Partially ionized plasma is a collection of charged (electrons, ions) and neutral (atoms, photons) particles collectively interacting with electromagnetic fields. Plasma is the most abundant state of matter in the Universe, which is naturally present on Earth in the form of lightning and the aurora borealis. Plasma is widely studied in the laboratories and is among the top technologies of the 21st century forming the foundation of microelectronics and material processing, with emerging applications in biomedical, agriculture and food safety industries.
The particle transport in plasmas can be described by kinetic or hydrodynamic (fluid) models. Kinetic models are based on Boltzmann, Vlasov, and Fokker-Planck kinetic equations, which can be solved using either statistical particle methods (Particle in Cell (PIC) and Direct Simulation Monte Carlo (DSMC)) or by direct numerical solution of the kinetic equations employing discrete velocity methods. Fluid models express conservation laws for density, mean velocity and temperature of plasma species. They can be derived from the kinetic equations and describe the system on a macroscopic (coarse-grained) level providing coupling between the particle transport and electromagnetics (Maxwell equations) via electric charge density and currents. Fluid equations are easier to solve but have limited range of applicability.
Several groups worldwide have been working on hybrid kinetic-fluid models combining the accuracy of kinetic solvers with efficiency of fluid models. This is particularly important for plasma, which is characterized by a wide range of time scales due to the disparity of electron mass and the masses of heavy species (ions, atoms). The research challenges in this area are associated with identifying correct criteria for selecting appropriate models (which are different for electrons, ions, atoms, and photons), coupling kinetic and fluid solvers at interfaces (which can dynamically evolve), and implementing these hybrid algorithms into smart software for practical engineering on modern computing systems.
This Research Topic welcomes original contributions and review papers devoted to adaptive kinetic-fluid models and their applications to plasma engineering and scientific discovery. Specific contributions are thought for theory and algorithms linking kinetic and fluid descriptions and resolving multi-scale challenges, plasma simulations with dynamically adaptive meshes, hybrid kinetic-fluid solvers for electrons, ions and neutral components, coupling particle transport with electromagnetics, plasma generation, self-organization and interaction with boundaries including soft and biomatter. Hybrid models of radiation, heat, and electricity transport in solids, liquids and gases, and phenomena at interfaces between different states of matter may also be relevant to this Topic.
With the end of Moore’s Law on the horizon, unconventional and nature-inspired computing become areas of active research. Due to collective and nonlinear nature, plasma is very attractive for hybrid analog-digital computing. This Topic seeks original contributions to this field.
Partially ionized plasma is a collection of charged (electrons, ions) and neutral (atoms, photons) particles collectively interacting with electromagnetic fields. Plasma is the most abundant state of matter in the Universe, which is naturally present on Earth in the form of lightning and the aurora borealis. Plasma is widely studied in the laboratories and is among the top technologies of the 21st century forming the foundation of microelectronics and material processing, with emerging applications in biomedical, agriculture and food safety industries.
The particle transport in plasmas can be described by kinetic or hydrodynamic (fluid) models. Kinetic models are based on Boltzmann, Vlasov, and Fokker-Planck kinetic equations, which can be solved using either statistical particle methods (Particle in Cell (PIC) and Direct Simulation Monte Carlo (DSMC)) or by direct numerical solution of the kinetic equations employing discrete velocity methods. Fluid models express conservation laws for density, mean velocity and temperature of plasma species. They can be derived from the kinetic equations and describe the system on a macroscopic (coarse-grained) level providing coupling between the particle transport and electromagnetics (Maxwell equations) via electric charge density and currents. Fluid equations are easier to solve but have limited range of applicability.
Several groups worldwide have been working on hybrid kinetic-fluid models combining the accuracy of kinetic solvers with efficiency of fluid models. This is particularly important for plasma, which is characterized by a wide range of time scales due to the disparity of electron mass and the masses of heavy species (ions, atoms). The research challenges in this area are associated with identifying correct criteria for selecting appropriate models (which are different for electrons, ions, atoms, and photons), coupling kinetic and fluid solvers at interfaces (which can dynamically evolve), and implementing these hybrid algorithms into smart software for practical engineering on modern computing systems.
This Research Topic welcomes original contributions and review papers devoted to adaptive kinetic-fluid models and their applications to plasma engineering and scientific discovery. Specific contributions are thought for theory and algorithms linking kinetic and fluid descriptions and resolving multi-scale challenges, plasma simulations with dynamically adaptive meshes, hybrid kinetic-fluid solvers for electrons, ions and neutral components, coupling particle transport with electromagnetics, plasma generation, self-organization and interaction with boundaries including soft and biomatter. Hybrid models of radiation, heat, and electricity transport in solids, liquids and gases, and phenomena at interfaces between different states of matter may also be relevant to this Topic.
With the end of Moore’s Law on the horizon, unconventional and nature-inspired computing become areas of active research. Due to collective and nonlinear nature, plasma is very attractive for hybrid analog-digital computing. This Topic seeks original contributions to this field.