Diagnostics play a pivotal role in nuclear fusion experimental reactors, supporting physical studies, modelling, and plasma control. However, most diagnostics provide limited and partial information about the plasma’s status. For instance, magnetic probes measure only external magnetic fields, while interferometers, polarimeters, and bolometers deliver line-integrated measurements, necessitating specific inversion algorithms to extract local information. In the case of bolometers, tomographic inversions are particularly complex due to the variety of radiative patterns observed, with regularization equations often only weakly approximating the intricate physics involved. To address these challenges, it is essential to develop innovative algorithms that enhance the accuracy of the inversion processes, thereby ensuring reliable results for physics understanding, modelling, and plasma control. This work introduces new methodologies based on Physics-Informed Neural Networks (PINNs) to perform time-resolved emission tomography from bolometer data. These methodologies are first evaluated using synthetic cases (phantoms) and compared with one of the most advanced tomographic inversion techniques in the literature. Subsequently, they are applied to reconstruct specific radiative anomalies, such as Edge Localized Modes, Multifaceted Asymmetric Radiation from the Edge, and excessive core radiation leading to temperature hollowness at the Joint European Torus. The study demonstrates that PINNs not only enhance the overall accuracy of tomographic inversions but also offer advanced capabilities like super-resolution, data projection, and self-modelling. These features make time-resolved PINNs a valuable tool for analysing radiative patterns in transient phenomena. Although this work only considers tomography, the technology is perfectly suited to tackle any kind of inverse problem and can therefore provide significant benefits for both research and practical applications in nuclear fusion.

Introduction In this work, we aim to explore numerically the behavior of beam energetic particles in the Divertor Tokamak Test (DTT), a superconductive device equipped with a Neutral Beam Injection (NBI) system capable of injecting neutrals up to 510 keV.Method We explore beam ionization and beam slowing down for different DTT plasma scenarios. Numerical simulations are performed using the ASCOT suite of codes, including a wide-range scan of plasma density and beam injection energy. For different plasma conditions, we estimate shine-through losses, including the heat fluxes on the first wall thanks to dedicated particle tracing simulations. Orbits of newly-born fast ions are characterized by means of the constant of motion phase space, showing how trapped energetic particles' population and prompt losses change with plasma density and NBI energy.Results and discussion Slowing down simulations show that NBI injection at 510 keV is well coupled to DTT plasmas. DTT NBI will be one of the sources of auxiliary ion heating, with an absorbed power ratio of up to similar to 50% depending on plasma and beam parameters. At low plasma densities, energetic particle confinement is less efficient, and NBI power and/or energy reduction is expected.

Tokamak a configuration variable (TCV), recently celebrating 30 years of near-continual operation, continues in its missions to advance outstanding key physics and operational scenario issues for ITER and the design of future power plants such as DEMO. The main machine heating systems and operational changes are first described. Then follow five sections: plasma scenarios. ITER Base-Line (IBL) discharges, triangularity studies together with X3 heating and N2 seeding. Edge localised mode suppression, with a high radiation region near the X-point is reported with N-2 injection with and without divertor baffles in a snowflake configuration. Negative triangularity (NT) discharges attained record, albeit transient, beta(N) similar to 3 with lower turbulence, higher low-Z impurity transport, vertical stability and density limits and core transport better than the IBL. Positive triangularity L-Mode linear and saturated ohmic confinement confinement saturation, often-correlated with intrinsic toroidal rotation reversals, was probed for D, H and He working gases. H-mode confinement and pedestal studies were extended to low collisionality with electron cyclotron heating obtaining steady state electron iternal transport barrier with neutral beam heating (NBH), and NBH driven H-mode configurations with off-axis co-electron cyclotron current drive. Fast particle physics. The physics of disruptions, runaway electrons and fast ions (FIs) was developed using near-full current conversion at disruption with recombination thresholds characterised for impurity species (Ne, Ar, Kr). Different flushing gases (D2, H2) and pathways to trigger a benign disruption were explored. The 55 kV NBH II generated a rich Alfvenic spectrum modulating the FI fas ion loss detector signal. NT configurations showed less toroidal Alfven excitation activity preferentially affecting higher FI pitch angles. Scrape-off layer and edge physics. gas puff imaging systems characterised turbulent plasma ejection for several advanced divertor configurations, including NT. Combined diagnostic array divertor state analysis in detachment conditions was compared to modelling revealing an importance for molecular processes. Divertor physics. Internal gas baffles diversified to include shorter/longer structures on the high and/or low field side to probe compressive efficiency. Divertor studies concentrated upon mitigating target power, facilitating detachment and increasing the radiated power fraction employing alternative divertor geometries, optimised X-point radiator regimes and long-legged configurations. Smaller-than-expected improvements with total flux expansion were better modelled when including parallel flows. Peak outer target heat flux reduction was achieved (>50%) for high flux-expansion geometries, maintaining core performance (H-98 > 1). A reduction in target heat loads and facilitated detachment access at lower core densities is reported. Real-time control. TCV's real-time control upgrades employed MIMO gas injector control of stable, robust, partial detachment and plasma beta feedback control avoiding neoclassical tearing modes with plasma confinement changes. Machine-learning enhancements include trajectory tracking disruption proximity and avoidance as well as a first-of-its-kind reinforcement learning-based controller for the plasma equilibrium trained entirely on a free-boundary simulator. Finally, a short description of TCV's immediate future plans will be given.

An overview is presented of the progress since 2021 in the construction and scientific programme preparation of the Divertor Tokamak Test (DTT) facility. Licensing for building construction has been granted at the end of 2021. Licensing for Cat. A radiologic source has been also granted in 2022. The construction of the toroidal field magnet system is progressing. The prototype of the 170 GHz gyrotron has been produced and it is now under test on the FALCON facility. The design of the vacuum vessel, the poloidal field coils and the civil infrastructures has been completed. The shape of the first DTT divertor has been agreed with EUROfusion to test different plasma and exhaust scenarios: single null, double null, X-divertor and negative triangularity plasmas. A detailed research plan is being elaborated with the involvement of the EUROfusion laboratories.

This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals

In 2021 JET exploited its unique capabilities to operate with T and D-T fuel with an ITER-like Be/W wall (JET-ILW). This second major JET D-T campaign (DTE2), after DTE1 in 1997, represented the culmination of a series of JET enhancements-new fusion diagnostics, new T injection capabilities, refurbishment of the T plant, increased auxiliary heating, in-vessel calibration of 14 MeV neutron yield monitors-as well as significant advances in plasma theory and modelling in the fusion community. DTE2 was complemented by a sequence of isotope physics campaigns encompassing operation in pure tritium at high T-NBI power. Carefully conducted for safe operation with tritium, the new T and D-T experiments used 1 kg of T (vs 100 g in DTE1), yielding the most fusion reactor relevant D-T plasmas to date and expanding our understanding of isotopes and D-T mixture physics. Furthermore, since the JET T and DTE2 campaigns occurred almost 25 years after the last major D-T tokamak experiment, it was also a strategic goal of the European fusion programme to refresh operational experience of a nuclear tokamak to prepare staff for ITER operation. The key physics results of the JET T and DTE2 experiments, carried out within the EUROfusion JET1 work package, are reported in this paper. Progress in the technological exploitation of JET D-T operations, development and validation of nuclear codes, neutronic tools and techniques for ITER operations carried out by EUROfusion (started within the Horizon 2020 Framework Programme and continuing under the Horizon Europe FP) are reported in (Litaudon et al Nucl. Fusion accepted), while JET experience on T and D-T operations is presented in (King et al Nucl. Fusion submitted).

Within the 9th European Framework programme, since 2021 EUROfusion is operating five tokamaks under the auspices of a single Task Force called ‘Tokamak Exploitation’. The goal is to benefit from the complementary capabilities of each machine in a coordinated way and help in developing a scientific output scalable to future largre machines. The programme of this Task Force ensures that ASDEX Upgrade, MAST-U, TCV, WEST and JET (since 2022) work together to achieve the objectives of Missions 1 and 2 of the EUROfusion Roadmap: i) demonstrate plasma scenarios that increase the success margin of ITER and satisfy the requirements of DEMO and, ii) demonstrate an integrated approach that can handle the large power leaving ITER and DEMO plasmas. The Tokamak Exploitation task force has therefore organized experiments on these two missions with the goal to strengthen the physics and operational basis for the ITER baseline scenario and for exploiting the recent plasma exhaust enhancements in all four devices (PEX: Plasma EXhaust) for exploring the solution for handling heat and particle exhaust in ITER and develop the conceptual solutions for DEMO. The ITER Baseline scenario has been developed in a similar way in ASDEX Upgrade, TCV and JET. Key risks for ITER such as disruptions and run-aways have been also investigated in TCV, ASDEX Upgrade and JET. Experiments have explored successfully different divertor configurations (standard, super-X, snowflakes) in MAST-U and TCV and studied tungsten melting in WEST and ASDEX Upgrade. The input from the smaller devices to JET has also been proven successful to set-up novel control schemes on disruption avoidance and detachment.

This paper is dealing with the physics basis used for the design of the Divertor Tokamak Test facility (DTT), under construction in Frascati (DTT 2019 DTT interim design report (2019)) Italy, and with the description of the main target plasma scenarios of the device. The main goal of the facility will be the study of the power exhaust, intended as a fully integrated core-edge problem, and eventually to propose an optimized divertor for the European DEMO plant. The approach used to design the facility is described and their main features are reported, by using simulations performed by state-of-the-art codes both for the bulk and edge studies. A detailed analysis of MHD, including also the possibility to study disruption events and Energetic Particles physics is also reported. Eventually, a description of the ongoing work to build-up a Research Plan written and shared by the full EUROfusion community is presented.

The main purpose of the divertor tokamak test (DTT) facility is to study alternative solutions to mitigate the issue of the power exhaust, under integrated physics and technical conditions relevant to ITER and DEMO. One of the most complex and innovative subsystems of the entire project is certainly the negative-ion-based neutral beam injector (NBI), meant to inject 10 MW of auxiliary power with a beam of 510 keV deuterium neutrals. This contribution describes the conceptual design of the beamline for the DTT NBI system, with a particular focus on the innovative technical solutions adopted to fulfill the requirements and maximize the performance. The DTT NBI is required to operate with high efficiency in several operating scenarios, covering a large range of beam energies, between 10% and 100% of the nominal value (510 keV). To reach this challenging goal, an innovative accelerator design, the spherical and lemon hyperlens grids (SLHGs), has been developed. The implementation of this design concept of the accelerator has recently become possible thanks to recent improvements in additive manufacturing (AM) technology. Another original aspect of the DTT NBI, compared to existing devices, regards the vacuum pumping system, which will be based on nonevaporable getter (NEG) pumps. This will represent the first application of the NEG technology to an NBI for the heating and current drive system of a fusion experiment, with a possible simplification of the overall construction, with respect to typical solutions based on cryogenic pumps. Other innovative solutions are the cylindrical sawtooth structure (CSS) for the neutralizer panels and the stray field shielding system (SFSS) with encapsulated neutralizer. This article provides an overview of the injector for DTT NBI with a particular focus on innovative technical solutions.

Disruptions are abrupt collapses of the configuration that have afflicted all tokamaks ever operated. Reliable observers are a prerequisite to the definition and the deployment of any realistic strategy of countermeasures to avoid or mitigate disruptions. Lacking first principle models of the dynamics leading to disruptions, in the past decades empirical predictors have been extensively studied and some were even installed in JET real-time network. Having been conceived as engineering tools, they were often very abstract. In this work, physics and data-driven methodologies are combined to identify the main macroscopic precursors of disruptions: magnetic instabilities, abnormal kinetic profiles and radiation patterns. Machine learning predictors utilising these observers can not only detect and classify these anomalies but also determine their probability of occurrence and estimate the time remaining before their onset. These tools have been applied to a database of about two thousand JET discharges with various isotopic compositions including DT, in conditions simulating in all respects real time deployment. Their performance would meet ITER requirements, and they are expected to be easily transferrable to larger devices, because they rely only on normalised quantities, form factors, and physical/empirical scaling laws.

To produce fusion reactions efficiently, thermonuclear plasmas have to reach extremely high temperatures, which is incompatible with their coming into contact with material surfaces. Confinement of plasmas using magnetic fields has progressed significantly in the last years, particularly in the tokamak configuration. Unfortunately, all tokamak devices, and particularly metallic ones, are plagued by catastrophic events called disruptions. Many disruptions are preceded by anomalies in the radiation patterns, particularly in ITER-relevant scenarios. These specific forms of radiation emission either directly cause or reveal the approaching collapse of the configuration. Detecting the localization of these radiation anomalies in real time requires an innovative and specific elaboration of bolometric measurements, confirmed by visible cameras and the inversion of sophisticated tomographic algorithms. The information derived from these measurements can be interpreted in terms of local power balances, which suggest a new quantity, the radiated power divided by the plasma internal energy, to determine the criticality of the plasma state. Combined with robust indicators of the temperature profile shape, the identified anomalous radiation patterns allow determination of the sequence of macroscopic events leading to disruptions. A systematic analysis of JET campaigns at high power in deuterium, full tritium, and DT, for a total of almost 2000 discharges, proves the effectiveness of the approach. The warning times are such that, depending on the radiation anomaly and the available actuators, the control system of future devices is expected to provide enough notice to enable deployment of effective prevention and avoidance strategies.

In many engineering fields and scientific disciplines, the results of experiments are in the form of time series, which can be quite problematic to interpret and model. Genetic programming tools are quite powerful in extracting knowledge from data. In this work, several upgrades and refinements are proposed and tested to improve the explorative capabilities of symbolic regression (SR) via genetic programming (GP) for the investigation of time series, with the objective of extracting mathematical models directly from the available signals. The main task is not simply prediction but consists of identifying interpretable equations, reflecting the nature of the mechanisms generating the signals. The implemented improvements involve almost all aspects of GP, from the knowledge representation and the genetic operators to the fitness function. The unique capabilities of genetic programming, to accommodate prior information and knowledge, are also leveraged effectively. The proposed upgrades cover the most important applications of empirical modeling of time series, ranging from the identification of autoregressive systems and partial differential equations to the search of models in terms of dimensionless quantities and appropriate physical units. Particularly delicate systems to identify, such as those showing hysteretic behavior or governed by delayed differential equations, are also addressed. The potential of the developed tools is substantiated with both a battery of systematic numerical tests with synthetic signals and with applications to experimental data.

Dimensional analysis is a well-known approach to model building in engineering, because it can contribute to identifying more parsimonious and meaningful equations for describing complex phenomena. Unfortunately, it is not always exploited to the full, because it is typically applied prior to any form of statistical evaluation, often resulting in poor choices of the dimensionless quantities and consequently in suboptimal models. A completely general and data driven technique is proposed, which integrates dimensional and statistical analysis with the help of genetic programming supported symbolic regression and neural computing. The methodology exploits the potential of various machine-learning techniques and allows extracting mathematical models in terms of dimensionless quantities directly from the dimensional databases available. A battery of numerical tests and examples from fluid dynamics and thermonuclear fusion illustrate the unquestionable advantages of the approach for statistical inference and for the interpretation of the large amounts of data produced by modern physics experiments and engineering studies.

The Divertor Tokamak Test (DTT) is a new experimental facility whose construction is starting in Frascati, Rome, Italy; its main goals are improving the understanding of plasma-wall interactions and supporting the development of ITER and DEMO. DTT will be equipped with a Neutral Beam Injector (NBI) based on negative deuterium ions, designed to inject 10 MW of power to the tokamak. A fundamental system for the good operations of the DTT NBI will be its Gas injection and Vacuum System (GVS). Indeed, the efficiency of the entire NBI strongly depends on the good performance of its GVS.The GVS for DTT NBI will be composed of two systems working in parallel: a grounded section connected to the main vacuum vessel, and a high voltage part connected to the ion source vessel and working at -510 kV voltage. The grounded part will feature a fore vacuum system (given by screw and roots pumps) plus a high vacuum system based on turbo-molecular pumps located on the side walls of the vessel and Non-Evaporable Getter (NEG) pumps located inside the vessel on the upper and lower surfaces. On the other hand, the high voltage part will feature a fore vacuum system (given by two compact screw pumps mounted on the external surface for the ion source vessel) plus a high vacuum system based on turbo-molecular pumps also located on the sidewalls of the ion source vessel. A dedicated deuterium gas injection will feed the process gas to the ion source and the neutralizer.This paper gives a description of the conceptual design of the GVS for DTT NBI, and of the procedure followed to optimize this system considering the operational requirements and the other constraints of the DTT NBI.

The Sequence of Physics Events (SPE) that leads the plasma towards a disruption can be different in different discharges. An Event, in this framework, refers to the signature of a physical phenomenon (e.g. a peaked ne profile or bolometric peaking factor), whose duration may vary from few ms to several hundreds ms. The SPE have a direct influence on the evolution of the pulses. Then, it is reasonable to assume that their identification can provide relevant information to keep the discharges within a safe operational space. In this work, first, a set of Event detectors (each one targeting a distinctive disruption-related phenomenon) have been considered. These detectors have been applied to JET disruptive discharges in the range of #94152 to #97137 (June 2019 - March 2020, corresponding to Baseline scenario experiments). Each one of these disruptive shots has been characterized by its corresponding SPE. Once the main signatures of the disruptive behaviour have been determined, the last step of the work consists of the analysis of these SPEs. The objective is to identify which ones are more prone to occur and therefore useful to describe their impact on the discharge evolution. This first step opens the possibility of applying the method to a considerable larger database, including also non-disruptive discharges, in order to better understand when and why similar trajectories end (or not) in a disruption.

In metallic devices, various forms of radiation collapse are one of the major causes of plasma degradation leading to disruptions. Some of the most advanced scenarios, with impurity seeding or detachment, are prone to developing excessive edge cooling, which very often ends with the collapse of the discharge. This type of disruption is also typically preceded by the formation of an instability called Multifaceted Asymmetric Radiation From the Edge (MARFE). This manifests itself as a blob of radiation above the X point or in various other regions of the plasma edge. This work reports on the development of image processing algorithms to detect MARFE as early as possible, to contribute to disruption prediction. Particular attention has therefore been devoted to general techniques, which can identify the features typical of MARFEs in any region of the plasma cross-section. The developed tools are sufficiently flexible to allow selecting a good trade-off between sensitivity and specificity. The solutions adopted are also all compatible with the implementation in real time networks. The examples provided have been collected using JET cameras during the H and T campaigns, run in preparation of the recently concluded JET campaign in D-T. Given the warning times achieved, it is expected that the early and accurate detection of MARFEs can contribute significantly to the prevention of the disruptions caused or preceded by these instabilities.

The objective of the Fourth Technical Meeting on Fusion Data Processing, Validation and Analysis was to provide a platform during which a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolating needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics and synthetic diagnostics data analysis (including image processing, regression analysis, inverse problems, deep learning, machine learning, big data and physics-based models for control) reported at the meeting. The progress in these areas highlight trends observed in current major fusion confinement devices. A special focus is dedicated on data analysis requirements for ITER and DEMO with a particular attention paid to artificial intelligence for automatization and improving reliability of control processes.

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.

The control of macroscopic instabilities, such as Edge Localised Modes (ELMs) and sawteeth, is becoming an essential ingredient in the optimisation of scenarios in preparation for the next generation of tokamaks and the demonstrative reactor. Various pacing experiments have been indeed successfully carried out in many devices but various details of their interactions with the plasma remain poorly understood, in particular the assessment of the relative contribution of driver phase and amplitude to frequency synchronization. In this paper, a data analysis methodology for investigating the details of pacing experiments is proposed. The approach is based on the wavelet decomposition of the signals and the following implementation of information theoretic indicators, to determine the basic form of the interactions. The main analysis tool deployed is the conditional mutual information, equivalent to the informational transfer entropy, which can detect actual causal influences and not only statistical correlations between signals. The most flexible type of ELM pacing, the vertical kicks, permits to clearly confirm the potential of the analysis tools developed. In the dedicated experiments to optimise the ELMs pacing with pellets and sawteeth synchronization with ICRH modulation, the main effect is due to the influence of the amplitude of the external perturbations. Some evidence of phase synchronization has been found, in both JET and AUG, which indicates the direction of possible future optimization of the interventions. Indeed, adjusting the phase of the pacing schemes would free density and ICRH power to give more leeway to other feedback loops, an important aspect, particularly for the integrated control of the next generation of devices. The long-term objective of this line of research is therefore twofold. On the one hand, it is expected to contribute to better physical interpretation of the pacing experiments and possibly also of their target instabilities. From an operational perspective, hopefully these insights will help in the development of reactor relevant scenarios, as exemplified by examples of ELM a pacing with pellets carried out in preparation for the DT campaign on JET.

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.