Bulletin of the American Physical Society
63rd Annual Meeting of the APS Division of Plasma Physics
Volume 66, Number 13
Monday–Friday, November 8–12, 2021; Pittsburgh, PA
Session NI02: MFE III: Integrated Modeling and MHD
9:30 AM–12:30 PM,
Wednesday, November 10, 2021
Room: Ballroom C
Chair: Francois Waelbroeck, University of Texas - Austin
Abstract: NI02.00006 : Predictive applications of the QuaLiKiz neural network within integrated modelling for JET scenarios*
12:00 PM–12:30 PM
Presenter:
Aaron Ho
(DIFFER)
Authors:
Aaron Ho
(DIFFER)
Jonathan Citrin
(DIFFER)
Clarisse Bourdelle
(CEA)
Francis J Casson
(CCFE)
Clive D Challis
(CCFE)
Karel L van de Plassche
(DIFFER)
Collaboration:
JET Contributors
This work applies JINTRAC+QLKNN on the ohmic ramp-up phase of a recent JET D hybrid scenario discharge, representing data not used to train the NN. This scenario has a hollow Te profile, which is due to impurity accumulation and increases with isotope mass [Challis NF 2020]. Its effect on the q-profile can lead to the formation of locked MHD modes and disruptions [Pucella NF 2021]. The dynamic simulation evolves j, ne, Te, and Ti for 7.25 s, along with the equilibrium and impurities (Be, Ni, W). This reveals the impurity impact on the Te hollowing, along with local Zeff and Qrad contributions. In addition, the q evolution was qualitatively validated by comparing it with the observed sawteeth onset.
The isotope effect on this validated simulation and density boundary condition sensitivities were also studied. This assisted the JET teams in their T campaign preparations, by affirming trends from previous H experiments and setting engineering targets to recreate the D q-profile in T experiments. This demonstrates the potential of using NN surrogates for fast routine analysis and discharge design, while stressing the inclusion of edge plasma considerations in these models.
*The work described here has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 and 2019-2020 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission.
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