arXiv subjects

F. Felici

Publications and source records attributed to F. Felici.

9 recordsLinked to original sources

arXiv2026

Applications of a novel model-based real-time observer for electron density profile control experiments in TCV

Real-time control of tokamak plasmas encompasses sustaining a high-performance stationary state, avoiding disruptions, and managing ramp-up and ramp-down phases. Real-time estimation and control of electron density is fundamental for monitoring and controlling particle confinement, heating efficiency, exhaust conditions, impurity concentration, fusion power, and proximity to the density limit. Building on the integration of a multi-rate observer based on RAPDENS into the TCV control system, this study explores its application to density profile control for detachment studies, ECH, and NBH L-mode plasmas, and high-performance H-mode scenarios. TCV experiments demonstrate the observer's capability to support detachment studies in complex divertor geometries, controlling the line-averaged density within the last-closed flux surface while rejecting interferometer pick-up from Scrap-Off Layer density in the divertor. The estimated density profile enables local control of central density in ECH/NBH L-mode plasmas below cutoff conditions; heating-induced profile peaking modification is treated as a disturbance to the control task. Real-time estimation of time-varying transport coefficients, such as the pinch velocity-to-diffusivity ratio, improves model predictive capabilities, and the underlying turbulent transport is characterized via linear and non-linear gyrokinetic simulations with GENE. Simultaneous control of edge-normalized density and toroidal beta in H-mode plasmas is then demonstrated, yielding good confinement, scenario reproducibility, and a diagnostics-independent edge-density metric, while avoiding density limits and diagnostic faults propagation.

physics.plasm-ph↗

arXiv2026

Kinetic Equilibrium Prediction at TCV using RAPTOR and FBT

We present results from a new Kinetic-Equilibrium Prediction (KEP) workflow and shot preparation for full TCV discharges, by coupling predict-first RAPTOR transport simulations with FBT inverse equilibrium calculations. RAPTOR is a 1.5D transport code which has been extensively used for plasma shot optimization and real-time modeling. We show that rapid pre-shot simulations can be performed directly using information from the pulse schedule across a wide range of plasma shapes and scenarios, given an estimate of the confinement quality factor H98(y,2) and line-averaged density. The resulting p' and TT' profiles are then provided to the pre-shot equilibrium computation performed by FBT - a static free-boundary solver routinely used at TCV - achieving convergence between the two codes in a few iterations. Finally, we show that this coupling, when integrated into the TCV shot preparation, improves the evaluation of the coil currents needed to match the target plasma shape; in particular providing an accurate estimate of critical quantities such as the internal inductance $l_i$ and normalized pressure $\beta_N$, giving more realistic information to tokamak operators about the expected pulse behavior and enabling them to adjust the plan correspondingly.

physics.plasm-ph↗

arXiv2026

A tutorial on inversion-based shape control with design application to NSTX-U

One of the most common designs for magnetic control in tokamaks is to ``linearize an equilibrium'' to obtain a sensitivity mapping, then invert this mapping in order to determine the feedback control currents or voltages. For this work, we refer to this broad class of methods as inversion-based shape control (IBSC). In this work we describe the IBSC framework in a comprehensive manner and show how variations such as using dynamic voltage mappings and quadratic-program constrained control fit naturally into the framework. Despite the prevalence of IBSC, some of these extensions and the challenges associated with specific variations of IBSC are less widely known. We pay special attention to the challenge of decoupling the interaction between shape control and vertical control, which was a source of degraded vertical control performance on NSTX-U. This work is intended to provide a systematic overview of IBSC, and to that end, we have provided background material, proposed design procedures, and tutorials on the magnetic control design process within the appendices. Applying the systematic design procedure to NSTX-U, we find that the vertical control bobble on NSTX-U can be removed via decoupling, and that the vertical control phase margin can be improved by 6 degrees just by including PF1A and PF2 as vertical actuators.

physics.plasm-ph↗

arXiv2025

Feedforward equilibrium trajectory optimization with GSPulse

One of the common tasks required for designing new plasma scenarios or evaluating capabilities of a tokamak is to design the desired equilibria using a Grad-Shafranov (GS) equilibrium solver. However, most standard equilibrium solvers are time-independent and do not include dynamic effects such as plasma current flux consumption, induced vessel currents, or voltage constraints. Another class of tools, plasma equilibrium evolution simulators, do include time-dependent effects. These are generally structured to solve the forward problem of evolving the plasma equilibrium given feedback-controlled voltages. In this work, we introduce GSPulse, a novel algorithm for equilibrium trajectory optimization, that is more akin to a pulse planner than a pulse simulator. GSPulse includes time-dependent effects and solves the inverse problem: given a user-specified set of target equilibrium shapes, as well as limits on the coil currents and voltages, the optimizer returns trajectories of the voltages, currents, and achievable equilibria. This task is useful for scoping performance of a tokamak and exploring the space of achievable pulses. The computed equilibria satisfy both Grad-Shafranov force balance and axisymmetric circuit dynamics. The optimization is performed by restructuring the free-boundary equilibrium evolution (FBEE) equations into a form where it is computationally efficient to optimize the entire dynamic sequence. GSPulse can solve for hundreds of equilibria simultaneously within a few minutes. GSPulse has been validated against NSTX-U and MAST-U experiments and against SPARC feedback control simulations, and is being used to perform scenario design for SPARC. The computed trajectories can be used as feedforward inputs to inform and improve feedback performance. The code for GSPulse is available open-source at https://github.com/jwai-cfs/GSPulse_public.

physics.plasm-ph↗

arXiv2020

Integrated real-time supervisory management for off-normal-event handling and feedback control of tokamak plasmas

For long-pulse tokamaks, one of the main challenges in control strategy is to simultaneously reach multiple control objectives and to robustly handle in real-time (RT) unexpected events (off-normal-events -- ONEs) with a limited set of actuators. We have developed in our previous work a generic architecture of the plasma control system (PCS) including a supervisor and an actuator manager to deal with these issues. We present in this paper recent developments of real-time decision-making by the supervisor to switch between different control scenarios (normal, backup, shutdown, disruption mitigation, etc.) during the discharge, based on off-normal-event states. We first standardize the evaluation of ONEs and thereby simplify significantly the supervisor decision logic, as well as facilitate the modifications and extensions of ONE states in the future. The whole PCS has been implemented on the TCV tokamak, applied to disruption avoidance with density limit experiments, demonstrating the excellent capabilities of the new RT integrated strategy.

eess.SY↗

arXiv2019

Classification of tokamak plasma confinement states with convolutional recurrent neural networks

During a tokamak discharge, the plasma can vary between different confinement regimes: Low (L), High (H) and, in some cases, a temporary (intermediate state), called Dithering (D). In addition, while the plasma is in H mode, Edge Localized Modes (ELMs) can occur. The automatic detection of changes between these states, and of ELMs, is important for tokamak operation. Motivated by this, and by recent developments in Deep Learning (DL), we developed and compared two methods for automatic detection of the occurrence of L-D-H transitions and ELMs, applied on data from the TCV tokamak. These methods consist in a Convolutional Neural Network (CNN) and a Convolutional Long Short Term Memory Neural Network (Conv-LSTM). We measured our results with regards to ELMs using ROC curves and Youden's score index, and regarding state detection using Cohen's Kappa Index.

physics.data-an↗

arXiv2015

Measurement and comparison of individual external doses of high-school students living in Japan, France, Poland and Belarus -- the "D-shuttle" project --

Twelve high schools in Japan (of which six are in Fukushima Prefecture), four in France, eight in Poland and two in Belarus cooperated in the measurement and comparison of individual external doses in 2014. In total 216 high-school students and teachers participated in the study. Each participant wore an electronic personal dosimeter "D-shuttle" for two weeks, and kept a journal of his/her whereabouts and activities. The distributions of annual external doses estimated for each region overlap with each other, demonstrating that the personal external individual doses in locations where residence is currently allowed in Fukushima Prefecture and in Belarus are well within the range of estimated annual doses due to the background radiation level of other regions/countries.

physics.med-ph↗

arXiv2015

Real-time capable first principle based modelling of tokamak turbulent transport

A real-time capable core turbulence tokamak transport model is developed. This model is constructed from the regularized nonlinear regression of quasilinear gyrokinetic transport code output. The regression is performed with a multilayer perceptron neural network. The transport code input for the neural network training set consists of five dimensions, and is limited to adiabatic electrons. The neural network model successfully reproduces transport fluxes predicted by the original quasilinear model, while gaining five orders of magnitude in computation time. The model is implemented in a real-time capable tokamak simulator, and simulates a 300s ITER discharge in 10s. This proof-of-principle for regression based transport models anticipates a significant widening of input space dimensionality and physics realism for future training sets. This aims to provide unprecedented computational speed coupled with first-principle based physics for real-time control and integrated modelling applications.

physics.plasm-ph↗

arXiv2012

Development and Validation of a Tokamak Skin Effect Transformer model

A control oriented, lumped parameter model for the tokamak transformer including the slow flux penetration in the plasma (skin effect transformer model) is presented. The model does not require detailed or explicit information about plasma profiles or geometry. Instead, this information is lumped in system variables, parameters and inputs. The model has an exact mathematical structure built from energy and flux conservation theorems, predicting the evolution and non linear interaction of the plasma current and internal inductance as functions of the primary coil currents, plasma resistance, non-inductive current drive and the loop voltage at a specific location inside the plasma (equilibrium loop voltage). Loop voltage profile in the plasma is substituted by a three-point discretization, and ordinary differential equations are used to predict the equilibrium loop voltage as function of the boundary and resistive loop voltages. This provides a model for equilibrium loop voltage evolution, which is reminiscent of the skin effect. The order and parameters of this differential equation are determined empirically using system identification techniques. Fast plasma current modulation experiments with Random Binary Signals (RBS) have been conducted in the TCV tokamak to generate the required data for the analysis. Plasma current was modulated in Ohmic conditions between 200kA and 300kA with 30ms rise time, several times faster than its time constant L/R\approx200ms. The model explains the most salient features of the plasma current transients without requiring detailed or explicit information about resistivity profiles. This proves that lumped parameter modeling approach can be used to predict the time evolution of bulk plasma properties such as plasma inductance or current with reasonable accuracy; at least in Ohmic conditions without external heating and current drive sources.

physics.plasm-ph↗