5 papers
An Inverse Grad-Shafranov Neural Network Approach to Tokamak Magnetic Control
Allen M. Wang, Adriano Mele, Cosmas Heiß +12
A new approach to tokamak magnetic control enabling high-precision plasma shaping and novel real-time adaptability is experimentally demonstrated on the Tokamak a Configuration Var…
Machine learning methods for modelling local, linear gyrokinetic simulations of MAST-U pedestal turbulence
Anna Niemelä, Daniel Jordan, Aaro Järvinen +8
Gyrokinetic (GK) stability strongly influences the performance of high-confinement-mode pedestals in spherical tokamak plasmas. High-fidelity gyrokinetic codes such as GENE can mod…
Experimental validation of a fast control-oriented, physics-informed surrogate model for plasma equilibrium reconstruction in the TCV tokamak
M. Grandin, A. Mele, C. Galperti +5
Magnetic equilibrium reconstruction provides the plasma state estimate required for real-time shape control in tokamaks. We present a fast, physics-informed neural network surrogat…
Improved n=1 Empirical Error Field Penetration Threshold Scaling with Ohmic and L-Mode Conventional Tokamak Plasma Discharges
E. M. Bursch, J. K. Park, N. C. Logan +12
This paper presents an updated n=1 error field penetration threshold scaling, which increases fit quality compared to previous error field scaling laws, is produced from an expande…
First experimental demonstration of plasma shape control in a tokamak through Model Predictive Control
Adriano Mele, Maria A. Topalova, Cristian Galperti +3
In this work, a Model Predictive Controller (MPC) is proposed to control the plasma shape in the Tokamak à Configuration Variable (TCV). The proposed controller relies on models ob…