Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8
datasets available to search
ShareScore release 0.9.0
Dataset results
8 results for “tokamak”
Thermophysical properties for the published article "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments"
<p>In order to model the macroscopic metallic melt motion realized in the poor-versus-efficient thermionic emitter leading edge exposures in the ASDEX-Upgrade outer divertor [1], the material library of the MEMENTO melt dynamics code, that previously only concerned tungsten [2] and beryllium [3], had to be extended to iridium and niobium. </p> <p>Reliable experimental data have been analyzed for the latent heats, specific isobaric heat capacity, electrical resistivity, thermal conductivity, mass density, vapor pressure, work function, total hemispherical emissivity and absolute thermoelectric power from the room temperature up to the normal boiling point of iridium and niobium as well as for the surface tension and the dynamic viscosity across the liquid state. Analytical expressions are recommended for the temperature dependence of these thermophysical properties, which involve high temperature extrapolations given the absence of extended liquid iridium and liquid niobium measurements. The analytical expressions, the details of their construction and the main references are included in the accompanying pdf.</p> <p>[1] S. Ratynskaia, K. Paschalidis, P. Tolias, K. Krieger, Y. Corre, M. Balden, M. Faitsch, A. Grosjean, Q. Tichit, R.A. Pitts, the ASDEX-Upgrade team, the WEST team and the Eurofusion MST1 team, "Experiments and modelling on ASDEX Upgrade and WEST in support of tool development for tokamak reactor armour melting assessments", Nucl. Mater. Energy 33 (2022) 101303.<br> [2] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid tungsten relevant for fusion applications", Nucl. Mater. Energy 13 (2017) 42.<br> [3] P. Tolias, "Analytical expressions for thermophysical properties of solid and liquid beryllium relevant for fusion applications", Nucl. Mater. Energy 31 (2022) 101195.</p>
A new set of analytical formulae for the computation of the bootstrap current and the neoclassical conductivity in tokamaks
<p>Provided data includes data sets that have been used to develop a new set of analytical formulas for calculating the bootstrap current and the neoclassical conductivity.</p>
Fluid and kinetic studies of tokamak disruptions using Bayesian optimization
<p>The codes and the data in this directory corresponds to the code and results used in the paper [I. Ekmark et al (2024) J. Plasma Phys., Fluid and kinetic studies of tokamak disruptions using Bayesian optimization, http://arxiv.org/abs/2402.05843]. References to figures below refer to this publication. </p> <p>The optimizations have been performed using the Python package by Fernando Nogueira [https://github.com/bayesian-optimization/BayesianOptimization] and the simulations are performed using the disruptions simulation simulation tool DREAM [https://github.com/chalmersplasmatheory/DREAM, git hash: 0d786e859f6228185ef68b6b3639747e8d96172d], for more information on the latter code visit https://ft.nephy.chalmers.se/dream/.</p> <p>The codes:<br> - BayesianOptimization.py: Runs the optimization, first in fluid and then in isotropic mode. For activated simulations, use the flag "-A".<br> - BlackBox.py: Contains the functions that are run in the optimizations and sets up the simulations.<br> - utils.py: Contains the settings for the simulations as well as some other functions needed in BlackBox.py<br> - ITER.py: Contains all the ITER specific settings.<br> - Exceptions.py: Contains exceptions needed during the simulations. <br> - CostFunction.py: Contains the functions used for evaluating the cost function value for specified values of the representative runaway current, final Ohmic current, current quench time and transported heat fraction.<br> - RunCases.py: Sets up simulations for the cases of table 1 in the paper, as well as for all the optima found.</p> <p>The data:<br> - Optimization results:<br> <br> - Data/OptimizationResults/optresult_fluid.json: Contains the optimization data for the non-activated case using the fluid model. Used to produce figure 1.a. <br> - Data/OptimizationResults/optresult_isotropic.json: Contains the optimization data for the non-activated case using the isotropic model. Used to produce figure 1.b. <br> - Data/OptimizationResults/optresult_fluid_activated.json: Contains the optimization data for the activated case using the fluid model. Used to produce figure 5.a.<br> - Data/OptimizationResults/optresult_isotropic_activated.json: Contains the optimization data for the activated case using the isotropic model. Used to produce figure 5.b.<br> <br> - Data/OptimizationResults/components_fluid.json: Contains the cost function components for each sample from the optimization of the non-activated case using the fluid model. Used to produce figure 2.a. <br> - Data/OptimizationResults/components_isotropic.json: Contains the cost function components for each sample from the optimization of the non-activated case using the isotropic model. Used to produce figure 2.b. <br> - Data/OptimizationResults/components_fluid_activated.json: Contains the cost function components for each sample from the optimization of the activated case using the fluid model. Used to produce figure 6.a.<br> - Data/OptimizationResults/components_isotropic_activated.json: Contains the cost function components for each sample from the optimization of the activated case using the isotropic model. Used to produce figure 6.b.<br> <br> - Cases:<br> - Data/Cases/nonActivatedOpts/fluidOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the non-activated scenario using the fluid model.<br> - Data/Cases/nonActivatedOpts/isoOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the non-activated scenario using the isotropic model.<br> - Data/Cases/activatedOpts/fluidOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the activated scenario using the fluid model.<br> - Data/Cases/activatedOpts/isoOpt/: Contains outputfiles for fluid and isotropic simulations of the optimal case found for the activated scenario using the isotropic model.<br> - Data/Cases/[circle, cross, square, triangle]: Contains outputfiles for fluid and isotropic simulations corresponding to the cases presented in table 1.</p>
Analytical model for collisional impurity transport in tokamaks at arbitrary collisionality
<p>Database of NEO simulations used to develop a set of formulae for an analytical model for collisional impurity transport in tokamaks.</p>
Gyrokinetic linear instabilities and quasilinear fluxes for variations of ITER tokamak baseline parameters
<p>Linear instability and quasilinear fluxes calculated with the <a href="https://genecode.org">GENE</a> plasma microturbulence code. The input parameters correspond to variations of ITER baseline scenario parameters calculated by integrated modelling using the <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/-/wikis/home">QuaLiKiz</a> transport model, as described in <a href="https://iopscience.iop.org/article/10.1088/1361-6587/ab5ae1">P. Mantica et al (2019) Plasma Physics and Controlled Fusion 62 014021</a>. <br> <br> The quasilinear fluxes were calculated with a bespoke saturation rule calibrated to dedicated GENE nonlinear simulations carried out in the same ITER regime. The quasilinear flux dataset was fit with a neural network (NN) regression model, which was then used for ITER baseline integrated modelling and performance projections. Alongside the linear stability dataset, the two separate quasilinear datasets correspond to an unfiltered dataset, and a filtered and data-augmented dataset used for the NN regression. For full details, please see reference [J. Citrin et al (2023) <em>submitted to Physics of Plasmas</em>]. <br> <br> The dimensionless input and output variables correspond to the IMAS gyrokinetic IDS standards. The major radius was taken as the reference length. A key and further details are found below.</p> <table> <caption><strong>Description of CSV file columns</strong></caption> <tbody> <tr> <td>rhoN</td> <td>Normalized toroidal flux coordinate</td> </tr> <tr> <td>ky</td> <td>Binormal wavenumber, normalized to the reference (ion scale) gyroradius</td> </tr> <tr> <td>omt_DT</td> <td>Normalized logarithmic main ion temperature gradient (<span class="math-tex">\(R/L_{Ti}\)</span>)</td> </tr> <tr> <td>omt_el</td> <td>Normalized logarithmic electron temperature gradient (<span class="math-tex">\(R/L_{Te}\)</span>)</td> </tr> <tr> <td>omn_el</td> <td>Normalized logarithmic electron density gradient (<span class="math-tex">\(R/L_{ne}\)</span>)</td> </tr> <tr> <td>s</td> <td>Magnetic shear</td> </tr> <tr> <td>q</td> <td>Safety factor (q-profile)</td> </tr> <tr> <td>gamma</td> <td>Instability growth rate (gyroBohm normalisation with IMAS standard)</td> </tr> <tr> <td>omega</td> <td>Instability frequency (gyroBohm normalisation). Positive frequencies correspond to the ion diamagnetic direction</td> </tr> <tr> <td>kperp2</td> <td>Square of perpendicular wavenumber weighted over poloidal mode structure <span class="math-tex">\(\langle{k_\perp^2}\rangle\)</span></td> </tr> <tr> <td>Q_DT</td> <td>ky-dependent ion heat flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>Q_el</td> <td>ky-dependent electron heat flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>G_el</td> <td>ky-dependent electron particle flux, normalized by the square of the electrostatic potential</td> </tr> <tr> <td>QDT</td> <td>Quasilinear ion heat flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>Qe</td> <td>Quasilinear electron heat flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>Ge</td> <td>Quasilinear electron particle flux, following summation of modes and a saturation rule</td> </tr> <tr> <td>QDT_ITG</td> <td>Quasilinear ion heat flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Qe_ITG</td> <td>Quasilinear electron heat flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Ge_ITG</td> <td>Quasilinear electron particle flux, when considering ITG modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>QDT_TEM</td> <td>Quasilinear ion heat flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Qe_TEM</td> <td>Quasilinear electron heat flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> <tr> <td>Ge_TEM</td> <td>Quasilinear electron particle flux, when considering TEM modes only. GyroBohm normalized with IMAS convention</td> </tr> </tbody> </table> <p> </p>
The global energy balance of the ASDEX Upgrade tokamak determined with the revised cooling water calorimetry
<p>Provided data includes data sets required for running the AUG calorimetry. The database consists of the flow rate, time correction and the distance between the temperature measuring points of inlet and outlet cooling water of each cooling unit. </p>
Study on the impact of N/Ne impurity seeding on neutrals in HL-2A tokamak
<p>Study on the impact of N/Ne impurity seeding on neutrals in HL-2A tokamak</p> <p>The N/Ne injection rates were 2×1019~6×1019 particles/s in the different simulation cases, and all the input parameters were basically the same except for the species used for the impurity seeding.</p> <p>share the one of the computational case, Look forward to your criticism and correction.</p>
Detection and tracking of turbulent structures in the edge of tokamak plasmas using machine learning
<p>This repository contains the data used in the study entitled "Detection and tracking of turbulent structures in the edge of tokamak plasmas: use of an ultra-fast camera and comparison of machine learning and Kalman filter methods". The data was collected using an ultra-fast camera on the COMPASS device.</p> <p><strong><em>This repository includes:</em></strong></p> <p><strong>Fast passive imaging data:</strong> High-frequency captures of turbulent structures present in the edge of tokamak plasmas.<br><strong>Labels :</strong> Annotation of turbulent structures for training and validation of detection and tracking algorithms.<br><strong>Detection and tracking results using YOLO:</strong> Outputs from the YOLO (You Only Look Once) model applied to turbulent data.<br><br><strong><em>Objective</em></strong><br>This dataset is intended to provide a complete and reproducible set for research into the detection and tracking of turbulent structures in tokamak plasmas. It can be used to compare the performance of machine learning methods with traditional techniques, and to encourage further research in this crucial area for plasma physics and nuclear fusion.</p> <p><br>Researchers are encouraged to use these data to:</p> <p>Replicate the results of the original study.<br>Develop and test new techniques for detecting and tracking turbulent structures.Compare the performance of machine learning algorithms with conventional methods.</p> <p><br>References Please cite the repository as follows: S. Chouchene et al., (2024). Detection and tracking of turbulent structures in the edge of tokamak plasmas using machine learning. Zenodo. https://doi.org/10.5281/zenodo.12608068</p>
ScienceDex guides
Understand access before you commit
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.