Simulation of profile evolution from ramp-up to ramp-down and optimization of tokamak plasma termination with the RAPTOR code. (30th October 2017)

• Record Type: Journal Article
• Authors: Teplukhina, A A; Sauter, O; Felici, F; Merle, A; Kim, D
• Other Names: collab.; collab.; collab.
• Is Part Of: Plasma physics and controlled fusion. Volume 59:Number 12(2017:Dec.)
• Publication Date: 2017-10-30
• Subjects: transport modeling -- electron heat diffusivity -- plasma termination -- ramp-down optimization; Plasma (Ionized gases) -- Periodicals; Controlled fusion -- Periodicals; 530.44
• DOI: 10.1088/1361-6587/aa857e
• Languages: English
• ISSNs: 0741-3335

Abstract

Abstract: The present work demonstrates the capabilities of the transport code RAPTOR as a fast and reliable simulator of plasma profiles for the entire plasma discharge, i.e. from ramp-up to ramp-down. This code focuses, at this stage, on the simulation of electron temperature and poloidal flux profiles using prescribed equilibrium and some kinetic profiles. In this work we extend the RAPTOR transport model to include a time-varying plasma equilibrium geometry and verify the changes via comparison with ATSRA code simulations. In addition a new ad hoc transport model based on constant gradients and suitable for simulations of L–H and H–L mode transitions has been incorporated into the RAPTOR code and validated with rapid simulations of the time evolution of the safety factor and the electron temperature over the entire AUG and TCV discharges. An optimization procedure for the plasma termination phase has also been developed during this work. We define the goal of the optimization as ramping down the plasma current as fast as possible while avoiding any disruptions caused by reaching physical or technical limits. Our numerical study of this problem shows that a fast decrease of plasma elongation during current ramp-down can help in reducing plasma internal inductance. An early transition from H- to L-mode allows us to reduce the drop in poloidal beta, which is also important for plasma MHD stability and control. This work shows how these complex nonlinear interactions can be optimized automatically using relevant cost functions and constraints. Preliminary experimental results for TCV are demonstrated.