The transport of neutral particles in turbulent plasmas is addressed from the prospect of developing coarse-grained transport models which can be implemented in code suites like B2-EIRENE, currently used for designing the ITER divertor. The statistical properties of turbulent fluctuations are described by a multivariate Gamma distribution able to retain space and time correlations through a proper choice of covariance function. We show that in the scattering free case, relevant for molecules and impurity atoms, the average neutral particle density obeys a Boltzmann equation with an ionization rate renormalized by fluctuations. This result lends itself to a straightforward implementation in the EIRENE Monte Carlo solver for neutral particles. Special emphasis is put on the inclusion of time correlations, and in particular on the ballistic motion of coherent turbulent structures. The role of these time dependent effects is discussed for D₂ molecules and beryllium atoms. The sensitivity of our results to the assumptions on the statistical properties of fluctuations is investigated.

We propose in this paper the use of artificial intelligence, especially deep learning algorithms, for the isotopic ratio determination for hydrogen–deuterium mixtures. Our approach is based on the Balmer-α line emitted by hydrogen and deuterium, but unlike the standard method, it does not consist of fitting the Hα/Dα line spectra. Instead, only some basic spectroscopic features such as the line peak-to-dip wavelength separation, peak-to-peak and dip-to-peak intensity ratios of the Zeeman–Doppler-broadened Hα/Dα line spectra are used by the regression algorithm for training. We demonstrate the proof-of-principle of our approach by applying deep learning from the open-access machine-learning platform TensorFlow to Hα/Dα line profiles, which we have synthetized with pre-determined parameters such as neutral temperatures, the magnetic field strength and the H/(H+D) isotopic ratio. The used regression algorithm allowed us to retrieve with a good accuracy the isotopic ratios used for the synthetized line profiles.

For a given set of plasma parameters, along a single series (Lyman, Balmer, etc.) the lines with higher principal quantum number (n) lines get progressively wider, closer to each other, and start merging for a certain critical n. In the present work, four different codes (with further options) are used to calculate the entire Balmer series for moderate and high electron densities. Particular attention is paid to the relevant physics, such as the cutoff criteria, strong and penetrating electron collisions.

Modeling of the spectral line and continuum radiation emitted by hydrogen isotopes in peripheral regions of magnetic fusion is presented through profiles of the Zeeman-Doppler broadened Hα/Dα line and those of the Stark broadened high-n Balmer lines extending beyond the series limit for recombining plasmas. The Hα/Dα line profiles should be modelled while accounting for several populations of neutrals to mimic real situations and analyze experimental data for isotopic ratio determination. On the other side, high-n Balmer lines of hydrogen are used for plasma electron density and temperature diagnostics. Moreover, modelling whole spectra including the continuum radiation contributes to the development of synthetic diagnostics for future magnetic fusion devices for which they can give predictive results through coupling to numerical simulation tools.

The shape of atomic spectral lines in plasmas contains information on the plasma parameters, and can be used as a diagnostic tool. Under specific conditions, the plasma located at the edge of tokamaks has parameters similar to those in magnetic white dwarf stellar atmospheres, which suggests that the same line shape models can be used. A problem common to tokamak and magnetic white dwarfs concerns the modeling of Stark broadening of hydrogen lines in the presence of an external magnetic field and the related Zeeman effect. In this work, we focus on a selection of issues relevant to Stark broadening in magnetized hydrogen plasmas. Various line shape models are presented and discussed through applications to ideal cases.

This article presents a machine learning approach to tungsten spectroscopy analysis using measurements performed in WEST tokamak plasmas. We developed a Random Forest algorithm to predict the maximum electron temperature (Temax) based on 45–65 Å tungsten brightness spectra measured along a mobile line of sight. The model achieves prediction errors typically below 5% across a broad temperature range (0.5–4 keV). Feature importance analysis highlights both physically meaningful wavelengths and others that, despite lacking direct interpretability, improve prediction stability and accuracy. In addition, a principal component analysis is conducted to investigate the relation between spectral variance and Temax unraveling that unaccounted-for parameters influence the spectral shape. Combined with the wavelength importance analysis, this study could potentially inform atomic structure and collisional-radiative models to better understand the tungsten spectral emission.