Non-equilibrium of charged particles in swarms and plasmas-from binary collisions to plasma effects

• Published in: Plasma physics and controlled fusion 2017-01, Vol.59 (1), p.14026
• Main Authors: Petrovi, Z Lj, Simonovi, I, Marjanovi, S, Bošnjakovi, D, Mari, D, Malovi, G, Dujko, S
• Format: Article
• Language: English
• Subjects: Boltzmann equation; charged particle swarms; Monte Carlo simulations; non-equilibrium plasma; positron traps; resistive plate chambers; skewness
• ISSN: 0741-3335; 1361-6587
• DOI: 10.1088/0741-3335/59/1/014026

In this article we show three quite different examples of low-temperature plasmas, where one can follow the connection of the elementary binary processes (occurring at the nanoscopic scale) to the macroscopic discharge behavior and to its application. The first example is on the nature of the higher-order transport coefficient (second-order diffusion or skewness); how it may be used to improve the modelling of plasmas and also on how it may be used to discern details of the relevant cross sections. A prerequisite for such modeling and use of transport data is that the hydrodynamic approximation is applicable. In the second example, we show the actual development of avalanches in a resistive plate chamber particle detector by conducting kinetic modelling (although it may also be achieved by using swarm data). The current and deposited charge waveforms may be predicted accurately showing temporal resolution, which allows us to optimize detectors by adjusting the gas mixture composition and external fields. Here kinetic modeling is necessary to establish high accuracy and the details of the physics that supports fluid models that allows us to follow the transition to streamers. Finally, we show an example of positron traps filled with gas that, for all practical purposes, are a weakly ionized gas akin to swarms, and may be modelled in that fashion. However, low pressures dictate the need to apply full kinetic modelling and use the energy distribution function to explain the kinetics of the system. In this way, it is possible to confirm a well established phenomenology, but in a manner that allows precise quantitative comparisons and description, and thus open doors to a possible optimization.