Numerical research is conducted to investigate the effects of plasma boundary shape on the tearing mode triggering explosive bursts in toroidal tokamak plasmas. In this work, $m/n=2/1$ mode is responsible for the triggering of the explosive burst. Plasma boundary shape can be adjusted via the adjustment of the parameters triangularity ${\delta}$ and elongation ${\kappa}$. The investigations are conducted both under low $\beta$ (close to zero) and under finite $\beta$ regimes. In the low $\beta$ regime, triangularity and elongation both have stabilizing effect on the explosive burst, and the stabilizing effect of elongation is stronger. Under a large elongation (${\kappa =2.0}$), the elongation effect can evidently enhance the stabilizing effect in a positive triangularity regime, but barely affects the stabilizing effect in a negative triangularity regime. In the finite $\beta$ regime, the explosive burst is delayed in comparison with that in the low $\beta$ regime. Similar to the low $\beta$ cases, the effects of triangularity and elongation both are stabilizing. Under a large elongation (${\kappa =2.0}$), the elongation effect can evidently enhance the stabilizing effect on the explosive burst in a positive triangularity regime, but impair the stabilizing effect in a negative triangularity regime. The explosive burst disappears in the large triangularity case (${\delta =0.5}$), indicating that the explosive burst can be effectively prevented in experiments via carefully adjusting plasma boundary shape. Moreover, strong magnetic stochasticity appears in the negative triangularity case during the nonlinear phase.

Machine learning opens up new possibilities for research of plasma confinement. Specifically, models constructed using machine learning algorithms may effectively simplify the simulation process. Previous first-principles simulations could provide physics-based transport information, but not fast enough for real-time applications or plasma control. To address this issue, this study proposes SExFC, a surrogate model of the Gyro-Landau Extended Fluid Code (ExFC). As an extended version of our previous model ExFC-NN, SExFC can capture more features of transport driven by the ion temperature gradient mode and trapped electron mode, using an extended database initially generated with ExFC simulations. In addition to predicting the dominant instability, radially averaged fluxes and radial profiles of fluxes, the well-trained SExFC may also be suitable for physics-based rapid predictions that can be considered in real-time plasma control systems in the future.

Microturbulence excited by ion temperature gradient (ITG)-dominant and trapped electron mode (TEM)-dominant instabilities is investigated by employing an extended fluid code (ExFC) based on the so-called Landau fluid model, which includes the trapped electron dynamics. Firstly, the global effect is emphasized through direct comparison of ITG and TEM instability domains based on local and global simulations. The global effect makes differences in both linear instability and nonlinear transport, including the fluxes and the structure of zonal flow. The transitions among ITG, TEM, and ITG & TEM (ITG & TEM represents that ITG and TEM coexist with different wavelengths) instabilities/turbulence depend not only on the three key drive forces $({R/L_{\rm n}, R/L_{\rm Te}, R/L_{\rm Ti}})$ but also on their global (profile) effects. Secondly, a lot of electrostatic linear gyro-fluid simulations are concluded to obtain a distribution of the instability.