Modern numerical methods for plasma tomography optimisation

摘要

Tomography is a technique which is widely applied to fusion plasmas as it can provide improved understanding of plasma emissivity distributions. It is challenging because of the sparse nature of data available from the measured plasma projections. An optimised version of robust and fast tomographic algorithm based on the Tikhonov regularisation constrained to Minimum Fisher Information is presented in this contribution. A new regularisation matrix enforcing preferential emissivity smoothness along magnetic flux surfaces is introduced. The paper also details application of advanced numerical methods which lead to a substantial decrease in computation time. Subsequent implementation of fast presolvers of the inverse problem further contributes to the algorithm's efficiency and also an improved stability of the tomography reconstruction. Finally, reliability and performance of the tomography algorithm is exemplified by the reconstruction of soft X-ray data evolution following tungsten ablation into a JET plasma. The resulting speed of reconstruction is compared to other referenced tomographic algorithms.