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90 results for “fluid flow”
Videos of fluid flow in contact interfaces
<p>These videos demonstrate the capabilities of the computational framework presented in [1] to solve complex coupled problem of viscous thin fluid flow in contact interfaces while handling the possibility of the fluid to be trapped in pockets surrounded by contact zones.</p> <p>[1] Andrei G. Shvarts, Julien Vignollet, Vladislav A. Yastrebov "Computational framework for monolithic coupling for thin fluid flow in contact interfaces" https://arxiv.org/abs/1912.11292v3</p>
Data from: Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space
<p>This data accompanies the paper entitled <strong>Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space.</strong></p> <p> </p> <p>The zip archive contains the results of Lattice Boltzmann Molecular Dynamics simulations of the systems investigated and presented in the manuscript. Computational Fluid Dynamics data are in VTK format. Molecular Dynamics trajectories are in XYZ format.</p>
HyUSPRe Report & Data on 'New experimental data on reactions between H2 and well cement and effects on fluid flow and mechanical properties of well cement
<p>In this study, new experimental data is presented of the effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties of oil well (class G) cement, relevant for underground hydrogen storage operations. Changes in mechanical properties (Young’s modulus, Poisson’s ratio and ultimate strength) have been analyzed using unconfined compressive strength (UCS) tests and confined cyclic loading tests on class G cement samples that were unreacted (cured for 3 days at 80°C) and exposed to lime-saturated brine and N<sub>2</sub> or H<sub>2</sub> for 1 and 2 months. Changes in cement mineralogy were analyzed by XRD analysis of the unreacted and exposed samples. The mechanical properties of elastic modulus and Poisson’s ratio are within the expected range of an oil well cement. Differences in Young’s modulus, Poisson’s ratio and ultimate strength are limited between unreacted, N<sub>2</sub>-exposed and H<sub>2</sub>-exposed samples, when comparing UCS tests or confined cyclic loading tests. Repeated UCS tests seem to indicate that the variation in Young’s modulus and ultimate strength increases after N<sub>2</sub> and H<sub>2</sub> exposure, but this observation needs to be confirmed in additional tests. During cyclic axial loading of confined cement samples, irreversible (plastic) deformation (compaction) occurs that affect static Young’s modulus. Also, effects of exceeding yield and failure strength on Young’s modulus are observed. Dynamic Young’s moduli and Poisson’s ratios derived from acoustic velocity measurements during confined cyclic tests show limited variation, in particular if static and dynamic Young’s modulus are compared. The mineralogical changes as identified using XRD analysis suggest minor changes between unexposed and H<sub>2</sub>- and N<sub>2</sub>-exposed samples, although XRD patterns indicate some minerals that could not be identified. The main conclusion is that effects of H<sub>2</sub> exposure and cyclic loading on mechanical properties and mineralogical changes of class G cement is limited compared to unreacted or N<sub>2</sub> exposed samples for the investigated conditions. There is no indication that changes in mechanical properties of cement are such that cement integrity of wells used for underground hydrogen storage will be significantly affected. It should be emphasized that this conclusion is based on experiments on one type of cement (class G) and a limited set of conditions. In particular, additional tests to assess the reproducibility of current results and tests on samples that were exposed longer to H<sub>2</sub> and N<sub>2</sub> are of interest. Detailed effects of changing properties for the durability and integrity of wells can be derived by performing a parameter sensitivity analysis with well integrity modelling for the range in mechanical properties measured in this study.</p>
3D Flow Field of a subaqueous cylindrical pendulum from large eddy simulation with fluid structure interaction
<p>Relevant data of fluid structure interaction large eddy simulation of a subaqueous cylindrical pendulum to reproduce the major findings of the article "Fluid structure interaction of a subaqueous pendulum: Analyzing the effect of wake correction via large eddy simulations" published in Physics of Fluids. The article has been published as open access: https://doi.org/10.1063/5.0086557</p>
The multilayer volume-of-fluid method for multiphase flows across scales: breaking waves, microfluidics, and membrane-less electrolyzers
<p>Supplementary movies to PhD thesis <a href="https://doi.org/10.3929/ethz-b-000547518">10.3929/ethz-b-000547518</a></p>
FIGURE 4 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 4. Visualization of the flow within the 3D printed model (Re = 0.376, see Table 1). Flow in the folds consists of horizontal bands of distinct red and blue color, indicating no adoral component to flow and no mixing within the folds, consistent with Hypothesis 2 (see text, Figure 2.2). The still used in the print version of this paper is a single frame from the flow pattern observed, showing the steady-state flow pattern after nine minutes of flow. The animation is sped up 16x (for video see palaeo-electronica.org/content/2015/1073-blastoid-hydrospire-fluid-flow).
FIGURE 2. Schematic showing hypothesized flow patterns within the hydrospire folds. 2.1 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 2. Schematic showing hypothesized flow patterns within the hydrospire folds. 2.1, In Hypothesis 1, the flow has an adoral component representing respiratory leakage. 2.2, In Hypothesis 2, the flow is entirely radial, without leakage. See text for further discussion.
FIGURE 3 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 3. Digital and physical models use to visualize fluid flow. 3.1, Digital solid model of approximately the lower quarter of a hydrospire of Pentremites rusticus, using Blender (see text). 3.2, 3D-printed rendering of the digital model, shown with inlet headers connected.
FIGURE 1 in Visualizing the fluid flow through the complex skeletonized respiratory structures of a blastoid echinoderm
FIGURE 1. Anatomy of the hydrospires of the blastoid Pentremites rusticus. 1.1, Location of one of the five radially distributed hydrospires within the calyx, showing incurrent hydrospire pores, and excurrent spiracle (inferred direction of water flow indicated by the arrows). 1.2, Oblique view of a section of a hydrospire and associated structures. Modified from Schmidtling and Marshall (2010).
Fully-coupled pressure-based finite-volume framework for the simulation of fluid flows at all speeds in complex geometries (Supporting Data)
<p>This data accompanies the paper "Fully-coupled pressure-based finite-volume framework for the simulation of fluid flows at all speeds in complex geometries", published in Journal of Computational Physics (2017), http://dx.doi.org/10.1016/j.jcp.2017.06.009.</p>
Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"
<p>Dataset of paper "Predicting the size of silver nanoparticles synthesised in flow reactors: Coupling population balance models with fluid dynamic simulations"</p>
Experimental Study on the Krauklis Wave Under Fluid Flow
<p>This data set contains the code and data for the "Experimental Study on the Krauklis Wave Under Fluid Flow" and is reffered to the paper submitted in the Journal of Geophysical Research: Solid Earth named "Possibility of Fluid Flow Characterization via a Geophysical Signal: Experimental Study on the Krauklis Wave Under Fluid Flow".</p>
Seagrass deformation affects fluid instability and tracer exchange in canopy flow
<p>Data and code used for the preparation of the manuscript "Seagrass deformation affects fluid instability and tracer exchange in canopy flow" (Vieira, Allshouse & Mahadevan 2022).</p> <p><em>Data and Code Repository Organization</em></p> <ul> <li><strong>data/ </strong>: contains the data presented in the manuscript (in .cdf and .mat format);</li> <li><strong>code/ </strong>: contains the code used for the numerical simulations (PSOM) and in processing the data and generating figures (MATLAB)</li> </ul> <p><em>Manuscript Abstract:</em></p> <p>Monami is the synchronous waving of a submerged seagrass bed in response to unidirectional fluid flow. Here we develop a multiphase model for the dynamical instabilities and flow-driven collective motions of buoyant, deformable seagrass. We show that the impedance to flow due to the seagrass results in an unstable velocity shear layer at the canopy interface, leading to a periodic array of vortices that propagate downstream. Each passing vortex locally weakens the along-stream velocity at the canopy top, reducing the drag and allowing the deformed grass to straighten up just beneath it. This causes the grass to oscillate periodically. Crucially, the maximal grass deflection is out of phase with the vortices. A phase diagram for the onset of instability shows its dependence on the fluid Reynolds number and an effective buoyancy parameter. Less buoyant grass is more easily deformed by the flow and forms a weaker shear layer, with smaller vortices and less material exchange across the canopy top. While higher Reynolds number leads to stronger vortices and larger waving amplitudes of the seagrass, waving is maximized at intermediate grass buoyancy. All together, our theory and computations correct some misconceptions in interpretation of the mechanism and provide a robust explanation consistent with a number of experimental observations.</p>
Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 21 dataset
<p><strong>The data includes the trajectories and the swimming velocities of individual microswimmers embedded in a turbulent fluid flow of Taylor-scale Reynolds number <span class="math-tex">\(Re_{\lambda} = 21\)</span> .</strong></p> <p>The net swimming velocity of a microswimmer is the sum of the intrinsic swimming velocity and the fluid velocity. The intrinsic swimming velocity is dictated by the swimming parameters, that is, the swimming speed <span class="math-tex">\(v_s\)</span> and the reorientation time <span class="math-tex">\(B\)</span>.</p> <p>Different values of swimming parameters are explored.</p> <table> <tbody> <tr> <td><strong>Item</strong></td> <td><strong><span class="math-tex">\(v_s\)</span></strong></td> <td><strong><span class="math-tex">\(B\)</span></strong></td> </tr> <tr> <td>R1</td> <td>2.20</td> <td>10</td> </tr> <tr> <td>R2</td> <td>0</td> <td>0</td> </tr> <tr> <td>R3</td> <td>2.20</td> <td>30</td> </tr> <tr> <td>R4</td> <td>2.20</td> <td>50</td> </tr> <tr> <td>R5</td> <td>1.10</td> <td>10</td> </tr> <tr> <td>R6</td> <td>0.22</td> <td>10</td> </tr> <tr> <td>R7</td> <td>4.39</td> <td>10</td> </tr> <tr> <td>R8</td> <td>1.76</td> <td>10</td> </tr> <tr> <td>R9</td> <td>2.85</td> <td>10</td> </tr> <tr> <td>R10</td> <td>6.58</td> <td>10</td> </tr> <tr> <td>R11</td> <td>2.20</td> <td>20</td> </tr> <tr> <td>R12</td> <td>5.49</td> <td>10</td> </tr> <tr> <td>R13</td> <td>3.29</td> <td>10</td> </tr> <tr> <td>R14</td> <td>3.60</td> <td>10</td> </tr> </tbody> </table> <p>Each column in the file corresponds to the position and net velocity of particles, the fluid vorticity at particle location, particle id, and time step.</p> <table> <tbody> <tr> <td>column number</td> <td>item</td> </tr> <tr> <td>0</td> <td>x - position</td> </tr> <tr> <td>1</td> <td>y - position</td> </tr> <tr> <td>2</td> <td>z - position</td> </tr> <tr> <td>3</td> <td>x - velocity</td> </tr> <tr> <td>4</td> <td>y - velocity</td> </tr> <tr> <td>5</td> <td>z - velocity</td> </tr> <tr> <td>15</td> <td>x - vorticity</td> </tr> <tr> <td>16</td> <td>y - vorticity</td> </tr> <tr> <td>17</td> <td>z - vorticity</td> </tr> <tr> <td>19</td> <td>particle id</td> </tr> <tr> <td>20</td> <td>time step</td> </tr> </tbody> </table> <p> </p>
Microswimmers in turbulent fluid flow of Taylor-scale Reynolds number Re = 59 dataset
<p><strong>The data includes the trajectories and the swimming velocities of individual microswimmers embedded in a turbulent fluid flow of Taylor-scale Reynolds number <span class="math-tex">\(Re_{\lambda} = 59\)</span> .</strong></p> <p>The net swimming velocity of a microswimmer is the sum of the intrinsic swimming velocity and the fluid velocity. The intrinsic swimming velocity is dictated by the swimming parameters, that is, the swimming speed <span class="math-tex">\(v_s\)</span> and the reorientation time <span class="math-tex">\(B\)</span>.</p> <p>Different values of swimming parameters are explored.</p> <table> <tbody> <tr> <td>Item</td> <td><span class="math-tex">\(v_s\)</span></td> <td>B</td> </tr> <tr> <td>Tr</td> <td>0</td> <td>0</td> </tr> <tr> <td>T13</td> <td>1</td> <td>10</td> </tr> <tr> <td>T16</td> <td>3</td> <td>10</td> </tr> <tr> <td>T18</td> <td>5</td> <td>10</td> </tr> <tr> <td>T19</td> <td>10</td> <td>0.3</td> </tr> <tr> <td>T25</td> <td>10</td> <td>1</td> </tr> <tr> <td>T27</td> <td>10</td> <td>3</td> </tr> <tr> <td>T29</td> <td>10</td> <td>10</td> </tr> <tr> <td>T30</td> <td>30</td> <td>10</td> </tr> <tr> <td>T31</td> <td>10</td> <td>30</td> </tr> <tr> <td>T33</td> <td>10</td> <td>50</td> </tr> <tr> <td>T38</td> <td>20</td> <td>10</td> </tr> <tr> <td>T43</td> <td>15</td> <td>10</td> </tr> <tr> <td>T50</td> <td>25</td> <td>10</td> </tr> <tr> <td>T56</td> <td>8</td> <td>10</td> </tr> </tbody> </table> <p>Each column in the file corresponds to the position and net velocity of particles, the fluid vorticity at particle location, particle id, and time step.</p> <p> </p> <table> <tbody> <tr> <td>column number</td> <td>item</td> </tr> <tr> <td>0</td> <td>x - position</td> </tr> <tr> <td>1</td> <td>y - position</td> </tr> <tr> <td>2</td> <td>z - position</td> </tr> <tr> <td>3</td> <td>x - velocity</td> </tr> <tr> <td>4</td> <td>y - velocity</td> </tr> <tr> <td>5</td> <td>z - velocity</td> </tr> <tr> <td>15</td> <td>x - vorticity</td> </tr> <tr> <td>16</td> <td>y - vorticity</td> </tr> <tr> <td>17</td> <td>z - vorticity</td> </tr> <tr> <td>19</td> <td>particle id</td> </tr> <tr> <td>20</td> <td>time step</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
InSAR data for "Transcrustal compressible fluid flow explains the Altiplano-Puna deformation anomaly"
<p>InSAR data presented in the paper "Transcrustal compressible fluid flow explains the Altiplano-Puna deformation anomaly". See file "README" for detailed descriptions of each item.</p>
Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models [source code]
<p>J. Heiland & Y. Kim, 'Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models', GAMM 2024</p>
Fluid flow drives phenotypic heterogeneity in bacterial growth and adhesion on surfaces
<p>Raw data for the study reported by Hubert et al., Nature Communications 2024 (accepted).</p> <p>Each of the 4 folders within the archive, denoted 'Ulow', 'Low', 'Med', and 'High' contains 3 sets of raw experimental data, each corresponding to an experiment performed in the 'Ulow', 'Low', 'Med', and 'High' shear stress regime, in accordance with the folders' names.</p> <p>Each of these data sets is bundled into a single .avi video, consisting of raw images recorded at a 1/60 Hz acquisition frequency (i.e., 1 image per minute).</p> <p>The folder 'Supplementary_Software'contains Matlab and Python scripts used to treat the raw data, with a raw data sample. The file 'HOWTO_use_the_scripts.txt' explains how to use the scripts.</p> <p>Except for this HOWTO file, the files inside the various subfolders of folder 'Supplementary_Software' are not listed in the file tree below. Only the subfolders are listed.</p> <p>./<br>├── README_data_sets_and_treatment_scripts.txt (this file)<br>├── Ulow/<br> | ├── Ulow_set1.avi<br> | ├── Ulow_set2.avi<br> | └── Ulow_set3.avi<br>├── Low/<br> | ├── Low_set1.avi<br> | ├── Low_set2.avi<br> | └── Low_set3.avi<br>├── Med/<br> | ├── Med_set1.avi<br> | ├── Med_set2.avi<br> | └── Med_set3.avi<br>└── High/<br> | ├── High_set1.avi<br> | ├── High_set2.avi<br> | └── High_set3.avi<br>└── Supplementary_Software/<br> ├── HOWTO_use_the_scripts.txt<br> ├── step1_matlab/ <br> | ├── Pos0_100_every_1000/ <br> | ├── matlab_image_processing/ <br> | ├── images_treated_20201120_220854/ <br> | └── Outputs/<br> └── step2_python/<br> └── Outputs/</p> <p> </p> <p> </p>
Fluid Lavage of Open Wounds (FLOW): Pilot Trial
ClinicalTrials.gov study NCT01069315. IPD Sharing: NO. Countries: 3. Publications: 27.
Intrarenal Venous Flow Change During Fluid Removal in Critically Ill Patients: A Prospective Exploratory Study
ClinicalTrials.gov study NCT06216119. IPD Sharing: YES. Countries: 1. Publications: 17.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.