Eni strongly relies on the use of Artificial Intelligence (AI) based solutions, with the purpose of progressively making operations more efficient and sustainable, from enhancing safety for personnel to ensuring the integrity of Eni's assets through predictive maintenance solutions and aiding the Research & Development department in crafting innovative technologies to achieve net-zero...
We present three case studies demonstrating the minimisation or elimination of human intervention from the process of generating data sets with relevance to different problems in tokamak fusion experiment design and control.
The design of new tokamaks, optimisation of plasma scenarios and construction of real-time control systems in tokamaks require a comprehensive and often expensive...
Accurate simulation of fusion plasma turbulence is required for reactor operation and control, but is either too slow or lacks accuracy with present techniques.
The FASTER project aims to circumvent these conflicting constraints of accuracy and tractability and provide real-time capable turbulent transport models with increased physics fidelity for tokamak temperature, density, and rotation...
During the WEST experimental fusion plasma discharges, several diagonistics are employed to collect diverse data. Among these diagnostics, two high-definition cameras, operating within the visible spectrum, broadcast in real time the plasma inside the vacuum vessel.
The intent of our study is to investigate the possibility of determining the plasma state from this live video for each...
In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. Also this controller is implemented on digital signal processor (DSP) control system.
In the first stage, a nonlinear model is identified for plasma vertical position, based on the...
Intelligent control of the thermal loads is required to guarantee the safety of fusion devices such as W7-X and ITER during quasi-steady-state operation with reactor-relevant performance. This feedback control system should implement preemptive strategies, enabling long-plasma operation by impeding thermal load escalation to dangerous levels and minimizing plasma terminations triggered by the...
Previous work [1,2] has successfully applied neural network (QLKNN) surrogates for the
quasi-linear gyrokinetic simulation code QuaLiKiz [3] to predict core tokamak transport heat
and particle fluxes, resulting in 3-5 orders of magnitude reduction in computation time with
minimal (up to 10%, case dependent) loss of precision. The current study aims to apply this
concept using the...
It is a crucial challenge that disruption prediction should learn from limited data due to the considerable expense of obtaining an extensive experimental dataset in future tokamaks. To reduce the data requirements for future tokamaks, utilizing existing knowledge of disruption physics and tokamak discharge could be helpful. IDP-PGFE (Interpretable Disruption Predictor based on Physics-Guided...
A digital twin for plasma dynamics in a tokamak is useful for optimising and validating experimental scenario proposals, developing plasma control systems and more. Physics-based modelling of the entire tokamak discharge process is challenging due to nonlinear, multi-scale, multi-physics characteristics of the tokamak and demands time from a diverse team of experts as well as computational...
In KSTAR tokamak, frequency-modulated continuous wave (FMCW) reflectometry has been used to measure plasma density profiles with high spatial and temporal resolution. The data analysis process involves extracting time-varying phase differences from the incident swept signal and reflected wave’s signal and subsequently calculating profiles through the numerical inversion process. However,...
The quality of energetic particle confinement in a nuclear fusion reactor is a key factor in the reactor's efficiency. One way of studying the behavior of energetic particles in detail is to integrate "test particle" trajectories into a previously calculated turbulent electric potential field. The high cost of calculating the turbulent field, and the size of the data, make it very difficult to...
Single-chord interferometry is widely used in plasma physics to obtain the line-integrated density of a plasma. In this work, we propose the use of a (deep) neural network (NN) to assist in the development of a novel diagnostic technique which allows the estimation of the plasma density profile from a single interferometry measurement. The purpose of the NN is to solve the inverse-scattering...
The decay photon fields produced by components activated during the normal operation of a nuclear plant are of particular interest for the maintenance and decommissioning functions of the plant itself, due to the threat they may pose to the health of exposed workers and to the integrity of the electronics components. For the design of the shielding for these radiation fields, extremely...
Artificial Intelligence (AI) and Machine Learning (ML) have imposed themselves as the standard toolkit for image processing. This happens also in experimental science, where the tools and methods of AI/ML (use case definition, annotated database creation, learning and inference) prove fruitful at solving science and technology challenges ill-addressed by conventional processes. At WEST, an...
In large tokamak reactors, one unmitigated disruption will bring intolerable damage to them. Accurate plasma disruption prediction system is needed to trigger the disruption mitigation system. Currently machine learning disruption predictor is the most promising way of solving this problem. But it does need data from the target machine to be trained. However, the future machine will not be...
Tokamaks require magnetic control across a wide range of plasma scenarios. The coupled behavior of plasma dynamics makes deep learning a suitable candidate for efficient control in order to fulfil these high-dimensional and non-linear situations. For example, on TCV, deep reinforcement learning has already been used for tracking of the plasma’s magnetic equilibrium [1]. In this work, we apply...
The aim of this work is the development and analysis of Al tools for welding success rate prediction and the posterior output processing of PAUT applied to welding defects detection in the ITER Vacuum Vessel manufacturing.
Due to its complexity, the manufacturing of this large equipment - based on the French nuclear design and manufacturing code (RCC-MR) - has generated a large amount of...
Artificial neural network (NN) surrogate models have been developed and trained on magnetic, magnetic + motional stark effect (MSE), and kinetic DIIII-D equilibria to accelerate tokamak equilibrium reconstructions for offline, between-shot, and real-time applications. Adaptation of the ML/AI algorithms has been facilitated through the recently developed device-independent portable equilibrium...
For fusion diagnostics implementing line-integrated measurement, tomography problem has to be addresses in order to reconstruct a spatially resolved 2D imagine by inverting a limited number of line-integrated data. However, most routinely used inversion methods are still not eligible for real-time application due to the time costing algorithms (i.e. iteration) adopted in these classical...
Tokamak is a promising device for producing nuclear fusion energy. Due to the high speed of physical processes, tokamak requires automation of control system to provide the most effective use. Researchers already proposed some essential automation such as gas puffing control system [[1]] and disruption mitigation system [[2]]. In turn, performance of those proposed automations highly relies on...
To reconstruct the electron density profile of tokamak plasma, the phase of reflected microwave is to be captured by tracing the signal along the frequency as an imaginary pathway on the spectrogram. From the blurry and broken lines among the unwanted clutters, the actual path should be estimated as a continuation of the tone heights at each vertical slice of the wavelet transform. Thus, we...
Plasma disruptions pose a major threat to burning plasma devices. As a part of avoidance, mitigation, resilience, and recovery (AMRR) efforts, it is desirable to develop emergency shutdown scenarios that 1) minimize ramp down time while avoiding disruptions, and 2) adapt to the real-time conditions of the plasma. Prior works involved performing a constrained trajectory optimization on the...
Fusion plasma devices have generated in the past years large amounts of shots exhibiting Alfvén activity, which is usually detected using external magnetic sensors (Mirnov coils), but also by other diagnostics. The behaviour of Magnetohydrodynamic (MHD) modes is commonly analysed through spectrograms from Mirnov signals. However, extracting physical information of individual mode activity from...
