Towards Predictive Fluid Modeling of Plasma Processing Applications
Low-temperature plasmas have multiple vital scientific and industrial applications, including plasma processing, electric propulsion, arc discharges, and many more. Plasmas exhibit complex phenomena in a wide range of both spatial and temporal scales, spanning frequencies from gigahertz to kilohertz and length scales from microns to meters. High-fidelity computational models that are computationally feasible and capable of accurately simulating these processes are necessary to develop predictive modeling capabilities for low-temperature plasmas. In this collaboration between the Plasma Dynamics Modeling Laboratory (PDML) at Stanford University and Laboratoire de Physique des Plasmas (LPP) in France, we will expand the capabilities of fluid models by incorporating closure models and plasma chemistry modules by benchmarking the models developed both at PDML and LPP. The project will develop advanced simulation setups, including multi-dimensional simulations, advanced plasma chemistry, and more sophisticated collision and source models for relevant plasma applications, such as Hall-effect thruster and industrial plasma simulations. The advancement of predictive plasma models is vital for the understanding of the underlying processes that govern plasma dynamics and reactions.