Description
The ITER baseline scenario (IBL) is a key operational scenario for ITER and has been jointly investigated on several tokamaks, including JET, AUG and TCV. The IBL is an inductive H-mode scenario characterized by Edge Localized Modes (ELMs), aiming at fusion power $P_{\mathrm{fus}} \sim 500\,\mathrm{MW}$ and fusion gain $Q \sim 10$, with $I_P = 15\,\mathrm{MA}$ and $B_T = 5.3\,\mathrm{T}$. This scenario operates at low safety factor $q_{95} \sim 3$, high $\beta_N \sim 1.8$, elongation $\kappa \sim 1.8$, Greenwald fraction $f_G \sim 0.8$ and strong positive triangularity $\delta \sim 0.5$. In this scenario, confinement is improved by increasing the positive triangularity, which enhances the average top pedestal pressure through improved edge stability [1].
In TCV, the performance of the IBL scenario is frequently limited by the onset of Neoclassical Tearing Modes (NTMs), in particular with $m=2$, $n=1$ periodicity, being $m$ the poloidal and $n$ the toroidal mode numbers. These modes are often observed to be triggered by large ELM crashes [2]. This recurrent phenomenology involves the co-evolution in time of multiple physical parameters that influence each other, making it difficult to evaluate the role of the temporal evolution of individual quantities in the onset of NTMs.
This work aims to improve the physical understanding of plasma conditions favouring the NTM onset in the IBL scenario at TCV, with the ultimate objective of informing control strategies and operational scenario design. Given the multi-physics nature of the problem, the adopted approach is based on predictive core transport modelling with the JETTO/JINTRAC code [3], used to reproduce the experimental trends and investigate how selected operational parameters affect the pre-NTM plasma evolution. The objective is to identify physically relevant correlations, characteristic response times and critical parameter ranges associated with NTM onset and subsequent plasma performance degradation. The analysis is also carried out using the DEFUSE framework [4], which enables a systematic treatment of experimental data.
[1] O. Sauter et al., IAEA FEC 2020
[2] B. Labit et al., Plasma Phys. Control. Fusion 66 (2024)
[3] M. Romanelli et al., Plasma and Fusion Research 9 (2014) 3403023
[4] A. Pau et al., IAEA FEC 2021