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69 results for “inflow”
Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods
<p>Input and output data of open-source softwares for computational fluid dynamics simulation involving fluid-structure interaction.</p> <p> </p> <p><strong>Data from two studies</strong></p> <ol> <li>Verifications of the weakly-compressible smoothed particle hydrodynamics (WCSPH) method, open-source code <a href="https://www.sphinxsys.org">SPHinXsys</a>, when applied to the flow of idealized 2D valve models.</li> <li>Validations of inflow-outflow model in moving particle semi-implicit (MPS) method, open-source code <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow">PolyMPS</a>.</li> </ol> <p> </p> <p><strong>Folders and Files</strong></p> <p><strong>valve-2D.zip </strong>is the folder with data from the idealized models of vertical and curved 2D valves:</p> <ul> <li>Vertical valves with parameters provided in <a href="https://doi.org/10.1016/j.jcp.2010.08.005">Gil et al., 2010</a></li> <li>Curved valves with parameters provided in <a href="http://doi.org/10.1007/s00466-013-0890-3">Wick, 2014</a></li> <li>source files (.cpp): input data (physical and numerical parameters) for SPHinXsys</li> <li>text files: SPHinXsys (.dat) and Reference (.tsv) results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>inflow-outflow-3D.zip </strong>is the folder with data from the inflow-outflow model in MPS:</p> <ul> <li>Fluid physical properties of water <ul> <li><span>\(\rho=1000kg/m^3 , \,\, \nu=10^{-6}m/s^{-2}\)</span></li> </ul> </li> <li>Pipes of length <span>\(L=0.15m\)</span>: <ul> <li>circular section of diameter <span>\(D=0.1m\)</span>.</li> <li>square section of sides <span>\(S=0.1m\)</span>.</li> </ul> </li> <li>Constante pressure variation (<span>\(\Delta P = 30 \,\, or \,\, 50 \,\, Pa\)</span>) between inflow and outflow: <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L}, \\ \Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> </ul> <ul> <li>Sinusoidal pressure variation (<span>\(\Delta P =700Pa \,\, , \,\, T = 2.0s\)</span>) between inflow and outflow <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L} \sin \omega t \, \\ \omega = \frac{2\pi}{T} \\ Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> <li>input data (.json, .grid, .stl): physical properties, numerical parameters and geometries for PolyMPS can be found at <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow/input">https://github.com/rubensamarojr/polymps/tree/inOutflow/input</a></li> <li>text files (.txt): PolyMPS and OpenFOAM results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>References</strong></p> <p><a href="https://doi.org/10.1016/j.jcp.2010.08.005">A. J. Gil. The Immersed Structural Potential Method for haemodynamic applications. J. Comput. Phys., 229 (2010), pp. 8613-8641</a></p> <p><a href="https://doi.org/10.1007/s00466-013-0890-3">T. Wick. Flapping and contact FSI computations with the fluid–solid interface-tracking/interface-capturing technique and mesh adaptivity. Comput Mech 53, 29–43 (2014)</a></p> <p><a href="https://doi.org/10.1016/j.cma.2014.10.040">D. Kamensky, et al. An immersogeometric variational framework for fluid–structure interaction: Application to bioprosthetic heart valves Comput. Methods Appl. Mech. Engrg., 284 (2015), pp. 1005-1053</a></p> <p><a href="https://doi.org/10.1016/j.cma.2015.12.023">C. Kadapa et al. A fictitious domain/distributed Lagrange multiplier based fluid–structure interaction scheme with hierarchical B-Spline grids. Comput. Methods Appl. Mech. Engrg., 301 (2016), pp. 1-27</a></p> <p><a href="https://doi.org/10.1016/j.jcp.2015.10.015">Jie Liu. A second-order changing-connectivity ALE scheme and its application to FSI with large convection of fluids and near contact of structures. J. Comput. Phys., 304 (2016), pp. 308-423</a></p>
Organic carbon, temperature, oxygen, and inflow volume time series for Falling Creek Reservoir, Vinton, Virginia: Summer 2014
Weekly and subweekly monitoring data collected from June to October 2014 were used to study organic carbon dynamics in Falling Creek Reservoir, a drinking water reservoir owned and managed by the Western Virginia Water Authority and located in Vinton, Virginia, USA. The dataset consists of: 1) daily inflow volumes and the temperature of water entering the reservoir, measured at a gauged weir on Falling Creek Reservoir’s primary inflow tributary; 2) weekly to subweekly depth profiles of temperature and oxygen collected at the deepest site of the reservoir adjacent to the dam; and 3) approximately weekly grab samples of dissolved organic carbon and dissolved carbon dioxide collected from subsurface (0.1 m depth) and just above the sediments (9.0 m depth). This dataset also includes approximately weekly grab samples of organic carbon and dissolved carbon dioxide in the inflow tributary measured at the gauged weir, dissolved oxygen concentrations in the inflow tributary measured at the gauged weir, and organic carbon deposition rates measured at 8 m depth at the deepest site of the reservoir approximately every 4 weeks with sedimentation traps.
Constraints for Rotor Inflow Benchmarks - Unstable Cases
<p>Data to be used as constraints for the rotor inflow benchmarks (unstable cases) as defined in https://rotor-inflow-benchmark.readthedocs.io/</p> <p> </p> <p>GL*.csv : time-averaged wind speed and veer over each period, obtained from ground-based vertically profiling lidar</p> <p>UVW_real*.csv : high-frequency (20 Hz) time series of three-dimensional wind velocity vector over 24 hours centered on the periods of interest, obtained from meteorological tower sonic anemometers</p> <p>NL*.nc: low-frequency (1 Hz) time series of longitudinal wind velocity over each period, obtained from nacelle-mounted scanning lidar after quality control, temporal upsampling, interpolation to a regular grid and the use of a machine learning model to fill in gaps introduced by blade passage in front of the lidar</p> <p>LES*.nc: low-frequency (1 Hz) time series of longitudinal wind velocity over each period, obtained from large-eddy simulations performed to match the experimental field measurements</p> <p>UVW_les*.csv: high-frequency (16 Hz) time series of three-dimensional wind velocity vector at two heights for the 704 seconds of the large-eddy simulations</p>
Deciphering the impacts of main inflowing rivers on dissolved organic matter in Lake Daye using isotopes, optical spectroscopy, and FT-ICR-MS during non-flood season
<p>The uploaded data include water quality data, isotopes, DOM fluorescence index and FT ICR MS data of Daye Lake and its inflowing rivers.</p>
Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal
<p>Data Supplement for 'Curled Wake Development of a Yawed Wind Turbine at Turbulent and Sheared Inflow' - Wind Energy Science Journal</p> <p>This database contains the measurement using a model wind turbine with 0.6m diameter(D) in a wind tunnel. A short-range Lidar WindScanner facilitated mapping the wake with a high spatial and temporal resolution in vertical, cross-stream planes at different downstream locations and in a horizontal plane at hub height.</p> <p>The measurement campaign was conducted in the large wind tunnel at ForWind-University of Oldenburg. The wind tunnel has a test section cross-section with the dimensions of 3m x3m. For this study three movable test section elements of 6m length were attached for a total enclosed length of 18m. The roof of the test section was adjusted to compensate for boundary layer growth to achieve a zero pressure gradient for the target wind speed of the experiments, nominally 7.5m/s, with an empty tunnel with no grid or turbine installed. The three-bladed MoWiTO 0.6 wind turbine model(Schottler et al.(2016)), with a hub height (h) of 0.77m and a diameter of 0.58m was placed at a distance of 2.4D downstream of the test section inlet, where the distance was measured to the centre of the rotor. In addition, the distance between the rotor center and the tower center is 110mm.<br> The flow blockage, based on rotor swept area and tower flow-facing area, was 2.7%. The wind turbine controller is based on the torque of the generator (Petrovi ́c et al. (2018)) leading to a tip speed ratio of 5.7 at the operational point during non-misaligned cases with no grid. More information can be found in the paper.</p> <p>The folder contains 12 unique .mat files each containing a matlab structure. The matlab structure conatins the vertical and horizontal scan for each inflow and operational condition:<br> With the upstream turbine installed:<br> - Yaw0_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yaw30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal<br> - Yawneg30_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D, Horizontal</p> <p> - Yaw0_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_Uniform_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p> - Yaw0_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yaw30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal<br> - Yawneg30_BoundaryLayer_PassiveGrid<br> - 1D, 2D, 3D, 5D, 7D, 10D, Horizontal </p> <p>Without the upstream turbine installed:<br> - NoTurbine_Uniform_NoGrid<br> - 1D, 2D, 3D, 5D, 13D, 16D<br> - NoTurbine_Uniform_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D<br> - NoTurbine_BoundaryLayer_PassiveGrid<br> - 0D, 1D, 2D, 3D, 5D, 7D, 10D </p> <p>Within each substructure the following parameters are provided:<br> - v_los [m/s] ----------> Line of sight velocity<br> - sigma [m/s] ----------> Spectrum width<br> - x_Global_frame [m] ---> x-coordinate referenced at the lower grid midpoint<br> - y_Global_frame [m] ---> y-coordinate referenced at the lower grid midpoint<br> - z_Global_frame [m] ---> z-coordinate referenced at the lower grid midpoint<br> - xx [m] ---------------> Grid of the x-coordinate referenced at the lower grid midpoint<br> - yy [m] ---------------> Grid of the y-coordinate referenced at the lower grid midpoint<br> - zz [m] ---------------> Grid of the z-coordinate referenced at the lower grid midpoint<br> - uu [m/s] -------------> Horizontal wind speed at the position of the gridded coordinates, these data have been interpolated onto the grid<br> </p> <p>When using this database please reference to the journal paper.</p> <p>All data has been included without warranty, express or implied.</p> <p>For further questions, please contact the corresponding author.<br> </p>
Dataset - Experimental analysis of the dynamic inflow effect due to coherent gusts
<p>Here the processed experimental data of the accepted paper given below is documented and made available:</p> <p>Berger, F., Neuhaus, L., Onnen, D., Hölling, M., Schepers, G., and Kühn, M.: Experimental analysis of the dynamic inflow effect due to coherent gusts, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-2, accepted, 2022.</p>
Reservoirs Inflows, Outflows, Storage
<p>Inflows [m3/s], outflows [m3/s] and storages [m3] for 11 reservoirs around the world as used in Hydrol. Earth Syst. Sci., 22, 1–21, 2018<br> https://doi.org/10.5194/hess-22-1-2018</p>
Supplementary Files: Lower Order Description and Reconstruction of Sparse Scanning Lidar Measurements of Wind Turbine Inflow using Proper Orthogonal Decomposition
<p>Gappy POD reconstruction of the line-of-sight velocities from a nacelle-mounted scanning SpinnerLidar with 30% missing data for two LES cases.</p>
Data for: Species composition of shoreline wolf spider communities vary with salinity but their diets vary with wrack inflow
<p>Wolf spiders are typically the most common group of arthropod predators on both lake and marine shorelines, because of the high prey availability in these habitats. However, shores are also harsh environments due to flooding and, in proximity to marine waters, to toxic salinity levels. Here, we describe the spider community, prey availabilities, and spider diets between shoreline sites with different salinities, albeit with comparatively small differences (5 vs. 7‰). Despite the small environmental differences, spider communities between low and higher-saline sites showed an almost complete species turnover. At the same time, differences in prey availability or spider gut contents did not match changes in spider species composition but rather changed with habitat characteristics within the region, where spiders collected at sites with thick wrack beds had a different diet than sites with little wrack. These data suggest that shifts in spider communities are due to habitat characteristics rather than prey availabilities, and the most likely candidate restricting species in high salinity would be saline sensitivity. At the same time, species' absences from low-saline habitats remain unresolved.</p>
Data from: Environmental DNA metabarcoding revealed the impacts of anthropogenic activities on phytoplankton diversity in Dianchi Lake and its three inflow rivers
<p>Phytoplankton diversity is closely related to environmental variables and has been widely used in ecological health assessment of rivers and lakes. Combining the advantages of DNA-based identification and high-throughput sequencing technology, environmental DNA (eDNA) metabarcoding permits a new measurement for biodiversity monitoring in aquatic ecosystems. However, it had rarely been used to explore the variability and similarity of phytoplankton diversity between a lake and its inflow rivers and the effects of environmental variables on phytoplankton. This study utilized eDNA metabarcoding to investigate the spatial distribution of phytoplankton and the impacts of environmental variables on the phytoplankton diversity in Dianchi Lake (one of the most polluted urban lakes in China) and its main inflow rivers (Panlong River, Baoxiang River and Chai River). The OTU table is the original table about phytoplankton taxa. A total of 243 distinct phytoplankton taxa were detected, covering 9 phyla, 30 classes, 84 orders and 132 families, and the taxonomic richness of rivers was higher than that of Dianchi Lake.</p>
Apulian Aqueduct demo site: daily time series of estimated inflows (natural springs and reservoirs) for climate projections
<p>This dataset contains the daily time series of net estimated inflows (natural springs and main reservoirs of Apulian aqueduct - demo site 1) computed with a lumped rainfall-runoff model starting from climate projections of precipitation and temperature.</p> <p>This dataset considers two representative concentration pathways (RCP 4.5 and 8.5) and two decades, in the medium (2050-2059) and long-term future (2090-2099).</p> <ul> <li>Temporal coverage: 2050-2059; 2090-2099</li> <li>Spatial coverage: Springs: Sele, Calore; Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo</li> <li>Unit of measure: m3/s</li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô <a href="https://zenodo.org/record/7576611">Deliverable D4.4</a>. </p>
Data from: Environmental DNA metabarcoding revealed the impacts of anthropogenic activities on phytoplankton diversity in Dianchi Lake and its three inflow rivers
Open the record for dataset details and reuse information.
Data for: Species composition of shoreline wolf spider communities vary with salinity but their diets vary with wrack inflow
Open the record for dataset details and reuse information.
Evaluation of global forcing data sets for hydropower inflow simulation in Nepal
<p>We are thankful to the Department of Hydrology and Meteorology, Government of Nepal for providing observed hydrological and Meteorological data sets for this study. Some of the data presented here are subject of copyright so we request to contact author (corresponding author: bikasbhattarai@gmail.com) for further use for publication. </p>
Inflow dynamics in weakly stratified lakes subject to large isopycnal displacements (figure data)
<p>Data displayed in the figures of the article entitled "Inflow dynamics in weakly stratified lakes subject to large isopycnal displacements". Water Resources Research, 56, e2019WR026578. <a href="https://doi.org/10.1029/2019WR026578">https://doi.org/10.1029/2019WR026578</a></p>
Data for paper "Effects of inflow conditions on plunge points and vertical profiles of turbidity currents on sloping flume based on 3D numerical simulation"
<p>This set of xlsx files, metadata of model results and Matlab code accompanies the manuscript "Effects of inflow conditions on plunge points and vertical profiles of turbidity currents on sloping flume based on 3D numerical simulation" by Ruoyin Zhang, Baosheng Wu, and Bangwen Zhang. This is the first release of the data.</p>
Supplementary data for "Quantifying the Impact Factors of Landslide Dam Breaching: A Numerical Study on the Influence of Lake Geometry and Upstream Inflow"
<p>The supplementary data provides a summary of previously published data and natural case studies related to landslide dam breaching, which were utilized in the study titled <em>"Quantifying the Impact Factors of Landslide Dam Breaching: A Numerical Study on the Influence of Lake Geometry and Upstream Inflow."</em></p>
Raw data files for "Dialectical characteristics of morphologies of the delta unit driven by water inflow"
<p>The following dataset is a collection of analytical data of a limited repetition of lab-scale delta evolution for evaluating the dialectical characteristics of delta morphology.</p>
Daily inflow and rainfall data
<p>Daily inflow and rainfall time series used within the simulation of a small hydropower plant at Achelous river basin. These data are used in the paper titled " Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data", by Korina-Konstantina Drakaki, Georgia-Konstantina Sakki, Ioannis Tsoukalas, Panagiotis Kossieris, and Andreas Efstratiadis, published in Advance in Geosciences ( https://doi.org/10.5194/adgeo-1-1-2022).</p>
Augmented Net Primary Production in Arctic Ocean Sustained by Increased Subarctic Inflow Water
<p>Developing an optical classification-based chlorophyll (Chla) retrieval algorithm for the Arctic Ocean by matching in situ and remote sensing data. The statistical relationship between mixed layer depth integrated primary productivity (NPP) and sea surface Chla was also regressed from a large field measurement data set. Through principal component analysis and correlation analysis, NPP environmental driving factors were analyzed. Data sets include remote sensing reflectance data used to invert NPP and environmental drivers of NPP changes.</p>
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OpenNeuro
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