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6 results for “shear viscosity”
Hysteresis of viscosity in a solid-liquid two-phase system with shear
<p>In this study, the role of fluids in a slow slip events (SSEs) regime was examined by investigating the rheological properties of the solid-liquid two-phase system on a laboratory scale. We specifically investigated how the rheological properties change with the shear rate when the granular layer is fluid-rich. We used a velocity-controlled rheometer to apply shear to a liquid-saturated granular layer. The results show that the liquid-saturated granular layer’s shear viscosity depended on the liquid viscosity and exhibited power-law like behavior. Additionally, the saturated granular layered exhibited hysteresis, which increased as the viscosity of the liquid decreased. After a quantitative discussion through Jop et al. (2006), Bagnold (1954) and Perrin et al. (2019) framework, we discuss the role of fluid in (SSE) regime. (Abstract in this study)</p>
Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations"
<p>Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations"</p> <p>Includes the data for 20 heartbeats. Divided into external and internal walls regions. </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>
Bayesian estimation of the specific shear and bulk viscosity of the quark-gluon plasma
<p>This repository contains all data necessary to reproduce Bayesian parameter estimates of a computational model of relativistic heavy-ion collisions.</p> <p>The computational collision model which generated the raw data is available at <a href="https://github.com/Duke-QCD/hic-eventgen">https://github.com/Duke-QCD/hic-eventgen</a>. The analysis code which postprocessed the raw data and performed the parameter estimation is available at <a href="https://github.com/jbernhard/hic-param-est">https://github.com/jbernhard/hic-param-est</a>.</p> <p>For more information, see the author's dissertation, <a href="https://arxiv.org/abs/1804.06469">Bayesian parameter estimation for relativistic heavy-ion collisions (arXiv:1804.06469 [nucl-th])</a>. In particular, chapter 3 describes the computational collision model, chapter 4 describes the parameter estimation method, and section 5.3 presents the results based on this dataset.</p> <p><strong>Usage</strong></p> <p>hic-param-est-cache.tar.gz is designed to be used with the analysis code (<a href="https://github.com/jbernhard/hic-param-est">https://github.com/jbernhard/hic-param-est</a>).<br> See the documentation at <a href="http://qcd.phy.duke.edu/hic-param-est">http://qcd.phy.duke.edu/hic-param-est</a>. After downloading the code, extract the data archive in the hic-param-est folder. This will create the cache directory containing the parameter design files, model calculations, and experimental data. Note that this data archive includes Python pickle files created by <a href="https://joblib.readthedocs.io/en/latest/persistence.html">joblib</a>, which is included with <a href="https://scikit-learn.org">scikit-learn</a>, a dependency of the analysis code.</p> <p>alternate-format.hdf contains the same data in HDF5 format. This is not intended for use directly with the analysis code, but may be more convenient for other purposes.</p> <p>chain.hdf contains the posterior distribution sample (the "chain") generated by MCMC sampling in HDF5 format. It may be used with the analysis code by placing it in a subfolder "mcmc" in the hic-param-est folder. It can also be regenerated by the analysis code, although this can take a long time.</p>
The Diagnostic Value of Shear Wave Elastography and Viscosity Imaging Techniques in Diabetic Peripheral Neuropathy
ClinicalTrials.gov study NCT06773793. IPD Sharing: NO. Countries: 1. Publications: 0.
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