Lunsford, R.; Bortolon, A.; Roquemore, A.L.; Mansfield, D.K.; Jaworski, M.A.; Kaita, R.; Maingi, R.; Nagy, A.
By employing a neutral gas shielding (NGS) model to characterize impurity granule
injection the pedestal atomic deposition for three different species of granule:
lithium, boron, and carbon are determined. Utilizing the duration of ablation
events recorded on experiments performed at DIII-D to calibrate the NGS model we
are able to quantify the ablation rate and mass deposition location with respect
to the plasma density profile. The species specific granule shielding constant
is then used to model granule ablation within NSTX-U discharges. Simulations of
300, 500 and 700 micron diameter granules injected at 50 m/sec are presented for
NSTX-U L-mode type plasmas as well as H-mode discharges with low natural ELM
frequencies. Additionally, ablation calculations of 500 micron granules of each
species are presented at velocities ranging from 50 � 150 m/sec. In H-mode type
discharges these simulations show that the majority of the injected granule is
ablated within or just past the steep gradient region of the discharge. At this
radial position, the perturbation to the background plasma generated by the ablating
granule can lead to conditions advantageous for the rapid triggering of an ELM crash
Stotler, D.P.; Lang, J.; Chang, C.S.; Churchill, R.M.; Ku, S.-H.
The effects of recycled neutral atoms on tokamak ion temperature
gradient (ITG) driven turbulence have been investigated in a steep
edge pedestal, magnetic separatrix configuration, with the full-f
edge gryokinetic code XGC1. Ion temperature gradient turbulence is
the most fundamental and robust edge plasma instability, having a long
radial correlation length and an ability to impact other forms of
pedestal turbulence. The neutral atoms enhance the ITG turbulence,
first, by increasing the ion temperature gradient in the pedestal via
the cooling effects of charge exchange and, second, by a relative
reduction in the ExB shearing rate.
Bejjanki, Vikranth R.; da Silveira, Rava Azeredo; Cohen, Jonathan D.; Turk-Browne, Nicholas B.
Multivariate decoding methods, such as multivoxel pattern analysis (MVPA), are highly effective at extracting information from brain imaging data. Yet, the precise nature of the information that MVPA draws upon remains controversial. Most current theories emphasize the enhanced sensitivity imparted by aggregating across voxels that have mixed and weak selectivity. However, beyond the selectivity of individual voxels, neural variability is correlated across voxels, and such noise correlations may contribute importantly to accurate decoding. Indeed, a recent computational theory proposed that noise correlations enhance multivariate decoding from heterogeneous neural populations. Here we extend this theory from the scale of neurons to functional magnetic resonance imaging (fMRI) and show that noise correlations between heterogeneous populations of voxels (i.e., voxels selective for different stimulus variables) contribute to the success of MVPA. Specifically, decoding performance is enhanced when voxels with high vs. low noise correlations (measured during rest or in the background of the task) are selected during classifier training. Conversely, voxels that are strongly selective for one class in a GLM or that receive high classification weights in MVPA tend to exhibit high noise correlations with voxels selective for the other class being discriminated against. Furthermore, we use simulations to show that this is a general property of fMRI data and that selectivity and noise correlations can have distinguishable influences on decoding. Taken together, our findings demonstrate that if there is signal in the data, the resulting above-chance classification accuracy is modulated by the magnitude of noise correlations.
A scintillator type fast ion loss detector measures the gyroradius and pitch angle distribution of superthermal ions escaping from a magnetically confined fusion plasma at a single location. Described here is a technique for optimizing the angular orientation of such a detector in an axisymmetric tokamak geometry in order to intercept losses over a useful and interesting ranges of pitch angle. The method consists of evaluating the detector acceptance as a function of the fast ion constants of motion, i.e. energy, canonical toroidal momentum, and magnetic moment. The detector acceptance can then be plotted in a plane of constant energy and compared with the relevant orbit class boundaries and fast ion source distributions. Knowledge of expected or interesting mechanisms of loss can further guide selection of the detector orientation. The example of a fast ion loss detector for the National Spherical Torus Experiment-Upgrade (NSTX-U) is considered.
The National Spherical Torus Experiment (NSTX) has undergone a major upgrade, and the NSTX Upgrade (NSTX-U) Project was completed in the summer of 2015. NSTX-U first plasma was subsequently achieved, diagnostic and control systems have been commissioned, H-Mode accessed, magnetic error fields identified and mitigated, and the first physics research campaign carried out. During 10 run weeks of operation, NSTX-U surpassed NSTX-record pulse-durations and toroidal fields, and high-performance ~1MA H-mode plasmas comparable to the best of NSTX have been sustained near and slightly above the n=1 no-wall stability limit and with H-mode confinement multiplier H98y2 above 1. Transport and turbulence studies in L-mode plasmas have identified the coexistence of at least two ion-gyro-scale turbulent micro-instabilities near the same radial location but propagating in opposite (i.e. ion and electron diamagnetic) directions. These modes have the characteristics of ion-temperature gradient and micro-tearing modes, respectively, and the role of these modes in contributing to thermal transport is under active investigation. The new second more tangential neutral beam injection was observed to significantly modify the stability of two types of Alfven Eigenmodes. Improvements in offline disruption forecasting were made in the areas of identification of rotating MHD modes and other macroscopic instabilities using the Disruption Event Characterization and Forecasting (DECAF) code. Lastly, the Materials Analysis and Particle Probe (MAPP) was utilized on NSTX-U for the first time and enabled assessments of the correlation between boronized wall conditions and plasma performance. These and other highlights from the first run campaign of NSTX-U are described.
The WallDYN package has recently been applied to a number of tokamaks to self-consistently model
the evolution of mixed-material plasma facing surfaces. A key component of the WallDYN model is the
concentration-dependent surface sputtering rate, calculated using SDTRIM.SP. This modeled sputtering
rate is strongly influenced by the surface binding energies (SBEs) of the constituent materials, which
are well known for pure elements but often are poorly constrained for mixed-materials. This work examines
the sensitivity of WallDYN surface evolution calculations to different models for mixed-material
SBEs, focusing on the carbon/lithium/oxygen/deuterium system present in NSTX. A realistic plasma background is reconstructed from a high density, H-mode NSTX discharge, featuring an attached outer strike
point with local density and temperature of 4e20 m^-3 and 4 eV, respectively. It is found that various
mixed-material SBE models lead to significant qualitative and quantitative changes in the surface evolution
profile at the outer divertor, with the highest leverage parameter being the C-Li binding model.
Uncertainties of order 50%, appearing on time scales relevant to tokamak experiments, highlight the importance of choosing an appropriate mixed-material sputtering representation when modeling the surface
evolution of plasma facing components. These results are generalized to other fusion-relevant materials
with different ranges of SBEs.
Linear stability analysis of the national spherical torus experiment (NSTX) Li-conditioned
ELM-free H-mode equilibria is carried out in the context of the extended
magneto-hydrodynamic (MHD) model in NIMROD. The purpose is to investigate the physical
cause behind edge localized mode (ELM) suppression in experiment after the Li-coating of
the divertor and the first wall of the NSTX tokamak. Besides ideal MHD modeling, including
finite-Larmor radius effect and two-fluid Hall and electron diamagnetic drift contributions,
a non-ideal resistivity model is employed, taking into account the increase of Z eff after
Li-conditioning in ELM-free H-mode. Unlike an earlier conclusion from an eigenvalue code
analysis of these equilibria, NIMROD results find that after reduced recycling from divertor
plates, profile modification is necessary but insufficient to explain the mechanism behind
complete ELMs suppression in ideal two-fluid MHD. After considering the higher plasma
resistivity due to higher Z eff , the complete stabilization could be explained. A thorough
analysis of both pre-lithium ELMy and with-lithium ELM-free cases using ideal and
non-ideal MHD models is presented, after accurately including a vacuum-like cold halo
region in NIMROD to investigate ELMs.
In this paper we present data from experiments on NSTX-U where it is shown for the first time that small amounts of high pitch-angle beam ions can strongly suppress the counter-propagating Global Alfvén Eigenmodes (GAE). GAE have been implicated in the redistribution of fast ions and modification of the electron power balance in previous experiments on NSTX. The ability to predict the stability of Alfvén modes, and developing methods to control them, is important for fusion reactor like the International Tokamak Experimental Reactor (ITER) which are heated by a large population of non-thermal, super-Alfvénic ions consisting of fusion generated alphas and beam ions injected for current profile control. We present a qualitative interpretation of these observations using an analytic model of the Doppler-shifted ion-cyclotron resonance drive responsible for GAE instability which has an important dependence on k⊥ρL. A quantitative analysis of this data with the HYM stability code predicts both the frequencies and instability of the GAE prior to, and suppression of the GAE after the injection of high pitch-angle beam ions.
Recent advances in experimental techniques have allowed the simultaneous recordings of
populations of hundreds of neurons, fostering a debate about the nature of the collective
structure of population neural activity. Much of this debate has focused on the
empirical findings of a phase transition in the parameter space of maximum entropy
models describing the measured neural probability distributions, interpreting this phase
transition to indicate a critical tuning of the neural code. Here, we instead focus on the
possibility that this is a first-order phase transition which provides evidence that the
real neural population is in a `structured', collective state. We show that this collective
state is robust to changes in stimulus ensemble and adaptive state. We find that the
pattern of pairwise correlations between neurons has a strength that is well within the
strongly correlated regime and does not require fine tuning, suggesting that this state is
generic for populations of 100+ neurons. We find a clear correspondence between the
emergence of a phase transition, and the emergence of attractor-like structure in the
inferred energy landscape. A collective state in the neural population, in which neural
activity patterns naturally form clusters, provides a consistent interpretation for our