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251. Simulating pitch angle scattering using an explicitly solvable energy-conserving algorithm

Author(s):
Zhang, Xin; Fu, Yichen; Qin, Hong
Abstract:
Particle distribution functions evolving under the Lorentz operator can be simulated with the Langevin equation for pitch angle scattering. This approach is frequently used in particle based Monte-Carlo simulations of plasma collisions, among others. However, most numerical treatments do not guarantee energy conservation, which may lead to unphysical artifacts such as numerical heating and spectra distortions. We present a novel structure-preserving numerical algorithm for the Langevin equation for pitch angle scattering. Similar to the well-known Boris algorithm, the proposed numerical scheme takes advantage of the structure-preserving properties of the Cayley transform when calculating the velocity-space rotations. The resulting algorithm is explicitly solvable, while preserving the norm of velocities down to machine precision. We demonstrate that the method has the same order of numerical convergence as the traditional stochastic Euler-Maruyama method.
Type:
Dataset
Issue Date:
September 2020

252. Deep Potential training data for subcritical and supercritical water

Author(s):
Calegari Andrade, Marcos; Ko, Hsin-Yu; Car, Roberto
Abstract:
Data set used to train a Deep Potential (DP) model for subcritical and supercritical water. Training data contain atomic forces, potential energy, atomic coordinates and cell tensor. Energy and forces were evaluated with the density functional SCAN. Atomic configurations were extracted from DP molecular dynamics at P = 250 bar and T = 553, 623, 663, 733 and 823 K. Input files used to train the DP model are also provided.
Type:
Dataset
Issue Date:
19 August 2020

253. Data from "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water"

Author(s):
Gartner, Thomas III; Zhang, Linfeng; Piaggi, Pablo; Car, Roberto; Panagiotopoulos, Athanassios; Debenedetti, Pablo
Abstract:
This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water", by Gartner et al., 2020. In this work, we used neural networks to generate a computational model for water using high-accuracy quantum chemistry calculations. Then, we used advanced molecular simulations to demonstrate evidence that suggests this model exhibits a liquid-liquid transition, a phenomenon that can explain many of water's anomalous properties. This dataset contains links to all software used, all data generated as part of this work, as well as scripts to generate and analyze all data and generate the plots reported in the publication.
Type:
Dataset
Issue Date:
July 2020

254. Geometric concepts for stellarator permanent magnet arrays

Author(s):
Hammond, K. C.; Zhu, C.; Brown, T.; Corrigan, K.; Gates, D. A.; Sibilia, M.
Abstract:
The development of stellarators that use permanent magnet arrays to shape their confining magnetic fields has been a topic of recent interest, but the requirements for how such magnets must be shaped, manufactured, and assembled remain to be determined. To address these open questions, we have performed a study of geometric concepts for magnet arrays with the aid of the newly developed MAGPIE code. A proposed experiment similar to the National Compact Stellarator Experiment (NCSX) is used as a test case. Two classes of magnet geometry are explored: curved bricks that conform to a regular grid in cylindrical coordinates, and hexahedra that conform to the toroidal plasma geometry. In addition, we test constraints on the magnet polarization. While magnet configurations constrained to be polarized normally to a toroidal surface around the plasma are unable to meet the required magnetic field parameters when subject to physical limitations on the strength of present-day magnets, configurations with unconstrained polarizations are shown to satisfy the physics requirements for a targeted plasma.
Type:
Dataset
Issue Date:
July 2020

255. Shakespeare and Company Project Dataset: Lending Library Books

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Green, Elspeth; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Mahoney, Cate; McCarthy, Jesse D.; Naydan, Mary; Ruehl, Isabel; Thode, Sylvie; VanSant, Camey; Wulfman, Clifford E.
Abstract:
This dataset includes information about approximately 6,000 books and other items with bibliographic data as well as summary information about when the item circulated in the Shakespeare and Company lending library and the number of times an item was borrowed or purchased.
Type:
Dataset
Issue Date:
July 2020

256. Shakespeare and Company Project Dataset: Lending Library Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Green, Elspeth; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Mahoney, Cate; McCarthy, Jesse D.; Naydan, Mary; Ruehl, Isabel; Thode, Sylvie; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The events dataset includes information about approximately 33,700 lending library events including membership activities such as subscriptions, renewals and reimbursements and book-related activities such as borrowing and purchasing. For events related to lending library cards that are available as digital surrogates, IIIF links are provided.
Type:
Dataset
Issue Date:
July 2020

257. Shakespeare and Company Project Dataset: Lending Library Members

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Green, Elspeth; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Mahoney, Cate; McCarthy, Jesse D.; Naydan, Mary; Ruehl, Isabel; Thode, Sylvie; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Members dataset includes information about approximately 5,700 members of Sylvia Beach's Shakespeare and Company lending library.
Type:
Dataset
Issue Date:
July 2020

258. Shakespeare and Company Project Dataset: Lending Library Members, Books, Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Alagappan, Serena; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chow, Jin; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Green, Elspeth; Hicks, Benjamin; Joelson, Madeleine E.; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Mahoney, Cate; McCarthy, Jesse D.; Naydan, Mary; Ruehl, Isabel; Thode, Sylvie; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project makes three datasets available to download in CSV and JSON formats. The datasets provide information about lending library members; the books that circulated in the lending library; and lending library events, including borrows, purchases, memberships, and renewals. The datasets may be used individually or in combination site URLs are consistent identifiers across all three. The DOIs for each dataset are as follows: Members (https://doi.org/10.34770/ht30-g395); Books (https://doi.org/10.34770/g467-3w07); Events (https://doi.org/10.34770/2r93-0t85).
Type:
Dataset
Issue Date:
July 2020

259. Enhanced Pedestal H-mode at low edge ion collisionality on NSTX

Author(s):
Battaglia, D.J.; Guttenfelder, W.; Bell, R.E.; Diallo, A.; Ferraro, N.;, Fredrickson, E.; Gerhardt, S.P.; Kaye, S.M.; Maingi, R.; Smith, D.R.
Abstract:
The Enhanced Pedestal (EP) H-mode regime is an attractive wide-pedestal ELM-free high-betap scenario for NSTX-U and next-step devices as it achieves enhanced energy confinement (H98y,2 > 1.5), large normalized pressure (betaN > 5) and significant bootstrap fraction (f_BS > 0.6) at I_p/B_T = 2 MA/T. This regime is realized when the edge ion collisionality becomes sufficiently small that a positive feedback interaction occurs between a reduction in the ion neoclassical energy transport and an increase in the particle transport from pressure-driven edge instabilities. EP H-mode was most often observed as a transition following a large ELM in conditions with low edge neutral recycling. It is hypothesized that the onset of pressure-driven instabilities prior to the full recovery of the neutral density leads to a temporary period with elevated ion temperature gradient that triggers the transition to EP H-mode. Linear CGYRO and M3D-C1 calculations are compared to beam emission spectroscopy (BES) and magnetic spectroscopy in order to describe the evolution of the edge particle transport mechanisms during the ELM recovery and the saturated EP H-mode state. The observations are consistent with the hypothesis that the onset of pressure-driven edge instabilities, such as the KBM and kink-peeling, can be responsible for the increased particle transport in EP H-mode.
Type:
Dataset
Issue Date:
June 2020

260. Gyrokinetic understanding of the edge pedestal transport driven by resonant magnetic perturbations in a realistic divertor geometry

Author(s):
Hager, R.; Chang, C. S.; Ferraro, N. M.; Nazikian R.
Abstract:
Self-consistent simulations of neoclassical and electrostatic turbulent transport in a DIII-D H-mode edge plasma under resonant magnetic perturbations (RMPs) have been performed using the global total-f gyrokinetic particle-in-cell code XGC, in order to study density-pump out and electron heat confinement.The RMP field is imported from the extended magneto-hydrodynamics (MHD) code M3D-C1, taking into account the linear two-fluid plasma response.With both neoclassical and turbulence physics considered together, the XGC simulation reproduces two key features of experimentally observed edge transport under RMPs: increased radial particle transport in the pedestal region that is sufficient to account for the experimental pump-out rate, and suppression of the electron heat flux in the steepest part of the edge pedestal.In the simulation, the density fluctuation amplitude of modes moving in the electron diamagnetic direction increases due to interaction with RMPs in the pedestal shoulder and outward, while the electron temperature fluctuation amplitude decreases.
Type:
Dataset
Issue Date:
June 2020