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103 results for “Flow control”
Liquid Flow and Control Without Solid Walls
<p>This repository contains additional data related to the publication: 10.26434/chemrxiv.7207001</p> <p>Contained in python_magneto_fluidics.zip are all the files needed to calculate magnetic fields of any assembly of cuboid permanent magnets such as used in this paper, along with the equilibrium diameters for each antitube-ferrofluid combination.</p> <p>data figures.zip contains all the experimental data plotted in the figures, consisting of data in figures:</p> <p>Main Text: 2, 3, 4<br> Extended Data: E2, E3, E4, E6, E8</p>
Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction
<p>The dataset named “Dataset: Flow manipulation in a Hele-Shaw cell with an electrically-controlled viscous obstruction” consists of Raw time-averaged images, which are generated by sequence of 100 frames extracted from experimental videos captured at various voltages (5V, 10V, 15V, 20V, and 50V), and saved as .tif files. These images were analysed to produce the data used in figure 2 and 3 of the article. The dataset also includes two Excel files named as “Figure 2_Experimental data.xlsx” and “Figure 3_Experimental data.xlsx”. These excel files contain the data used to create the experimental plots shown in Figure 2C, and Figure 3 of the research article respectively.</p> <p>In the “Figure 2C_Experimental Data.xlsx” excel file, each sheet corresponds to a different voltage value shown in the figure, and contains three columns: A, B, and C. which represents the X-location, Y-location, and orientation angle (in degrees) of the experimental plot (red rods in the figure) respectively. This plot is overlaid on the model data (black rods in the figure) and displayed in Figure 2C given in the article.</p> <p><span>The “Figure 3_Experimental data.xlsx” file contains three sheets for each voltage (5V, 10V, 15V, 20V, and 50V) and each of these three sheets provide data at three different X-locations (X=579, X= 1079, and X= 1779) as a function of Y-location as shown in the Figure 3 of the article. Each sheet has five columns: A, B, C, D, and E. These columns represent the X-location, Y-location, Orientation angle (in degrees), Coherency, and Error in the orientation angle (in degrees), respectively. These data points are used to create the experimental scatter plot shown in Figure 3 of the article.</span></p>
Data files for the manuscript "Extended kinetic theory applied to pressure-controlled shear flows of frictionless spheres between rigid, bumpy planes"
<p>This depository contains the data of all DEM simulations used in the manuscript titled "Extended kinetic theory applied to pressure-controlled shear flows of frictionless spheres between rigid, bumpy planes" submitted to Soft Matter in July 2024.</p> <p>The data in the excel file are the measurements obtained after the coarse graining procedure.</p>
Biogeochemical rate data and sediment properties of samples used for a controlled flow through experiment testing the effect of nitrate on organic matter decomposition, PIE LTER, Plum Island Sound estuary, Massachusetts.
In this dataset, we used a controlled flow-through reactor (FTR) experiment to test the role of nitrate as an electron acceptor, and its effect on organic matter decomposition and the associated microbial community in salt marsh sediments. Organic matter decomposition significantly increased in response to nitrate, even at sediment depths typically considered resistant to decomposition. The use of isotope tracers suggests this pattern was largely driven by stimulated denitrification. Nitrate addition also significantly altered the microbial community and decreased alpha diversity, selecting for taxa belonging to groups known to reduce nitrate and oxidize more complex forms of organic matter. Fourier Transform-Infrared Spectroscopy further supported these results, suggesting that nitrate facilitated decomposition of complex organic matter compounds into more bioavailable forms. Taken together, these results suggest the existence of organic matter pools that only become accessible with nitrate and would otherwise remain stabilized in the sediment. The existence of such pools could have important implications for carbon storage, since greater decomposition rates as N loading increases may result in less overall burial of organic-rich sediment. Given the extent of nitrogen loading along our coastlines, it is imperative that we better understand the resilience of salt marsh systems to nutrient enrichment, especially if we hope to rely on salt marshes, and other blue carbon systems, for long-term carbon storage.
Source data belonged to "Geometric flow control in lateral flow assays: Macroscopic single-phase modeling"
<p>This record contains all the necessary data to obtain the results of the study "Geometric flow control in lateral flow assays: Macroscopic single-phase modeling" (<a href="https://doi.org/10.1063/5.0093316">https://doi.org/10.1063/5.0093316</a>).</p>
Dataset for "Tidal control of the flow through long, narrow straits: a modeling study for the Seto Inland Sea"
<p>This dataset contains simulated results used to draw figures in the following paper.</p> <p>Kurogi, M. & Hasumi, H. Tidal control of the flow through long, narrow straits: a modeling study for the Seto Inland Sea, Sci. Rep. 9, 11077 (2019).</p> <p>==== List of data</p> <p> dep_T.nc: Depth of tracer point<br> dep_V.nc: Depth of velocity point<br> ssh.nc: Sea Surface Height for TIDE (hourly snapshots of July 2012)<br> ubt_TIDE.nc: Eastward barotropic velocity for TIDE (monthly mean)<br> vbt_TIDE.nc: Northward barotropic velocity for TIDE (monthly mean)<br> ubt_NTIDE.nc: Eastward barotropic velocity for NTIDE (monthly mean)<br> vbt_NTIDE.nc: Northward barotropic velocity for NTIDE (monthly mean)<br> ubt_NTIDE_DV0.nc: Eastward barotropic velocity for NTIDE_DV0 (monthly mean)<br> vbt_NTIDE_DV0.nc: Northward barotropic velocity for NTIDE_DV0 (monthly mean)<br> ubt_NTIDE_D.nc: Eastward barotropic velocity for NTIDE_D (monthly mean)<br> vbt_NTIDE_D.nc: Northward barotropic velocity for NTIDE_D (monthly mean)<br> ubt_NTIDE_DV.nc: Eastward barotropic velocity for NTIDE_DV (monthly mean)<br> vbt_NTIDE_DV.nc: Northward barotropic velocity for NTIDE_DV (monthly mean)<br> ubt_NTIDE_DVA.nc: Eastward barotropic velocity for NTIDE_DVA (monthly mean)<br> vbt_NTIDE_DVA.nc: Northward barotropic velocity for NTIDE_DVA (monthly mean)<br> ubt_inout.nc: Eastward barotropic velocity for the inflow-outflow model (monthly mean)<br> vbt_inout.nc: Northward barotropic velocity for the inflow-outflow model (monthly mean)<br> amv_TIDE.nc: Vertical average of vertical viscosity for TIDE (monthly mean)<br> ahv_TIDE.nc: Vertical average of vertical diffusivity for TIDE (monthly mean)<br> amv_NTIDE.nc: Vertical average of vertical viscosity for NTIDE (monthly mean)<br> ahv_NTIDE.nc: Vertical average of vertical diffusivity for NTIDE (monthly mean)<br> ke.nc: Vertical average of kinetic energy per unit mass for TIDE (monthly mean)<br> ke_bt.nc: Vertical average of barotropic kinetic energy per unit mass for TIDE (monthly mean)</p> <p> </p>
Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"
<p>Uploaded on 20. February 2023</p> <p>This is the supplementary data for the publication</p> <p>"Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann</p> <p>Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl<br> Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands</p> <p>The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES. </p> <p>The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583</p>
Metadata with submitted Emission Control Science and Technology Journal manuscript Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems
<p>Metadata with submitted Emission Control Science and Technology Journal <em>Traceable uncertainty of exhaust flow meters embedded in portable emission measurement systems</em></p> <p>Link to article: https://link.springer.com/article/10.1007/s40825-025-00260-z</p>
FarmConners Wind Farm Flow Control Benchmark: Blind Test with CL-WINDCON Wind Tunnel Data
<p>This is the dataset used for running the fourth Blind Test of the FarmConners Wind Farm Flow Control Benchmark. The Blind Test was performed with an extensive dataset gathered while testing a cluster of three scaled wind turbines within a large boundary layer wind tunnel. The experimental dataset has been compared against the predictions provided by 5 different control-oriented wind farm flow models. The resulting comparison is described in the paper "FarmConners Wind Farm Flow Control Benchmark: Blind Test Results, Part 2", by Campagnolo et al, (2023). The dataset consists of:</p> <ol> <li>measurements of the flow within the wake shed by one or two machines, as well as measurements of the power, loads (on the rotating shaft and at tower base), and pitch/yaw/torque actuators states of the three scaled machines. The measurements have been performed under a wide range of inflow and machines operating conditions. The time series of the measured data are provided in the format of Matlab structures saved in .mat files.</li> <li>Predictions provided by the models used by the Blind Test participants</li> <li>Matlab scripts used for comparing the experimental dataset and the numerical predictions provided by the Blind Test participants</li> <li>Additional data provided to the Blind Test participants. This includes a FAST model of the scaled wind turbine and the mapping of the inflow of the empty wind tunnel. </li> </ol>
Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs
<p>This dataset and pre-trained models are released as a companion to our OOPSLA '20 publication: "Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs":</p> <ol> <li>The dataset file (<a href="https://zenodo.org/api/files/0e98c909-50b4-43e3-8ab2-bb9673eed786/nero_dataset_binaries.tar.gz?versionId=b943752a-e89e-43c1-b32f-19d4a7420e00">nero_dataset_binaries.tar.gz</a>) is composed from packages of binary executables created by compiling several GNU source-code packages. We used these executables to evaluate our approach as implemented in our prototype "Nero" and compare it to other approaches. All executables contain debug information which serves as the ground truth for the procedure name predictions. The packages are split into three sets: training, validation and test. <ol> <li>The executable file name structure is: "<compiler>-<compiler version>__O<Optimization level(u for default)>__<Package name>[-<optional package version>]__<Executable name>". For example "gcc-5__Ou__cssc__sccs".</li> </ol> </li> <li>The procedure representation file (<a href="https://zenodo.org/api/files/0e98c909-50b4-43e3-8ab2-bb9673eed786/procedure_representations.tar.gz?versionId=fef2380c-c869-4a3f-8e32-bc4972dbb219">procedure_representations.tar.gz</a>) contains: <ol> <li>The raw representations for all the binary procedures in the above dataset. Each procedure is represented by one line in the relevant file for each set (training.json, validation.json and test.json) </li> <li>The above representations preprocessed for training.</li> </ol> </li> <li>The pre-trained model file (<a href="https://zenodo.org/api/files/0e98c909-50b4-43e3-8ab2-bb9673eed786/nero_gnn_model.tar.gz">nero_gnn_model.tar.gz</a>) was created using the above preprocessed dataset and contains: <ol> <li>Pre-trained model.</li> <li>Training log.</li> <li>Prediction results log.</li> </ol> </li> </ol> <p>For the code of the "Nero" prototype, and more information about the above artifacts see <a href="https://github.com/tech-srl/Nero">our Github repo</a></p>
Climatic-oceanographic controls on Amundsen Sea productivity and bottom flow evolution since 280 ka
<p>Understanding the mechanisms regulating productivity and bottom flow variability in the Amundsen Sea is vital for constraining cryosphere-climate feedbacks in the South Pacific Seasonal Sea Ice Zone. To enhance our comprehension of the mechanisms underlying productivity variations in this region, we present an extensive set of multi-proxy records from gravity core ANT36/A3-08 (LATITUDE: -69.02 and LONGITUDE: -120.06), retrieved from the southernmost upwelling boundary of the modern Circumpolar Deep Water in the Amundsen Sea.</p> <p>Surface productivity is reconstructed using a suite of biogeochemical proxies, including Ti-normalized bromine, biogenic opal, total nitrogen, and excess barium normalized to Ti. These proxies reflect marine biogenic export production and nutrient cycling. Bottom flow strength is inferred from sedimentological and geochemical indicators such as mean sortable silt, sortable silt percentage, zirconium-to-rubidium, and zirconium-to-aluminum ratios.</p>
dataset for "Corner-bridge flow as pore-scale flow mechanism controlling capillary pressure curve at low saturations"
<p>dataset for "Corner-bridge flow as pore-scale flow mechanism controlling capillary pressure curve at low saturations", including raw images from drainage experiments</p>
Controlling rheology via boundary conditions in dense granular flows
<p>Boundary shape, particularly roughness, strongly controls the amount of wall slip in dense granular flows. We aim to quantify and understand which aspects of a dense granular flow are controlled by the boundary conditions and to incorporate these observations into a cooperative nonlocal model characterizing slow granular flows. To examine the influence of boundary properties, we perform experiments on a quasi-2D annular shear cell with a rotating inner wall and a fixed outer wall; the latter is selected among 6 walls with various roughnesses, local concavity, and compliance. Measuring flow field and stress field throughout the material in experimental studies of granular materials is an ongoing challenge due to the complex nature of these materials. Here, we use innovative techniques to quantify stress field and flow field when different boundaries were used. We find that we can successfully capture the full flow profile using a single set of empirically determined model parameters, with only the wall slip velocity set by direct observation. Through the use of photoelastic particles, we observe how the internal stresses fluctuate more for rougher boundaries, corresponding to a lower wall slip, and connect this observation to the propagation of nonlocal effects originating from the wall. Our measurements indicate a universal relationship between dimensionless fluidity and velocity.</p>
Channel flow with hydrodynamically controlled benthic-pelagic coupled oxygen fluxes
<p>This dataset was used for the manuscript "Hydrodynamic control of sediment-water fluxes: Consistent parameterization and impact in coupled benthic-pelagic models" by Umlauf et al. (2023, JGR Oceans, <a href="https://doi.org/10.1029/2023JC019651" target="_blank" rel="noopener">10.1029/2023JC019651</a>).</p>
On-Demand Inkjet Printed Hydrophilic Coatings for Flow Control in 3D-Printed Microfluidic Devices Embedded with Organic Electrochemical Transistors
<p>Dataset of the journal article "On-Demand Inkjet Printed Hydrophilic Coatings for Flow Control in 3D-Printed Microfluidic Devices Embedded with Organic Electrochemical Transistors", article DOI: 10.1002/admt.202300127</p>
Prospective, Multicenter, Self-controlled Clinical Trial to Validate Optical Ultrasonic Flow Ratio (OUFR)
ClinicalTrials.gov study NCT06726252. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from a randomized controlled trial: Comparison between high-flow nasal cannula (HFNC) therapy and noninvasive ventilation (NIV) in children with acute respiratory failure by bronchiolitis
Open the record for dataset details and reuse information.
Controlling rheology via boundary conditions in dense granular flows
Open the record for dataset details and reuse information.
Data from: Flow heterogeneity controls dissolution dynamics in topologically complex rocks
Open the record for dataset details and reuse information.
Data for the effect of optic flow cues on honeybee flight control in wind
<p><span>To minimise the risk of colliding with the ground or other obstacles, flying animals need to control both their ground speed and ground height. This task is particularly challenging in wind, where head winds require an animal to increase its airspeed to maintain a constant ground speed and tail winds may generate negative airspeeds, rendering flight more difficult to control. In this study, we investigate how head and tail winds affect flight control in the honeybee <i>Apis mellifera</i>, which is known to rely on the pattern of visual motion generated across the eye – known as optic flow – to maintain constant ground speeds and heights. We find that, when provided with optic flow cues in both the longitudinal and transverse directions of flight, honeybees maintain a constant ground speed but fly lower in head winds and higher in tail winds, a response that is also observed when longitudinal optic flow cues are minimised. This change in height with wind does not appear to result in a constant rate of optic flow in the ventral visual field, suggesting that honeybees may rely on a combination of mechanosensory and visual information when controlling flight in wind. We also find that, when the transverse component of optic flow is minimised, or when all optic flow cues are minimised, the effect of wind on ground height is abolished. We propose that the regular sidewards oscillations that the bees make as they fly may be used to extract information about the distance to the ground, independently of the longitudinal optic flow that they use for ground speed control. This computationally simple strategy could have potential uses in the development of lightweight and robust systems for guiding autonomous flying vehicles in natural environments.</span></p>
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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.