Bulletin of the American Physical Society
66th Annual Meeting of the APS Division of Plasma Physics
Monday–Friday, October 7–11, 2024; Atlanta, Georgia
Session PP12: Poster Session VI:
MFE Analytical, Computational and Data Science Techniques and Machine Learning
MFE Active Control and Whole Device Modelings
MFE MHD and Stability
DIII-D and Conventional Tokamaks II
Warm Dense Matter
Particle acceleration, beams and relativistic plasmas: Laser-plasma wakefield or direct laser accelerators
2:00 PM - 5:00 PM
Wednesday, October 9, 2024
Hyatt Regency
Room: Grand Hall West
Abstract: PP12.00038 : Real-time estimation and control of the electron density with a novel multi-rate observer on TCV*
Presenter:
Francesco Pastore
(École Polytechnique Fédérale de Lausanne)
Authors:
Francesco Pastore
(École Polytechnique Fédérale de Lausanne)
Olivier Sauter
(SPC-EPFL)
Federico Felici
(Google DeepMind)
Daniela Kropackova
(Czech Technical University, Prague)
Ondrej Kudlacek
(Max-Planck-Institut für Plasmaphysik)
N. M. T. Vu
(ITER Organization)
Alessandro Pau
(SPC-EPFL)
Cristian Galperti
(SPC-EPFL)
Timo Ravensbergen
(ITER Organization)
Simon Van Mulders
(ITER Organization)
Kenneth Lee
(SPC-EPFL)
Benjamin Vincent
(SPC-EPFL)
Collaboration:
TCV team
Building on the integration of RAPDENS into the TCV plasma control system [1], this follow-up study explores its application to density control for detachment studies and high-performance H-mode scenarios.
The RAPDENS-based density profile observer combines the spatial resolution of Thomson Scattering with the high time resolution of the FIR to obtain a reliable density profile estimate, which is then employed to control the normalized edge density [2] for advanced tokamak scenarios in H-mode plasmas.
Experiments demonstrated the observer’s capability to support detachment studies in complex divertor geometries by accurately controlling the line-averaged density within the LCFS while avoiding divertor pick-up of the FIR signal.
Unknown plasma parameters can be estimated and adapted in real-time through the Kalman filter algorithm, such as fuelling efficiency and particle transport coefficients, for enhanced model robustness.
[1] F. Pastore et al., Fus. Eng. Des vol.192, p.113615,doi:10.1016/j.fusengdes.2023.113615, 2023.
[2] M. Bernert et al 2015 Plasma Phys. Control. Fusion 57 014038, doi: 10.1088/0741-3335/57/1/014038
*This work has been carried out within the framework of the EUROfusion Consortium, via the Euratom Research and Training Programme (Grant Agreement No 101052200 — EUROfusion) and funded by the Swiss State Secretariat for Education, Research and Innovation (SERI). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union, the European Commission, or SERI. Neither the European Union nor the European Commission nor SERI can be held responsible for them. This work was supported in part by the Swiss National Science Foundation.
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