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99 results for “Flow system”
Simulated river flow and temperature in regulated river systems in the southeastern United States
<p>Streamflow and stream temperature are important water resources variables, and regional-scale simulations are essential for water resources management and multi-sector assessment for large regions, e.g., regional ecological assessment and power system planning. In large-scale stream temperature modeling practices, reservoir thermal stratification is mostly ignored. We have synthesized a process-based modeling approach, consisting of a series of established models, to simulate streamflow and stream temperature for a complicated river-reservoir system, which explicitly considers thermal stratification. This approach consists of a large-scale, spatially-distributed hydrological model (Variable Infiltration Capacity or VIC; Liang et al., 1994; Hamman et al., 2018), a river routing model (Model for Scale Adaptive River Transport or MOSART; Li et al., 2013), coupled to a spatially-distributed water management model (WM; Voisin et al., 2013, 2017), and a stream temperature model (River Basin Model or RBM; Yearsley, 2009; 2012) that includes a two-layer reservoir thermal stratification module (2L; Niemeyer et al., 2018). To generate this dataset, we applied this modeling approach at a temporal resolution of 1 day and a spatial resolution of 1/8º to river systems in the southeastern United States that include 271 major reservoirs. We used an ensemble of downscaled meteorological forcing data from 20 global climate models (GCM) based on RCP8.5 to simulate potential climate change impacts. This dataset includes simulated river flow and temperatures for both historical (1980-2009; 1980s) and future periods (2070-2099; 2080s). The simulations for the 1980s are based on the gridMet data set (Abatzolglou, 2013), which also forms the basis for the statistical downscaling that is applied to each of the climate models. All simulations for the 2080s are based on downscaled climate model outputs. This dataset includes streamflow and stream temperature using both unregulated and regulated model setups to quantify the impacts of reservoir regulations. The unregulated setup does not account for withdrawals and impoundments in the river system. The stream temperature in the unregulated model setups is constant in each river cross section. In the regulated setup, we explicitly considered reservoir regulation, thermal stratification, and water withdrawal. For a more detailed description of the model configuration, please see Cheng et al. (2020).</p> <p> </p> <p>File structure and filenames: The archive includes two directories, named “streamflow/” and “stream_temperature/”, which contain model output for streamflow and stream temperature, respectively. Within each directory, subdirectories named “regulated/” and “unregulated/” contain model output for the regulated and unregulated model setups, respectively. All data files are in netCDF format and provide model outputs at a temporal resolution of 1 day and a spatial resolution of 1/8º. The unit for streamflow is m<sup>3</sup>/s and the unit for stream temperature is °C.</p> <p> </p> <p>Files are constructed as follows:</p> <p>SERC.<climate simulation>.RCP85.<model setup>.<variable>.nc</p> <p>where</p> <ul> <li><climate simulation> is either ‘historical’ for the simulation that represents the 1980s or an abbreviation that indicates the climate model for the simulations that represent the 2080s. The abbreviations for the climate models are shown in column 1 in the Table below.</li> <li><model setup> is either ‘regulated’ or ‘unregulated’ for the regulated and unregulated model setups, respectively.</li> <li><variable> is either ‘streamflow’ or ‘stream_temperature’ for streamflow and stream temperature, respectively.</li> </ul> <p>More details please see README.pdf</p>
Example models and queries for Flow Systems Ontology
<p><strong>Example models and queries for Flow Systems Ontology</strong></p> <p>This repository contains the files used to produce the examples in the article manuscript introducing the <a href="https://w3id.org/fso">Flow Systems Ontology (FSO)</a>. This includes both the triples of the example models, and the SPARQL queries used to demonstrate the use cases.</p> <p>The manuscript has been published as "<a href="https://doi.org/10.1016/j.autcon.2021.104067">An ontology to support flow system descriptions from design to operation of buildings</a>" in Automation in Construction.</p> <p>For further details, see the included README.md.</p>
Replication data for: Energy flow analysis of an industrial ammonia refrigeration system
<p>This dataset includes energy data acquired from a pelagic fish processing plant, including data from an industrial ammonia refrigeration system that provides cooling and freezing. In addition, production data is included. Data from the system was analysed within the KSP project PCM-STORE (308847) supported by the Research Council of Norway and industry partners. PCM-STORE aims at building knowledge on novel PCM technologies for low-temperature thermal energy storage. Collecting and analysing data is an important part of evaluating the potential for reduction of CO2 emissions and increasing energy efficiency. Many processing plants measure and log data, but it is not often published. This dataset includes specific energy demand, peak power demand, power demand for different sections of the plant, ambient temperatures, and production volumes. The data was collected in 2021. The included graphics show the refrigeration system and some resulting tables and graphs. Production follows a seasonal cycle throughout the year, with no (or very low) production in the spring (Mar-May), and peak production in the autumn (Sep-Nov). The cycle is linked to the seasonal availability of fish. Annual SEC numbers (200-247 kWh/tonnes) were found to be in line with other Norwegian pelagic plants. A strong dependency between SEC and volume throughput were also found, where months of low production resulted in high SEC values and vice versa. Knowledge about the processes indicates that a fillet production is more energy intensive compared to round production, due to more energy demand from the fillet sections, higher mass (fish and brine) in each box and higher requirement of hot water for cleaning. This dataset is related to the conference paper "Energy flow analysis of an industrial ammonia refrigeration system and potential for a cold thermal energy storage" presented at the 15th IIR Gustav Lorentzen Conference on Natural Refrigerants, Trondheim, Norway 13-15 June 2022.</p>
Supplementary movies and figures for geophysical flows impacting a flexible barrier system
<p>The supplementary movies S1, S2, and S3 (presented in Figs. 1 and 2) show typical debris flow, debris avalanche, and rock avalanche impacting a flexible ring net barrier with vint = 6 m/s, respectively.</p> <p>As a supporting figure for Figs. 3b, 3c and 3d, Fig. S1 presents free surfaces of flowing layers and boundaries of dead zones measured at peak impacts for (a ~ d) DF, (e ~ h) DA and (i ~ l) RA cases near the slow-to-fast transitions.</p> <p>As a supplementary figure for Fig. 4, Fig. S2 presents the detailed barrier load-deformation cures until the peak barrier load is reached. It compares three Fr-dependent load-deflection modes of a flexible ring net barrier measured in all rock avalanche, debris avalanche and debris flow cases.</p>
Molecular simulations of nanoscale two-phase Couette flow of a water-hexane system on a hydrophobic substrate
<p>This dataset contains the output of Molecular Dynamics simulations (MD) of two-phase Couette flow of water/hexane biphasic systems, in terms of density, velocity and temperature fields. Instructions on how to read and analyze the output files in the <code>.tar.gz</code> archives can be found in these previously-published datasets: <a href="https://doi.org/10.5281/zenodo.8077915">https://doi.org/10.5281/zenodo.8077915</a>, <a href="https://doi.org/10.5281/zenodo.6541983">https://doi.org/10.5281/zenodo.6541983</a></p> <p>The run output files are labeled using the following pattern: <code>hex-ca<capillary-number>-q<partial-charge>.tar.gz</code>. It is possible to obtain the wall speed/contact line speed from the capillary number using the following formula: <code>u_w = U_0*<capillary-number></code>, with <code>U_0 = 37.246 m/s</code>.</p> <p>To reproduce the runs it is necessary to use a specific version of Gromacs that allows for a special algorithm of pressure scaling with position restraints. The code can be obtained by cloning <a href="https://github.com/MicPellegrino/gromacs-flow-field.git">https://github.com/MicPellegrino/gromacs-flow-field.git</a>, and switching to the <code>flow-field-grid-visco-coms-deform</code> branch.</p> <p>The folder <code>conf-wat-hex.zip</code> contains the configuration files to reproduce MD simulations. To prepare the equilibration runs at constant pressure, run after having installed Gromacs:</p> <p><code>gmx grompp -f npt.mdp -p topology.top -c before-npt.gro -r before-npt.gro -o system-npt.tpr</code></p> <p>while to prepare the shear runs:</p> <p><code>gmx grompp -f shear.mdp -p topology.top -c after-npt.gro -r lambda0.gro -rb lambda1.gro -o system-shear.tpr</code></p> <p>Simulations are launched by running:</p> <p><code>gmx mdrun -v -s <tpr-file-name>.tpr <possibly-other-mdrun-flags></code></p> <p>Have fun simulating!</p>
Molecular simulations of nanoscale two-phase Couette flow of a water-hexane system on a hydrophilic substrate
<p>This dataset contains the output of Molecular Dynamics simulations (MD) of two-phase Couette flow of water/hexane biphasic systems, in terms of density, velocity and temperature fields. Instructions on how to read and analyze the output files in the <code>.tar.gz</code> archives can be found in these previously-published datasets: <a href="https://doi.org/10.5281/zenodo.8077915">https://doi.org/10.5281/zenodo.8077915</a>, <a href="https://doi.org/10.5281/zenodo.6541983">https://doi.org/10.5281/zenodo.6541983</a></p> <p>The run output files are labeled using the following pattern: <code>hex-ca<capillary-number>-q<partial-charge>.tar.gz</code>. It is possible to obtain the wall speed/contact line speed from the capillary number using the following formula: <code>u_w = U_0*<capillary-number></code>, with <code>U_0 = 37.246 m/s</code>.</p> <p>To reproduce the runs it is necessary to use a specific version of Gromacs that allows for a special algorithm of pressure scaling with position restraints. The code can be obtained by cloning <a href="https://github.com/MicPellegrino/gromacs-flow-field.git">https://github.com/MicPellegrino/gromacs-flow-field.git</a>, and switching to the <code>flow-field-grid-visco-coms-deform</code> branch.</p> <p>The folder <code>conf-wat-hex.zip</code> contains the configuration files to reproduce MD simulations. To prepare the equilibration runs at constant pressure, run after having installed Gromacs:</p> <p><code>gmx grompp -f npt.mdp -p topology.top -c before-npt.gro -r before-npt.gro -o system-npt.tpr</code></p> <p>while to prepare the shear runs:</p> <p><code>gmx grompp -f shear.mdp -p topology.top -c after-npt.gro -r lambda0.gro -rb lambda1.gro -o system-shear.tpr</code></p> <p>Simulations are launched by running:</p> <p><code>gmx mdrun -v -s <tpr-file-name>.tpr <possibly-other-mdrun-flags></code></p> <p>Have fun simulating!</p>
Sample Records (PRISMA Checklist and Flow diagram): Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>This dataset includes: the PRISMA Checklist and the <span>PRISMA Flow diagram of the study titled: Disinformation as an obstructionist strategy in climate change mitigation: A review of the scientific literature for a systemic understanding of the phenomenon.</span></p>
Picophytoplankton and bacteria abundances analyzed with flow cytometry (FCM) from CCE-CalCOFI Augmented cruises in the California Current System, 2004 - 2023 (ongoing).
Picophytoplankton populations and non-pigmented prokaryotes are sampled within the California Current Ecosystem (CCE) for abundances from 3 to 8 depths at CalCOFI stations. Seawater is collected from Niskin bottles and cells are fixed in the field aboard the survey cruises (since 2004, ongoing) with paraformaldehyde, and stained with a DNA-specific dye back in the laboratory. The cells are enumerated by an Altra flow cytometer (with a syringe pump for volumetric sample delivery) simultaniously with argon ion lasers, to distinguish three major populations of photoautotrophs (Prochlorococcus, Synechococcus, and pico-eukaryotes) and the assemblage of heterotrophic prokaryotes collectively referred to as H-Bact.
Picophytoplankton and bacteria total carbon estimates from cell counts analyzed with flow cytometry (FCM) from CCE-CalCOFI Augmented cruises in the California Current System, 2004 - 2023(ongoing).
Picophytoplankton populations and non-pigmented prokaryotes are sampled within the California Current Ecosystem (CCE) for abundances from 3 to 8 depths at CalCOFI stations. Seawater is collected from Niskin bottles and cells are fixed in the field aboard the survey cruises (since 2004, ongoing) with paraformaldehyde, and stained with a DNA-specific dye back in the laboratory. The cells are enumerated by an Altra flow cytometer (with a syringe pump for volumetric sample delivery) simultaniously with argon ion lasers, to distinguish three major populations of photoautotrophs (Prochlorococcus, Synechococcus, and pico-eukaryotes) and the assemblage of heterotrophic prokaryotes collectively referred to as H-Bact. FCM abundance estimates for each are converted to carbon biomass equivalents using mixed-layer estimates.
FIGURE 2 in Speciation with gene flow in marine systems
FIGURE 2 Cladogram of the hypotheses of Animalia (after Dunn et al., 2014) and Chromista (after CavalierSmith, 2018) phylogenies. Animalia and Chromista clades in which marine species have been described, are marked blue, with clades in which potential cases of ecological speciation with gene flow Downloaded from Brill.com 12/12/2023 03:01:49PM have been identified in orangevia. Open LettersAccess refer.toThis examples is an of openspecies accessshown articlein fig. 1. distributed under the terms of the prevailing CC-BY license at the time of publication. http://creativecommons.org/licenses/by-nc/4.0
FIGURE 1 in Speciation with gene flow in marine systems
FIGURE 1 Examples of some species which potentially arose through ecological speciation with gene flow. The octocoral Eunicea flexuosa (a; photo by Y.W. Lau), the Caribbean hamlet Hypoplectrus unicolor (b; photo by O. Puebla), the intertidal gastropod Littorina saxatilis (c; photo by P. Larsson), the nudibranch Phestilla minor (d; photo by A. Fritts-Penniman), the Japanese greenling Hexagrammos otakii (e; photo by Z. Kanamoto), the corallivorous gastropod Coralliophila violacea (f; photo by S.E. Simmonds), the Downloaded from Brill.com 12/12/2023 03:01:49PM seahorse Hippocampus erectus (via g; Open photo by Access E.. RoseThis), the is an snapping open access shrimparticle Alpheus armatus distributed(h;under photothe terms by A. Anker), the undescribed, coral-associated of gobytheGobiodon prevailingsp. B (CC-BY i; photo licenseby P. at L. the Mundaytime),of and the publication. Caribbean sponge Chondrilla caribensis (j; photo by N.J. de Voogdhttp):. //creativecommons.org/licenses/by-nc/4.0
FIGURE 3 in Speciation with gene flow in marine systems
FIGURE 3 Genetic mechanisms of single traits driving divergence. Disruptive selection acting on a mating cue (e.g., colour pattern or body size) targeted by a preference locus elsewhere in the genome ('classic magic trait', after Servedio et al., 2011) results into assortative mating (a). In case of habitat or host differentiation and mating on the host species, disruptive selection may directly lead to assortative mating (b), without the need for a preference locus ('automatic magic trait'). Figures after Servedio et al. (2011). Downloaded from Brill.com 12/12/2023 03:01:49PM via Open Access. This is an open access article distributed under the terms of the prevailing CC-BY license at the time of publication. http://creativecommons.org/licenses/by-nc/4.0
Data for: Water system simulation modeling with hydropower optimization and environmental flows: An example with Pywr
<p>This dataset was used in the CenSierraPywr model created for the project "Optimizing Hydropower Operations While Sustaining Ecosystem Functions in a Changing Climate", for the California Energy Commission. Specifically, this data is to support reproducibility of the article describing the basic methods (Rheinheimer et al., in review). The model was built in Pywr, an open-source, linear programming-based Python package for modeling basin-scale water systems in the Central Sierra Nevada, California. Here, we focus on the Stanislaus and Upper San Joaquin River basins as they have high elevation hydropower typically operated to maximize revenue. CenSierraPywr consists of daily water allocations that include both hydroeconomic drivers for hydropower and more advanced environmental flows. Piecewise linear electricity prices from simulated hourly price data are used to drive discretionary hydropower, while environmental flows include the addition of ramping rates. Hydrological inputs include runoff data at the sub-basin level, based on the historical (1950 to 2011) daily gridded (1/16 degree) runoff data generated by the Variable Infiltration Capacity (VIC) hydrologic model developed by Livneh et al. (2013), forced with observed meteorological data and bias-corrected using local gauge data. All data inputs for reproducibility of CenSierraPywr for the Stanislaus and Upper San Joaquin Rivers are included, including original and preprocessed electricity and hydrological data and management-related data specific to certain hydropower projects or facilities.</p>
Global Positioning System Based on Optical Flow and Convolutional Neural Network
<p>Two datasets used in the paper Global Positioning System Based on Optical Flow and Convolutional Neural Network, to evaluate the proposed CNN model to the task of position estimation. The datasets cover 2 different modes of motion of a drone, and were captured using Google API.</p>
High-pressure flow digestion system
<p><span>Buildup of the presented high-pressure flow-through microwave-assisted digestion system. The gray shaded components including the digestion vessel indicate the pressurized part. The counter-current nitrogen stream enters the digestion system at the nitrog</span>en inlet port/ sample exit port and leaves the system at the pressure interface. (A, H) nitric acid 5% (v/v) rinsing solution, (B) sample (1% (w/w) wine), (C) waste (D) digested sample, (E) precision dispenser, (F) six-port high performance valve, (G) sample loop, (I) Knauer HPLC pump, (J) pressure interface including the nitrogen exit port, (K) cooling unit, (L) high pressure vessel, (M) magnetron, (N) nitrogen inlet port/ sample exit port, (O) pressure restriction loop</p>
Figure.4. Proposed system's flow chart-Single Trial Classification of Evoked EEG Signals Due to RGB Colors
<p>In this paper we proved the possibility to perform a single trial classification the EEG signals which are evoked by the RGB color stimulus. The required time to do this process is much shorter than the time which is required by any other stimulus, such as imagery and spelling words, which is presented in the previous researches. This result proves the main idea behind using colors in the next generation of BCI systems, which is based on introducing more efficient and faster systems that are able to give a quicker response than any other time. As a future work, we are going to conduct a BCI application that controls a cursor movement on PC by using those signals. This is unlike earlier BCI systems where cursor controlled movement application is controlled by the imagination of foot and hand movement, but no one has controlled it with colored stimuli before. Such study would be used to simulate an environment where a disabled person would be expected to drive a vehicle in a virtual environment with a possible uniform background, in which the vehicle will either start and/or stop moving on appearance of Green and Red lights respectively.</p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 5. Emotional flow after 3 hours of graphical editing
<p>To resume, we started with a product interface, found a way to determine two opposite states, than used that way to map user interaction with the product and determine the emotional answer to that interface. The results can then be used to improve product design, to elicit certain emotional responses, etc. For example, in this case, due to the rapid movement (anger) patterns in the aligning phase, a layout that minimizes this can be developed, using a shortcut menu or dynamically appearing guidelines. Furthermore, the user interface can be imagined to be able to learn working patterns and shift the shortcut menu from an aligning menu, if it detects anger patterns, into a color/shape picking menu, if it detects relaxation patterns, therefore assessing the emotional impact and improving the design of the interface in the same time.</p>
District heating modelling data for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems"
<p>Modelling data for a district heating system model which has been used for the publication "Integration of feed flow temperatures in unit commitment models of future district heating systems" on the 4th Generation District Heating (4GDH) conference 2018.</p>
Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows
<p>Title: <strong>Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows</strong> Author: Pamela A. Green (<a href="mailto:pg@pamelaagreen.com">pg@pamelaagreen.com</a>), Advanced Science Research Center, CUNY, New York, NY USA <a href="https://orcid.org/0009-0006-7803-8182">https://orcid.org/0009-0006-7803-8182</a></p> <p>The python Jupyter Notebook <strong>SafeJustEarthSysBnd_EstressCUNY-Griffith2022-23.ipynb</strong> and accompanying data sets represent spatial modelling for development of the safe and just surface water target for Working Group 3 of the Earth Commission for the Earth Commission Long Report and the "Safe and Just Earth Systems Boundaries" publication. The surface water target includes spatial modelling of the extent of global-scale hydrological alteration of environmental flows.</p> <p>All input datasets required to run the model are located under the <strong>ModelInput</strong> folder with raster data in zipped format to minimize space requirements. The code extracts the zipped files and then deletes the uncompressed files upon completion. All model outputs are located under the <strong>ModelOutput</strong> folder.</p> <p>Please reference the <strong>README.xlsx</strong> file for a full listing of the model input and output data files.</p>
Dataset: A Neutral pH Aqueous Biphasic System Applied to both Static and Flow Membrane-free Battery
<p>Dataset for the results shown in the publication "A Neutral pH Aqueous Biphasic System Applied to both Static and Flow Membrane-free Battery"</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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