In modern tokamaks like ITER or WEST, with tungsten (W) instead of traditional carbon as the main plasma-facing material to minimize erosion and tritium retention in the walls, the essential issue of heavy impurity radiation has been raised. Monitoring and real-time control of W concentration below 0.01% in the plasma core will be indeed necessary to avoid significant cooling of the plasma by impurity radiation, in particular in the soft x-ray (SXR) energy range of 0.1–20 keV, and to select adequate mitigation strategies. In this context, this paper describes recent work to reconstruct W concentration in WEST plasma core from SXR, electron profiles measurements and magnetic equilibrium in a fast and automatized way, with the support of machine learning. A significant data reduction is performed, with the parametrization of the magnetic equilibrium and electron radial profiles, to limit the number of inputs feeding the considered neural network (NN). The NN architecture, training strategy and experimental dataset used to perform fast reconstructions are introduced. The NN is trained with 2023 WEST data and tested on a set of 2024 plasma discharges. NN predictions are compared with the ones calculated with a synthetic diagnostic tool for different ranges of plasma temperature and density. Perspectives for further improvements and extension of this study are also discussed.
This paper describes a method to quickly and automatically reconstruct the concentration of tungsten, a heavy impurity, in the core of WEST tokamak plasmas using machine learning. This is important to monitor and control the impurity levels, which can significantly cool the plasma through radiation. The method uses soft X-ray, electron profile, and magnetic equilibrium data as inputs to a neural network model.