A Novel Self-Aware Dynamic Mesh Refinement Technique for Plasma Fluid Simulations of Microwave Breakdown

Abstract

The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) is based on plasma density and plasma temperature. The conventional methods for electron density measurements offer axial and radial profiles for any given linear LTP device. These methods have major disadvantages of operational range (not very wide), cumbersome instrumentation, and complicated data analysis procedures. To address such practical concerns, the article proposes a novel machine learning (ML) assisted microwave-plasma interaction based strategy which is capable enough to determine the electron density profile within the plasma. The electric field pattern due to microwave scattering is measured to estimate the density profile. The proof of concept is tested for a simulated training data set comprising a low-temperature, unmagnetized, collisional plasma. Different types of Gaussian-shaped density profiles, in the range 10 16 − 10 19 m −3 , addressing a range of experimental configurations have been considered in our study. The results obtained show promising performance in estimating the 2D radial profile of the density for the given linear plasma device. The performance of the proposed deep learning based approach has been evaluated using three metrics-SSIM, RMSLE and MAPE. The favourable performance affirms the potential of the proposed ML based approach in plasma diagnostics.