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Stefano Marchioni

Publications and source records attributed to Stefano Marchioni.

2 recordsLinked to original sources
arXiv2025

FGE: A Fast Free-Boundary Grad-Shafranov Evolutive Solver

Accurate and rapid simulation of the free boundary tokamak plasma equilibrium evolution is essential for modern plasma control, stability analysis, and scenario development. This paper presents the Free-Boundary Grad-Shafranov Evolutive (FGE) code, a highly flexible and control-oriented solver designed to address the challenges posed by advanced plasma configurations across a range of devices. FGE evolved from the FBT and LIUQE codes and is part of the MEQ suite, sharing many of the low-level optimized functions. It self-consistently solves the free-boundary Grad-Shafranov equation coupled with circuit equations for external conductors and models for plasma profile evolution. The code implements a fully non-linear, Newton-based framework with multiple, highly optimized solver options, state representations, and residual formulations that enable rapid computation across different simulation setups. A key capability is the self-consistent integration of various 0D and 1D current diffusion equations (CDEs) to model the resistive evolution of the plasma current profile as well as the ability to model plasmas with multiple magnetic axes (Doublets). Furthermore, FGE allows to linearize the plasma dynamics around a given equilibrium to generate a state-space model suitable for controller design and analysis. Numerical studies are presented demonstrating the code's speed of convergence and validating its performance against experimental data. Furthermore, benchmarks against the RAPTOR and KINX codes for profile evolution and vertical growth rate estimates are presented highlighting FGE's capabilities for both prediction and analysis.

physics.plasm-ph↗
arXiv2025

Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak \`a Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM's ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

physics.plasm-ph↗