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289 results for “hydraulics”
Hydraulic Constraints on Two Life History Stages of Larrea tridentata in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (2002-2003)
Maintaining high rates of water loss during times of high resource availability could allow establishing woody desert perennials to grow quickly by allowing them to take advantage of the fleeting but abundant monsoonal moisture typical of warm deserts like the Chihuahuan. However, a plant cannot endlessly increase water loss in order to grow faster --there are hydraulic constraints on rates of water loss. The hydraulic properties of each particular plant xylem and soil microsite, as well as the AR:AL absorbing root area to transpiring leaf area ratio) interact to set limits on rates of water loss. If transpiration rates become too high, cavitation may limit the ability of the xylem to supply water to the leaves. The main objective of this study was to test two hypotheses on a population of Larrea tridentata at the Sevilleta LTER in central New Mexico (1) do small plants grow faster and use water less conservatively than large, and (2) are there differences in the hydraulic constraints on small and large plants. Measurements were made every six weeks in the spring, summer and fall from April 2002 - August 2003. Field measurements of shoot growth, gas exchange and plant and soil water potentials were made to determine growth rates and water use. Measurements of leaf specific conductance determined the ability of the xylem to supply water to the leaves. Excavation findings were used to estimate (AR:AL). Xylem vulnerability curves and soil texture analysis were used to determine the hydraulic properties of the plant xylem and soil. A model determined where the limiting conductance occurred in the plant-soil continuum.
Hydraulic Properties of Expansive Yazoo Clay subjected to wet dry cycles
<p>The data file includes the investigation of the behavior of yazoo clay under different wet-dry cycles. The investigation was conducted using laboratory testing and numerical analysis.</p>
Dataset related to the article "Laboratory-scale hydraulic fracturing dataset for benchmarking of Enhanced Geothermal System simulation tools"
<p>Experimental results from hydraulic fracturing experiments performed in granite and marble samples of size 30 cm × 30 cm × 45 cm under well-defined boundary conditions.</p> <p>Datasets include:</p> <ul> <li>pressure versus flow-rate response</li> <li>acoustic emission data from a dense network of 32 seismic sensors</li> <li>detailed description of the experimental set-up and adopted test protocol</li> <li>mechanical and petrophysical properties of the samples</li> <li>python code for seismic data processing</li> </ul> <p>This complete collection of data, obtained within the framework of European Union’s Horizon 2020 project GEMex, is rare in its kind and indispensable for verification of model assumptions and constitutive relationships of numerical codes used for designing field-scale hydraulic fracturing experiments.</p>
Effects of plant hydraulic traits on the flammability of live fine canopy fuels in 62 Australian plant species
<ol> <li><span>Plant species vary in how they regulate moisture and this has implications for their flammability during wildfires. We explored how fuel moisture is shaped by variation within six hydraulic traits: saturated moisture content, cell wall rigidity, cell solute potential, symplastic water fraction and tissue capacitance.</span></li> <li><span>Using pressure-volume curves, we measured these hydraulic traits distal shoots (<i>i.e.</i> twigs + leaves) in 62 plant species across four wooded communities in south-eastern Australia. For a subset of 30 of those species, we also measured hydraulic traits of twigs using moisture-release curves. Moisture content of fine fuels was then estimated for circumstances typical of fire weather. These projections were made assuming that under the hot, dry, windy conditions typical of large wildfires, leaves and fine twigs would function at internal water pressures close to wilting point (<i>i.e. </i>turgor loss point, TLP). The effect of different moisture contents at TLP on ignition time was then modelled using a fully mechanistic, finite element model of biomass ignition based on standard principles of physical chemistry.</span></li> <li><span>We also measured predawn water potential, an indication of plant access to soil water that is influenced by root architecture. These data were used to model how root traits influence fuel moisture and ignition time.</span></li> </ol>
Dataset on soil hydraulic properties under contrasted plant covers and agricultural practices
<p>Complete dataset on the temporal variation of soil infiltrability along a homogeneous fluvisol, on bare soil or soil planted with two plant species with contrasted root systems (a Malvaceae with a tap-root system and a Poaceae with a fibrous root system), and impacted by three different management practices (burning, mowing, and chemical weeding). An original protocol, based on specific ring infiltrometers, able to measure the temporal dynamics of soil infiltrability was used. This dispositive takes into account the variability of the measurement across space.</p> <p> </p>
Map of the Roman mining hydraulic system - Las Médulas, León, Spain
<p>This resource is related with the Roman hydraulic system linked to Las Médulas gold mining complex in northwest Iberia. The dataset includes a detailed digital cartography of the hydraulic network, which extends over 1100km. It identifies 41 canals distributed between La Cabrera and El Bierzo regions, (33 and 8, respectively), with 14 canals supplying water to Las Médulas. Additionally, the dataset includes the locations of the Roman mining sites.</p> <p><a title="Map of the Roman mining hydraulic system - Las Médulas, León, Spain" href="https://earth.google.com/earth/d/1argDbqGaEMdlE7Uomb4z70LGYce9pCBb?usp=sharing">View on Google Earth (3D)</a></p> <p><a title="Map of the Roman mining hydraulic system - Las Médulas, León, Spain" href="https://www.google.com/maps/d/edit?mid=12yHtyAN3qJcmrt-P_3abv3rd6PaI_ZQ&usp=sharing">View on Google Maps (2D)</a></p> <p> </p>
hydraulic accumulator 600ml PURfoamPPI60 p0 40bar p1 60bar 20degreeCelsius
<p>This is a dataset publication with multiple measurements seperated into different .mat files created at the Institut für Fluidsystemtechnik (Chair of Fluid Systems) at the Technische Universität Darmstadt (Technical University of Darmstadt (https://ror.org/05n911h24)).</p> <p>The experimental data include dynamic characterisations of gas accumulators. The data were obtained on a dynamic uni-axial test machine. Each measurement-file contains the measurement data at a monofrequent harmonic excitation.</p> <p>This dataset publication in this version doesn't contain an RDF (Resource Description Framework) 'METADATA.ttl' file in the RDF Turtle format that would contain metadata of the measurements. This decision is made, since the information model marked a interim stage to FAIR measurement data and is currently under major reconstruction.</p> <p>The different measurement files in the .mat format contain different time series and corresponding metadata. The various measurements differ in various parameters (e.g. excitation frequency, excitation amplitude, ambient temperature, etc.).</p> <p>If you have further questions, please feel free to contact info*AT*fst.tu-darmstadt.de .<br>Thank you.</p> <p>Yours sincerely<br><br></p> <p><strong>CHANGELOG:</strong><br>1. This version contains the same data inside the .mat files but with updated/corrected metadata<br><br></p> <p><strong>LICENSE NOTICE:</strong><br>This dataset is licensed under the 'CC-BY-4.0' License (https://creativecommons.org/licenses/by/4.0/)</p>
hydraulic accumulator 600ml PURfoamPPI80 p0 40bar p1 60bar 20degreeCelsius
<p>This is a dataset publication with multiple measurements seperated into different .mat files created at the Institut für Fluidsystemtechnik (Chair of Fluid Systems) at the Technische Universität Darmstadt (Technical University of Darmstadt (https://ror.org/05n911h24)).</p> <p>The experimental data include dynamic characterisations of gas accumulators. The data were obtained on a dynamic uni-axial test machine. Each measurement-file contains the measurement data at a monofrequent harmonic excitation.</p> <p>This dataset publication in this version doesn't contain an RDF (Resource Description Framework) 'METADATA.ttl' file in the RDF Turtle format that would contain metadata of the measurements. This decision is made, since the information model marked a interim stage to FAIR measurement data and is currently under major reconstruction.</p> <p>The different measurement files in the .mat format contain different time series and corresponding metadata. The various measurements differ in various parameters (e.g. excitation frequency, excitation amplitude, ambient temperature, etc.).</p> <p>If you have further questions, please feel free to contact info*AT*fst.tu-darmstadt.de .<br>Thank you.</p> <p>Yours sincerely</p> <p> </p> <p><strong>CHANGELOG:</strong><br>1. This version contains the same data inside the .mat files but with updated/corrected metadata</p> <p> </p> <p><strong>LICENSE NOTICE:</strong><br>This dataset is licensed under the 'CC-BY-4.0' License (https://creativecommons.org/licenses/by/4.0/)</p>
Hydraulic Processes based on Managed Realignment
<p>As a part of the primary requirement for the Data Science task, it has been written a document under name of Data Management Plan. For this purpose we used a tool provided by TU Wien - DMP Tool. The DMP has been build in a way to comply with Science Europe guidlines and hence following FAIR principles.</p> <p>This is s Verison 1 of DMP and the updates will be performed through the research if that's required. </p>
Hydraulic geometry and whitewater coverage for a steep proglacial stream -- data sets and scripts
<p>Data sets and scripts used in the analyses for the following article:</p> <p>Dufficy, A.L., Eaton, B.C. and Moore, R.D. <span>Quantifying hydraulic geometry and whitewater coverage for steep proglacial streams to support stream temperature modelling. <em>Hydrological Processes</em>, DOI: 10.1002/hyp.70003.<br></span></p> <p><span>The number in the file names for the R scripts indicates the order in which the scripts should be run.</span></p> <p><span>The study was funded by the Natural Sciences and Engineering Research Council of Canada and the Faculty of Arts, University of British Columbia.</span></p> <p> </p>
Source parameter of hydraulic-fracturing-induced earthquakes in the Kiskatinaw area, northeast British Columbia
<p>Source parameter of 2475 hydraulic-fracturing-induced earthquakes (1927 events using P-wave estimates and 1882 events using S-wave estimates) in the Kiskatinaw area, northeast British Columbia between 1 July 2017 to 31 July 2020. The Study area covers parts of the Montney Formation, a major shale gas play within the Western Canada Sedimentary Basin. Source parameters include seismic moment, moment magnitude, spectral corner frequency, and static stress drop values using three different approaches (single spectrum fitting, a clustered event approach, and spectral-ratio method). Detailed information about the methods are stated in the corresponding publication:</p> <p>Roth, M. P., K. B. Kemna, R. M. Harrington, and Y. Liu (2022). Source properties of hydraulic-fracturing-induced earthquakes in the Kiskatinaw area, British Columbia, Canada. Journal of Geophysical Research: Solid Earth. <a href="https://doi.org/10.1029/2021JB022750">https://doi.org/10.1029/2021JB022750</a></p>
Hydraulic scale model experiments on the two-dimensional run-up and overtopping of solitary waves at a vertical wall and a dam-like structure
<p>This dataset includes the experimental data, which were generated during the study on the run-up and overtopping of solitary waves at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the dataset accompanying the paper: Zeitfogel et al., Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity, published at Geoderma, 2023 (https://doi.org/10.1016/j.geoderma.2023.116418).</p> <p>Soil property and Ksat maps for Austria. The digital soil maps were generated based on a Machine Learning and PTF-based approach (indirect approach) and a pure Machine Learning based approach (direct approach). By downloading the datasets, you agree that we nor the provider of the used source datasets cannot be liable for the data provided.</p> <p>This study was funded by the Austrian Federal Ministry of Agriculture, Regions and Tourism (Project InfCapAT), the Austrian Academy of Science (Project RechAUT) and the Austrian Science Fund project P 31213.</p> <p> </p>
Can we use hydraulic handbooks in blind trust? Two examples from a real-world complex hydraulic system
<p>This database includes the data used to produce the results for the following article:</p> <p>Bellos, V., Kossieris, P., Efstratiadis, A., Papakonstantis, I., Papanicolaou, P., Dimas, P., Makropoulos, C. 2022. Can we use hydraulic handbooks in blind trust? Two examples from a real-world complex hydraulic system. 7th IAHR Europe Congress, September 7th – 9th, 2022, Athens, Greece (accepted paper for oral presentation, in press).</p>
Data supplement to "From Grains to Plastics: Modeling Nourishment Patterns and Hydraulic Sorting of Fluvially Transported Materials in Deltas"
<p>Supplementary data and codes for "From Grains to Plastics: Modeling Nourishment Patterns and Hydraulic Sorting of Fluvially Transported Materials in Deltas". Zipped files contain ANUGA hydrodynamic model outputs, dorado particle-routing simulation outputs, Python scripts for running additional dorado simulations, and other metadata used in the analysis of dorado outputs. See README for additional details about directory contents. Note that this directory does not contain the model software itself, which is available on GitHub and has been archived elsewhere (relevant links can be found in README).</p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Data from: The PDI model system for parameterizing soil hydraulic properties
<p>The PDI ("Peters-Durner-Iden") model system represents a robust framework for parameterizing soil hydraulic properties, i.e. the water retention curve and the hydraulic conductivity curve, across the entire soil moisture spectrum. This model accounts for water retention and hydraulic conductivity in completely and partially-filled pores, including adsorption and film-flow. The model was developed in stages and a comprehensive overview of the model development and the model equations is provided in Peters et al. (2024). In this repository, we provide a Python file named "pdi.py" which can be used to compute the various submodels (PDI-VG, PDI-KOS, PDI-FX, ...) of the PDI model system. One MS Excel file is provided for easy access to one PDI model, the PDI-VG. The PYTHON functions contained in "pdi.py" can be used to calculate the water retention curve, the unsaturated hydraulic conductivity curve, the specific water capacity function, and the soil water diffusivity function. In addition, we provide five python scripts which illustrate how to call the various PDI functions in different contexts. Notably, "pdi.py" incorporates a utility function, 'export_hydrus_materin', which generates an ASCII file named "MATER.IN". This file serves as input for simulations with Hydrus-1D and Hydrus-2D3D, offering seamless integration with these simulation platforms. It's important to emphasize that the provided Python scripts and accompanying documentation are closely aligned with the research article by Peters et al. (2024). To streamline accessibility, the repository refrains from redundantly restating the theory or equations already detailed in the referenced publication.</p>
Template matching catalog of the seismicity induced by hydraulic fracturing operations at Preston New Road (UK) in 2019
<p>The file PNR2_TMcatalog.csv contains the catalog of seismicity induced by the hydraulic fracturing operations carried out at Preston New Road (UK) in 2019. The catalog was created with template matching using a single downhole sensor located in a monitoring well.</p> <p>Here is the description of the columns found in the file:</p> <ul> <li>ARRTIME: P-wave arrival times at the reference station.</li> <li>X, Y, Z: hypocentral coordinates (easting, northing and depth). Northing and easting are expressed in the United Kingdom Ordnance Survey coordinate system (EPSG:27700). Depth is expressed in meters below sea level.</li> <li>MW: moment magnitudes.</li> <li>CLUSTER: cluster ID. Earthquakes with the same cluster ID were detected by the same template.</li> </ul>
Data set: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain
<p>Data set of Wind Energy Science (WES) paper: Control design, implementation and evaluation for an in-field 500 kW wind turbine with a fixed-displacement hydraulic drivetrain</p>
Summary Table of AHG Parameters, Hydraulics, Morphological and geophysical variables, and Suspending Sediment Concentration Derived at 1246 USGS River Monitoring Stations
<p>This table contains information about:</p> <p>1) At-A-Station Hydraulic Geometry Parameters,</p> <p>2) Hydraulics Variables,</p> <p>3) Morphological and Geophysical Variables, and</p> <p>4) Suspending Sediment Concentration and Fraction of Sand, Silt, and Clay,</p> <p>derived at 1246 USGS river monitoring stations across the conterminous United States. </p>
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Allen Brain Atlas
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International Brain Laboratory public data
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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.