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M. V. Gorelenkova

Publications and source records attributed to M. V. Gorelenkova.

3 recordsLinked to original sources

Predictive capabilities of the integrated modeling TRANSP code for tokamak plasmas

This paper expands on the TRANSP description given in [Computer Physics Communications 312 (2025) 109611] by describing recent progress in TRANSP's predictive functionality and emphasizing development of the PT_SOLVER module and integration of the high-fidelity T3D/GX framework for plasma profile prediction using a high-fidelity gyrokinetic model for turbulent transport. PT_SOLVER is a modular, multi-region, parallel solver for coupled transport equations of particle density, electron and ion energy, and toroidal angular momentum that uses an implicit Newton method to advance the solution of these equations. The numerical formulation includes source coupling, moving-geometry terms, and nonlinear stabilization based on modified Péclet numbers, thereby enabling the PT_SOLVER to handle the stiffness associated with gradient-dependent transport models. Stabilization occurs via a nonlinear function controlling discretization in zones of steep gradients or rapidly changing transport coefficients. Source terms that account for heating, current drive, alpha-particle effects, and collisional energy exchange are handled thoroughly, and both residual norms and profile-change measures are used to assess convergence. Verification is carried out using analytical benchmark solutions, manufactured solution benchmarks, convergence studies of stiff gradient-dependent diffusivities, and code-to-code comparisons of TGYRO using the TGLF/NEO models for anomalous and neoclassical transport. A multi-level parallel decomposition strategy in PT_SOLVER is described for both flux surface and TGLF wavenumber parallelism, along with representative scaling results. This paper also describes the TRANSP Interface to the modular T3D/GX workflow and presents verification examples related to the interface for coupled prediction simulations.

physics.plasm-ph↗

Assessing time-dependent temperature profile predictions using reduced transport models for high performing NSTX plasmas

Time-dependent, predictive simulations were performed with the 1.5D tokamak integrated modeling code TRANSP on a large set of well-analyzed, high performing discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate how well modern reduced transport models can reproduce experimentally observed temperature profiles in spherical tokamaks. Overall, it is found that simulations using the Multi-Mode Model (MMM) more consistently agree with the NSTX observations than those using the Trapped Gyro-Landau Fluid (TGLF) model, despite TGLF requiring orders of magnitude greater computational cost. When considering all examined discharges, MMM has median overpredictions of electron temperature ($T_e$) and ion temperature ($T_i$) profiles of 28% and 27%, respectively, relative to the experiment. TGLF overpredicts $T_e$ by 46%, with much larger variance than MMM, and underpredicts $T_i$ by 25%. As $β$ is increased across NSTX discharges, TGLF predicts lower $T_e$ and significant flattening of the $T_i$ profile, conflicting with NSTX observations. When using an electrostatic version of TGLF, both $T_e$ and $T_i$ are substantially overpredicted, underscoring the importance of electromagnetic turbulence in the high $β$ spherical tokamak regime. Additionally, calculations with neural net surrogate models for TGLF were performed outside of TRANSP with a time slice flux matching transport solver, finding better agreement with experiment than the TRANSP simulations, highlighting the impact of different transport solvers and simulation techniques. Altogether, the reasonable agreement with experiment of temperature profiles predicted by MMM motivates a more detailed examination of the sensitivities of the TRANSP simulations with MMM to different NSTX plasma regimes in a companion paper, in preparation for self-consistent, time-dependent predictive modeling of NSTX-U scenarios.

physics.plasm-ph↗

Sensitivities of time-dependent temperature profile predictions for NSTX with the Multi-Mode Model

The Multi-Mode Model (MMM) for turbulent transport was applied to a large set of well-analyzed discharges from the National Spherical Torus Experiment (NSTX) in order to evaluate its sensitivities to a wide range of plasma conditions. MMM calculations were performed for hundreds of milliseconds in each discharge by performing time-dependent predictive simulations with the 1.5D tokamak integrated modeling code TRANSP. A closely related study concluded that MMM predicted electron ($T_e$) and ion ($T_i$) temperature profiles that were in reasonable agreement with NSTX observations, generally outperforming a different reduced transport model, TGLF, motivating a more thorough investigation of the characteristics of the MMM predictions. The simulations with MMM have electron energy transport dominated by electron temperature gradient modes for relatively low plasma $β$ and high collisionality, transitioning to a mixture of different modes for higher $β$ and lower collisionality. The thermal ion diffusivity predicted by MMM is much smaller than the neoclassical contribution, in line with previous experimental analysis of NSTX. Nonetheless, the $T_e$ and $T_i$ profiles are coupled via collisional energy exchange and thus sensitive to which transport channels are predicted. The simulations with MMM are robust to the simulation start time, converging to remarkably similar temperatures later during the discharge. MMM typically overpredicts confinement relative to NSTX observations, leading to the prediction of overly steep profiles. Plasmas with spatially broader $T_e$ profiles, higher $β$, and longer energy confinement times tend to be predicted by MMM with better agreement with the experiment. These findings provide useful context for understanding the regime-dependent tendencies of MMM in anticipation of self-consistent, time-dependent predictive simulations of NSTX-U discharges.

physics.plasm-ph↗