Verification and validation of linear gyrokinetic and kinetic-MHD simulations for internal kink instability in DIII-D tokamak

Abstract

Verification and linear validation of the internal kink instability in tokamak have been performed for both gyrokinetic (GTC) and kinetic-MHD codes (GAM-solver, M3D-C1-K, NOVA, XTOR-K). Using realistic magnetic geometry and plasma profiles from the same equilibrium reconstruction of the DIII-D shot #141216, these codes exhibit excellent agreement for the growth rate and mode structure of the internal kink mode when all kinetic effects are suppressed. The simulated radial mode structures, obtained from linear simulations, are in reasonable agreement with the normalised electron cyclotron emission measurement after adjusting, within the experimental uncertainty, the safety factor q=1 flux-surface location in the equilibrium reconstruction. Compressible magnetic perturbations strongly destabilize the kink, while poloidal variations of the equilibrium current density reduce the growth rate of the kink. Furthermore, kinetic effects of thermal ions are found to decrease the kink growth rate in kinetic-MHD simulations, but increase the kink growth rate in gyrokinetic simulations, due to the additional drive of the ion temperature gradient and parallel electric field. Kinetic thermal electrons are found to have negligible effects on the internal kink instability.

Saturation of fishbone instability by self-generated zonal flows in tokamak plasmas

“…When including zonal flows however, M3D-C1 and GTC saturation amplitudes at δT e,max ∼ 200 eV match very well with the experimental one. The significant m=2 harmonic in GTC simulations leads to a quantitative agreement with the ECE measurement, which provides a nonlinear validation for GTC regarding fishbone instabilities, completing the linear one obtained in [38] for kink instabilities. Nonlinear scans for the fishbone saturation amplitude performed over the radial position and amplitude of q min recover the same significant mitigation by zonal flows.…”

“…-The DIII-D discharge #178631 is studied numerically mostly through gyrokinetic simulations with the GTC code [6][29] [30][31], and with kinetic-MHD simulations using the M3D-C1 [32][33] [34] and XTOR-K [35][36] [37] codes. GTC capability at simulating MHD modes was recently verified and validated on DIII-D experiments [38]. The magnetic configuration is reproduced from the EFIT code at t=1580ms.…”

Deep learning based surrogate models for first-principles global simulations of fusion plasmas

“…In the right panel of Figure 7, we compared the mean squared error of the prediction of the growth rate for unstable cases with growth rate smaller than 50 kHz. The yellow bar represents the difference between GTC simulation and simulation result from four other MHD codes M3D-C1, GAM-solver, NOVA, and XTOR-K [Brochard 2021] for the DIII-D shot number 141216 at 1750 ms, which has unstable kink mode in the MHD limit with a growth rate around 50 kHz. The details of this benchmark is presented in [Brochard 2021].…”

“…The yellow bar represents the difference between GTC simulation and simulation result from four other MHD codes M3D-C1, GAM-solver, NOVA, and XTOR-K [Brochard 2021] for the DIII-D shot number 141216 at 1750 ms, which has unstable kink mode in the MHD limit with a growth rate around 50 kHz. The details of this benchmark is presented in [Brochard 2021]. These results show that SGTC has strong predictive power for the kink linear growth rate.…”

Reconstruction of tokamak plasma safety factor profile using deep learning

“…The final reconstruction and the subsequent physical analysis can depend sensitively on the capability of measurements. EFIT traditionally used data 5 These authors contributed equally to this work. * Author to whom any correspondence should be addressed.…”