Validating and speeding up x-ray tomographic inversions in tokamak plasmas

被引：1

作者：

Jardin, A. ^{[1
]}

Mazon, D. ^{[2
]}

Bielecki, J. ^{[1
]}

Dworak, D. ^{[1
]}

Guibert, D. ^{[2
]}

Krol, K. ^{[1
]}

Savoye-Peysson, Y. ^{[2
]}

Scholz, M. ^{[1
]}

Walkowiak, J. ^{[1
]}

机构：

[1] Polish Acad Sci IFJ PAN, Inst Nucl Phys, PL-31342 Krakow, Poland

[2] CEA, IRFM, F-13108 St Paul Les Durance, France

来源：

PLASMA PHYSICS AND CONTROLLED FUSION | 2024年 / 66卷 / 08期

关键词：

tokamaks; plasma diagnostics; tomography; soft x-rays; neural networks; real-time; RECONSTRUCTION; REGULARIZATION; EQUILIBRIUM;

D O I：

10.1088/1361-6587/ad5b85

中图分类号：

O35 [流体力学]; O53 [等离子体物理学];

学科分类号：

070204 ; 080103 ; 080704 ;

摘要：

X-ray tomography is a precious tool in tokamaks that provides rich information about the core plasma, such as local impurity concentration, electron temperature and density as well as magnetic equilibrium (ME) and magnetohydrodynamic activity. Nevertheless, inferring the local plasma emissivity from a sparse set of line-integrated measurements is an ill-posed problem that requires dedicated regularization and validation methods. Besides, speeding up the inversion algorithm in order to be compatible with real-time control systems is a challenging task with traditional approaches. In this contribution, in a first part we introduce tools aiming at validating and speeding up the x-ray tomographic inversions based on Tikhonov regularization, including ME constraint and parameter optimization, taking the WEST geometry as an example. In a second part, an alternative approach compatible with real-time, based on a set of neural networks is proposed and compared with the Tikhonov approach for an experimental case.

引用

页数：15

共 55 条

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