arXiv subjects

A. Pau

Publications and source records attributed to A. Pau.

5 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↗

arXiv2022

First-principles density limit scaling in tokamaks based on edge turbulent transport and implications for ITER

A first-principles scaling law, based on turbulent transport considerations, and a multi-machine database of density limit discharges from the ASDEX Upgrade, JET and TCV tokamaks, show that the increase of the boundary turbulent transport with the plasma collisionality sets the maximum density achievable in tokamaks. This scaling law shows a strong dependence on the heating power, therefore predicting for ITER a significantly larger safety margin than the Greenwald empirical scaling (Greenwald et al, Nucl. Fusion, 28(12), 1988) in case of unintentional H-L transition.

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↗