Model-based estimation of tokamak plasma profiles and physics parameters: integration with improved equilibrium reconstruction and experimental data

Abstract Plasma state reconstruction methods combining measurements and physics modeling improve the estimation of physics quantities in real-time and in post-discharge analysis. We present a workflow to reconstruct the dynamic evolution of a set of internal tokamak plasma profiles with consistent equilibria, as applied in this paper for experimental data from TCV L and H-mode discharges. Plasma profile estimates for electron temperature T e , electron density n e and parallel current density <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>j</mml:mi> <mml:mrow> <mml:mi>par</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> are obtained by data assimilation of Thomson scattering (TS) measurements into RAPTOR modeling, using an Extended Kalman Filter (EKF). A new kinetic equilibrium reconstruction method ensures mutual consistency of free-boundary equilibrium reconstruction, core plasma profile estimates and mapping of the TS measurements to flux surface coordinates. The RAPTOR code captures the coupled dynamics of electron heat, particle and current density transport and includes a model for the onset conditions of sawtooth instabilities and the resulting profile relaxations after a sawtooth crash, enabling a realistic q profile reconstruction even in the absence of direct measurements. During ohmic phases with sawtooth instabilities, the sawtooth period and inversion radius inferred from soft x-ray measurements are in excellent agreement with RAPTOR EKF inner q profile reconstructions and the predicted sawtooth dynamics, even for transient phases. In addition to the plasma profiles, the EKF allows to infer unknown physics quantities such as the effective charge <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>Z</mml:mi> <mml:mrow> <mml:mi>eff</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> and the on-axis ion-to-electron temperature ratio <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mi>T</mml:mi> <mml:mrow> <mml:mi>i</mml:mi> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:msub> <mml:mi>T</mml:mi> <mml:mrow> <mml:mi>e</mml:mi> <mml:mn>0</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> , as well as transport model parameters. Continuously updating the transport model parameters for electron heat and density transport, based on the available measurements, is an effective way to reduce the model-to-reality gap, as required for real-time model-based control of fusion reactor plasmas.