled fronts: I. Planar fronts

Details Spatially hybrid computations for streamer discharges with generic features of pulled fronts: I. Planar fronts Spatially hybrid computations for streamer discharges with generic features of pulled fronts: I. Planar fronts

2009-04-20

arxiv.org/abs/0904.2968v1

Journal of Computational Physics Volume 229, Issue 1, 1 January 2010, Pages 200-220

Physics

Plasma Physics

33 pages, 15 figures and 2 tables

Scientific paper

10.1016/j.jcp.2009.09.027

Streamers are the first stage of sparks and lightning; they grow due to a strongly enhanced electric field at their tips; this field is created by a thin curved space charge layer. These multiple scales are already challenging when the electrons are approximated by densities. However, electron density fluctuations in the leading edge of the front and non-thermal stretched tails of the electron energy distribution (as a cause of X-ray emissions) require a particle model to follow the electron motion. As super-particle methods create wrong statistics and numerical artifacts, modeling the individual electron dynamics in streamers is limited to early stages where the total electron number still is limited. The method of choice is a hybrid computation in space where individual electrons are followed in the region of high electric field and low density while the bulk of the electrons is approximated by densities (or fluids). We here develop the hybrid coupling for planar fronts. First, to obtain a consistent flux at the interface between particle and fluid model in the hybrid computation, the widely used classical fluid model is replaced by an extended fluid model. Then the coupling algorithm and the numerical implementation of the spatially hybrid model are presented in detail, in particular, the position of the model interface and the construction of the buffer region. The method carries generic features of pulled fronts that can be applied to similar problems like large deviations in the leading edge of population fronts etc.

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