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Reciprocating probes are a classic and widespread tool for the investigation of the edge and Scrape-Off Layer of magnetic fusion plasmas. In the Wendelstein 7-X (W7-X) stellarator, the Multi-Purpose Manipulator serves as a multi-user platform for probe measurements of various kinds. This paper presents a review on reciprocating probe operation during the first operation phase of W7-X with a test divertor (2017-2018). It gives an overview of the diverse zoo of probe heads and presents lessons learned about probe operation in complex magnetic geometries, operation safety, and probe head design. A few examples of probe measurements with a focus on unexpected observations are presented.

Overload detection is crucial in nuclear fusion experiments to prevent damage to plasma-facing components (PFCs) and ensure the safe operation of the reactor. At Wendelstein 7-X (W7-X), real-time monitoring and prediction of thermal events are essential for maintaining the integrity of PFCs. This paper proposes a machine learning approach for developing a real-time overload detector, trained and tested on OP1.2a experimental data. The analysis showed that Self-Organizing Maps (SOMs) are efficient in detecting the overload risk starting from a set of plasma parameters that describe the magnetic configuration, the energy behavior, and the power balance. This study aims to thoroughly evaluate the capabilities of the SOM in recognizing overload risk levels, defined by quantizing the maximum criticality across different IR cameras. The goal is to enable detailed monitoring for overload prevention while maintaining high-performance plasmas and sustaining long pulse operations. The SOM proves to be a highly effective overload risk detector. It correctly identifies the assigned overload risk level in 87.52% of the samples. The most frequent error in the test set, occurring in 10.46% of cases, involves assigning a risk level to each sample adjacent to the target one. The analysis of the results highlights the advantages and drawbacks of criticality discretization and opens new solutions to improve the SOM potential in this field.

This paper analyzes the neutral fluxes in the divertor region of the W7-X standard configuration for different input powers, both under attached and detached conditions. The performed analysis is conducted through EMC3-EIRENE simulations. They show the importance of the horizontal divertor to generate neutrals, and resolve the neutral plugging in the divertor region. Simulations of detached cases show a decrease in the number of generated neutrals compared to the attached simulations, in addition to a higher fraction of the ion flux arriving on the baffles during detachment. As the ionization takes place further inside the plasma during detachment, a larger percentage of the generated neutral particles leave the divertor as neutrals. The leakage in the poloidal and toroidal direction increases, just as the fraction of collected particles at the pumping gap. The fraction of pumped particles increases with a factor two, but stays below one percent. This demonstrates that detachment with the current target geometry, although it improves the power exhaust, is not yet leading to an increased particle exhaust.

The quench detection system of the fusion experiment Wendelstein 7-X monitors the superconducting magnet system consisting of 50 non-planar and 20 planar coils, 14 current leads and bus bars. The commissioning phase contained the wiring check of the electrical contact between the 486 quench detection units and the superconducting magnet system, the verification of an unbroken and overlapping monitoring, the balance adjustment of the quench detection unit voltage measuring bridge and the parameterisation of the detection criteria: detection level and integration time. During the operation phases there were two unexpected fast discharges initiated through the trim-coils and a fast decay of the bootstrap current. As a result the interplay of the detection level and integration time was re-evaluated. The quench detection system worked stable over the first both operating phases of W7-X, performed from 2015 to 2018. There were no failures of the quench detection units or other parts of the quench detection system.

The computational cost of constructing 3D magnetohydrodynamic (MHD) equilibria is one of the limiting factors in stellarator research and design. Although data-driven approaches have been proposed to provide fast 3D MHD equilibria, the accuracy with which equilibrium properties are reconstructed is unknown. In this work, we describe an artificial neural network (NN) that quickly approximates the ideal-MHD solution operator in Wendelstein 7-X (W7-X) configurations. This model fulfils equilibrium symmetries by construction. The MHD force residual regularizes the solution of the NN to satisfy the ideal-MHD equations. The model predicts the equilibrium solution with high accuracy, and it faithfully reconstructs global equilibrium quantities and proxy functions used in stellarator optimization. We also optimize W7-X magnetic configurations, where desiderable configurations can be found in terms of fast particle confinement. This work demonstrates with which accuracy NN models can approximate the 3D ideal-MHD solution operator and reconstruct equilibrium properties of interest, and it suggests how they might be used to optimize stellarator magnetic configurations.

In magnetic confinement fusion research, the achievement of high plasma pressure is key to reaching the goal of net energy production. The magnetohydrodynamic (MHD) model is used to self-consistently calculate the effects the plasma pressure induces on the magnetic field used to confine the plasma. Such MHD calculations serve as input for the assessment of a number of important physics questions. The VMEC code is the most widely used to evaluate 3D ideal-MHD equilibria, as prominently present in stellarators. However, considering the computational cost, it is rarely used in large-scale or online applications. Access to fast MHD equilbria is a challenging problem in fusion research, one which machine learning could effectively address. In this paper, we present artificial neural network (NN) models able to quickly compute the equilibrium magnetic field of W7-X. Magnetic configurations that extensively cover the device operational space, and plasma profiles with volume averaged normalized plasma pressure \\(\\langle \\beta \\rangle\\) (\\(\\beta\\) = \\(\\frac{2 \\mu_0 p}{B^2}\\)) up to 5% and non-zero net toroidal current are included in the data set. By using convolutional layers, the spectral representation of the magnetic flux surfaces can be efficiently computed with a single network. To discover better models, a Bayesian hyper-parameter search is carried out, and 3D convolutional neural networks are found to outperform feed-forward fully-connected neural networks. The achieved normalized root-mean-squared error ranges from 1% to 20% across the different scenarios. The model inference time for a single equilibrium is on the order of milliseconds. Finally, this work shows the feasibility of a fast NN drop-in surrogate model for VMEC, and it opens up new operational scenarios where target applications could make use of magnetic equilibria at unprecedented scales.

In this work, a Serpent 2 neutronics model of the Wendelstein 7-X (W7-X) stellarator is prepared, and an response function for the Scintillating-Fibre neutron detector (SciFi) is calculated using the model. The neutronics model includes the simplified geometry for the key components of the stellarator itself as well as the torus hall. The objective of the model is to assess the 14.1 MeV neutron flux from deuteron-triton fusions in W7-X, where the neutrons are modelled only until they have slowed down to 1 MeV energy. The key messages of this article are: demonstration of unstructured mesh geometry usage for stellarators, W7-X in particular; technical documentation of the model and first insights in fast neutron behaviour in W7-X, especially related to the SciFi: the model indicates that the superconducting coils are the strongest scatterers and block neutrons from large parts of the plasma. The back-scattering from e.g. massive steel support structures is found to be small. The SciFi will detect neutrons from an extended plasma volume in contrast to having an effective line-of-sight.

Neutron production rates in fusion devices are determined not only by the kinetic profiles but also the fast ion slowing-down distributions. In this work, we investigate the effect of magnetic configuration on neutron production rates in future deuterium plasmas in the Wendelstein 7-X (W7-X) stellarator. The neutral beam injection, beam and triton slowing-down distributions, and the fusion reactivity are simulated with the ASCOT suite of codes. The results indicate that the magnetic configuration has only a small effect on the production of 2.45 MeV neutrons from thermonuclear and beam-target fusion. The 14.1 MeV neutron production rates were found to be between \\(1.49 \\times 10^{12}\\) \\(\\mathrm{s}^{-1}\\) and \\(1.67 \\times 10^{12}\\) \\(\\mathrm{s}^{-1}\\), which is estimated to be sufficient for a time-resolved detection using a scintillating fiber detector, although only in high-performance discharges.

The ECE diagnostic at W7-X in its standard mode of operation measures in X2 mode polarization with a 32 channel radiometer in the frequency band around 140 GHz for central magnetic field 2.5T. The radiometer is calibrated by a noise source and the overall system absolutely calibrated by means of a hot-cold source placed outside the torus in front of a Gaussian telescope optics with identical geometry and transmission line as it is installed for the measurements in the plasma vessel. The system is supplemented with a 16 channel zoom device with 4 GHz span for higher frequency resolution at a suitable radial range and a Michelson interferometer for the characterization of higher harmonics sharing the same line of sight.

The detachment regime has a high potential to play an important role in fusion devices on the road to a fusion power plant. Complete power detachment has been observed several times during the experimental campaigns of the Wendelstein 7-X (W7-X) stellarator. Automatic observation and signaling of such events could help scientists to better understand these phenomena. With the growing discharge times in fusion devices, machine learning models and algorithms are a powerful tool to process the increasing amount of data. We investigate several classical supervised machine learning models to detect complete power detachment in the images captured by the Event Detection Intelligent Camera System (EDICAM) at the W7-X at each given image frame. In the dedicated detached state the plasma is stable despite its reduced contact with the machine walls and the radiation belt stays close to the separatrix, without exhibiting significant heat load onto the divertor. To decrease computational time and resources needed we propose certain pixel intensity profiles (or intensity values along lines) as the input to these models. After finding the profile that describes the images best in terms of detachment, we choose the best performing machine learning algorithm. It achieves an F1 score of 0.9836 on the training dataset and 0.9335 on the test set. Furthermore, we investigate its predictions in other scenarios, such as plasmas with substantially decreased minor radius and several magnetic configurations.