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149 results for “Viscosity”
2D visco-elasto-plastic subduction models in the presence of a high density and viscosity continental block
<p>Three main models with different rheological conditions and density are incorporated for an imposed intrusion between the upper and lower continental crust. Output files for a model with a crust without intrusions are also incorporated.</p> <p>The file contains data on the conditions of plate motion velocity, plate age, plate thickness in Excel (Input_Data_Age_Vel.xlsx), a table with the rheology used to reproduce different numerical models incorporating the intrusion (Table_S3.pdf), and a file High_density_materials.m showing the construction of the intrusion within the continental crust.</p> <p>High_density_materials.m m should be imposed on the original I2ELVIS code provided by Taras Gerya - ETH Zürich, Institut für Geophysik, Switzerland - email: taras.gerya@erdw.ethz.ch</p> <p>The file has the main time steps as a function of temperature, viscosity, density and rock composition into the folders. If you want to visualize them please run the following Script PLOT_OUTPUT_DATA.m where,</p> <p>str=string(2040), represent the time_step</p> <p>Files are organized in a matrix form, each folder has the</p> <p>grid_x :coordinate matrix, <br>gridy_: coordinate matrix, </p> <p>matrix_temperature_, density and viscosity.</p> <p>To plot the Rock composition.</p> <p>mx and my, tracer coordinates in x-direction and y-direction<br>matrix_markcom is matrix tracers.</p> <p> </p> <p>Model 1</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 310= 12 Ma, 670 = 9 Ma, 1120 = 6 Ma, 1620 = 3 Ma, 2040 = 0 Ma)</p> <p> </p> <p>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 1. <br>This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. <br>The high density material between the upper and lower crust in the vicinity of the slab is not included in Model 1. <br>The model evolves over a period of the last 15 Myr and shows a shallow subduction. </p> <p><br>Model 2</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 380= 12 Ma, 850 = 9 Ma, 1490 = 6 Ma, 2090 = 3 Ma, 2540 = 0 Ma)</p> <p>With high density block<br>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 2. This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. The high density material between the upper and lower crust in the vicinity of the slab is included in Model 2. The model evolves over a period of the last 15 Myr and shows a steep subduction.</p> <p><br>Model 3</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 310= 12 Ma, 670 = 9 Ma, 1120 = 6 Ma, 1690 = 3 Ma, 2040 = 0 Ma)</p> <p>Without high density block and high viscosity</p> <p>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 3. This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. The initial high viscosity material and without high density between the upper and lower crust in the vicinity of the slab is included in Model 3. The model evolves over a period of the last 15 Myr and shows subduction with a high dip angle. </p> <p><br>Model4</p> <p>(Time step= 10= 14.99 Ma, 120= 14 Ma, 330= 12 Ma, 770 = 9 Ma, 1190 = 6 Ma, 1640 = 3 Ma, 2040 = 0 Ma)<br> <br>With high density block and high viscosity<br>The temperature, viscosity, density, and rock composition for the PHS slab are shown in this animation for Model 4. This model is reproduced under the conditions of plate motion velocity and age from Figure 3 with an initial dip angle for the weak zone of 20°. The high density and high viscosity material between the upper and lower crust in the vicinity of the slab is included in Model 4. The model evolves over a period of the last 15 Myr and shows a steep subduction</p>
Supporting dataset for manuscript "Direct viscosity measurement of peridotite melt under lower-mantle conditions supports a fractional magma ocean solidification at top lower mantle conditions"
<p>Supporting material for manuscript "<strong>Direct viscosity measurement of peridotite melt under lower-mantle conditions supports a fractional magma ocean solidification at top lower mantle conditions"</strong></p>
Bumblebees' food preferences are jointly shaped by rapid valuation of nectar sugar concentration and viscosity
<p>Animals are often assumed to follow a strategy of energy maximisation, and therefore should evaluate feeding options based on energy intake rates. Contrastingly, rhesus macaque's learned food preferences are based on sensory properties, e.g. sweetness and resistance, regardless of energy differences. Here, we show that nectar sugar concentration (sweetness) and nectar viscosity (resistance) drive preferences of bumblebees, classical models for economic and foraging decision-making. Using a tasteless/odourless biopolymer (tylose), we created feeding options that differed in sweetness and resistance, properties that affect energy intake rate and can be immediately sensed. When energy intake rates were similar, bumblebees developed preferences based on sweetness and resistance. When energy intake rates were different but sweetness and resistance were balanced against each other, bees developed no preferences. Decision dynamics during training indicated that bumblebees simultaneously evaluated sweetness and resistance to make decisions quickly (in seconds). These results indicate that bumblebees' food preferences are jointly affected by the immediate sensation of nectar sweetness and resistance as positively and negatively reinforcing properties, respectively, irrespective of energy intake rate. From these findings we propose that subjective valuations of sensory food properties should be considered as constraints in models of foraging behaviour.</p>
EMD data for the paper "Impact of ad-hoc post-processing parameters on the lubricant viscosity calculated with equilibrium molecular dynamics simulations"
<p>This archive contains the post-processing data obtained from EMD simulations of <strong>2,2,4-Trimethylhexane</strong> lubricant molecule under various operating conditions. The EMD simulations were performed using LAMMPS with COMPASS force field. (See manuscript and README for details.)</p>
Effects of fluid saturation and viscosity on seismic dispersion characteristics in Berea sandstone
<p>These are the supplementary data files for a paper submitted to the Journal of Geophysical Research (JGR), "Effects of fluid saturation and viscosity on seismic dispersion characteristics in Berea sandstone". The uploaded excel files are the actual datasets of forced-oscillation and ultrasonic measurements. Detailed information is described in the word file. In addition, three Matlab code files used for data analysis are uploaded. </p>
A Benchmark Analytical Solution for Variable Viscosity Flow in Fractured Media: Development and Comparative Analysis with Numerical Simulations
<p>This data is associated with <strong>(a) Figure 3.</strong> Comparison between the velocities obtained through analytical calculations and those estimated numerically, and <strong>(b) Figure 4.</strong> Comparison of the BTCs obtained through analytical calculations and those estimated numerically.</p>
Dataset used in the paper "Verifying the parameterization of vertical eddy viscosity and diffusivity in the bottom boundary layer"
<p>This dataset is created from the data obtained over the continental shelf of the East China Sea in July 2020 during a cruise of the training ship Nagasaki-maru of Nagasaki University (NN055), and used in the manuscript entitled "Verifying the parameterization of vertical eddy viscosity and diffusivity in the bottom boundary layer" by Takahiro Endoh, Takuya Hirooka, and Yoshinobu Wakata, which will be submitted to Journal of Physical Oceanography.</p>
Tables of results for lower mantle viscosity estimates
<p>Data on diffusivity, grain size, and viscosity calculations are presented in Figs. 7-9 and Figs. S4-S5 of Okamoto and Hiraga's "A Common Diffusional Mechanism for Creep and Grain Growth in Polycrystalline Rocks: Application to Lower Mantle Viscosity Estimates."</p>
The evolvement of ULVZ at LLSVP margins--selected models with various buoyancy number and compositional viscosity
<p>This tar.gz file is created and uploaded for the submission of the manuscript titled '<em>The evolvement of dense ULVZs at LLSVP margins and implications</em>' to the journal <em>Geophysical Research Letters</em>.</p> <p>This tar.gz file is for selected models with various buoyancy number (B) and compositional viscosity (etaC), which are described and compared with the reference model (in doi 10.5281/zenodo.8394637) in the manuscript. The collection includes the raw output data and post-processed data illustrated in Figures 3. </p> <p>1. the /scratch directory</p> <p>This directory contains the raw output data of three models listed below. All models are calculated by CitcomS (modified from the CitcomS-3.3.1 version) and the data follows the CitcomS output convention. Please read the description of a previous upload (doi 10.5281/zenodo.8394637, titled 'The evolvement of ULVZ at LLSVP margins--reference model with B=3.0 and etaC=1.0') for more information.</p> <p>The ULVZ-v1B6_h10l10/ directory: the model with B=6.0 and etaC=1.0, illustrated in the top panel of Figure 3 </p> <p>The ULVZ-v1B1-5_h10l10/ directory: the model with B=1.5 and etaC=1.0, illustrated in the middle panel of Figure 3 </p> <p>The ULVZ-v01B3_h10l10/ directory: the model with B=3.0 and etaC=0.1, illustrated in the bottom panel of Figure 3 </p> <p>2. the data_plots/ directory: data extracted from the raw outputs and plotted in Figure 3.</p> <p>The time frame of 14800 is for the left column in Figure 3, and the other time frames are for the right column in Figure 3. </p> <p>temperature: *temp* files</p> <p>composition: *comp* files</p> <p>velocity: *velo_Sv* files</p> <p>density anomaly: *density* files</p>
Comparative Efficacy of Ticagrelor Versus Aspirin on Blood Viscosity in Peripheral Artery Disease Patients With Type 2 Diabetes
ClinicalTrials.gov study NCT02325466. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Loss of intracellular ATP affects axoplasmic viscosity and pathological protein aggregation in mammalian neurons
Open the record for dataset details and reuse information.
Bumble bees’ food preferences are jointly shaped by rapid evaluation of nectar sugar concentration and viscosity
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Frequency-dependent viscosity of salmon ovarian fluid has biophysical implications for sperm-egg interactions
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Dataset for "Predicting the influence of particle size on the glass transition temperature and viscosity of secondary organic material"
<p>The archive file predictingInfluenceParticleSize2020.zip contains scripts and data to generate the figures in the manuscript. Datafiles are in the src/Data folder. Scripts are written in the Julia language. </p> <p>The file predicting_influence_particle2020.tar.gz contains a Docker container. The docker container is a virtual machine that contains all software and dependencies needed to execute the code. The freely available Docker engine must be installed on the local computer (https://docs.docker.com/install/). To install the container run</p> <p>docker load < predicting_influence_particle2020.tar.gz</p> <p>It can be started through the command:</p> <p>docker run -it -p 8888:8888 mdpetters/predicting_influence_particle2020:final</p> <p>Please refer to the supplement of the paper for further instructions.</p>
9-10-1288 Does the viscosity of the sub-phase effect the oxidation kinetics of surface active films on "glassy" cloud droplets
<p>Binned Neutron reflectivity data for the titled experiment.</p>
Data for "Bayesian inference of mantle viscosity from whole-mantle density models"
<p><strong>Supplementary Data</strong><br> Rudolph, M.L., Moulik, P., and Lekic, V. (2020). Bayesian inference of mantle viscosity from whole-mantle density models. Geochemistry, Geophysics, Geosystems</p> <p>This data archive contains files needed to reproduce the figures from our 2020 G-Cubed paper, including the full ensemble solutions for mantle viscosity structure.</p>
Data from: Promiscuity resolves constraints on social mate choice imposed by population viscosity
Population viscosity can have major consequences for adaptive evolution, in particular for phenotypes involved in social interactions. For example, population viscosity increases the probability of mating with close kin, resulting in selection for mechanisms that circumvent the potential negative consequences of inbreeding. Female promiscuity is often suggested to be one such mechanism. However, whether avoidance of genetically similar partners is a major selective force shaping patterns of promiscuity remains poorly supported by empirical data. Here, we show (i) that fine-scale genetic structure constrains social mate choice in a pair-bonding lizard, resulting in individuals pairing with genetically similar individuals, (ii) that these constraints are circumvented by multiple mating with less related individuals and (iii) that this results in increased heterozygosity of offspring. Despite this, we did not detect any significant effects of heterozygosity on offspring or adult fitness or a strong relationship between pair relatedness and female multiple mating. We discuss these results within the context of incorporating the genetic context dependence of mating strategies into a holistic understanding of mating system evolution.
Physicochemical properties (viscosity, electrical conductivity) of betaine based deep eutectic solvents
<p>New deep eutectic solvents (DES) based on betaine as a hydrogen bond acceptor and urea, lactic acid, glycerol, 1,2-propanediol, and xylitol as hydrogen bond donors have been prepared. Viscosity and electrical conductivity were investigated. </p>
Quantification and qualification of in-flight fragmentation of bombs from low-viscosity magma
<p>The dataset contains both automatic and manual image analyses of four videos: from (i) fountaining and (ii) spattering activities at the 2021 Tajogaite eruption of Cumbre Vieja volcano, La Palma, Canary Islands, Spain; from the 2021 Mount Etna eruption (iii) fountaining activity, Sicily, Italy); and from Strombolian activity (iv) in 2023 at Stromboli, Aeolian Islands, Italy.</p> <p>This dataset consists of one MATLAB file and 15 CSV files. Fourteen CSV files are named according to the case studies (e.g., T. fountaining, T. spattering, E. fountaining, S. Strombolian), the acquisition mode (Manual or Automatic), the data type (e.g., Collision, Fragmenting, Non-Fragmenting, Total), and specific case details. Notably, the "T.spattering_Manual_Collision" files are split into two categories (e.g., coarser, finer) based on bomb size classification of the colliding-pairs. Additionally, the "E. fountaining_Manual_Collision / Fragmentation" dataset is divided into two regions of interest (ROI1 and ROI2) to reflect separate analyzed areas described in the manuscript. The 15th CSV file, “FragmentingModes_All.csv”, aggregates bomb counts by fragmenting mode across all case studies and corresponds to Figure 3 in the manuscript.</p> <p>Automated analyses were performed on five different frames: four used for measuring bomb velocity and size (saved in the MATLAB file “Fourframes.mat”), and a fifth frame used for measuring bombs velocity, size, and circularity (saved in the CSV files). The MATLAB file contains four matrices, named according to the case studies (e.g., T. fountaining, T. spattering, E. fountaining, S. Strombolian). Each matrix is composed of two columns: the first for velocity and the second for bomb size. The fifth frame’s data is saved in four separate CSV files for each case study, named as “namecasestudies_Auto_Total.csv”, where “namecasestudies” changes to reflect the specific case studies analyzed. Each CSV includes three columns: velocity, bomb size, and bomb circularity respectively.</p> <p>Manual data collected in .csv files include the velocity, size and circularity and additional parameters such as: area, position, major axis and minor axis. In the “Fragmenting” files additional fields detail the fragmenting mode, the number of pyroclasts generated, and the fragmentation direction (upward, transitional, or downward). In the “Collision” files an additional field includes the number of pyroclasts produced.</p> <p>The dataset is graphically represented in the manuscript, particularly in Figures 4,5, 6, and 7. </p>
Acoustic propulsion of nano- and microcones: dependence on the viscosity of the surrounding fluid
<p>Supplementary data for the following manuscript: Johannes Voß, Raphael Wittkowski, "Acoustic propulsion of nano- and microcones: dependence on the viscosity of the surrounding fluid".</p>
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