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Applied Sciences
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Recent Developments in Fusion Plasma Diagnostics
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Recent Developments in Fusion Plasma Diagnostics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 11699

Special Issue Editors

Dr. Teddy Craciunescu

E-Mail Website
Guest Editor
National Institute for Laser, Plasma and Radiation Physics, RO-077125 Magurele-Bucharest, Romania
Interests: computed tomography; imagine processing; time series analysis; complex networks; data mining; Monte Carlo simulations
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Murari

E-Mail Website
Guest Editor
Consorzio RFX (CNR, ENEA, INFN, Universita’ di Padova, Acciaierie Venete SpA), 35127 Padova, Italy
Interests: nuclear fusion; entropy; information theory; machine learning; evolutionary computation; tomography; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Michela Gelfusa

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Rome “Tor Vergata”, via del Politecnico 1, 00133 Roma, Italy
Interests: plasma diagnostics; inverse problems; data mining; time series analysis; genetic programming
Special Issues, Collections and Topics in MDPI journals
Dr. Joao Figueiredo

E-Mail Website
Guest Editor
Institute for Plasma Research and Nuclear Fusion (IPFN), Technical University of Lisbon, Lisbon, Portugal
Interests: Nuclear fusion; plasma diagnostics; diagnostics for burning plasmas; instrumentation; data acquisition

Special Issue Information

Dear Colleagues,

Thermonuclear plasmas are complex, open systems, kept well out of equilibrium by massive injection of energy and particles to achieve nuclear fusion conditions. The accurate measurement of their properties is essential for both understanding of the physics and real time control. In a fusion reactor, the experimental determination of the plasma properties is performed by a wide range of specifically designed devices, called diagnostics. With the increasing size of the devices and duration of the discharges, diagnostics are every day more systematically used also for the control of the plasma configurations.

To achieve all the previously mentioned functions, high temperature plasma diagnostics have reached nowadays a high level of sophistication. They implement all the basic measurement principles in physics. Moreover, they have to operate in a very hostile environment, characterized by high radiation fields, high temperatures and significant problems of electromagnetic compatibility. The limited access for measurements represents a challenge for the physical interpretation. All these boundary conditions and constraints require the development of innovative solutions.

This Special Issue is aimed at collecting papers that describe new solutions for the above-mentioned problems. The contributions can be based on (but not limited to) the following fields:

  • Magnetic diagnostics
  • Microwaves and millimetre waves diagnostics
  • Infrared polarimetry/interferometry
  • Spectroscopic and radiation measurements
  • Neutron/gamma diagnostics
  • Diagnostic for the plasma-wall interactions, erosion and migration
  • Tomography and imaging
  • Neutral beam and laser supported diagnostics

The construction of ITER, the design of DEMO and DTT and the imminent tritium campaigns in JET are all factors that are contributing to the recent emphasis on the diagnostics for the burning plasma. Wall protection and cleaning, pacing of instabilities and isotopic composition measurements have also become more central to the international programme. Papers related to these topics are particularly welcome.

Altogether, the diagnostics of laboratory plasmas can produce enormous amounts of information. On the Joint European Torus, the largest Tokamak in operation, in a well-diagnosed discharge more than 50 Gigabytes of data can be produced and stored. The limited experimental characterization of many phenomena and the presence of high noise levels in the data require quite sophisticated analysis techniques to draw reliable and sound conclusions. Solutions based on machine learning and data mining will be therefore considered with particular attention.

Dr. Teddy Craciunescu
Dr. Andrea Murari
Dr. Michela Gelfusa
Dr. Joao Figueiredo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (4 papers)

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Research

16 pages, 4286 KiB  
Article
A Multiphysics Ray Optics Model for the Propagation of Electromagnetic Waves in Plasmas and the Design of Laser-Based Diagnostics in Nuclear Fusion Reactors
by Luca Spolladore, Ivan Wyss, Riccardo Rossi and Pasquale Gaudio
Appl. Sci. 2021, 11(1), 434; https://doi.org/10.3390/app11010434 - 4 Jan 2021
Cited by 2 | Viewed by 1962
Abstract
Laser-based methods are widely used techniques for thermonuclear plasma diagnostics, since they can probe the internal of the plasma, being contactless and non-invasive. The interferometer, the polarimeter and Thomson scattering are the most widespread techniques, providing line-integral information of the electron density and [...] Read more.
Laser-based methods are widely used techniques for thermonuclear plasma diagnostics, since they can probe the internal of the plasma, being contactless and non-invasive. The interferometer, the polarimeter and Thomson scattering are the most widespread techniques, providing line-integral information of the electron density and the magnetic field (interferometer–polarimeter) and local information of the electron density and temperature (Thomson scattering). The design of the diagnostics is a fundamental step, which usually requires an iterative process to maximise the performances of the diagnostics and their durability. In the future reactors, such as ITER and DEMO, the working environment will be much challenging, due to the various electro-mechanical, thermal and nuclear loads which may affect the life of the components and degrade the performances of the diagnostics. This work aims to present the modelling of plasma interferometry, polarimetry and Thomson scattering applied to a ray optics code. The model, developed on the COMSOL Multiphysics software, can be easily interfaced with multiphysics problems, allowing the possibility to test the performances of the diagnostics in several conditions. Full article
(This article belongs to the Special Issue Recent Developments in Fusion Plasma Diagnostics)
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14 pages, 4250 KiB  
Article
Alternative Detection of n = 1 Modes Slowing Down on ASDEX Upgrade
by Emmanuele Peluso, Riccardo Rossi, Andrea Murari, Pasqualino Gaudio, Michela Gelfusa, on behalf of the ASDEX Upgrade Team and on behalf of the EUROfusion MST1 Team
Appl. Sci. 2020, 10(21), 7891; https://doi.org/10.3390/app10217891 - 6 Nov 2020
Cited by 4 | Viewed by 2246
Abstract
Disruptions in tokamaks are very often associated with the slowing down of magneto-hydrodynamic (MHD) instabilities and their subsequent locking to the wall. To improve the understanding of the chain of events ending with a disruption, a statistically robust and physically based criterion has [...] Read more.
Disruptions in tokamaks are very often associated with the slowing down of magneto-hydrodynamic (MHD) instabilities and their subsequent locking to the wall. To improve the understanding of the chain of events ending with a disruption, a statistically robust and physically based criterion has been devised to track the slowing down of modes with toroidal mode numbers n = 1 and mostly poloidal mode number m = 2, providing an alternative and earlier detection tool compared to simple threshold based indicators. A database of 370 discharges of axially symmetric divertor experiment—upgrade (AUG) has been studied and results compared with other indicators used in real time. The estimator is based on a weighted average value of the fast Fourier transform of the perturbed radial n = 1 magnetic field, caused by the rotation of the modes. The use of a carrier sinusoidal wave helps alleviating the spurious influence of non-sinusoidal magnetic perturbations induced by other instabilities like Edge localized modes (ELMs). The indicator constitutes a good candidate for further studies including machine learning approaches for mitigation and avoidance since, by deploying it systematically to evaluate the time instance for the expected locking, multi-machine databases can be populated. Furthermore, it can be thought as a contribution to a wider approach to dynamically tracking the chain of events leading to disruptions. Full article
(This article belongs to the Special Issue Recent Developments in Fusion Plasma Diagnostics)
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23 pages, 12083 KiB  
Article
Material Erosion and Dust Formation during Tungsten Exposure to Hollow-Cathode and Microjet Discharges
by Valentina Marascu, Cristian Stancu, Veronica Satulu, Anca Bonciu, Christian Grisolia and Gheorghe Dinescu
Appl. Sci. 2020, 10(19), 6870; https://doi.org/10.3390/app10196870 - 30 Sep 2020
Cited by 7 | Viewed by 2994
Abstract
Tungsten erosion and dust occurrence are phenomena of great interest for fusion technology. Herein, we report results concerning the material damage and dust formation in the presence of high temperature and large area or concentrated discharges in helium and argon. In order to [...] Read more.
Tungsten erosion and dust occurrence are phenomena of great interest for fusion technology. Herein, we report results concerning the material damage and dust formation in the presence of high temperature and large area or concentrated discharges in helium and argon. In order to generate adequate plasmas, we used tungsten electrodes in two experimental discharge systems, namely a hollow discharge and a microjet discharge. In both exposure cases, we noticed surface modification, which was assigned to sputtering, melting, and vaporization processes, and a significant dust presence. We report the formation on electrode surfaces of tungsten fuzz, nano-cones, nanofibers, and cauliflower- and faced-like particles, depending on the discharge and gas type. Dust with various morphologies and sizes was collected and analyzed with respect to the morphology, size distribution, and chemical composition. We noticed, with respect to erosion and particle formation, common behaviors of W in both laboratory and fusion facilities experiments. Full article
(This article belongs to the Special Issue Recent Developments in Fusion Plasma Diagnostics)
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22 pages, 2342 KiB  
Article
Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools
by Andrea Murari, Emmanuele Peluso, Michele Lungaroni, Riccardo Rossi, Michela Gelfusa and JET Contributors
Appl. Sci. 2020, 10(19), 6683; https://doi.org/10.3390/app10196683 - 24 Sep 2020
Cited by 13 | Viewed by 3323
Abstract
The inadequacies of basic physics models for disruption prediction have induced the community to increasingly rely on data mining tools. In the last decade, it has been shown how machine learning predictors can achieve a much better performance than those obtained with manually [...] Read more.
The inadequacies of basic physics models for disruption prediction have induced the community to increasingly rely on data mining tools. In the last decade, it has been shown how machine learning predictors can achieve a much better performance than those obtained with manually identified thresholds or empirical descriptions of the plasma stability limits. The main criticisms of these techniques focus therefore on two different but interrelated issues: poor “physics fidelity” and limited interpretability. Insufficient “physics fidelity” refers to the fact that the mathematical models of most data mining tools do not reflect the physics of the underlying phenomena. Moreover, they implement a black box approach to learning, which results in very poor interpretability of their outputs. To overcome or at least mitigate these limitations, a general methodology has been devised and tested, with the objective of combining the predictive capability of machine learning tools with the expression of the operational boundary in terms of traditional equations more suited to understanding the underlying physics. The proposed approach relies on the application of machine learning classifiers (such as Support Vector Machines or Classification Trees) and Symbolic Regression via Genetic Programming directly to experimental databases. The results are very encouraging. The obtained equations of the boundary between the safe and disruptive regions of the operational space present almost the same performance as the machine learning classifiers, based on completely independent learning techniques. Moreover, these models possess significantly better predictive power than traditional representations, such as the Hugill or the beta limit. More importantly, they are realistic and intuitive mathematical formulas, which are well suited to supporting theoretical understanding and to benchmarking empirical models. They can also be deployed easily and efficiently in real-time feedback systems. Full article
(This article belongs to the Special Issue Recent Developments in Fusion Plasma Diagnostics)
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