Highly efficient sparse-matrix inversion techniques and average procedures applied to collisional-radiative codes
M. Poirier, F. de Gaufridy de Dortan

TL;DR
This paper introduces a highly efficient LU-based algorithm for solving large, sparse linear systems in NLTE plasma modeling, significantly improving speed and accuracy over traditional methods, and discusses criteria for validating configuration averaging.
Contribution
The paper presents a novel LU decomposition algorithm tailored for sparse, large-scale matrices in plasma physics, outperforming existing methods and providing new criteria for configuration average validation.
Findings
The LU-based method is orders of magnitude faster than Gauss elimination.
No convergence or accuracy issues were encountered with the proposed method.
Configuration averaging validity can be assessed using a fictive temperature criterion.
Abstract
The behavior of non-local thermal-equilibrium (NLTE) plasmas plays a central role in many fields of modern-day physics, such as laser-produced plasmas, astrophysics, inertial or magnetic confinement fusion devices, or X-ray sources. The proper description of these media in stationary cases requires to solve linear systems of thousands or more rate equations. A possible simplification for this arduous numerical task may lie in some type of statistical average, such as configuration or superconfiguration average. However to assess the validity of this procedure and to handle cases where isolated lines play an important role, it may be important to deal with detailed levels systems. This involves matrices with sometimes billions of elements, which are rather sparse but still involve thousands of diagonals. We propose here a numerical algorithm based on the LU decomposition for such linear…
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