DOI: 10.5281/zenodo.7919618
title: Data from: Plasma acceleration in a magnetic arch

description: <strong>Data from: Plasma acceleration in a magnetic arch</strong> - Authors: Mario Merino, Diego García, Eduardo Ahedo - Contact emails: mario.merino@uc3m.es, dieggarc@ing.uc3m.es - Date: 2023-05-10 - Keywords: electric propulsion, electrodeless plasma thruster, magnetic arch, plasma expansion - Version: 1.0.2 - Digital Object Identifier (DOI): []() - License: This dataset is made available under the [Open Data Commons Attribution License](http://opendatacommons.org/licenses/by/1.0/) <strong>Abstract</strong> This dataset contains the data found in the plots of the paper: Mario Merino, Diego García, Eduardo Ahedo, "Plasma acceleration in a magnetic arch" Provisionally accepted in the journal Plasma Sources Science and Technology <strong>Dataset description</strong> The data in this repository has been extracted from the fluid simulations as described in the [article](link to the paper). For further information on the setup for the simulation please refer to the article. <strong>Data files</strong> The data files are in .csv format. They were produced in numpy using the [numpy.savetxt()](https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html) function and can be easily read with [numpy.loadtxt()](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html) loadtxt('path_to_file', delimiter = ',') or in any other language with the apropiate reader for .csv files. The files are organised following the order of the figures in the article. Therefore each file contains a different sized array. In the following one can find a description of all the data contained in each of the files: - fig2.csv - Applied magnetic field 'Ba/Ba0' - fig3.csv - Thermalised potential 'He' - Electron out of plane velocity 'uye' - fig4.csv - Plasma density 'n' - Electron temperature 'Te' - Electric potential 'phi' - Ion in-plane velocity 'uitilde' - Ion Mach number 'Mi' - fig5.csv - In-plane electric current density 'jitilde' - fig6.csv - Radial magnetic force density 'jyBz' - Axial magnetic force density '-jyBx' - fig7.csv - Thrust integral, beta = 0.00 case 'F_F0_beta_0.00' - Thrust integral, beta = 0.02 case 'F_F0_beta_0.02' - Thrust integral, beta = 0.04 case 'F_F0_beta_0.04' - Thrust integral, beta = 0.08 case 'F_F0_beta_0.08' - fig8.csv - Normalised induced magnetic field strength 'Bp_beta0_Ba0' - fig9.csv - Total magnetic field, beta = 0.00 case 'B_beta_0.00' - Total magnetic field, beta = 0.02 case 'B_beta_0.02' - Total magnetic field, beta = 0.04 case 'B_beta_0.04' - Total magnetic field, beta = 0.08 case 'B_beta_0.08' All files contain a matrix of comma separated values with 400 rows. The number of columns depends on the specific file, for the files corresponding to two dimensional maps (all files except fig7.csv) the number of columns is a multiple of 400, where the first 400 columns correspond to the Z positions values and the following 400 the X position values. These two 400 by 400 matrices correspond to a [meshgrid](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html) common in Matlab and NumPy. The following columns correspond to the values of each quantity in the positions given by the grid. For example, files containing only one field such as 'fig2.csv' have 400 rows and 1200 columns with columns 801 to 1200 corresponding to the values of the given field. As an example for files containing multiple fields let us take 'fig3.csv', this file contains 400 rows and 1600 columns where columns 801 to 1200 contain the values for 'He' and columns 1201 to 1600 contain 'uye'. The file 'fig7.csv' contains the data for a 1D plot with multiple lines. In this case the data is matrix with 400 rows and 5 columns where column 1 contains the z axis positions column 2 contains the values for 'F_F0_beta_0.00' column 3 contains 'F_F0_beta_0.02' and so on. All values are normalised as explained in the article. <strong>Citation</strong> Any works using this dataset or any part of it in any form shall cite it as follows: The prefered means of citation is to reference the publication as soon as it is available. The BibTeX entry(ies) are provided for convenience too: Optionally the dataset can be cited by referencing the DOI: 10.5281/zenodo.7919578 <strong>Acknowledgments</strong> This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/). The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).