Sparse data recovery of tomographic diagnostics for ultra-large-area plasmas

Abstract

As the size of plasma systems reaches multiple square meters, several phenomena lead to nonuniform plasma production; as a result, the requirement for real-time monitoring of plasma uniformity is increasing, particularly for industrial plasmas. Although non-intrusive diagnostics (e.g. optical emission spectroscopy) are preferred to monitor such plasmas, line (or volume)integrated measurement is indispensable. Thus, special attention has been paid to the tomographic reconstruction technique; however, limitations remain for tomographic diagnostics for ultra-large-area plasmas. Here, we report an inversion technique that remarkably improves the performance of tomographic reconstruction for large-area industrial plasmas. A computational tool for the tomographic reconstruction with Phillips-Tikhonov regularization was prepared, and reconstruction performance tests were conducted using artificial (phantom) images (namely, expected plasma emission distributions). While very sparse line-of-sight and projection data result in poor reconstruction performance, two suggested operators, which constrain the reconstruction solution, noticeably improve the reconstruction performance and reduce the overall reconstruction error. Although the projection data are contaminated by random noise (<5% of data), the reconstruction error was improved by 43.8% and 40.5% for flatand hollow-shaped phantoms, respectively. The computational analysis clearly demonstrates that tomographic plasma diagnostics with the proposed inversion technique can be used as a real-time tool for detecting arcing and monitoring plasma spatial uniformity in large-area industrial plasmas.

A Finite-Difference Approach for Plasma Microwave Imaging Profilometry

“…First of all, it is worth underlining that even if we are not considering minimum energy solution approaches (L 2 -norm), the SVD analysis can be retained still valid to understand the main features among the different measurement configurations and backgrounds, since we are considering sparse promoting approaches rather than exact compressive sensing-based approaches. Indeed, (19) entails a standard minimum energy solution objective function equipped with a regularization (penalty) 1 -norm enforcing step-wise sparsity. For this reason, we numerically perform the SVD of the relevant scattering operator with the aim to give a qualitative interpretation of the different results shown in the previous Section.…”

“…Plasma diagnostics using active microwave techniques is one of the most promising tools to optimize the production of plasma in both large and compact reactors because of its non-invasive nature and relatively modest access requirements [ 1 ]. Among the others, microwave imaging reflectometry (MIR), interferometric and polarimetric techniques are currently under investigation to extract plasma proprieties useful to model and optimize the heating process.…”

Three-dimensional tomographically reconstructed optical emission profiles of Hall thruster plasmas

“…Therefore, equation ( 2) becomes an ill-posed problem that has multiple solutions. To solve this, the P-T regularization algorithm with the constraint operator [30] was used in this study. The P-T regularization method has the advantage of being efficient and reliable when computing large-scale discrete problems with simple computational formulae [29,[31][32][33].…”