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195 results for “Equilibrium”
Fig. 2 in New insight in the determination of thermodynamic equilibrium thickness using heat budget over Barents Sea
Fig. 2 — Climatology of Sea Ice Area (SIA) and Oceanic Energy (OE) over Barents Sea for the span 2002 – 2020
Fig. 4 in New insight in the determination of thermodynamic equilibrium thickness using heat budget over Barents Sea
Fig. 4 — Histogram representing percentage contribution of Barents Sea Ice Thickness (SIT) over Arctic Sea Ice Thickness (SIT) during (a) November, (b) December, (c) January, (d) February, (e) March, and (f) April for the span of 2002 – 2020
Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature
<p>This archive includes data and python scripts to create the figures in</p> <p>Duetsch, M., Blossey, P. N., Steig, E. J., and Nusbaumer, J. M. (2019). Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature. Submitted to J. Adv. Model. Earth Sy.</p> <p><strong>Model</strong></p> <p>Model output data are saved in model.tar.gz</p> <p>For all simulations, monthly averages of the following variables are saved in h0 files (used for Figures 3, 6, 8, 9):<br> T: temperature (Si_real simulations only)<br> PS: surface pressure (Si_real simulations only)<br> U: zonal wind (Si_real simulations only)<br> V: meridional wind (Si_real simulations only)<br> LANDFRAC: fraction of surface area covered by land<br> PRECT_H2O: total precipitation rate for H2O<br> PRECT_HDO: total precipitation rate for HDO<br> PRECT_H218O: total precipitation rate for H218O<br> H2OV: H2O mixing ratio for vapor<br> HDOV: HDO mixing ratio for vapor<br> H218OV: H218O mixing ratio for vapor<br> H2OI: H2O mixing ratio for cloud ice<br> HDOI: HDO mixing ratio for cloud ice<br> H218OI: H218O mixing ratio for cloud ice<br> H2OL: H2O mixing ratio for cloud liquid<br> HDOL: HDO mixing ratio for cloud liquid<br> H218OL: H218O mixing ratio for cloud liquid</p> <p>For the control and Si_real microphysical sensitivity simulations, 6-hourly averages of the following variables are saved in h1 files (used for Figures 4, 5, 7):<br> PRECL_H2O: Large-scale precipitation rate for H2O (Si_real simulations only)<br> PRECL_HDO: Large-scale precipitation rate for HDO<br> PRECL_H218O: Large-scale precipitation rate for H218O<br> PRECL_SAT: Large-scale precipitation rate for Si tracer (Si_real simulations only)<br> PRECL_TMP: Large-scale precipitation rate for T tracer (Si_real simulations only)<br> PRECL_RVD: Large-scale precipitation rate for R^D tracer<br> PRECL_RVO: Large-scale precipitation rate for R^18O tracer</p> <p>To limit the size of the data set, only the first 5 years of the simulations are saved.</p> <p><strong>Measurements</strong></p> <p>Ice core measurements for present-day climate and last glacial maximum are saved in icecores_PD.txt and icecores_LGM.txt, respectively.</p> <p>LGM values are averaged from 19000 BCE to 16000 BCE, PD values are averaged from 1000 BCE to 2000 CE.</p> <p>Antarctic surface snow measurements from Masson-Delmotte et al. (2008) are available at https://doi.org/10.1594/PANGAEA.681697</p> <p><strong>Python scripts</strong></p> <p>Scripts run with python 3.6</p> <p>Required packages:<br> - numpy<br> - matplotlib<br> - netCDF4<br> - datetime<br> - Basemap<br> - copy<br> - intergrid<br> - cmocean</p>
Files for the numerical results shown in "Far-from-equilibrium travelling pulses in sloped semi-arid environments driven by autotoxicity effects" by Gabriele Grifò, Annalisa Iuorio, and Frits Veerman
<p>This folder contains the code used to produce Fig. 1 and Fig. 8-10 in the paper: "Far-from-equilibrium travelling pulses in sloped semi-arid environments driven by autotoxicity effects" by Gabriele Grifò, Annalisa Iuorio, and Frits Veerman. The preprint version of this paper is available at <a href="https://arxiv.org/abs/2405.1560">https://arxiv.org/abs/2405.1560</a>.</p> <p>In the subfolder "Direct_simulations", the code (to obtain Fig. 1) is based on a MATLAB routine which uses finite differences for spatial discretization with periodic boundary conditions together with MATLAB’s ode15s routine for time integration.</p> <p>The continuation procedure (to obtain Fig. 8-10) is shown in the subfolder "Continuation" based on the software AUTO to analyse the dependence of the constructed travelling pulse solutions of Eq. (3.2) with respect to three system parameters, i.e. A, D, and H. Correspondingly, in each folder 'Continuation_X' where X=A, D, H, the results can be obtained by running the file 'AUTO_script_cont_X', based on the two files 'parab_X.f90' and 'c.parab_X'.</p> <p>Selected solutions for each continuation are stored in the corresponding subfolders 'solutions_X', whereas the tables resulting from the continuation procedure in the scripts are shown in the files 'bifdiag_X.dat'. In each script file further details are provided in order to clarify how to extract the solutions used for the plots.</p>
General relativistic self-gravitating equilibrium disks around rotating neutron stars: dataset
<p><strong>Dataset containing the results from <em>arXiv:2406.00945 (Y.Kim et al. 2024)</em></strong></p> <ul> <li>`model_list.asc` contains the name of the model (can be looked up from the Table 4 in the manuscript) and basic information.</li> </ul> <p>All equilibrium solutions are generated with (N_s x N_mu) = (801 x 401) resolution. See section 3.4 of the manuscript.</p> <ul> <li>`radial_grid.dat` contains the radial grid in the unit of the neutron star coordinate radius. A zero value at the end of the file corresponds to the endpoint (infinity) of the radial grid, which can be ignored.</li> <li>Angular grid is simply a uniformly divided interval of `mu = cos (theta)`, which can be easily generated and is not included in this dataset. Note that the angle `theta` is measured from the polar axis (z) toward the equatorial plane.</li> </ul> <p>Main data files (`<model_name>_<quantity_name>.dat`) include a flattened 2D array of the four metric functions, rest mass density, and the angular velocity of the fluid. They all contain a single long column of numbers, and can be opened with normal text editors or loaded with other packages (e.g. numpy) without difficulty. Data value at a grid point (mu_i, r_j) is located as a (801 * i + j)th entry with zero-based indexing. For example, the first 801 numbers correspond to the data on the equator (mu=0), then a radial profile along `mu=1/400`, then `mu=2/400`, and so on.</p> <ul> <li>The metric function `rho` and `gamma` correspond to `nu - beta` and `nu + beta` (see Eq 8-9 of Komatsu+1989: https://ui.adsabs.harvard.edu/abs/1989MNRAS.237..355K/abstract).</li> <li>Other metric functions `alpha` and `omega` have the same definition.</li> <li>`restenergydensity` is the rest energy density, and `angvel` is the coordinate angular velocity (see Eq 5 of the manuscript) of the fluid.</li> <li>Neutron star (r<=r_e) is modeled with K=100, Gamma=2 polytropic EoS, where the disk is modeled with K=0.468, Gamma=4/3 polytropic EoS. See the section 4 of the paper.</li> </ul> <p> </p> <p>If you use this dataset, please cite the Zenodo DOI and the companion manuscript <em>Y.Kim et al. 2024 (arXiv:2406.00945).</em></p>
Precisely controlled colloids: A playground for path-wise non-equilibrium physics
<p>Particle Trajectories from Non Equilibrium Steady State (Driven) and Equilibrium state.</p> <p>Number of trajectories can be found in the Book1.xlx </p> <p>File format description on the individual *.dat files is explained in the instructions.pdf</p> <p> </p>
Dataset of "Direct Observation of Shallow Trap States in Thermal Equilibrium with Band-Edge Excitons in Strongly Confined CsPbBr3 Perovskite Nanoplatelets""
<p>Dataset underpinning the published article:</p> <p>Direct Observation of Shallow Trap States in Thermal Equilibrium with Band-Edge Excitons in Strongly Confined CsPbBr<sub>3</sub> Perovskite Nanoplatelets</p> <p><em>Adv. Optical Mater.</em> <strong>2021</strong>, <em>9</em>, 2001308. DOI: <a href="http://doi.org/10.1002/adom.202001308">10.1002/adom.202001308</a></p>
Raw plot data for: Accelerating equilibrium isotope effect calculations. II. Stochastic implementation of direct estimators
<p>Data for publication: K. Karandashev, J. Vanicek, Accelerating equilibrium isotope effect calculations. II. Stochastic implementation of direct estimators, J. Chem. Phys. <strong>151</strong>, 134116 (2019) <a href="https://doi.org/10.1063/1.5124995">https://doi.org/10.1063/1.5124995</a></p> <p>This data set contains the raw numerical data for reproducing Figures 1-8 in the publication.</p>
ANI-1E: An equilibrium database from the ANI-1 database
<p><strong>ANI-1E: An equilibrium database from the ANI-1 database v.2.0</strong></p> <p><em>Authors: Luis Itza Vazquez-Salazar and Markus Meuwly</em><br> E-mail contact: litzavazquezs@gmail.com and m.meuwly@unibas.ch</p> <p>From the SMILES strings, provided by <a href="https://www.nature.com/articles/sdata2017193">Smith et al.,</a> initial geometries using OpenBabel were generated.<br> Subsequently, geometries were optimised using PM7 implemented in MOPAC2016, before a final geometry optimisation and frequency calculation at the ωB97x/6-31G(d) level of theory performed using Gaussian09.<br> The final results were checked to assure that they did not contain imaginary frequencies and therefore correspond to a minimum on the potential energy surface, which can be different from the global minima for the molecule.<br> The total number of molecules is 57455; 7 molecules were unstable for optimisation. The format of the files is .xyz, following the style of the <a href="https://www.nature.com/articles/sdata201422">QM9 database</a> and it contains the geometry minimal in energy, rotational constants, dipole moments, polarizabilities, along with energies of HOMO and LUMO, electronic spatial extent, zero-point energy, enthalpies, and free energies of atomisation. The header of the .xyz file follows the format<br> given in <a href="https://www.nature.com/articles/sdata201422/tables/4">Table 3</a> of the QM9 paper with the difference that the TAG is 'ANI-1E'. Additionally, a file with the original smiles of the ANI-1 dataset and the smiles of ANI-1E is added. The seven molecules (56176,56177,56213,56214,56215,56216,56217) that do not converge are not included<br> in the new database. The .xyz of the final structures are available in the folder 'failed'. The output files for<br> all optimizations are available upon reasonable request to the authors.</p> <p><br> We acknowledge Alfred Andersson and Prof. David van der Spoel for attracting our attention to<br> the problems on the first version of our database.</p>
Datasets for "Cross-helicity effect on alpha-type dynamo in non-equilibrium turbulence"
<p>This directory contains an index.html file with links to the run directories for Runs A-E and idl plotting routines with secondary data for the other figures for the paper "Cross-helicity effect on alpha-type dynamo in non-equilibrium turbulence" by Mizerski, Yokoi, & Brandenburg.</p>
Data for "Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size"
<p>This record contains <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE databases</a> in sqlite format that contain the minimum energy structures of nanoparticles of Ag, Au, Cu, and Pd as determined in the paper "<em>Beyond Magic Numbers: Atomic Scale Equilibrium Nanoparticle Shapes for Any Size</em>", Rahm and Erhart, Nano Letters <strong>17</strong>, 5775 (2017); <a href="http://doi.org/10.1021/acs.nanolett.7b02761">doi: 10.1021/acs.nanolett.7b02761</a></p> <p>The <code>access-database.py</code> script included in this record demonstrates how to access the databases. It requires the <a href="https://wiki.fysik.dtu.dk/ase/">ASE package</a> to be installed.</p>
Shear viscosity coefficient of acqueous glycerol from non-equilibrium Molecular Dynamics simulations
<p>This dataset contains the results of non-equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using the periodic perturbation technique [1]. </p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains three sets of simulations, with different perturbation force parameters ("Em*"), where configuration files necessary to reproduce molecular simulations simulations are provided. Maps of density and velocity field are in "Em*"->"Flow".</p> <p>A small self-contained Python script to fit the velocity fields to a periodic cosine perturbation is provided (fit-periodic.py). Alternatively, viscosity can be obtained from energy outputs by running:</p> <pre><code>gmx energy -f ener.edr</code></pre> <p>and selecting "1/Viscosity". Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p> <p>References:</p> <p>[1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) <a href="https://doi.org/10.1063/1.1421362">https://doi.org/10.1063/1.1421362</a></p>
Shear viscosity coefficient of acqueous glycerol from equilibrium Molecular Dynamics simulations
<p>This dataset contains the results of equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using linear response theory (Einstein relations) [1]. The post-processing of simulation results is inspired by the method of Zhang et al. [2].</p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol, being "000" pure water (0%) and "100" pure glycerol (100%). Zip folders contain</p> <ul> <li>Expected value and integral of the square of the off-diagonal components of the pressure gradient ("EnergyOutputs")</li> <li>Initial configurations used to start the ensemble of replicas from which viscosity is computed ("InitConfReplicas")</li> <li>Output, state and topology of replica simulations ("MdrunOutputs")</li> </ul> <p>A self-contained Python script (compute-visco.py) for the computation of viscosity from the output of an ensemble of simulations is provided. Integrals used to quantify viscosity via Einstein's relation can be obtained from energy output files (.edr) by running:</p> <pre><code>gmx energy -f ener.edr -evisco -eviscoi -vis</code></pre> <p>Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p> <p>References:</p> <p>[1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) <a href="https://doi.org/10.1063/1.1421362">https://doi.org/10.1063/1.1421362</a></p> <p>[2] Y. Zhang et al., Reliable Viscosity Calculation from Equilibrium Molecular Dynamics Simulations: A Time Decomposition Method, J. Chem. Theory Comput. 2015, 11, 3537−3546, <a href="https://doi.org/10.1021/acs.jctc.5b00351">https://doi.org/10.1021/acs.jctc.5b00351</a></p>
Equilibrium contact angle of acqueous glycerol on a silica-like surface from Molecular Dynamics
<p>This dataset contains the results of Molecular Dynamics simulations of quasi-2D water-glycerol liquid droplets, spreading on silica-like surfaces. The goal of the simulations is to quantify the equilibrium contact angle of said droplets.</p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains configuration files and compressed output molecular trajectories. The contact angle is computed from density maps binned on-the-fly using a customized Gromacs version (<a href="https://github.com/pjohansson/gromacs-flow-field">https://github.com/pjohansson/gromacs-flow-field</a>); frames are placed in a tarball ("flow-***p.tar.gz").</p> <p>The zipped folder 'scripts.zip' contains a self-contained library of functions to read density maps and a Jupyter notebook with an example of density reading and plotting.</p> <p>Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p>
Cloud Feedbacks in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2023).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2023).</p>
Data from: Ancient and modern genomes reveal microsatellites maintain a dynamic equilibrium through deep time
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Stability of patch-turnover relationships under equilibrium and nonequilibrium metapopulation dynamics driven by biogeography
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The Plasma Distribution in Saturn's Inner Magnetosphere from 2.4 to 10 Rs: A Diffusive Equilibrium Model
<p>This is the supporting data set for the paper by the same title published in AGU JGR Space Physics, <a href="https://doi.org/10.1029/2019JA027545">10.1029/2019JA027545</a></p> <p>Abstract:</p> <p>Electron density measurements have been obtained by the Cassini Radio and Plasma Wave Science (RPWS) instrument covering the period from 30 June, 2004 to 19 April, 2017, spanning latitudes up to ~30<sup>o </sup>and <em>L</em> values from 2.4 to 10. Near the F ring, electron densities are derived from RPWS measurements of electron plasma oscillations at high latitudes and from the Langmuir Probe (RPWS/LP) sweep data at low latitudes. The electron density measurements from the ring-grazing orbits, beginning in December 2016, have made it possible to extend the work of a previous diffusive equilibrium model to include the distribution of the ring plasma. Beyond the ring-grazing orbits, the densities are derived from RPWS measurements of the upper hybrid resonance frequency. These density measurements are used to anchor the fit of an expanded diffusive equilibrium density model for a two-species plasma consisting of water group and hydrogen ions in Saturn’s inner magnetosphere. Density contour plots for the two ion species and the electrons are presented. The distribution of the derived plasma densities is consistent with two primary sources, the Enceladus plumes and the extended ring atmosphere. There is also an indication of a weaker plasma source at Dione. In the region just outside the A Ring and in the region including the Enceladus orbit, the diffusive equilibrium model also clearly shows the expansion of lighter ions and electrons to higher latitudes along the magnetic field lines with evidence of a weaker plasma expansion between the orbits of Tethys and Dione.</p>
Source Data - Ilzhofer et al. - Phase coherence in out-of-equilibrium supersolid states of ultracold dipolar atoms
<p>Source data for following publication:</p> <p>"Phase coherence in out-of-equilibrium supersolid states of ultracold dipolar atoms" (2019)</p> <p>P. Ilzhofer and M. Sohmen and G. Durastante and Claudia Politi and A. Trautmann and G. Morpurgo and T. Giamarchi and L. Chomaz and M. Mark and F. Ferlaino</p> <p>Institut f ̈ur Quantenoptik und Quanteninformation, ̈Osterreichische Akademie der Wissenschaften, 6020 Innsbruck, Austria</p> <p>Institut f ̈ur Experimentalphysik und Zentrum f ̈ur Quantenoptik,Universit ̈at Innsbruck, Technikerstraße 25, 6020 Innsbruck, Austria</p> <p>DQMP, University of Geneva, 24 Quai Ernest-Ansermet, CH-1211 Geneva, Switzerland</p>
Replication package for: Credit Shocks and Equilibrium Dynamics in Consumer Durable Goods Markets
<p>Alessandro Gavazza and Andrea Lanteri, Credit Shocks and Equilibrium Dynamics in Consumer Durable Goods Markets, Review of Economic Studies</p> <p>The package includes four folders: data, stata_code, matlab_code, and figures. Please see the readme.pdf file for details.</p>
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International Brain Laboratory public data
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OpenNeuro
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