Cole M; Hager R; Moritaka T; Dominski J; Kleiber R; Ku S; Lazerson S; Riemann J; Chang C
Abstract:
XGC (X-point Gyrokinetic Code) is a whole-volume, total-f gyrokinetic particle-in-cell code developed for modelling tokamaks.In recent work, XGC has been extended to model more general 3D toroidal magnetic configurations, such as stellarators.These improvements have resulted in the XGC-S version.In this paper, XGC-S is benchmarked in the reduced delta-f limit for linear electrostatic ion temperature gradient-driven microinstabilities, which can underlie turbulent transport in stellarators.An initial benchmark of XGC-S in tokamak geometry shows good agreement with the XGC1, ORB5, and global GENE codes.A benchmark between XGC-S and the EUTERPE global gyrokinetic code for stellarators has also been performed, this time in geometry of the optimised stellarator Wendelstein 7-X.Good agreement has been found for the mode number spectrum, mode structure, and growth rate.
Vertical displacement events (VDEs) can occur in elongated tokamaks causing large currents to flow in the vessel and other adjacent metallic structures. To better understand the potential magnitude of the associated forces and the role of the so called ``halo currents'' on them, we have used the M3D-C1 code to simulate potential VDEs in ITER. We used actual values for the vessel resistivity and pre-quench temperatures and, unlike most of the previous studies, the halo region is naturally formed by triggering the thermal quench with an increase in the plasma thermal conductivity. We used the 2D non-linear version of the code and vary the post-thermal quench thermal conductivity profile as well as the boundary temperature in order to generate a wide range of possible cases that could occur in the experiment. We also show that, for a similar condition, increasing the halo current does not increase the total force on the wall since it is offset by a decrease in the toroidal contribution.
Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data. We also introduce a simple technique, context normalization, that encourages representations that emphasize the relations between objects. We find that this technique enables a significant improvement in the ability to extrapolate, considerably outperforming a number of competitive techniques.
Gilson, Erik; Lee, H; Bortolon, A; Choe, W; Diallo, A; Hong, SH; Lee, HM; Maingi, R; Mansfield, DK; Nagy, A; Park, SH; Song, IW; Song, JI; Yun, SW; Nazikian, R
Abstract:
Results from KSTAR powder injection experiments, in which tens of milligrams of boron nitride (BN) were dropped into low-power H-mode plasmas, show an improvement in wall conditions in subsequent discharges and, in some cases, a reduction or elimination of edge-localized modes (ELMs). Injected powder is distributed by the plasma flow and is deposited on the wall and, over the course of several discharges, was observed to gradually reduce recycling by 33%, and decrease both the ELM amplitude and frequency. This is the first demonstration of the use of BN for ELM mitigation. In all of these experiments, an Impurity Powder Dropper (IPD) was used to introduce precise, controllable amounts of the materials into ELMy H-mode KSTAR discharges. The plasma duration was between 10 s and 15 s, 𝐼𝑝 = 500 kA, 𝐵𝑇 = 1.8 T, 𝑃NBI = 1.6 MW, and 𝑃ECH = 0.6 MW. Plasma densities were between 2 and 3 × 1019 m−3. In all cases, the pre-fill and startup gas-fueling was kept constant, suggesting that the decrease in baseline D𝛼 emission is in fact due to a reduction in recycling. The results presented herein highlight the viability of powder injection for intra-shot and between-shot wall conditioning.
Basic physics of drift-wave turbulence and zonal flows has long been studied within the framework of wave-kinetic theory. Recently, this framework has been re-examined from first principles, which has led to more accurate yet still tractable "improved" wave-kinetic equations. In particular, these equations reveal an important effect of the zonal-flow "curvature" (the second radial derivative of the flow velocity) on dynamics and stability of drift waves and zonal flows. We overview these recent findings and present a consolidated high-level picture of (mostly quasilinear) zonal-flow physics within reduced models of drift-wave turbulence.
Yoo, Jongsoo; Jara-almonte, J.; Yerger, Evan; Wang, Shan; Qian, Tony; Le, Ari; Ji, Hantao; Yamada, Masaaki; Fox, William; Kim, Eun-Hwa; Chen, Li-Jen; Gershman, Daniel
Abstract:
Whistler wave generation near the magnetospheric separatrix during reconnection at the dayside magnetopause is studied with data from the Magnetospheric Multiscale (MMS) mission. The dispersion relation of the whistler mode is measured for the first time near the reconnection region in space, which shows that whistler waves propagate nearly parallel to the magnetic field line. A linear analysis indicates that the whistler waves are generated by temperature anisotropy in the electron tail population. This is caused by loss of electrons with a high velocity parallel to the magnetic field to the exhaust region. There is a positive correlation between activities of whistler waves and the lower-hybrid drift instability (LHDI) both in laboratory and space, indicating the enhanced transport by LHDI may be responsible for the loss of electrons with a high parallel velocity.
Hogikyan, Allison; Resplandy, Laure; Yang, Wenchang; Fueglistaler, Stephan
Abstract:
Dataset constructed from GFDL-FLOR preindustrial control experiment run by Wenchang Yang (wenchang@princeton.edu) on Princeton University's tiger CPU. Processing by Allison Hogikyan (hogikyan@princeton.edu) on Princeton University's tigress data processing node. June 2021.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples.
Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples.
Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
The bitKlavier Grand consists of sample collections of a new Steinway D grand piano from nine different stereo mic images, with: 16 velocity layers, at every minor 3rd (starting at A0); Hammer release samples; Release resonance samples; Pedal samples. Release packages at 96k/24bit, 88.2k/24bit, 48k/24bit, 44.1k/16bit are available for various applications.
