Model-based electron density estimation using multiple diagnostics on TCV

Estimation of the dynamic evolution of electron plasma density during a tokamak discharge is cru- cial since it directly affects the plasma performance, confinement and stability, therefore it needs to be monitored and controlled. Knowledge of the density profile can also be used to control in a more direct way the desired aspects of the plasma density, for example choosing to control the core, volume averaged or edge density, replacing control methods that rely e.g. on a single line-averaged electron density from a specific interferometer chord. The reconstruction of the density profile can be performed with the RAPDENS code [1], employing the Extended Kalman Filter technique. The code collects the electron plasma density measurements from the available real-time diagnostics and uses them to constrain the solution of a predictive model that describes the 1D particle transport equation for the electron plasma density. Following recent improvements to the code for use on ASDEX-Upgrade [2], we report on the applica- tion of this method for routine reconstruction of density profiles in the TCV tokamak. In particular the transport coefficients of the electron diffusion equation, as well as the ionization depth of the fueling gas, have been tuned heuristically with the available off-line diagnostics data for higher fi- delity reconstruction of density profiles in L-mode, H-mode and negative triangularity discharges in TCV. We also present an extensive comparison of real-time estimated density profiles with the off-line Thomson scattering measurements across a wide range of experimental conditions. Finally, we report the first results on the upgraded detection and correction of interferometer errors with a model-based approach relying on the observer. References: [1] T. C. Blanken et al. “Control-oriented modeling of the plasma particle density in tokamaks and application to real-time density profile reconstruction”. In: Fusion Engineering and Design 126 (2018). issn: 09203796. doi: 10.1016/j.fusengdes.2017.11.006. [2] T.O.S.J. Bosman et al. “Kalman filter density reconstruction in ICRH discharges on ASDEX Upgrade”. In: Fusion Engineering and Design 170 (Sept. 2021). issn: 09203796. doi: 10.1016/ j.fusengdes.2021.112510.1