Data Assimilation to Estimate the State of Partially Ionized Plasmas in Space Propulsion Systems

Abstract

Spacecraft electric propulsion plays a critical role in in-space missions. Understanding the state of the ionized gas, i.e., plasmas, is important for the characterization of thruster performance and the lifetime. In this talk, we present the recent development of data assimilation (DA) framework that estimates state variables and parameters obtained from a physics-based dynamical model with noisy experimental data. We have developed DA techniques based on variants of Kalman filters to estimate state and parameters in plasma systems governed by coupled nonlinear ordinary and partial differential equations.