The dataset contains the model file for the Global Adjoint Tomography Model 25 (GLAD-M25). The model file contains parameters defined on the spectral-element mesh and is recommend to be used in SPECFEM3D GLOBE for seismic wave simulation at the global scale.
Conditions for net fast ion drive are derived for beam-driven, sub-cyclotron compressional (CAE) and global (GAE) Alfven eigenmodes, such as those routinely observed in spherical tokamaks such as NSTX(-U) and MAST. Both co- and counter-propagating CAEs and GAEs are investigated, driven by the ordinary and anomalous Doppler-shifted cyclotron resonance with fast ions. Whereas prior results were restricted to vanishingly narrow distributions in velocity space, broad parameter regimes are identified in this work which enable an analytic treatment for realistic fast ion distributions generated by neutral beam injection. The simple, approximate conditions derived in these regimes for beam distributions of realistic width compare well to the numerical evaluation of the full analytic expressions for fast ion drive. Moreover, previous results in the very narrow beam case are corrected and generalized to retain all terms in omega/omega_{ci} and k_{||}/kperp, which are often assumed to be small parameters but can significantly modify the conditions of drive and damping when they are non-negligible. Favorable agreement is demonstrated between the approximate stability criterion, simulation results, and a large database of NSTX observations of cntr-GAEs.
China is the world's largest carbon emitter and suffers from severe air pollution. About one million deaths in China were attributable to air pollution in 2017. Alternative energy vehicles (AEVs), e.g. electric, hydrogen fuel cell, and natural gas vehicles, can help achieve both carbon emission mitigation and air quality improvement. However, climate, air quality and health co-benefit of AEVs powered by deeply decarbonized electricity generation remain poorly quantified. Here, we conduct a quantitative integrated assessment of the air quality, health, carbon emission mitigation and economic benefits of AEV deployment as the electricity grid decarbonizes in China. We find population-weighted annual PM2.5 and summer O3 concentration can decrease as large as 5.7μgm−3 and 4.9ppb. Annual avoided premature mortalities and years of life lost resulting from improved ambient air pollution can be as large as ~329,000 persons and ~1,611,000 years. We thus show that maximizing climate, air quality and health benefits of AEV deployment in China requires rapid decarbonization of the power system.
Recently published scenarios for fully non-inductive startup and operation on the National Spherical
Torus eXperiment Upgrade (NSTX-U) (Menard et al 2012 Nucl. Fusion 52 083015) show Electron
Cyclotron Resonance Heating (ECRH) as an important component in preparing a target plasma for
efficient High Harmonic Fast Wave and Neutral Beam heating. The modeling of the propagation and
absorption of EC waves in the evolving plasma is required to define the most effective window of
operation, and to optimize the launcher geometry for maximal heating and current drive during this
window. Here, we extend a previous optimization of O1-mode ECRH on NSTX-U to account for the
full time-dependent performance of the ECRH using simulations performed with TRANSP. We find
that the evolution of the density profile has a prominent role in the optimization by defining the time
window of operation, which in certain cases may be a more important metric to compare launcher
performance than the average power absorption. This feature cannot be captured by analysis on static
profiles, and should be accounted for when optimizing ECRH on any device that operates near the
cutoff density. Additionally, the utility of the electron Bernstein wave (EBW) in driving current and
generating closed flux surfaces in the early startup phase has been demonstrated on a number of
devices. Using standalone GENRAY simulations, we find that efficient EBW current drive is
possible on NSTX-U if the injection angle is shifted below the midplane and aimed towards the top
half of the vacuum vessel. However, collisional damping of the EBW is projected to be significant, in
some cases accounting for up to 97% of the absorbed EBW power
An electron beam is detected by a 1D floating potential probe array in a relatively high density (10e12 − 10e13 cm−3) and low temperature (∼ 5 eV) plasma of the Magnetic Reconnection Experiment (MRX). Clear perturbations in the floating potential profile by the electron beam are observed. Based on the floating potential profile and a current balance equation to the probe array tips, the effective width of the electron beam is determined, from which we determine the radial and toroidal beam current density profiles. After the profile of the electron beam is specified from the measured beam current, we demonstrate the consistency of the current balance equation and the location of the perturbation is also in agreement with field line mapping. No significant broadening of the electron beam is observed after the beam propagates for tens of centimeters through the high density plasma. These results prove that the field line mapping is, in principle, possible in high density plasmas.
In our study, we compare the three dimensional (3D) morphologic characteristics of Earth's first reef-building animals (archaeocyath sponges) with those of modern, photosynthetic corals. Within this repository are the 3D image data products for both groups of animals. The archaeocyath images were produced through serial grinding and imaging with the Grinding, Imaging, and Reconstruction Instrument at Princeton University. The images in this repository are the downsampled data products used in our study, and the full resolution (>2TB) image stacks are available upon request from the author. For the coral image data, the computed tomography (CT) images of all samples are included at full resolution. Also included in this repository are the manual and automated outline coordinates of the archaeocyath and coral branches, which can be directly used for morphological study.
Gas puff imaging (GPI) is a diagnostic of plasma turbulence which uses
a puff of neutral gas at the plasma edge to increase the local visible
light emission for improved space-time resolution of plasma
fluctuations. This paper reviews gas puff imaging diagnostics of edge
plasma turbulence in magnetic fusion research, with a focus on the
instrumentation, diagnostic cross-checks, and interpretation
issues. The gas puff imaging hardware, optics, and detectors are
described for about 10 GPI systems implemented over the past ~15
years. Comparison of GPI results with other edge turbulence diagnostic
results are described and many common features are observed. Several
issues in the interpretation of GPI measurements are discussed, and
potential improvements in hardware and modeling are suggested.
One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.
