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Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows
<div> <div><span># Data repository for the paper</span></div> <br> <div><span># </span><span>_Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows_</span></div> <br> <div><span>Corresponding author:</span></div> <div><span>Berend.van.Wachem@multiflow.org</span></div> <br> <div><span>This repository consists of the data and exemplary python scripts for the paper "Correlations for aerodynamic force coefficients of non-spherical particles in compressible flows" by Christian Gorges, Victor Chéron, Anjali Chopra, Fabian Denner and Berend van Wachem. The data stored in this repository have the following data format:</span></div> <br> <div><span>-</span><span> .csv files consisting the raw data of the simulations used for the coefficient plots in the results' chapter of the paper</span></div> <div><span> </span></div> <div><span>-</span><span> .py files containing python scripts serving as examples on how to use and plot the raw data of the .csv files and the correlations</span></div> <br> <div><span>The main folders of this repository are named as the non-spherical particle shapes (Oblate, Prolate, Rod-like) and a folder with the data on which the correlations are based.</span></div> <br> <div><span>The folders named after the non-spherical particle shapes contain the raw simulation data. For instance, the Oblate folder contains the individual .csv files of all simulations of the oblate spheroid for all Reynolds numbers, Mach numbers, and angles of attack.</span></div> <br> <div><span>The folder Correlations/ consists of the temporally averaged drag, lift and torque coefficients, which are written in .csv files and stored in the folder ResultsCoefficients/, as well as Python scripts for plotting the correlations. </span></div> <br> <div><span>The naming style of the raw data files and the subfolders for each section is explained in the following:</span></div> <br> <div><span>The file names of the .csv files within the particle shape folders consist of the Reynolds number, followed by the Mach number and the angle of attack. For example "log_Re100M2_0_alpha_90.csv" consists of the data for a Reynolds number of 100, a Mach number of 2.0 and an angle of attack of 90 degrees. The content in the .csv files is given as: "%f,%f,%f,%f\n" which corresponds to "Physical time, drag coefficient, lift coefficient, torque coefficient". The first row in each file gives the headers of each column.</span></div> <br> <div><span>The .csv files in the folder Correlations/ResultsCoefficients/ are split per coefficient, shape, and particle Reynolds numbers, which can be identified by the name of the .csv file. For instance, the results obtained for the lift coefficient of</span></div> <div><span>the prolate spheroid particle for at a particle Reynolds numbers 100 for all orientation angles and Mach numbers are given in the file:</span></div> <div><span>"Prolate_100_CL.csv". In these files, the results are ordered per orientation angle (rows) and Mach</span></div> <div><span>number (column). </span></div> <br> <div><span>The python scripts have been tested with Python 3.11.5.</span></div> <br> <div><span>PlotCoefficients.py is an example python script to read the .csv files and plot the aerodynamic force coefficients as it is done in the results section of the paper.</span></div> <br> <div><span>The python scripts in the directory Correlations/ are split in three main functions in two files:</span></div> <div><span>-</span><span> Getter.py (read the .csv files storing the coefficients - separate functions</span></div> <div><span> for the drag, lift and torque coefficients)</span></div> <div><span>-</span><span> ManuscriptCorrelation.py with all the correlations derived in this work for an</span></div> <div><span> effective implementation in any solver, and a plotting function to have visual</span></div> <div><span> representation of the correlations.</span></div> <div><span>-</span><span> generalmain.py (calls Getter and Plotter)</span></div> <br> <div><span>The Getter is called from the generalmain.py file. (run python3 generalmain.py) so that all coefficients can be gathered in a 3D array.</span></div> <div><span>First dimension : Reynolds number</span></div> <div><span>Second dimension : Orientation angle</span></div> <div><span>Third dimension : Mach number</span></div> <div><span>The user just needs to give the absolute path to the folder ResultsCoefficients/.</span></div> <br> <div><span>This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 447633787.</span></div> </div>
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments"
<p>Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments."</p> <p>Data includes representative real and simulated bead trajectories used in the manuscript.</p> <p>Code includes all simulations, analysis, and plot details for the Figures in the manuscript. </p> <p>See included README.txt for more details.</p>
Sensitivity maps of the Amundsen Sea Embayment to changes in external forcings using Automatic Differentiation
<p>Sensitivity maps of the final volume above flotation after 20 years to the basal friction coefficient, rheology factor, surface mass balance, and ocean-induced melting. These results were computed from STREAMICE and ISSM using automatic differentiation. See manuscript for complete description</p>
Students' perceived obstacles with Forced Online Distance Learning during the CoVID-19 outbreak and their preferences to continue with the introduced teaching methods after the reopening of the University of Maribor [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics,</li> <li>and SPSS dataset.</li> </ul>
Forced Continuance Intention Model of Distance Online Teaching during CoVID-19 outbreak at University of Maribor, Slovenia [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the first study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university teachers to the new situation. The project documentation provided for the Forced Online Distance Teaching (FODT) consist of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics, and</li> <li>SPSS dataset.</li> </ul>
Data plotted in Figures in O'Connor et al. paper on methane forcing, including plotting scripts
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Apportionment of the Pre-Industrial to Present-Day Climate Forcing by Methane using UKESM1" submitted for publication to J. Adv. Modeling Earth Sys as csv files. Scripts used for plotting also included. </p>
Automatic learning of hydrogen-bond fixes in an AMBER RNA force field - dataset
<p>Supporting data related to manuscript "Automatic learning of hydrogen-bond fixes in an AMBER RNA force field"</p>
La force des positions vulnerables : hégémonie financière et potentiel disruptif
<p>Dataset and R script related to the paper <a href="https://arcs.episciences.org/9233">La force des positions vulnérables : Hégémonie financière et potentiel disruptif</a>, <em>ARCS</em>, 2018.</p>
CH3CH2OCH3 molecule 200 ps MD trajectory with energies and forces
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p> </p> <p>SOAP params:</p> <p>species=["H", "C", "O"],</p> <p>periodic=False,</p> <p>rcut=5.0,</p> <p>sigma=0.5,</p> <p>nmax=5,</p> <p>lmax=5,</p> <p>average="outer" / "inner",</p> <p>crossover=True,</p> <p>dtype="float64",</p> <p>------------------------------------------------</p> <p>SOAP invariants were calculated with DScribe library (https://pypi.org/project/dscribe/1.2.1/)</p> <p> </p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
Can a knee sleeve influence ground reaction forces and knee joint power during a step-down hop in participants following ACL reconstruction? Discrete and time-continuous datasets
<p>Using a cross-over design, we estimated GRF and knee kinematics and kinetics during a step-down hop for 30 participants (age 26.1 [SD 6.7] years, 14 women) following ACL reconstruction (median 16 months post-surgery) with and without wearing a knee sleeve. In a subsequent randomised clinical trial, participants in the ‘Sleeve Group’ (n=9) then wore the sleeve for 6 weeks at least 1 hour daily, while a ‘Control Group’ (n=9) did not wear the sleeve. Statistical parametric mapping (SPM) was used to compare (1) GRF trajectories in the three planes as well as knee joint power between three conditions at baseline (uninjured side, unsleeved injured and sleeved injured side); (2) within-participant changes for GRF and knee joint power trajectories from baseline to follow-up between groups. We also compared discrete peak GRFs and power, rate of (vertical) force development, and mean knee joint power in the first 5% of stance phase. Time-continuous and discrete data are included in this dataset.</p>
Supporting material of deliverable D2.1 (StretchBio_D.2.1_Report on relation deformation-force for the nanopillars)
<p>Nanopillars - Analytical vs numerical bending - Spreadsheet</p> <p>Nanopillars - Deformation_pillars_on_SiO2substrate_figure10 at deliverable D2.1</p> <p>Nanopillars - vonMises_stress_pillars_on_SiO2substrate_figure14 at deliverable D2.1</p>
High Payload Collaborative Robot - Joint States/Motor Current/TCP Force-Torque
<p>This dataset contains bag files, with data related to robot joint position, motor currents, robot tcp pose, robot tcp Force torque values etc. that were used for the design and development of a redundant collision detection for collisions with the robotic tool. There are also data with measurements from an external F/T sensor for the validation of the approach.</p>
Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces: ftDFT code
<p>This is the complete ftDFT code used to generate the results reported in our Nov 2017 Nanoscale article "Solute particle near a nanopore: influence of size and surface properties on the solvent-mediated forces",DOI: 10.1039/C7NR07218J. </p>
Illustrative dataset for Ozone radiative forcing calculations using SOCRATES-RF
<p>This dataset provides to the reader/user with two netCDF files which illustrate the structure and properties of the input datasets (used directly by the software SOCRATES-RF) in support of the publication: "<strong>Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database</strong>". It comprises two examples of January (pre-industrial decade, 1850s): one based on CMIP5 ozone concentrations and other based on the recently available CMIP6 ozone dataset. Both were created with the procedure described on the supplementary information of the publication "Historical tropospheric and stratospheric ozone radiative forcing using the CMIP6 database", Checa-Garcia, R et al.</p> <p>The sources of information for these datasets are the CMIP5 / CMIP6 ozone dataset, the ERA-Interim reanalysis dataset (2000-01 to 2009-12) and the solar irradiance from SORCE and TIM projects. Please see the references:</p> <ul> <li>Cionni, I., Eyring, V., Lamarque, J. F., Randel, W. J., Stevenson, D. S., Wu, F., Bodeker, G. E., Shepherd, T. G., Shindell, D. T., and Waugh, D. W.: Ozone database in support of CMIP5 simulations: results and corresponding radiative forcing, Atmos. Chem. Phys., 11, 11267-11292, https://doi.org/10.5194/acp-11-11267-2011, 2011.</li> <li>Hegglin, M. I., D. Kinnison, D. Plummer, R.Checa-Garcia et al., Historical and future ozone database (1850-2100) in support of CMIP6, GMD, in preparation.</li> <li>Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F. (2011), The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553–597. doi: 10.1002/qj.828</li> <li>Kopp G., Heuerman K., Lawrence G. (2005) The Total Irradiance Monitor (TIM): Instrument Calibration. In: Rottman G., Woods T., George V. (eds) The Solar Radiation and Climate Experiment (SORCE). Springer, New York, NY</li> </ul>
Slipids Force Field v2.0 (2016)
<p>Slipids force field: files for Gromacs<br> -----------------------------------------------------------</p> <p>Authors: Joakim Jämbeck, Inna Ermilova, Alexander Lyubartsev<br> Department of Materials and Environmental Chemistry,<br> Stockholm University, Stockholm 10691 Sweden<br> e-mail: alexander.lyubartsev@mmk.su.se</p> <p>Content:</p> <p>SLipids_FF: directory containing the force field. Included into the Gromacs<br> topology file by: <br> #include "SLipids_FF/forcefield.itp"</p> <p>itp_files: itp files for various lipids</p> <p> </p> <p>The force field can be used together with the AMBER99SB/AMBER03/GAFF<br> for proteins</p> <p><br> !!!! MAKE SURE YOU CITE THE FOLLOWING REFERENCES WHEN USING THIS FORCE FIELD !</p> <p><br> Saturated PC lipids:</p> <p>Joakim P. M. Jämbeck and Alexander P. Lyubertsev, "Derivation and Systematic<br> Validation of a Refined All-Atom Force Field for Phosphatidylcholine Lipids",<br> J. Phys. Chem. B, 2012, 116, 3164-3179 (2012) DOI: 10.1021/jp212503e</p> <p>POPC, DOPC, SOPC, DOPE, POPE and similar:<br> <br> Joakim P. M. Jämbeck and Alexander P. Lyubertsev, "An Extension and Further<br> Validation of an All-Atomistic Force Field for Biological Membranes"<br> J. Chem. Theory Comput., 8, 2938-2948, (2012) DOI: 10.1021/ct300342n</p> <p>PS, PG, SM lipids and Cholesterol:</p> <p>Joakim P. M. Jämbeck and Alexander P. Lyubertsev, "Another Piece of the<br> Membrane Puzzle: Extending Slipids Further"<br> J. Chem. Theory Comput., 9 (1), 774-784 (2013) DOI: 10.1021/ct300777p</p> <p>Polyinsaturated lipids:</p> <p>Inna Ermilova and Alexander Lyubartsev:, "Extension of the Slipids Force<br> Field for Polyunsaturated Lipids",<br> J. Phys. Chem. B, 120 (50), 12826–12842 (2016)</p> <p><br> Cite also this paper on Charmm36 force field since bond and angle parameters,<br> as well as a part of Lennard-Jones parameters and torsion angles in the lipid<br> headgroups in SLipids are taken from the Charmm36 force field:</p> <p>Jeffery B. Klauda, Richard M. Venable, Alfredo Freites, Joseph W. O’Connor,<br> Douglas J. Tobias, Carlos Mondragon-Ramirez, Igor Vorobyov, Alexander D.<br> MacKerell, Jr. and Richard W. Pastor, "Update of the CHARMM all-atom additive<br> force field for lipids: Validation on six lipid types", J.Phys.Chem B, 114,<br> 7830–78 (2010)</p> <p> </p>
Neural force functional: dataset and model
<p>Dataset and trained model used in the publication:</p> <p>Neural force functional for non-equilibrium many-body colloidal systems, T. Zimmermann, F. Sammüller, S. Hermann, M. Schmidt, and D. de las Heras<br>arXiv 2406.03606 (2024).</p>
Input files for "Faster Simulations with a 5 fs Time Step for Lipids in the CHARMM Force Field"
<p>The performance of all-atom molecular dynamics simulations is limited by an integration time step of 2 fs, which is needed to resolve the fastest degrees of freedom in the system, namely, the vibration of bonds and angles involving hydrogen atoms. The virtual interaction sites (VIS) method replaces hydrogen atoms by massless virtual interaction sites to eliminate these degrees of freedom while keeping intact nonbonded interactions and the explicit treatment of hydrogen atoms. We have modified the existing VIS algorithm for most lipids in the popular CHARMM36 force field by increasing the hydrogen atom masses at regular intervals in the lipid acyl chains and obtained lipid properties and pore formation free energies in very good agreement with those calculated in simulations without VIS. Our modified VIS scheme enables a 5 fs time step resulting in a significant performance gain for all-atom simulations of membranes. The method has the potential to make longer time and length scales accessible in all-atom simulations of membrane–protein complexes.</p> <p>The file set contains individual lipid topologies for virtual interaction sites for standard CHARMM lipids, as well as a README file with instructions on how to implement the VIS algorithm for membranes or membrane-protein complexes</p> <p>Please Cite: <a href="//pubs.acs.org/doi/10.1021/acs.jctc.8b00267">10.1021/acs.jctc.8b00267</a></p> <p> </p>
Supporting Information for "An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"
<p>This supporting information provides the Data Set S1 used to produce Fig. 6 in <strong>"An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities"</strong> (Gieseler et al., 2017). It can be used to calculate the rigidity-dependent solar modulation potential <span class="math-tex">\(\phi(P)\)</span> for monthly intervals from 1973-2017 following Eq. 10 in Gieseler et al. (2017).</p> <p>If you use this data, please refer to and cite <strong>BOTH</strong> following publications:</p> <ul> <li>Gieseler, J., B. Heber, and K. Herbst, <em>An empirical modification of the force field approach to describe the modulation of galactic cosmic rays close to Earth in a broad range of rigidities</em>, J. Geophys. Res., 2017 (doi:10.1002/2017JA024763).</li> <li>Usoskin, I. G., G. A. Bazilevskaya, and G. A. Kovaltsov, <em>Solar modulation parameter for cosmic rays since 1936 reconstructed from ground-based neutron monitors and ionization chambers</em>, J. Geophys. Res., 2011 (doi:10.1029/2010JA016105).</li> </ul> <p>This data set contains the solar modulation potential values in MV for monthly intervals from 1973-2017 derived from the proton proxies IMP-8 He and ACE/CRIS C (Phi_pp), and from Usoskin et al. (2011) as provided by http://cosmicrays.oulu.fi/phi/phi.html (Phi_Uso11). The uncertainties of Phi_pp are given in column 4, those of Phi_Uso11 are 26 MV for the observed period. The LIS used to calculate the modulation potentials is that from Burger et al. (2000) as given by Usoskin et al. (2005).</p> <p>Column 1: Fractional year (start of interval)<br> Column 2: Month<br> Column 3: Phi_pp /MV<br> Column 4: Uncertainty of Phi_pp /MV<br> Column 5: Phi_Uso11 /MV</p> <p>Data also available at http://www.ieap.uni-kiel.de/et/ag-heber/cosmicrays</p>
A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, the Netherlands
<p>The data set contains 39 digital elevation models and 11 orthophotos of a beach-foredune system near Egmond aan Zee, the Netherlands, a high-wave storm-dominated site with an approximately 25 m high foredune. The elevation data set combines a long duration (six years; January 2013 - January 2019) with a high temporal resolution (typically 2-4 months) and is spatially extensive (1.4 km alongshore) with a high spatial (1 m) resolution. To facilitate the testing and further development of coastal dune evolution models, the data set is supplemented with high-frequency time series of offshore wave, water level and wind characteristics as well as several subtidal bathymetries.</p><p>The data set is described in detail in the following open-access, peer-reviewed paper:</p><p>Ruessink, G.; Schwarz, C.S.; Price, T.D.; Donker, J.J.A. A Multi-Year Data Set of Beach-Foredune Topography and Environmental Forcing Conditions at Egmond aan Zee, The Netherlands. <i>Data</i> <strong>2019</strong>, <i>4</i>, 73. <a href="https://doi.org/10.3390/data4020073">https://doi.org/10.3390/data4020073</a></p><p>Update December 7, 2023: The data descriptor paper contains a typo related to the rotation of the RD and local coordinate schemes. On page 4/15 it is said that this rotation angle is 177 degrees, it should be 172.8 degrees. A big thank-you to Haoyang Peng (UNSW, Australia) for pointing out that the 177 degrees is incorrect. </p><p> </p><p> </p>
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