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23 results for “plasma accelerators”
Data from: Plasma acceleration in a magnetic arch
<p><strong>Data from: Plasma acceleration in a magnetic arch</strong></p> <p>- Authors: Mario Merino, Diego García, Eduardo Ahedo</p> <p>- Contact emails: mario.merino@uc3m.es, dieggarc@ing.uc3m.es</p> <p>- Date: 2023-06-08</p> <p>- Keywords: electric propulsion, electrodeless plasma thruster, magnetic arch, plasma expansion</p> <p>- Version: 1.0.4</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.7919577</p> <p>- License: This dataset is made available under the [Open Data Commons Attribution License](http://opendatacommons.org/licenses/by/1.0/)</p> <p> </p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the paper:</p> <p>Mario Merino, Diego García, Eduardo Ahedo, "Plasma acceleration in a magnetic arch"</p> <p>Published in the journal Plasma Sources Science and Technology</p> <p> </p> <p><strong>Dataset description</strong></p> <p>The data in this repository has been extracted from the fluid simulations as described in the article. (https://iopscience.iop.org/article/10.1088/1361-6595/acd476).</p> <p>For further information on the setup for the simulation please refer to the article.</p> <p> </p> <p><strong>Data files</strong></p> <p>The data files are in .csv format. They were produced in numpy using the numpy.savetxt() function and can be easily read with numpy.loadtxt() or in any other language with the apropiate reader for .csv files.</p> <p>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:</p> <p>- fig2.csv</p> <p> - Applied magnetic field 'Ba/Ba0'</p> <p>- fig3.csv</p> <p> - Thermalised potential 'He'</p> <p> - Electron out of plane velocity 'uye'</p> <p>- fig4.csv</p> <p> - Plasma density 'n'</p> <p> - Electron temperature 'Te'</p> <p> - Electric potential 'phi'</p> <p> - Ion in-plane velocity 'uitilde'</p> <p> - Ion Mach number 'Mi'</p> <p>- fig5.csv</p> <p> - In-plane electric current density 'jitilde'</p> <p>- fig6.csv</p> <p> - Radial magnetic force density 'jyBz'</p> <p> - Axial magnetic force density '-jyBx'</p> <p>- fig7.csv</p> <p> - Thrust integral, beta = 0.00 case 'F_F0_beta_0.00'</p> <p> - Thrust integral, beta = 0.02 case 'F_F0_beta_0.02'</p> <p> - Thrust integral, beta = 0.04 case 'F_F0_beta_0.04'</p> <p> - Thrust integral, beta = 0.08 case 'F_F0_beta_0.08' </p> <p>- fig8.csv</p> <p> - Normalised induced magnetic field strength 'Bp_beta0_Ba0'</p> <p>- fig9.csv</p> <p> - Total magnetic field, beta = 0.00 case 'B_beta_0.00'</p> <p> - Total magnetic field, beta = 0.02 case 'B_beta_0.02'</p> <p> - Total magnetic field, beta = 0.04 case 'B_beta_0.04'</p> <p> - Total magnetic field, beta = 0.08 case 'B_beta_0.08'</p> <p>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 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'.</p> <p>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.</p> <p>All values are normalised as explained in the article.</p> <p> </p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication as soon as it is available.</p> <p>The BibTex is also provided for the sake of convinience:</p> <p>@article{Merino_2023,</p> <p>doi = {10.1088/1361-6595/acd476},</p> <p>url = {https://dx.doi.org/10.1088/1361-6595/acd476},</p> <p>year = {2023},</p> <p>month = {jun},</p> <p>publisher = {IOP Publishing},</p> <p>volume = {32},</p> <p>number = {6},</p> <p>pages = {065005},</p> <p>author = {Mario Merino and Diego García-Lahuerta and Eduardo Ahedo},</p> <p>title = {Plasma acceleration in a magnetic arch},</p> <p>journal = {Plasma Sources Science and Technology},</p> <p>abstract = {}</p> <p>}</p> <p>Optionally the dataset can be cited by referencing the DOI: 10.5281/zenodo.7919577</p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p>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).</p>
Multi-GeV Wakefield Acceleration in a Plasma-Modulated Plasma Accelerator
<p>Input decks for the particle-in-cell code WarpX used in a new study to simulate the accelerator stage of a recently proposed laser-plasma accelerator scheme [Phys. Rev. Lett. <strong>127</strong>, 184801 (2021)], dubbed the Plasma-Modulated Plasma Accelerator (P-MoPA).</p>
Experimental data, analysis scripts and simulations for "Emittance preservation in a plasma-wakefield accelerator"
<p>This dataset presents the experimental data, the analysis scripts and the accompanying simulations for the article <em>"Emittance preservation in a plasma-wakefield accelerator"</em> by C. A. Lindstrøm <em>et al</em>. [<a href="https://doi.org/10.1038/s41467-024-50320-1">Nat. Commun. 15, 6097 (2024)</a>].</p> <p>The data was collected at the FLASHForward facility at DESY (Hamburg, Germany). Simulations were performed using <a href="https://doi.org/10.5281/zenodo.5639467" target="_blank" rel="noopener">HiPACE++ v23.11</a>.</p> <p><strong>Folder structure:</strong></p> <ul> <li>Folders containing experimental data: <ul> <li>Folder <code>1A_DATA__OBJECT_PLANE_SCANS</code> contains all data from object-plane scans (emittance measurements).</li> <li>Folder <code>1B_DATA__SPECTRUM_MEASUREMENT</code> contains all data from energy-spectrum measurements.</li> <li>Folder <code>1C_DATA__TWO_BPM_TOMOGRAPHY</code> contains all data from two-BPM tomography measurements.</li> <li>Folder <code>1D_DATA__BEAM_RECONSTRUCTION</code> contains all data from beam-reconstruction measurements (including longitudinal-phase-space measurements).</li> <li>Folder <code>1E_DATA__PLASMA_DENSITY</code> contains all data from plasma-density measurements (spectral-line broadening).</li> </ul> </li> <li>Folder <code>2_ANALYSIS</code> contains all the data-analysis scripts, required for plotting experimental figures.</li> <li>Folder <code>3_SIMULATION</code> contains all simulation scripts, required for generating 6D beam phase spaces and plotting simulation figures.</li> <li>Folder <code>4_FIGURES</code> contains all figure-plotting scripts (17 figures total).</li> </ul> <p><br><strong>Dataset structure:</strong></p> <ul> <li>Each dataset is identified by a 5-digit number (e.g., <code>14275</code>)</li> <li>Metadata and beam-synchronous scalar values are contained in a <code>.mat</code> dataset file (e.g., <code>14275.mat</code>).</li> <li>The dataset file has the following fields: <ul> <li><code>.metadata</code> containing all the generic metadata</li> <li><code>.state</code> containing all the <em>non-beam-synchronous</em> data (once per dataset; magnet settings etc.)</li> <li><code>.scalars</code> containing all the <em>beam-synchronous scalar</em> data (once per shot; BPM readings etc.)</li> <li><code>.vectors</code> containing all the <em>beam-synchronous vector</em> data (once per shot; scope traces etc.)</li> <li><code>.images</code> containing all the <em>beam-synchronous image</em> data, with relative URLs (once per shot; spectrometer images etc.)</li> </ul> </li> <li>The corresponding images (linked from the <code>.mat</code> file) are contained in the <code>images</code> folder, sorted by scan step.</li> </ul> <p><br><strong>Instructions for plotting all figures*:</strong></p> <ol> <li>Change directory to <code>4_FIGURES/</code></li> <li>In MATLAB, run <code>plot_all_figures();</code></li> <li>The 4 main figures and 13 supplementary figures will be plotted</li> </ol> <p><strong>Instructions for generating the 6D phase space for simulations*:</strong></p> <ol> <li>Change directory to<code> 3_SIMULATION/input_beam_generation/</code></li> <li>In MATLAB, run <code>generate_beam_and_plasma();</code></li> <li>The full analysis will up to several minutes (the files are stored in the <code>_files</code> folder)</li> </ol> <p><strong>Instructions for performing HiPACE++ simulations*:</strong></p> <ol> <li>Change directory to e.g. <code>3_SIMULATION/simulations/experimental_cell_50mm/</code></li> <li>The HiPACE++ input file is called <code>input_file</code></li> <li>This file refers to the plasma profile (<code>plasma_short.csv</code>) and beam files (<code>beam.h5</code> and <code>driver.h5</code>) found in <code>3_SIMULATION/run_notebooks/inputs/</code></li> </ol> <p><strong>Instructions for re-performing all the analysis*:</strong></p> <ol> <li>Change directory to <code>2_ANALYSIS/</code></li> <li>In MATLAB, run <code>run_all_analyses();</code></li> <li>The full analysis will up to several hours (the files are stored in various <code>_files</code> folders)</li> </ol> <p><em>* The scripts use UNIX system calls and are only compatible with Linux and Mac, but not Windows.</em></p>
Reference dataset of multi-objective and multi-fidelity optimization in laser-plasma acceleration
<p>This repository contains a dataset used for the article "<em>Multi-objective and multi-fidelity Bayesian optimization of laser-plasma acceleration</em>" (<a href="https://arxiv.org/abs/2210.03484">arXiv:2210.03484</a>). The dataset consists of 2443 FBPIC particle-in-cell simulations of a laser wakefield accelerator that were selected using a Bayesian optimizer. The goal of the optimization was to perform multi-objective multi-fidelity optimization of electron beam parameters. The dataset contains simulations of different resolutions, accordingly with differing fidelities. The typical runtime at lowest (highest) resolution is approximately 1 (90) minutes.</p> <p>In the dataset we have <em>train_x </em>and <em>train_obj </em>numpy arrays with dimensions <em>(n,5)</em> and<em> (n,3)</em>, respectively. Here <em>n</em> is the number of FBPIC simulations. The five columns in <em>train_x </em>are [plasma density, upramp length, laser focus, downramp length, fidelity]. The fidelity parameter controls the resolution and hence the runtime of the simulation. The three columns in the <em>train_obj </em>are the [total charge, distance of median to target energy, bandwidth of electron beams]. For the distance, the target energy is fixed to 300 MeV and for the bandwidth is defined by the median absolute deviation around the median. The two columns have negative values since the optimizer assumes a maximization of all objectives while the distance and bandwidth in this study were being minimized.</p> <p>The different folders contain data of different kind of single and multi-objectives that were used to produce figures 2, 3, 5 in the associated paper. For more details please see the referred article. The folder "combined" contains the data of all simulations together and is most suitable for (5D x 3D) surrogate model generation.</p>
Stability of the Modulator in a Plasma-Modulated Plasma Accelerator
<p>Input decks for the particle-in-cell code WarpX used in a new study to simulate the modulator stage of a recently proposed laser-plasma accelerator scheme [Phys. Rev. Lett. <strong>127</strong>, 184801 (2021)], dubbed the Plasma-Modulated Plasma Accelerator (P-MoPA). </p>
Accurate modeling of plasma acceleration with arbitrary order pseudo-spectral particle-in-cell methods
<p>This is supplementary material to the publication "Accurate modeling of plasma acceleration with arbitrary order pseudo-spectral particle-in-cell methods" by S. Jalas et. al.</p> <p>Particle in Cell (PIC) simulations are a widely used tool for the investigation of both laser- and beam-driven plasma acceleration. It is a known issue that the beam quality can be artificially degraded by numerical Cherenkov radiation (NCR) resulting primarily from an incorrectly modeled dispersion relation. Pseudo- spectral solvers featuring infinite order stencils can strongly reduce NCR – or even suppress it – and are therefore well suited to correctly model the beam properties. For efficient parallelization of the PIC algorithm, however, localized solvers are inevitable. Arbitrary order pseudo-spectral methods provide this needed locality. Yet, these methods can again be prone to NCR. Here, we show that acceptably low solver orders are sufficient to correctly model the physics of interest, while allowing for parallel computation by domain decomposition.</p> <p>The given script can be run with the open source particle in cell codes FBPIC (https://github.com/fbpic/fbpic) and Warp (https://bitbucket.org/berkeleylab/warp)</p> <p>The produced data is conformant with the openPMD standard (openpmd.org) and can be analysed for example with the openPMD-viewer (https://github.com/openPMD/openPMD-viewer).</p>
Simulation input scripts for figures in the article "Experimental parameters for plasma wakefield acceleration in a narrow plasma column"
<p>Simulation input scripts for figures in the article "Experimental parameters for plasma wakefield acceleration in a narrow plasma column"</p> <p>Alter line "my_constants.dx_dz= 10e-6" to chage the misalignment degree</p>
Dataset for Stable laser-acceleration of high-flux proton beams with plasma collimation
<p>This repository contains data and analysis code in support of the publication "Stable laser-acceleration of high-flux proton beams with plasma collimation" in Nature Communications. It contains processed experimental data and the code required to produce plots contained within the publication. It also contains inputs and analysis for simulations used to interpret the experimental findings.</p>
Tail-Wave-Assisted Positron Acceleration in Nonlinear Laser Plasma Wakefields
<p>The data and vector diagrams of the results of positron trapping and acceleration when the initial parameters are modified.</p>
Dataset for Bright x-ray radiation from plasma bubbles in an evolving laser wakefield accelerator
<p>This dataset contains the raw data and analysis scripts for results presented in the paper. " Bright x-ray radiation from plasma bubbles in an evolving laser wakefield accelerator"</p>
Modeling of Emittance Growth Due to Coulomb Collisions in Plasma-based Accelerators
<p>This dataset contains some supplementary material for the paper titled "Modeling of Emittance Growth Due to Coulomb Collisions in Plasma-based Accelerators" published in the journal of Physics of Plasmas. There are two folders in the compressed file. One includes two input files that are used for the open-source particle-in-cell code WarpX(https://github.com/ECP-WarpX/WarpX/). The other includes the data file, figure files, and the plotting scripts.</p>
Modeling of a chain of three plasma accelerator stages with the WarpX electromagnetic PIC code on GPUs
<p>This archive contains the data used for plots 1, 2, 3, 6 and 7 of the Physics of Plasma paper "Modeling of a chain of three plasma accelerator stages with the WarpX electromagnetic PIC code on GPUs" by J.-L. Vay et al (2021).</p> <p> </p> <p>To produce plots 1, 2 and 3, cd into the corresponding directory and run the python script.</p> <p> </p> <p>To produce plots 6 and 7, cd in the directory Figures_6_7 and run the script mkzplots.</p> <p> </p> <p>Simulations were performed on Summit (OLCF) with WarpX Version 19.10-2404-gafa3d4808ff0-dirty (code with slight modification from repo is given in archive warpx_source.tar.gz).</p> <p> </p> <p>Loaded Modules:</p> <p> 1) hsi/5.0.2.p5 3) lsf-tools/2.0 5) DefApps 7) spectrum-mpi/10.3.1.2-20200121 9) ccache/3.7.9 11) boost/1.66.0 13) hdf5/1.10.4 15) openblas/0.3.9-omp 17) nano/2.6.3</p> <p> 2) xalt/1.2.1 4) darshan-runtime/3.1.7 6) gcc/6.4.0 8) cuda/10.1.243 10) fftw/3.3.8 12) cmake/3.18.2 14) adios2/2.5.0 16) netlib-lapack/3.8.0 18) python/3.6.6-anaconda3-5.3.0</p>
HiPACE++ input scripts for "Emittance-preserving acceleration of high-quality positron beams using warm plasma filaments"
<p>This dataset presents the <a href="https://github.com/Hi-PACE/hipace/">HiPACE++</a> input scripts required to re-create the data of the figures in the article "Emittance-preserving acceleration of high-quality positron beams using warm plasma filaments"</p>
Plasma electron contribution to beam emittance growth from Coulomb collisions in plasma-based accelerators
<p>This dataset contains some supplementary material for the paper published in the journal Physics of Plasmas. There are two folders in the compressed file. One includes two input files that are used for the open-source particle-in-cell code WarpX(https://github.com/ECP-WarpX/WarpX/). The other includes figure files, and the plotting scripts.</p>
Extended X-ray absorption spectroscopy using an ultrashort pulse laboratory-scale laser-plasma accelerator
<p>The data contained in this repository was used in the production of the publication "Extended X-ray absorption spectroscopy using an ultrashort pulse laboratory-scale laser-plasma accelerator"</p>
Optimization scripts used for "Bayesian optimization of laser-plasma accelerators assisted by reduced physical models"
<p>This dataset contains the optimization scripts needed to reproduce the results from the article "Bayesian optimization of laser-plasma accelerators assisted by reduced physical models" by A. Ferran Pousa, S. Jalas, M. Kirchen, A. Martinez de la Ossa, M. Thévenet, J. Larson, S. Hudson, A. Huebl, J.-L. Vay, and R. Lehe.</p>
Air Cold Atmospheric Pressure Plasma Treatment for Acceleration of Venous Ulcer Healing
ClinicalTrials.gov study NCT05894096. IPD Sharing: YES. Countries: 1. Publications: 12.
Proton and Helium Ion Acceleration at Magnetotail Plasma Jets
<p>Additional files</p>
HiPACE++ input scripts for "Self-stabilizing positron acceleration in a plasma column"
<p>This dataset presents the <a href="https://github.com/Hi-PACE/hipace">HiPACE++</a> input scripts required to re-create the data of Fig. 2 and 4 in the article "Self-stabilizing positron acceleration in a plasma column" by S. Diederichs, C. Benedetti, M. Thévenet, E. Esarey, J. Osterhoff, and C. B. Schroeder.</p>
HiPACE++ input scripts for "Temperature effects in plasma-based positron acceleration schemes using electron filaments"
<p>This dataset presents the <a href="https://github.com/Hi-PACE/hipace">HiPACE++</a> input scripts required to re-create the data of the figures in the article "Temperature effects in plasma-based positron acceleration schemes using electron filaments" by S. Diederichs, C. Benedetti, M. Thévenet, A. Sinn, E. Esarey, J. Osterhoff, and C. B. Schroeder.</p>
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