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101 results for “Rheology”
Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I
<p>Model Lagrangian trajectories and deformation estimates for sea-ice models participating in the Sea Ice Rheology Experiment (SIREx) - Part I. Model Lagrangian trajectories are integrated offline, starting on January 1st with all available raw RGPS cells positions (interpolated to January 1st 00:00:00 UTC). The trajectories are advected at an hourly time step with the models daily velocity output until March 31st. The trajectories are then sampled at a 3-day interval to match the RGPS composite time stamps, and the velocity derivatives (deformation) are calculated using the line integral approximations on the cells' contour. All model trajectories and Lagrangian deformation data therefore have nominal temporal and spatial scales of 3-days and 10-km (same as the RGPS composite), regardless of the original resolution of the model output. The model Lagrangian deformation estimates form the basis quantity for the statistical and spatio-temporal scaling analysis presented in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the model trajectory integration and deformation calculation.</p> <p>There is one netCDF file per model, per year (1997 and/or 2008). Data are organized in matrices where the (i,j) indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files, their structure, and how to cite. </p> <p> </p> <p><strong>1. File naming convention</strong></p> <p>"< Model simulation label >" + _ + "deformation" + _ + "< year >" </p> <p> </p> <p><strong>2. Variables included</strong></p> <ul> <li><em>(x1,y1), (x1,y2), (x3,y3), (x4,y4)</em>: Position of the cells' corners (Lagrangian trajectories) - (meters);</li> <li><em>A</em>: Cells' area - (meters squared);</li> <li><em>dudx, dudy, dvdx, dvdy</em>: Cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li><em>d_dudx, d_dudy, d_dvdx, d_dvdy</em>: Trajectory error on cells' velocity derivatives - (1/seconds);</li> <li><em>time</em>: Day of year.</li> </ul> <p><strong>*Note:</strong> the model trajectories are terminated if they move within 100 km from land. Before computing deformation statistics to compare with RGPS composite data, one should mask both deformation sets to only keep cells available in both the model and RGPS data sets.</p> <p> </p> <p><strong>3. Variable structure</strong></p> <p>All variables (except <em>time</em>) are matrices with axes (<em>it, i, j </em>), where <em>it</em> is the time stamp/iteration and<em> i,j </em>are the cells identifiers. See below for how the cells are defined: </p> <p> |--------------------------------------------------------------><sub> <strong>j-axis</strong> </sub> <br> | <br> | <strong>(</strong><strong>x1_ij,y1_ij</strong><strong>)</strong> <strong>o</strong> -------------------<strong> o</strong> <strong>(</strong><strong>x2_ij,y2_ij</strong><strong>)</strong> <br> | | | <br> | | <strong>A_ij or dudx_ij</strong> | <strong> </strong><br> | | | <br> | <strong>(</strong><strong>x4_ij,y4_ij</strong><strong>) </strong><strong>o</strong> ------------------- <strong>o</strong> <strong>(</strong><strong>x3_ij,y3_ij</strong><strong>)</strong> <br> | <br> |<br> V<sub><strong>i-axis</strong></sub> </p> <p> </p> <p>Hence, coordinates are repeated between neighbouring cells, for example: (x2_ij,y2_ij) = (x1_ij+1,y1_ij+1) and (x4_ij,y4_ij) = (x1_i+1j,y1_i+1j)</p> <p> </p> <p><strong>4. Recommended citation usage</strong></p> <p>If <em>all</em> simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx paper (Bouchat et al., 2022). If only <em>selected </em>simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers).</p>
Understanding the heterogeneous rheologic structure across the Longmenshan fault from ten-year postseismic GPS observations
<p>The two datasets are the 10-year cumulative displacements following the 2008 Wenchuan earthquake, GPS time-series observations for all sites, GPS time-series simulation for all sites and the secular velocity corresponding to the interseismic tectonic response, respectively.</p>
Investigating the Role of CNP and CNP aggregates in the Rheological Breakdown of Triglyceride Systems - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication "<em>Investigating the Role of CNP and CNP Aggregates in the Rheological Breakdown of Triglyceride Systems" </em></p> <p>The dataset includes:</p> <ul> <li>X-ray scattering 2D profiles </li> <li>Thixotropy results</li> </ul> <p>Relevant abbreviations: </p> <ul> <li>SSS - Tristearin</li> <li>PPP - Tripalmitin</li> <li>FHRO - Fully Hydrogenated Rapeseed Oil</li> <li>PS - Palm Stearin</li> </ul>
Data and codes for 'A Bayesian Approach to Blood Rheological Uncertainties in Aortic Hemodynamics'
<p>This submission is supplementary material in the form of data and codes used in and for the manuscript 'A Bayesian Approach to Blood Rheological Uncertainties in Aortic Hemodynamics' submitted to the International Journal of Numerical Methods in Biomedical Engineering (currently under review).</p>
Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"
<p><em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018.</p> <p>Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...)</p> <p>Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...)</p> <p>In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no</p> <p>This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).</p>
Dataset for publication Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718
<p>Dataset consisting of G-code for 3D mortar specimen's printing path and particle size distribution (Origin file) of materials used in the study Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718. <a href="https://doi.org/10.1016/j.jobe.2021.102718">https://doi.org/10.1016/j.jobe.2021.102718</a></p>
Near-surface rheology and hydrodynamic boundary condition of semi-dilute polymer solutions
<p>Data appearing in the figures of the article DOI:10.1039/D0SM02116D.</p>
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
<p>Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction.</p> <p>The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes.</p>
Linear rheology of liquid foam - data from "Delayed elastic contributions to the viscoelastic response of foams"
<p>Linear rheological data of Gillette shaving cream "normal skin" made by Gillette UK LTD, acquired at the Department of Physics, University of Fribourg, Switzerland, between 2019 and 2022. Dataset contains elastic modulus, creep-recovery, stress relaxation, and frequency sweep experimental data, together with model data, as described in "Delayed elastic contributions to the viscoelastic response of foams", The Journal of Chemical Physics, 2022, published in the special issue <em>Slow Dynamics</em> [DOI: 10.1063/5.0085773].</p>
Data accompanying "A new brittle rheology and numerical framework for large-scale sea-ice models"
<p>Data accompanying "A new brittle rheology and numerical framework for<br> large-scale sea-ice models" by E. Olason et al, accepted for publication in<br> Journal of Advances in Modelling Earth Systems (2022).</p> <p>Files:<br> * CS2SMOS.tar.bz2: Contains Cryosat2/SMOS data, post-precessed and used to<br> produce figures comparing modelled thickness to observations.<br> * deformation_maps_demo.ipynb: An example jupyter notebook to read pairs.npz<br> * OlasonEtAl_BBM.tar.bz2: Thickness fields from the MEB run used to produce<br> figure 1 (netCDF).<br> * OlasonEtAl_MEB.tar.bz2: Thickness fields from the BBM run used to produce<br> figure 8 (netCDF).<br> * OlasonEtAl_mEVP.tar.bz2: Thickness fields from the mEVP run used to produce<br> figure 8 (netCDF).<br> * pairs.npz: Displacement pairs derived from the model's Lagrangian mesh used<br> to produce figures 3, 4, and 5 (numpy data file).<br> * Winter2006_7_BBM.nc.bz2: Thickness, concentration, and velocity fields from<br> the BBM run for the winter 2006-7 widely used in the paper (netCDF).<br> * Winter2006_7_mEVP.nc.bz2: Thickness, concentration, and velocity fields from<br> the mEVP run for the winter 2006-7 used for comparison in the paper<br> (netCDF).</p> <p> </p>
Additional Sponge Rheology Data for "Rheology of Marine Sponge Tissue Reveals Anisotropic Mechanics and Tuned Dynamics"
<p>These plots further support the general conclusions of the manuscript "Rheology of Marine Sponge Tissue Reveals Anisotropic Mechanics and Tuned Dynamics". These data were collected on a Kinexus rheometer and the sponges were from a distinct shipment of sponges than those presented in the main manuscript. </p>
Marine demosponge rheology / dissection microscopy
<p>Sponges are animals that inhabit many aquatic environments while filtering small particles and ejecting metabolic wastes. They are composed of cells in a bulk extracellular matrix, often with an embedded scaffolding of stiff, siliceous spicules. We hypothesize that the mechanical response of this heterogeneous tissue to hydrodynamic flow influences cell proliferation in a manner that generates the body of a sponge. Toward a more complete picture of the emergence of sponge morphology, we dissected a set of species and subjected disks of living tissue to physiological shear and uniaxial deformations on a rheometer. Various species exhibited rheological properties such as anisotropic elasticity, shear softening and compression stiffening, negative normal stress, and non-monotonic dissipation as a function of both shear strain and frequency. Erect sponges possessed aligned, spicule-reinforced fibers which endowed three times greater stiffness axially compared with orthogonally. By contrast, tissue taken from shorter sponges was more isotropic but time-dependent, suggesting higher flow sensitivity in these compared with erect forms. We explore ecological and physiological implications of our results and speculate about flow-induced mechanical signaling in sponge cells.</p>
The rheological structure of East Asian continental lithosphere
<p>The data here includes the rheological results for the paper<br> entitled "The rheological structure of East Asian continental lithosphere ", which has been submitted to Tectonophysics.</p>
Agronomic, rheological and nutritional phenotypic data of 50 spelt varieties grown at 3 locations in Switzerland during 2 growing seasons (2021-2022)
<p>This dataset contains agronomic, rheological, and nutritional parameters of 50 winter spelt varieties tested during 2 growing seasons (2021-2022) at 3 locations in Switzerland. The dataset has been used to investigate the links between genotype and phenotype of spelt varieties, published in https://doi.org/10.1007/s10681-024-03400-8.</p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP, and under organic conditions, performed by GZPK. </p> <h3>Methods </h3> <p><em>Field trials </em></p> <div>Field trials were set up over the course of two growing seasons – 2020/2021, 2021/2022 – in three sites across the Swiss Central Plateau. The experimental sites were located in Changins (46°19′ N 6°14′ E, 455m a.s.l), Delley (46°55′ N 6°58′ E, 494m a.s.l) and Feldbach (47°14'24.00" N, 8°47'9.60" E, 410m a.s.l.).</div> <div>Each variety was grown in a plot of 7.1 m<sup>2</sup> (1.5 m*4.7 m) in Changins and Delley, and 4.5 m<sup>2</sup> (1.5 m*3 m) in Feldbach. We replicated the experiment three times per location. At each site, we used a complete randomized block design, with plots being randomized within each block. Density of sowing was 180 spikelets/m<sup>2</sup>. Plots were sowed mechanically each autumn. In Changins and Delley, the plots were mechanically fertilized with 100 kg N/ha (ammonium nitrate), applied in two splits (60 at heading stage—40 at flowering stage). In Feldbach, the fields were treated organically, and therefore no synthetic fertilizer was applied.</div> <div> </div> <div> </div> <div><em>Agronomic and morphological characteristics </em></div> <div> <p>For each plot, we recorded the heading date as the day of the year, in which 50% of the ears of the plot had fully emerged from the flag leaf. Once the plants and ears were fully developed, plant height was measured in each plot, by taking the average height in centimeters from the ground to the top of five random ears, excluding awns.</p> <p>At maturity, we harvested each plot with a combine harvester (Zürn 150, Schontal-Westernhausen, Switzerland). The harvested grains were weighed first, dehusked, sorted and cleaned with a sieve cleaner, and then weighted again. We measured specific weight and water content using a Dickey–John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content (%) was measured at the plot level with a near-infrared instrument (ProxiMate™, Büchi instruments). Thousand kernel weight (TKW, g), as well as kernel length and width (mm), were measured at the plot level with a Marvin seed analyzer (GTA Sensorik, Neubrandenburg, Germany).</p> <p>Additional measurements in Changins: we computed harvest index for each plot by cutting 30 individual culms just before harvest. Plants were cut just above the ground, oven-dried for 3 days at 80 °C and then weighed. We then threshed, dehusked, sieved and weighed the obtained grains. The harvest index was computed by taking the ratio of grain mass over total mass.</p> <p> </p> <p><em>Rheological characteristics </em></p> </div> <div> <p>At all sites, Zeleny sedimentation value (mL) was assessed based on the International Association for Cereal Science and Technology standard method 116/1.The analyses were performed by the analytical laboratory of DSP, Delley, at the variety level for each site—i.e., grains from the three replicates per site were pooled together and subsequently milled.</p> <p>Additional measurements in Changins were done for each variety, based on a pooled sample of the three replicates. Extensograph properties of the obtained dough were assessed according to ICC standard method 114/1; area under curve (energy, cm2), resistance to extension at 5 cm extension (EE), and extensibility of the dough (mm) were measured. The analyses were performed by the accredited laboratory “Versuchsanstalt für Getreideverarbeitung” based in Austria (<a href="https://www.vfg.or.at/">https://www.vfg.or.at/</a>).</p> <p> </p> <p><em>Nutritional characteristics </em></p> </div> <div> </div> <div>We assessed the structure of starch (amylose content) and the fatty acid composition for each variety in Changins. These analyses were done by pooling grains from the three replicates in Changins and milling them. The amylose and amylopectin contents of starch were determined enzymatically via an assay based on the precipitation of amylopectin complexes with the lectin concanavalin A, according to K-Amy 06/18. The fatty acid composition was analyzed by GC-FAME, via in situ transesterification, according to the method of Ampuero Kragten et al. (<a title="Kragten SA, Collomb M, Dubois S, Stoll P (2014) Determination of fatty acid composition in feed: analytical methods. Agrarforschung Schweiz 5(9):330&ndash;337" href="https://link.springer.com/article/10.1007/s10681-024-03400-8#ref-CR36">2014</a>). These analyses were performed at the accredited analytical laboratory of Agroscope, Posieux.</div> <div> </div> <div>Kragten SA, Collomb M, Dubois S, Stoll P (2014) Determination of fatty acid composition in feed: analytical methods. Agrarforschung Schweiz 5(9):330–337</div> <div> </div> <div> </div> <div><em>DNA extraction & Genotyping </em></div> <div> </div> <div>DNA was extracted from all cultivars, and sent to TraitGenetics (SGS institute Frenius, Gatersleben DE) for SNP genotyping on the 25 K XT Infinium array for wheat.</div> <div> </div> <div> </div>
Dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7
<p>Open dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7. https://doi.org/10.1186/s40069-022-00568-y</p> <p>File 1 - X-ray diffractogram and particle size distribution (laser granulometry) data - *.opju (Origin)<br> File 2 - Rheological test results - *.opju (Origin)<br> File 5 - Mechanical peformance (early strength - ultrasounds and compressive strength) and density test results - *.opju (Origin)<br> File 4 - Mercury intrusion porosimetry test data - *.opju (Origin)</p>
Pipe rheology of wet aqueous application foams
<p>Foam application of chemicals to the wet web is currently being developed for the paper and board industry. An important part of this work is to understand the rheology of the used application foams. Polyvinyl alcohol (PVOH) is widely used as a strength additive in paper and board, and it was the main surfactant in this study. The PVOH foam density varied between 100 kg/m<sup>3</sup> and 300 kg/m<sup>3</sup> and the dosage of PVOH varied between 0.5% to 6%. The foam viscosity and slip flow were determined with a pipe rheometer using three pipe diameters. The slip velocity was quantified by recording the foam motion in the vicinity of the wall of an acrylic pipe with a high-speed video camera. A measurement setup was also built for measuring the slip flow indirectly in opaque pipes. General formulas for the foam viscosity and slip flow, based on several physical quantities describing both the foam and the base liquid, were obtained using dimensional analysis. Specifically, dimensionless shear stress and dimensionless wall shear stress were found to be proportional to certain powers of the capillary number and slip capillary number, respectively. The contribution of the slip flow to the total flow rate was significant, especially with lower flow rates when most of the volumetric flow was due to the slip. In the literature, many papers have suggested that there is no slip flow in steel pipes. Our results suggest that this is due to the high pipe roughness used in those works. In our measurements, the slip velocity of a smooth-walled steel pipe was equal to the slip in an acrylic pipe. The obtained viscosity and slip models form a solid basis for developing and running various industrial processes including foam application processes. For new foam recipes, quite a small number of rheological measurements are needed to determine the model parameters.</p>
Data and Code for: Phospholipase-catalyzed Degradation Drives Domain Morphology and Rheology Transitions in Model Lung Surfactant Monolayers
Open the record for dataset details and reuse information.
Marine demosponge rheology / dissection microscopy
Open the record for dataset details and reuse information.
Data repository to "The thermal and rheological state of the Northern Argentinian foreland basins"
<p>This is the data repository to the doctoral thesis "The thermal and rheological state of the northern Argentinian foreland basins" by Christian Meeßen. It contains data to the following chapters</p> <ul> <li>Chapter 2.1: "Crustal structure of the Andean foreland in northern Argentina: Results from data-integrative three-dimensional density modelling"</li> <li>Chapter 3.1: "How do first-order controlling factors of subduction zones affect the thermal field of retroarc foreland basins?"</li> <li>Chapter 3.2: "Differences between transient and steady-state thermal fields in the central Andean foreland"</li> <li>Chapter 4: "The present-day thermal and rheological state of the Chaco-Paraná basin"</li> </ul>
Data for the article "New insights into the rheology of cohesive granular media".
<p>The compressed directory contains the data in .csv format for the Pyhton scripts of the figures. </p>
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