The Divertor Tokamak Test facility (DTT) is the new tokamak under construction in Frascati (Italy), with the main aim of testing different divertor magnetic configurations and technologies in view of DEMO. The Heating and Current Drive function will be provided also by one Neutral Beam Injector (NBI), accelerating negative Deuterium ions with an energy of 510 keV, giving an output power of 10 MW maximum. The Acceleration Grid Power Supply (AGPS), with 3 stages rated 167 kV each, will feed the NBI accelerator. For optical reasons, the insulating gap among the acceleration grids is at the limit of the voltage holding in vacuum, therefore frequent arc breakdowns (BD) among the grids will occur, requiring the quick switch-off of the AGPS to limit the arc energy. For the AGPS, beside the ITER-like scheme including gas-insulated HV components, an innovative approach based on the Modular Multilevel Converter (MMC) technology is being considered. This would include air-insulated HV converters, requiring a huge building volume, which at present is not available in close proximity to the tokamak building. The alternative of installing the MMC converters in a new building to be erected in an available area located far from the tokamak, and connecting the AGPS to the load with HV coaxial cables, is under study. This paper presents the studies carried out to verify the impact of the cables length on the arc energy, in case of breakdown among the grids, and to estimate the magnitude of the voltage oscillations due to resonance effects. These oscillations can occur not only between each hot pole and the return conductor, endangering the cable insulation, but also between the return conductor and local ground, with potential consequences on the laying method, the human safety and the electromagnetic compatibility with sensitive apparatus nearby, which have all been evaluated.

A methodology, to determine the causal relations between time series and to derive the set of equations describing the interacting systems, has been developed. The techniques proposed are completely data driven and they are based on ensembles of Time Delay Neural Networks (TDNNs) and Symbolic Regression (SR) via Genetic Programming (GP). With regard to the detection of the causal influences and the identification of graphical causal networks, the developed tools have better performances than those reported in the literature. For example, the TDNN ensembles can cope with evolving systems, non-Markovianity, feedback loops and multicausality. In its turn, on the basis of the information derived from the TDNN ensembles, SR via GP permits to identify the set of equations, i.e. the detailed model of the interacting systems. Numerical tests and real life examples from various disciplines prove the power and versatility of the developed tools, capable of handling tens of time series and even images. The excellent results obtained emphasize the importance of recording the time evolution of signals, which would allow a much better understanding of many issues, ranging from the physical to the social and medical sciences.

Identification of complex systems: diagnostics for Tokamak control as a case study.

Recently, a linear disruption predictor (Vega et al 2020 Nucl. Fusion 60 026001) was installed in the JET real-time network for disruption mitigation purposes. From a mathematical point of view, the predictor is based on computing centroids of disruptive examples and non-disruptive examples in a two-dimensional space. This is the reason for calling it centroid method (CM). It uses a single signal: the mode lock normalized to the plasma current. The predictor is not based on thresholds to trigger alarms but on the differences of amplitudes between consecutive samples. The article analyses its results for the range of discharges 94 152-97 137 (June 2019-March 2020), including discharges of both baseline scenario and hybrid scenario. The article presents a comparison between the CM predictor and several different disruption detection systems operational in the JET real-time event detection platform named PETRA (Plasma Events Triggering for Alarms). The CM predictor outperforms all the other classifiers implemented in PETRA, according to all the main statistical indicators normally used to qualify these tools.

The paper reports a systematic assessment of the radiation-hard Hall probes (RHP) magnetic diagnostic system of the JET tokamak, which is based on InSb semiconductor thin films, and describes the path that lead to the proposal of an innovative magnetic probe concept. A relevant account of RHP operation during the recent deuterium-tritium experimental campaign is also provided, showing correct operation under ITER-like intense neutron flux. The period considered for the systematic assessment of the RHP system ranges from October 2009 to March 2021, during which the machine produced more than 19 000 pulses. The RHP system consists of six three-dimensional Hall probes, which have built-in recalibration capability, thanks to the presence of microsolenoids that produce a local known field during a tailored automatic pre-pulse calibration sequence, that can also be initiated manually. During pulses, the microsolenoids can also be used as inductive sensors as their signals are recorded as well. Moreover, the system provides temperature measurements at the location of the probes, which are continuously recorded too. The assessment demonstrates accurate long-term operation of the RHP system. All the diagnostic channels reliably provide pre-pulse calibration data and pulse signals and the original sensitivities of the Hall sensors are preserved. Integration considerations and a data fusion analysis lead to the proposal of a high performance, compact, broadband, hybrid field probe, consisting of the combination of an inductive coil and a Hall sensor, to be manufactured by means of the coil technology developed for ITER or an alternative concept with improved radiation-hardness. The hybrid probe is expected to deliver the advantages of both inductive and Hall sensing technologies, essentially in the same package size of a single ITER magnetic discrete probe. In particular, it would solve the problem of the drift of the integrator for long lasting burning plasma discharges. The signals produced by the coil and the Hall sensor, processed by means of a Luenberger-Kalman observer, provide a magnetic field measurement which is non-drifting and low-noise. For these reasons, the hybrid probe has been proposed as the potential primary magnetic diagnostic sensor for future burning plasma experiments and demonstration fusion power plants.

In the era of Big Data, many scientific disciplines and engineering activities rely on cumulative databases, consisting of many entries derived from different experiments and studies, to investigate complex problems. Their contents can be analysed with much finer granularity than with the usual meta-analytic tools, based on summary statistics such as means and standard deviations. At the same time, not being primary studies, also traditional statistical techniques are not adequate to investigate them. New meta-analysis methods have therefore been adapted to study these cumulative databases and to ensure their validity and consistency. Information theoretic and neural computational tools represent a series of complementary techniques, which can be deployed to identify the most important variables to analyse the problem at hand, to detect whether quantities are missing and to determine the coherence between the entries provided by the individual experiments and studies. The performances of the developed methodologies are verified with a systematic series of tests with synthetic data. An application to thermonuclear fusion proves the capability of the tools to handle real data, in one of the most complex fields of modern physics.

It can be argued that the identification of sound mathematical models is the ultimate goal of any scientific endeavour. On the other hand, particularly in the investigation of complex systems and nonlinear phenomena, discriminating between alternative models can be a very challenging task. Quite sophisticated model selection criteria are available but their deployment in practice can be problematic. In this work, the Akaike Information Criterion is reformulated with the help of purely information theoretic quantities, namely, the Gibbs-Shannon entropy and the Mutual Information. Systematic numerical tests have proven the improved performances of the proposed upgrades, including increased robustness against noise and the presence of outliers. The same modifications can be implemented to rewrite also Bayesian statistical criteria, such as the Schwartz indicator, in terms of information-theoretic quantities, proving the generality of the approach and the validity of the underlying assumptions.

Start-up runaway electrons are one of the key challenges for ITER. Unlike disruption generated RE beams, the experimental data analysis in present does built on dedicated experiments but on existing databases of many shots as in most devices start-up RE occur naturally in certain percentage of discharges. On the other hand this allows us to map the effects of various parameters only in a small, natural range. Furthermore the initial phase of the discharge is often poorly diagnosed and controlled and once suitable conditions are found, operators often do not allow variation of the optimal parameters. Thus, dedicated experimental data with parameter scans motivated by physics understanding is in fact hard to obtain. This contribution aims on initiating the discussion on the best methods to analyse start-up RE databases and missing steps in applications and comparison with numerical models. An analysis of start-up REs in COMPASS is presented with the early electron density being apparently the most important parameter. The relation between early non-thermal ECE and early HXR is examined. An initial look on the ASDEX-U start-up RE database is also presented.

In recent years, a new tomographic inversion method based on the Maximum Likelihood (ML) approach has been adapted to JET bolometry. Apart from its accuracy and reliability, the key advantage is its ability to provide reliable estimates of the uncertainties in the reconstructions. The original algorithm was implemented and validated using the MATLAB software tool. This work presents the accelerated version of the algorithm implemented using a compatible ITER fast controller platform with the Ubuntu 18.04 or the ITER Codac Core System distributions (6.1.2). The algorithm has been implemented in C++ using the open-source libraries: ArrayFire, ALGLIB, and MATIO. These libraries simplify the management of specific hardware accelerators such as GPUs and increase performance. The speed-up factor obtained is approximately 10 times. The work presents the methodology followed, the results obtained, and the advantages and drawbacks of implementation.