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68 results for “mhd”
Additional evidence for a pulsar wind nebula in SN 1987A from multi-epoch X-ray data and MHD modelling
<p>This is a basic reproduction package for the paper "Additional evidence for a pulsar wind nebula in the hearth of sN 1987A from multi-epoch X-ray data and MHD modeling" by Greco et al. 2022. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
MHD Model of Ganymede's Magnetosphere: Predicted OCFB and magnetic footprint surface locations for Juno's flyby
<p>This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish coordinates for the predicted location of the open-closed-field line-boundary (OCFB) on Ganymede's surface. Additionally we provide coordinates of Juno's magnetic footprint, namely the surface locations that connect to Juno's trajectory through magnetic field lines.</p> <p>For the surface locations we use a western longitude planetographic coordinate system where 0° longitude is in direction of the y-axis and 90° in direction of the x-axis of the cartesian GPhiO system. The GPhiO system is defined by the primary direction<br> z parallel to Jupiter’s rotation axis, the secondary direction y is pointing towards Jupiter barycenter<br> and x completes the right-handed system approximately in direction of plasma flow.</p> <p><strong>Duling2022_JunoGanymede_modeled_surface_OCFB.txt</strong></p> <p>Columns:</p> <p>Longitude [°]<br> Northern OCFB latitude [°]<br> Southern OCFB latitude [°]</p> <p><strong>Duling2022_JunoGanymede_modeled_magnetic_footprint.txt</strong></p> <p>Columns:</p> <p>Spacecraft time [UTC]<br> Magnetic footprint longitude [°]<br> Magnetic footprint latitude [°]<br> Length of field line between Juno and surface [radii]<br> Length of field line between Juno and surface [km]<br> r coordinate of Juno [radii]<br> Latitude of Juno [°]<br> Longitude of Juno [°]<br> x of Juno in GPhiO [km]<br> y of Juno in GPhiO [km]<br> z of Juno in GPhiO [km]</p> <p><strong>Duling2022_JunoGanymede_surface_map.png</strong></p> <p>A plot that visualizes the data of this repository.</p>
Data sets for "Magnetic helicity dissipation and production in an ideal MHD code"
<pre>The tar archive Helicity_in_IdealMHDCode.tar contains an index.html file with links to a directory with "Add-ons" to the FLASH code and the flash.par file. We also list the IDL directory with secondary data and plot routines for each figure used in the paper "Magnetic helicity dissipation and production in an ideal MHD code" by Axel Brandenburg (Nordita) and Evan Scannapiecoo (Arizona State University) with the URL https://arxiv.org/abs/1910.06074.</pre>
MHD Model of Ganymede's Magnetosphere: Predicted magnetic field on Juno's trajectory
<p>This dataset contains model results from a magnetohydrodynamic (MHD) model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021. Here we publish predicted magnetic field components on Juno's trajectory that can be compared to MAG measurements and are displayed in Figure 3 of Duling et al. (2022).</p> <p>Each file contains data from one model. The dataset includes all models with parameter variations from Duling et al. (2022). These are summarized in Table 1 of Duling et al. (2022) and displayed in Figure 3 with the gray lines.</p> <p>If not varied, all models are run with the following parameters:</p> <p>Upstream Jovian background magnetic field B<sub>0 </sub>= (−15,24,−75) nT<br> Upstream plasma velocity v<sub>0</sub> = 140 km/s<br> Upstream plasma mass density <span class="math-tex">\(\rho\)</span><sub>0</sub> = 100 amu/cm<sup>3</sup><br> Upstream plasma thermal pressure p<sub>0</sub> = 2.8 nPa<br> Ionization frequency <span class="math-tex">\(\nu_{ion}\)</span> = 2.2e-8/s<br> Atmospheric surface mass density <span class="math-tex">\(n_{n,0}\)</span> = 8e6/cm<sup>3</sup><br> Dipole Gauss coefficient <span class="math-tex">\(g_1^0\)</span> = −716.8 nT</p> <p> </p> <p>The published data files correspond to the following models with each one parameter variation:</p> <table> <thead> <tr> <th scope="col">Parameter</th> <th scope="col">Value</th> <th scope="col">Filename Suffix</th> </tr> </thead> <tbody> <tr> <td>default model</td> <td> - </td> <td>default</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured before flyby)</td> <td>B<sub>0 </sub>= (−16,3,−70) nT</td> <td>B0before</td> </tr> <tr> <td>Upstream Jovian background magnetic field (measured after flyby)</td> <td>B<sub>0 </sub>= (−14,43,−80) nT</td> <td>B0after</td> </tr> <tr> <td>Upstream plasma velocity (min)</td> <td>v<sub>0</sub> = 120 km/s</td> <td>v-</td> </tr> <tr> <td>Upstream plasma velocity (max)</td> <td>v<sub>0</sub> = 160 km/s</td> <td>v+</td> </tr> <tr> <td>Upstream plasma mass density (min)</td> <td><span class="math-tex">\(\rho\)</span><sub>0</sub> = 10 amu/cm<sup>3</sup></td> <td>rho-</td> </tr> <tr> <td>Upstream plasma mass density (max)</td> <td><span class="math-tex">\(\rho\)</span><sub>0</sub> = 160 amu/cm<sup>3</sup></td> <td>rho+</td> </tr> <tr> <td>Upstream plasma thermal pressure (min)</td> <td>p<sub>0</sub> = 1.0 nPa</td> <td>p-</td> </tr> <tr> <td>Upstream plasma thermal pressure (max)</td> <td>p<sub>0</sub> = 5.0 nPa</td> <td>p+</td> </tr> <tr> <td>Ionization frequency (min)</td> <td> <span class="math-tex">\(\nu_{ion}\)</span> = 0.5e-8/s</td> <td>prod-</td> </tr> <tr> <td>Ionization frequency (max)</td> <td> <span class="math-tex">\(\nu_{ion}\)</span> = 10.0e-8/s</td> <td>prod+</td> </tr> <tr> <td>Atmospheric surface mass density (min)</td> <td> <span class="math-tex">\(n_{n,0}\)</span> = 1.6e6/cm<sup>3</sup></td> <td>nn-</td> </tr> <tr> <td>Atmospheric surface mass density (max)</td> <td> <span class="math-tex">\(n_{n,0}\)</span> = 40e6/cm<sup>3</sup></td> <td>nn+</td> </tr> <tr> <td>Dipole Gauss coefficient (min)</td> <td> <span class="math-tex">\(g_1^0\)</span> = −702.5 nT</td> <td>dipole-</td> </tr> <tr> <td>Dipole Gauss coefficient (max)</td> <td> <span class="math-tex">\(g_1^0\)</span> = −731.1 nT</td> <td>dipole+</td> </tr> </tbody> </table> <p>Magnetic Field components and Juno's position are in GPhiO system. GPhiO is defined by the primary direction z parallel to Jupiter’s rotation axis, the secondary direction y is pointing from Ganymede's towards Jupiter's barycenter and x completes the right-handed system approximately in direction of plasma flow.</p> <p>Columns:</p> <p>Spacecraft time [UTC]<br> Bx modeled magnetic field in GPhiO [nT]<br> By modeled magnetic field in GPhiO [nT]<br> Bz modeled magnetic field in GPhiO [nT]<br> B modeled magnetic field magnitude [nT]<br> x of Juno in GPhiO [km]<br> y of Juno in GPhiO [km]<br> z of Juno in GPhiO [km]</p>
New insights into the generalized Rutherford equation for nonlinear neoclassical tearing mode growth from 2D reduced MHD simulations
<p>Two dimensional reduced MHD simulations of neoclassical tearing mode growth and suppression by ECCD are performed. The perturbation of the bootstrap current density and the EC drive current density perturbation are assumed to be functions of the perturbed flux surfaces. In the case of ECCD, this implies that the applied power is flux surface averaged to obtain the EC driven current density distribution. The results are consistent with predictions from the generalized Rutherford equation using common expressions for $\Delta^\prime_{\rm bs}$ and $\Delta^\prime_{\rm ECCD}$. These expressions are commonly perceived to describe only the effect on the tearing mode growth of the helical component of the respective current perturbation acting through the modification of Ohm's law. Our results show that they describe in addition the effect of the poloidally averaged current density perturbation which acts through modification of the tearing mode stability index. Except for modulated ECCD, the largest contribution to the mode growth comes from this poloidally averaged current density perturbation.</p>
Annotation historische Semantik von mhd. ungehiure
<p>Der Datensatz enthält alle Daten, die als Grundlage für den Aufsatz Marion Darilek, Ungeheuerlich. Zur historischen Semantik des Monströsen am Beispiel der computergestützten Textannotation von mhd. <em>ungehiure</em>, in: <em>Euphorion</em> 118 (2024), erhoben wurden.</p> <p>Die Struktur des Datensatzes und der ZIP-Dateien ist in der txt-Datei "Dokumentation_Datensatz_Annotationen_ungehiure_DEU_ENG" auf Deutsch und Englisch erläuert.</p> <p> </p> <p>The dataset contains all data used as a basis for the article Marion Darilek, Ungeheuerlich. Zur historischen Semantik des Monströsen am Beispiel der computergestützten Textannotation von mhd. <em>ungehiure</em>, in: <em>Euphorion</em> 118 (2024).</p> <p>The structure of the data set and of the ZIP-files is explained in the txt file "Dokumentation_Datensatz_Annotationen_ungehiure_DEU_ENG" in German and English.</p>
Data for "The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment due to their Propagation with MHD Modes" by Strack & Saur
<div>This dataset contains data from the publication Strack & Saur, 2024 (<a href="https://doi.org/10.1029/2024JA033235">https://doi.org/10.1029/2024JA033235</a>), including the output of our MHD model as well as processed data used in Figures 4, 5, and 6.<br> <div> </div> <div>We use a Cartesian and a spherical coordinate system, both with the origin at the geometric center of Callisto. In the Cartesian system, the z-axis is parallel to Jupiter’s rotation axis, the y-axis points to the center of Jupiter and the x-axis, which completes the right-handed coordinate system, is approximately in direction of Callisto's orbital motion. In the spherical coordinate system, phi=0° is defined on the Jupiter-facing meridian (positive y-axis) and is counted in an easterly direction, i.e., phi=90° is the upstream direction (negative x-axis). Theta is taken from the positive z-axis.<br><br></div> <div> <div> <h2>Simulation Output</h2> <br> <div>The PLUTO simulation code (v4.4, Mignone et al. 2007, http://plutocode.ph.unito.it) was used for the numerical solution of the MHD model. A description of the model equations, boundary conditions and simulation process is given Strack & Saur, 2024.</div> <br> <div>The simulations were performed in spherical geometry (r, theta, phi). Each "*.flt" output file contains the model variables on the simulation grid for a single time step. The respective simulation grid is specified in the "grid.out" file. The model variables are:</div> <ul> <li>rho: Plasma mass density</li> <li>vx1: Plasma bulk velocity, r component</li> <li>vx2: Plasma bulk velocity, theta component</li> <li>vx3: Plasma bulk velocity, phi component</li> <li>Bx1: Magnetic field, r component</li> <li>Bx2: Magnetic field, theta component</li> <li>Bx3: Magnetic field, phi component</li> <li>prs: Thermal plasma pressure</li> </ul> <div> <div>Each simulation output file also contains the following additional variables:</div> <ul> <li>Bpx1: In our case, this is the same as Bx1</li> <li>Bpx2: In our case, this is the same as Bx2</li> <li>Bpx3: In our case, this is the same as Bx3</li> <li>Jx1: Electric current density, r component</li> <li>Jx2: Electric current density, phi component</li> <li>Jx3: Electric current density, theta component</li> </ul> <div>In the output files, all values are in normalized units. The normalization factors (in CGS units) are:</div> <ul> <li>norm_r = 2410e3 cm</li> <li>norm_t = 1.255e1 s</li> <li>norm_rho = 1.594e-24 g/cm^3</li> <li>norm_v = 1.92e7 cm/s</li> <li>norm_B = 8.593e-05 Gauss</li> <li>norm_prs = 5.877e-10 dyne/cm^3</li> <li>norm_J = 8.508e-04 statA/cm^2</li> </ul> <div>Since the simulation output files are in PLUTO's binary ".flt" format, we provide the Python script "read_data.py" to read the simulation data and grid specifications.</div> <br> <div>We provide the following simulation data:</div> <br> <div>For Section 4 in Strack & Saur, 2024</div> <ul> <li>`./symmetric_model_reference`: The reference simulation, i.e., moon-magnetosphere interactions only<br>`./symmetric_model_full_A075`: The (main) full simulation with A=0.75, i.e., moon-magnetosphere interactions and induced magnetic field<br>`./symmetric_model_full_A025`: The full simulation with A=0.25<br>`./symmetric_model_full_A050`: The full simulation with A=0.50<br>`./symmetric_model_full_A100`: The full simulation with A=1.00</li> </ul> <div>For Section 5 in Strack & Saur, 2024</div> <div> <ul> <li>`./C03_high_density_reference`: The reference simulation for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_high_density_full`: The full simulation with A=0.85 for the C03 flyby with the higher initial plasma mass density</li> <li>`./C03_low_density_reference`: The reference simulation for the C03 flyby with the lower initial plasma mass density</li> <li>`./C03_low_density_full`: The full simulation with A=0.85 for the C03 flyby with the lower initial plasma mass density</li> <li>`./C09_high_density_reference`: The reference simulation for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_high_density_full`: The full simulation with A=0.85 for the C09 flyby with the higher initial plasma mass density</li> <li>`./C09_low_density_reference`: The reference simulation for the C09 flyby with the lower initial plasma mass density</li> <li>`./C09_low_density_full`: The full simulation with A=0.85 for the C09 flyby with the lower initial plasma mass density</li> </ul> </div> <br> <div>Note that in the simulation data that is provided for the symmetric model (Section 4), the output numbers of the data files are different. This is because a higher output frequency was used for the reference simulation and the A=0.75 full simulation. All output files for the symmetric full simulations refer to the end of the propagation time span shown in Figure 4. For the reference simulation, the output is provided at the beginning and end of this time span.</div> <div> </div> <div> <div> <h2>Processed Data</h2> <p>In addition to the simulation output, we provide processed data used in Figures 4, 5 and 6 of Strack & Saur, 2024.</p> <p>The directory `./data_figure_4_and_5` contains the following files for each of the four panels in Figure 4:</p> <ul> <li>`fig4_panel_*_reference.csv`: The magnetic field of the reference simulation for the respective profile. Provided are the mean, minimum, and maximum values of each component (Bx, By, Bz) in the analyzed time period.</li> <li>`fig4_panel_*_full_Bx.csv`: The time series of the Bx magnetic field component of the full simulation for the respective profile. Each column contains values for a different position (given in the first row) and each row contains values for a different point in time (given in the first column).</li> <li>`fig4_panel_*_full_By.csv`, `fig4_panel_*_full_Bz.csv`: The time series of the By and Bz magnetic field components, respectively.</li> </ul> <p>The data given for panels a and b are also used in Figure 5.</p> <p>The directory `./data_figure_6` contains a single file `fig6_sample_data.csv` with the data used for Figure 6.</p> <ul> <li>The first three columns of the file give the Cartesian coordinates of the sample points</li> <li>"B_sec_infinity" is the magnitude of the induced magnetic dipole field in a vacuum environment with A=1.0 (Equation 1)</li> <li>"dB_reference" is the numerical variability of the reference simulation in its approximately stationary state</li> <li>The last four columns (e.g. "B_sec_A025") contain the transport altered induced magnetic field magnitudes in the plasma environment for a true dipole amplitude of A=0.25, A=0.50, A=0.75, and A=1.00</li> </ul> <p>Note that length, time and magnetic field in the processed data are given in units of Callisto radii (Rc), seconds and nanotesla.</p> </div> <h2>References:</h2> <div> <div>Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A Numerical Code for Computational Astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. https://doi.org/10.1086/513316</div> <br> <div>Strack, D., Saur, J. (2024). The Spatiotemporal Structure of Induced Magnetic Fields in Callisto's Plasma Environment Due to Their Propagation With MHD modes. Journal of Geophysical Research: Space Physics, 129(12), https://doi.org/10.1029/2024JA033235</div> </div> </div> </div> </div> </div> </div>
MHD model output for Ganymede's magnetosphere during Juno's flyby
<p>This dataset contains the complete simulation output from our MHD model of Ganymede's magnetosphere adapted to Juno's PJ34 flyby in 2021 (Duling et al. 2022).</p> <p>The data was obtained by our application of the PLUTO simulation code v4.4 (Mignone et al. 2007) (http://plutocode.ph.unito.it/) described in Duling et al. 2022.</p> <p>The dataset includes the simulation variables on the simulation grid for a single timestep after steady state was reached. The grid has spherical geometry (r, theta, phi) with phi=0° longitude pointing towards Jupiter (positive y axis of the GPhiO system), phi=90° longitude pointing in the upstream direction (negative x axis of GPhiO) and theta=0° latitude at Ganymede's north pole (positive z axis of GPhiO). The following model variables are included:</p> <p>rho: plasma mass density<br> prs: thermal plasma pressure<br> vx1: plasma velocity radial component<br> vx2: plasma velocity theta component<br> vx3: plasma velocity phi component<br> Bx1: magnetic field radial component<br> Bx2: magnetic field theta component<br> Bx3: magnetic field phi component</p> <p>Additionally the following derived variables are included:</p> <p>Jx1: electric current density radial component<br> Jx2: electric current density theta component<br> Jx3: electric current density phi component<br> Bpx1: plasma magnetic field radial component<br> Bpx2: plasma magnetic field theta component<br> Bpx3: plasma magnetic field phi component</p> <p>Plasma magnetic field means that part of the total magnetic field that results from the plasma interaction. It equals the total magnetic field subtracted by the homogeneous upstream field and Ganymede's intrinsic and induced field.</p> <p>All values are in normalized units with these normalization factors:</p> <p>NORMR = 2.631e8 cm<br> NORMV = 1.4e7 cm/s<br> NORMRHO = 1.661e-22 g/cm^3<br> NORMPRS = 3.255e-08 dyne/cm^2<br> NORMB = 6.395e-04 Gauss<br> NORMJ = 5.801e-03 statA/cm^2</p> <p>In Duling et al. 2022 we present results of a model sensitivity study. This dataset includes model output from our best guess setup (default setup) only.</p> <p>Since the data is in PLUTO's binary format "flt" we provide a Python code snippet that reads the data to data arrays.</p> <p><strong>grid.out</strong><br> This ASCII file contains the grid dimensions and coordinates of the cell boundaries.</p> <p><strong>data.0020.flt</strong><br> This binary file contains the simulation variables on the cell centers of the grid.</p> <p><strong>pluto.0.log</strong><br> This ASCII file contains the header of the PLUTO logfile.</p> <p><strong>read_data.py</strong><br> This Python code snippet helps with reading the data.</p>
Datasets for "Inverse cascading for initial MHD turbulence spectra between Saffman and Batchelor"
<pre>This directory contains an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Inverse cascading for initial MHD turbulence spectra between Saffman and Batchelor". If anything turns out to be incomplete, please email brandenb@nordita.org.</pre>
Dedalus output from 2D incompressible MHD fluid on a magnetized beta plane
<p>This dataset includes all output from simulations of the 2D incompressible MHD fluid on a magnetized beta plane that were used in the paper:</p> <blockquote> <p>Parker, J. B. and Constantinou, N. C. (2019). Magnetic eddy viscosity of mean shear flows in two-dimensional magnetohydrodynamics. Phys. Rev. Fluids, 4, 083701. DOI: 10.1103/PhysRevFluids.4.083701</p> </blockquote> <p>Simulations were performed using the spectral code Dedalus.</p>
Selected BATSRUS MHD output data and IE solver data for August 3, 2016
<p>This is a dataset for BATSRUS MHD model output and IE module output used to prepare Figures 2, 3, 4 for a second submission to GRL of a paper by A. M. Keese, N. Buzulukova, C. Mouikis and E. E. Scime "Mesoscale structures in Earth's magnetotail observed using energetic neutral atom imaging". The dataset has one file in .zip format.</p> <p>The file BATSRUS_IE_data_Fig2_3_4.zip contains the data for plotting BATSRUS results and IE module results for the Figures 2, 3, 4.</p> <p>Figure 2: file Fig2_imf_BATSRUS_input.dat has solar wind data used as an input to BATSRUS run.<br> file Fig2_ae_index.dat has model AU and AL indices</p> <p>Figure 3: files Fig3* are standard output files for IE module (ASCII) and could be plotted with spacepy package.</p> <p>Figure 4: file Fig4_BATSRUS_3D_0520UT_nx300_ny150_nz150.csv has 3D output from BATSRUS interpolated to a regular grid (nx=300, ny=150, nz=150) required to plot Figure 4. The Figure 4 could be reproduced with ParaVew free 3D plotting software.</p>
3D Multi-fluid MHD Simulation of the Early Time Behavior of an Artificial Plasma Cloud in the Bottom Side Ionosphere
<p>This repository contains the necessary files for reproducing each of the figures in Ober et. al (2021) - currently a manuscript. Each file is named after the associated figure number and stored in the HDF5 format - with maximum compression. In general all chemical species physical variables are stored with four indexed dimensions: (species, z-axis, y-axis, x-axis).</p> <p> </p> <p>The details for chemical species are based in corresponding index values with string array ‘species’:<br> </p> <p>0 -> Ba</p> <p>1 -> O</p> <p>2 -> O+</p> <p>3 -> Ba+</p> <p>4 -> e-</p> <p> </p> <p>Axis values are in units of meters (m) from spatial origin </p> <p>X-axis (1, 300)</p> <p>Y-axis (1, 300)</p> <p>Z-axis (1, 888)</p> <p> </p> <p>Chemical species number densities in units of meters cubed (m^3)</p> <p>number_density ( 5, 888, 300, 300 ) </p> <p> </p> <p>Chemical species Temperature in units of Kelvin (K)</p> <p>temperature ( 5, 888, 300, 300 ) </p> <p> </p> <p>Chemical species velocity component values - meters per second (m/s)</p> <p>velocity_x ( 5, 888, 300, 300 ) </p> <p>velocity_y ( 5, 888, 300, 300 ) </p> <p>velocity_z ( 5, 888, 300, 300 ) </p> <p> </p> <p>Magnetic field in units of Teslas (T)</p> <p>magnetic_x ( 1, 888, 300, 300 ) </p> <p>magnetic_y ( 1, 888, 300, 300 ) </p> <p>magnetic_z ( 1, 888, 300, 300 ) </p> <p> </p> <p>NOTE: To help meet the data set limit, magnetic field variables are excluded from file “_figure-1.h5”. For "__figure-7b.h5" and "__figure-7c.h5" only magnetic field components are included.</p>
Grad-Shafranov equation: MHD simulation of the new solution obtained from the Fadeev and Naval models
<p>This article aims to obtain a new analytical solution of a specific form of the Grad-Shafranov (GS) equation using Walker's formula. The new solution has magnetic field lines with X-type neutral points, magnetic islands and singular points. The singular points are located on the x-axis. The X-points and the center of the magnetic islands do not appear on the x-axis an island appears at $z>0$ and the other two at $z<0$. The aforementioned property allows us to use this solution as an initial condition at $t=0$ s in an magnetohydrodynamic (MHD) numerical simulation by excluding the singular points of the solution, i.e., the x-axis, and maintaining the magnetic structure of the islands, as well as the X-type neutral points. For this, we numerically solve the equations of the classical ideal MHD in two dimensions using the Newtonian CAFE code. The code is based on high resolution shock capturing methods using the Harten-Lax-van Leer-Einfeldt (HLLE) flux formula combined with MINMOD reconstructor. The MHD simulation shows a very fast dissipation in less than one second of the magnetic islands present in the initial configuration. Almost all structures left the integration region at $13.2$ s, and the magnetic field vector reverses its polarity very quickly. In addition, our simulation allows us to observe the fast temporal evolution of the magnetic islands turning into elongated current sheets. As a limitation of the model, the difficulty in relating it to a physical system because of fast temporal evolution is considered.</p>
Orszag Tang MHD Test result
<p>Orszag Tang MHD Test result</p>
Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics (Model Data)
<p>Model Data to reproduce plots from article "Exploring Localized Geomagnetic Disturbances in Global MHD: Physics and Numerics". README contains information on where to access model and visualization tools.</p>
Data for the paper "Computing MHD equilibria of stellarators with a flexible coordinate frame"
<p>Data for the revised paper "<span>Computing MHD equilibria of stellarators with a </span><span>flexible coordinate frame"</span></p> <p>Thetitle has changed from the submission title: "A generalized Frenet frame for computing MHD equilibria in stellarators"</p> <p>We provide the input and output files for all GVEC simluations presented at the "JOINT VARENNA - LAUSANNE INTERNATIONAL WORKSHOP: THEORY OF FUSION PLASMAS, 2024" and to be published in PPCF.</p> <p>An ipython script that generates the postprocessing /plots is also provided.</p> <p>New content computing the frame from a boundary surface obtained from quasr is now also part of this compilation.</p> <p>See the README.md file for details.</p>
Multispecies MHD study of ion escape at ancient Mars: effects of an intrinsic magnetic field and solar XUV radiation
<p>This dataset contains the simulation results in the paper "Multispecies MHD study of ion escape at ancient Mars: effects of an intrinsic magnetic field and solar XUV radiation" submitted to Journal of Geophysical Research: Space Physics.</p>
Comprehensive comparison of two global multi-species MHD models of Mars
<p>Understanding the interaction between Mars and the solar wind is crucial for comprehending the atmospheric evolution and climate change on Mars. To gain a comprehensive understanding of the Martian plasma environment, global numerical simulations are essential in addition to spacecraft observations. However, there are still discrepancies among different simulation models. This study investigates how these discrepancies stem from the considered physical processes and numerical implementations. We compare two global multispecies MHD models: the "Sun model" based on the BATS-R-US code and the "Sakata model" based on a newly developed multifluid model MAESTRO. By employing the same typical upstream conditions and the same neutral atmosphere for current Mars, along with similar numerical implementations such as inner boundary conditions, we obtain simulation results that exhibit unprecedented agreement between the two models. The dayside results are nearly identical, especially along the subsolar line, indicating the reliability of MHD models to predict dayside interaction under given upstream conditions and ionosphere assumptions. The escape rates of planetary ions are also in good agreement. However, discrepancies remain in the terminator and nightside regions. Detailed numerical implementations, including inner boundary conditions, magnetic field divergence control methods, and radial resolutions, are shown to influence certain aspects of the results greatly, such as magnetotail configuration and ion diffusion.</p>
Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations
<p>Data associated with JGR: Space Physics paper, "Modeling the Depletion and Recovery of the Outer Radiation Belt During a Geomagnetic Storm: Combined MHD and Test Particle Simulations".</p>
Data for the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'
<p> This is the data used by the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'.</p> <p> The uploaded data is the X-ray intensity data for all the five cases studied in the manuscritpt. 'Casen' (n=1, 2, 3, 4, and 5) in the name of each data file indicates the case number, and 'sat pointX' (X=A, B, C, D) show the satellite positions analyzed in the manuscript. </p> <p> The data can be read by IDL using the following program statments:</p> <p>openr,lun,datai,/get_lun<br> xgse=0. & ygse=0. & zgse=0.<br> readf,lun,xgse,ygse,zgse ;;;;(satellite position in the GSE coordinate)<br> xsat=0. & ysat=0. & zsat=0.<br> readf,lun,xsat,ysat,zsat ;;;;(satellite position in the GSM coordinate)<br> xpoint=0. & ypoint=0. & zpoint=0.<br> readf,lun,xpoint,ypoint,zpoint ;;;;(satellite pointing of SXI, aim point)<br> nthtmax=0L & nphimax=0L<br> readf,lun,nthtmax,nphimax ;;;;(number of the tht and phi grids)<br> thti=fltarr(nthtmax) & phii=fltarr(nphimax)<br> readf,lun,thti,format='(e14.6)' ;;;;(the tht grids)<br> readf,lun,phii,format='(e14.6)' ;;;;(the phi grids)<br> Pxraytp=fltarr(nthtmax,nphimax)<br> readf,lun,Pxraytp,format='(e14.6)' ;;;;(X-ray intensity)<br> close,lun<br> free_lun,lun</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.