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42 results for “computational fluid dynamics”
Dataset for "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport"
<p>Dataset for figures and tables of the article "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport" submitted to "International Journal of Multiphase Flow".</p>
FIGURE 12 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 12. Simulated Nautilus data plotted alongside live Nautilus behavior data from Niel and Askew (2018).
FIGURE 8 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 8. Plot of the coefficient of drag versus Reynolds number for each of the 10 morphotypes in this study. Drag coefficient and Reynolds number were calculated following the equations of Jacobs (1992). Only shells that had a uniform diameter of approx. 5 cm from aperture to venter are shown.
FIGURE 7 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 7. Plot of drag force versus velocity for each of the 10 different morphotypes used in this study. Only shells that had a uniform diameter of approx. 5 cm from aperture to venter are shown.
FIGURE 5 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 5. Coefficient of drag results from Scheme 1 (green) and Scheme 3 (blue) plotted against Re compared against the data from Jacobs (1992; black). Comparisons shown are for Sphenodiscus (left) and Oppelia (right).
FIGURE 3 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 3. An illustration of the computational domain of the simulation. The model target (an ammonoid in this case) is shown as a circle. Each arrow indicates a distance from the shell to a target face of the computational domain. These arrows represent the straight-line distance between the nearest edge of the shell (not the shell's midpoint) and the corresponding wall as per the methods of Shiino, Kuwazuru, and Yoshikawa (2009). Dimensions in the figured example correspond to those of Scheme 3 (see Table 1)
FIGURE 4 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 4. Hemisphere simulation data plotted as velocity versus % difference from the literature baseline (Blevins 1984). Velocities shown are within a range in which the drag coefficient of a hemisphere is relatively stable around a value of 1.17 (Blevins, 1984). The drag values used to derive this plot are given in Appendix 3.
FIGURE 1 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 1. An outline of the workflow from model creation to completed simulation. Boxes are colored based on the general process they are included in: Case generation (blue), Mesh generation (purple), and numerical set-up (green). Two tracks are shown for case generation: one in which a model is created in blender from measurement data (below the dotted line) and the other where the model is created using a Structure from Motion technique such as laser scanning or photogrammetry (above the dotted line). Software used in each process is noted in "()" outside its respective step.
FIGURE 11 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 11. Plots of pressure overlain with water velocity vectors for the Serpenticone and Oxycone shells at both 15 cm/s (A) and 5 cm/s (B) inlet velocities. At 15 cm/s the flow around the Serpenticone shell is more chaotic and there is a buildup of pressure at around the trailing coils compared to the Oxycone shell. This difference mostly disappears at 5 cm/s.
FIGURE 9 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 9. Water velocity around the Sphenodiscus shell at inlet velocities of 15 cm/s (A) and 5 cm/s (B). Areas of slow water velocity caused by viscous interactions are larger to the sides and immediately behind the shell at the lower velocity because water is less readily shed.
Cloud-Repro: Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics
<p>In a new effort to make our research transparent and reproducible by others, we developed a workflow to run computational studies on a public cloud. It uses Docker containers to create an image of the application software stack. We also adopt several tools that facilitate creating and managing virtual machines on compute nodes and submitting jobs to these nodes. The configuration files for these tools are part of an expanded "reproducibility package" that includes workflow definitions for cloud computing, in addition to input files and instructions. This facilitates re-creating the cloud environment to re-run the computations under the same conditions.</p> <p>The present Zenodo dataset contains all secondary data required to reproduce the figures of the manuscript ("Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics") without running the CFD simulations again.</p>
Fig. 1 in Hydrodynamic performance of psammosteids: new insights from computational fluid dynamics simulations
Fig. 1. Box-shaped flow domain, mesh and coordinate system used for computational fluid dynamics (A ); A , enlargement view on the mesh.
Fig. 2 in Hydrodynamic performance of psammosteids: new insights from computational fluid dynamics simulations
Fig. 2. Distribution of pressure on the fish bodies at flow velocity 1.5 ms-1, in anterior (A) and lateral (A) views.
Fig. 4. Vorticity patterns using Q-criterion for 0 in Hydrodynamic performance of psammosteids: new insights from computational fluid dynamics simulations
Fig. 4. Vorticity patterns using Q-criterion for 0 angles of attack and flow velocity 1.5 ms-1 in Errivaspis (A), Guerichosteus (B), and Tartuosteus (C). Models displayed in left lateral (A 1 –C 1) and top (A 2 –C 2) views. Iso-vorticity surface is colored by the magnitude of velocity.
Micro-urban environment experimental dataset to validate performance of different Computational Fluid Dynamics methodologies.
<p><span><span>This dataset enclosed wind 3D geolocated wind flow and air concentrations </span><span>5-minutal </span><span>data </span><span>collected in </span><span>El Prat del Llobregat (Spain) </span><span>between January and August 2022 in the context of the experiment 1012-ibam of the FF4EuroHPC European project. The intention of this dataset is to provide a </span><span>resource to do performance benchmark of micro-urban chemical – dispersion models to assess their performance</span><span>. To do so, we enclose experimental data collected by </span><span>Bettair</span><span> Mk2 Series Air quality monitors, 2 Air Quality Monitoring stations equipped with reference instruments f</span><span>rom “La </span><span>Xarxa</span><span> de </span><span>Vigilància</span> <span>i</span> <span>Previsió</span><span> de la </span><span>Contaminació</span> <span>Atmosfèrica</span><span> (XVPCA)”</span><span>, and different data from the repository of the ECMWF Era-5 land and CAMS. We also provide the </span><span>3D watertight geometry model of the </span><span>el</span><span> Prat de Llobregat (Spain) in step file format</span><span> (layout from 2020)</span><span>.<br></span></span></p>
Comparative analysis of patient-specific aortic dissections through computational fluid dynamics suggests increased likelihood of degeneration in partially thrombosed aorta
<p>Aortic dissection is a life-threatening cardiovascular disease associated with high rates of morbidity and mortality, especially in medically under-served communities. It compromises the hemodynamics of the arteries that originate from the aorta, and its outcomes include visceral ischemia and aortic rupture in the acute phase and aneurysmatic degeneration in the chronic phase. Understanding patients’ blood flow patterns is pivotal for non-invasive evidence-based treatment as they greatly influence both the disease onset and its outcome. In this paper, we combine diagnostic imaging techniques and computational fluid dynamics to analyze the flow patterns of three aorta dissections (fully perfused, partially thrombosed, and fully thrombosed), and compare them to a healthy aorta. Besides flow kinematics, we focus on time averaged wall shear stress and oscillatory shear index that are recognized risk factors for aneurysm and rupture. Our analysis shows that partially thrombosed dissection is the most prone to false lumen degeneration. In all dissections, the arteries connected to the false lumen are generally poorly supplied with blood. Further, both true and false lumens present higher turbulence levels than the healthy aorta, and critical stagnation points. Mesh sensitivity and a thorough comparison against literature data together support the methodology robustness.</p>
Data from: Computational fluid dynamics confirms drag reduction associated with trilobite queuing behaviour
<p>Queuing behaviour has been documented in marine arthropods from Cambrian to modern oceans. One possible explanation of this behaviour is drag reduction, with trilobites in the following positions hypothesized to produce less drag than those leading. In this study, we evaluate the hydrodynamics of queuing behaviour in the Devonian trilobite Trimerocephalus chopini using computational fluid mechanics. Our results show that the drag forces of the trilobites following in the queue were substantially lower than those produced by the leader (75.1% lower at 2 cm s-1). Drag reduction is positively correlated with the movement speed of the trilobites, but decreases with increasing distance from the leader. Our results support the hypothesis that the queuing behaviour of trilobites was an adaptation for reducing hydrodynamic drag. This drag reduction effect compensated for the energy cost of movement, which would have been particularly advantageous during migration.</p>
Merging computational fluid dynamics and machine learning to reveal animal migration strategies
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Data from: Computational fluid dynamics confirms drag reduction associated with trilobite queuing behaviour
Open the record for dataset details and reuse information.
Effect of space diffuser on flow characteristics of a centrifugal pump by computational fluid dynamic analysis
<p><span>Achieving an optimal configuration of the diffuser is indispensable for high pump performances. In this work, a numerical study on diffuser configuration is conducted for a high pump performance using a computational fluid dynamics code, and the effects of the wrap angle and the relative position of the diffuser vane to the impeller on pump performances are included. The results indicate that the modified diffuser with a suitable wrap angle may improve the pump hydraulic efficiency and the head by approximately 4% and 8%, respectively, while a suitable position of the diffuser vane can enhance the pump head by more than 4%. Meanwhile, the pressure recovery coefficient and the local Euler head of the diffuser are adopted to evaluate the diffuser performance. For a high pump performance, the local Euler head of the diffuser has a peak value at the leading edge with the change rate of zero along the meridian streamline, meaning that no blade loading at the leading edge of the diffuser guarantees a better match between the impeller and the diffuser.</span></p>
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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.