Machine Learning model for real-time SPARC vertical stability observers

Given the demanding requirements of the SPARC high-field tokamak (B₀=12.2 T) and its operation with high elongated plasma (κ_sep=1.97), robust real-time-compatible vertical stability observers are paramount. In this work, we present fast ML surrogate modeling for observers (such as VDE n=0 growth rate, stability margins ​​(m_s), inductive stability margins (m_i), max-Z, & frequency components), integrating advanced 2D electro-mechanical circuits and dynamic plasma response models [1]. These surrogate observers employ transformer-based ML networks; trained to replicate and predict the results of filamentary rigid body MEQ-RZIp and deformable non-linear MEQ-FGE (and its linearized version FGElin) plasma response models [2]. The training dataset incorporates simulated SPARC primary reference discharge scenarios and the C-Mod (hot VDEs) 2012-2016 disruption database. These observers will also be trained over a range of simulated L- and H-mode scenarios, including periods without and with ELM (via artificial vertical kicks [2]). We will report on the assessment of observers' sensitivity to the RZIp & FGE models and their proximity to stability boundaries. Conclusions regarding SPARC's vertical stability control are further employed to inform ARC design and its operational max-Z stability limits.

1. Walker & Humphreys (2006), 50:4,473-489

2. Carpanese et al. 2020 EPFL PhD thesis no. 7914

3. Sartori et al Proc. 35th EPS Conf. on Plasma Physics vol 32D P5.045