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J. G. Bak

Publications and source records attributed to J. G. Bak.

4 recordsLinked to original sources

Study of Low-Frequency Core-Edge Coupling in a Tokamak: I. Experimental Observation in KSTAR

Double-peaked fishbone events across multiple KSTAR discharges are investigated. The normalized beta $β_{\mathrm{N}}$ and the edge safety factor $q_{\mathrm{95}}$ under which the fishbones appear vary depending on the presence and form of external magnetic perturbations. The fishbone strength is closely related to $β_{\mathrm{N}}$ and $q_{\mathrm{95}}$: as $β_{\mathrm{N}}$ increases and $q_{\mathrm{95}}$ decreases, the fishbone strength increases. Measured fishbone-relevant signals are decomposed into amplitude envelope and phase components in the temporal domain, which are analyzed separately. In terms of the amplitude envelope component, the edge electron temperature fluctuation $\tilde T_\mathrm{e}^{\mathrm{Edge}}$ becomes more correlated with the poloidal magnetic fluctuation $\dot{B}_\mathrmθ$ compared to the core electron temperature fluctuation $\tilde T_\mathrm{e}^{\mathrm{Core}}$ as fishbone strength increases. In terms of the phase component, the phase of $\tilde T_\mathrm{e}^{\mathrm{Edge}}$ precedes the phase of $\tilde T_\mathrm{e}^{\mathrm{Core}}$ except in the case of very weak fishbones where the phase relations are inconclusive due to weak fishbone activity at the edge plasma, which is comparable to background fluctuations. The investigation suggests the possibility that the edge activity is not a mere side effect of the core activity, but could play an active role.

physics.plasm-ph

Deep neural network Grad-Shafranov solver constrained with measured magnetic signals

A neural network solving Grad-Shafranov equation constrained with measured magnetic signals to reconstruct magnetic equilibria in real time is developed. Database created to optimize the neural network's free parameters contain off-line EFIT results as the output of the network from $1,118$ KSTAR experimental discharges of two different campaigns. Input data to the network constitute magnetic signals measured by a Rogowski coil (plasma current), magnetic pick-up coils (normal and tangential components of magnetic fields) and flux loops (poloidal magnetic fluxes). The developed neural networks fully reconstruct not only the poloidal flux function $ψ\left( R, Z\right)$ but also the toroidal current density function $j_ϕ\left( R, Z\right)$ with the off-line EFIT quality. To preserve robustness of the networks against a few missing input data, an imputation scheme is utilized to eliminate the required additional training sets with large number of possible combinations of the missing inputs.

physics.plasm-ph

Bayesian with Gaussian process based missing input imputation scheme for reconstructing magnetic equilibria in real time

A Bayesian with GP(Gaussian Process)-based numerical method to impute a few missing magnetic signals caused by impaired magnetic probes during tokamak operations is developed such that the real-time reconstruction of magnetic equilibria, whose performance strongly depends on the measured magnetic signals and their intactness, are affected minimally. Likelihood of the Bayesian model constructed with the Maxwell's equations, specifically Gauss's law of magnetism and Ampère's law, results in infinite number of solutions if two or more magnetic signals are missing. This undesirable characteristic of the Bayesian model is remediated by coupling the model with the Gaussian process. Our proposed numerical method infers the missing magnetic signals correctly in less than $1$\:msec suitable for real-time reconstruction of magnetic equilibria during tokamak operations. The method can also be used for a neural network that reconstructs magnetic equilibria trained with a complete set of magnetic signals. Without our proposed imputation method, such a neural network would become useless if missing signals are not tolerable by the network.

physics.plasm-ph

First evidence of Alfven wave activity in KSTAR plasmas

We report on first evidence of wave activity during neutral beam heating in KSTAR plasmas: 40 kHz magnetic fluctuations with a toroidal mode number of n=1. Our analysis suggests this a beta-induced Alfven eigenmode resonant with the q=1 surface. A kinetic analysis, when coupled with electron temperature measurements from electron cyclotron emission and ion/electron temperature ratios from crystallography, enables calculation of the frequency evolution, which is in agreement with observations. Complementary detailed MHD modelling of the magnetic configuration and wave modes supports the BAE mode conclusion, by locating an n=1 mode separated from the continuum in the core region. Finally, we have computed the threshold to marginal stability for a range of ion temperature profiles. These suggest the BAE can be driven unstable by energetic ions when the ion temperature radial gradient is sufficiently large. Our findings suggest that mode existence could be used as a form of inference for temperature profile consistency in the radial interval of the mode, thereby extending the tools of MHD spectroscopy.

physics.plasm-ph