Predictive JET current ramp-up modelling using QuaLiKiz-neural-network

Nuclear Fushion

article

A. Ho¹  J. Citrin¹  C.D. Challis²  C. Bourdelle³  F.J. Casson²  J. Garcia³  J. Hobirk⁴  A. Kappatou⁴  D.L. Keeling²  D.B. King²  F. Koechl²  E. Lerche²  C.F. Maggi²  J. Mailloux² 

[1] DIFFER—Dutch Institute for Fundamental Energy Research;United Kingdom Atomic Energy Authority, Culham Centre for Fusion Energy, Culham Science Centre;CEA Cadarache;Max-Planck-Institut für Plasmaphysik

关键词: integrated modelling;    tokamak;    current ramp-up;    surrogate model;    isotope effect;    impurity transport;   

DOI  :  10.1088/1741-4326/acc083

来源: Institute of Physics Publishing Ltd.

【 摘 要 】

This work applies the coupled JINTRAC and QuaLiKiz-neural-network (QLKNN) model on the ohmic current ramp-up phase of a JET D discharge. The chosen scenario exhibits a hollow T_mathrm{e}profile attributed to core impurity accumulation, which is observed to worsen with the increasing fuel ion mass from D to T. A dynamic D simulation was validated, evolvingj , n_mathrm{e} , T_mathrm{e} , T_mathrm{i} ,n _Be,n _Ni, andn _W for 7.25 s along with self-consistent equilibrium calculations, and was consequently extended to simulate a pure T plasma in a predict-first exercise. The light impurity (Be) accounted forZ _eff while the heavy impurities (Ni, W) accounted forP _rad. This study reveals the role of transport on the T_mathrm{e}hollowing, which originates from the isotope effect on the electron-ion energy exchange affecting T_mathrm{i} . This exercise successfully affirmed isotopic trends from previous H experiments and provided engineering targets used to recreate the Dq -profile in T experiments, demonstrating the potential of neural network surrogates for fast routine analysis and discharge design. However, discrepancies were found between the impurity transport behaviour of QuaLiKiz and QLKNN, which lead to notable T_mathrm{e}hollowing differences. Further investigation into the turbulent component of heavy impurity transport is recommended.

【 授权许可】

Unknown