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26 results for “energy flow”
Pythia8 Quark and Gluon Jets for Energy Flow
<p>Two datasets of quark and gluon jets generated with Pythia 8, one with all kinematically realizable quark jets and one that excludes charm and bottom quark jets (at the level of the hard process). The one without c and b jets was originally used in <a href="https://arxiv.org/abs/1810.05165">Energy Flow Networks: Deep Sets for Particle Jets</a>. Generation parameters are listed below:</p> <ul> <li>Pythia 8.226 (without bc jets), Pythia 8.235 (with bc jets), <span class="math-tex">\(\sqrt{s}=14\,\text{TeV} \)</span></li> <li>Quarks from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos</li> <li>FastJet 3.3.0, anti-ki jets with R=0.4</li> <li><span class="math-tex">\(p_T^\text{jet}\in[500,550]\,\text{GeV},\,|y^\text{jet} |<1.7\)</span></li> </ul> <p>There are 20 files in each dataset, each in compressed NumPy format. Files including charm and bottom jets have 'withbc' in their filename. There are two arrays in each file</p> <ul> <li>X: (100000,M,4), exactly 50k quark and 50k gluon jets, randomly sorted, where M is the max multiplicity of the jets in that file (other jets have been padded with zero-particles), and the features of each particle are its pt, rapidity, azimuthal angle, and pdgid.</li> <li>y: (100000,), an array of labels for the jets where gluon is 0 and quark is 1.</li> </ul> <p>If you use this dataset, please cite this Zenodo record as well as the corresponding paper:</p> <ul> <li>P. T. Komiske, E. M. Metodiev, J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165.</li> </ul> <p>For the corresponding dataset of Herwig jets, see <a href="https://zenodo.org/record/2664330">this Zenodo record</a>. The datasets can be downloaded and read into python automatically using the <a href="https://energyflow.network/docs/datasets/#quark-and-gluon-jets">EnergyFlow Python package</a>.</p> <p>Changes:</p> <ul> <li>v1 - Added files with b and c quark jets.</li> </ul>
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>
Silica dissolution under a flow of pure water at pressure and temperature conditions relevant to geothermal energy extraction
<p>This dataset is a published product of the 'REFLECT' Project - a Horizon Europe project which aims to inform the processes of geothermal energy extraction by determining the effect of relevant fluid properties and reactions in order to enhance predictive geochemical modelling and thus the energy exploitation and life-time of geothermal power plants.</p><p>The dataset records the concentration of silica measured in water that had been passed through a packed column of quartz grains at temperatures from 200 to 450°C and pressures from 150 to 450 bar. Concentrations are reported as g/ml SiO2, measured photometrically. The reader is referred to the full report for deliverable 1.4 of the REFLECT project for details of the experimental set up and interpretation of the data.</p><p>Silica concentrations marked with (a) are believed to be artificially reduced compared to the rest of the dataset due to a reduction in the surface density of active sites during some of the highest dissolution experiments. The final column lists the chronological order in which the measurements were taken to assist with interpretation of this factor.</p><p>The density marked with (b) represents the density of water at 150 bar and 342°C, rather than the measured condition of 148 bar and 344°C. This is to reflect the fact that the solubility measurement suggests the presence of a liquid phase - the conditions chosen represent the closest point on the phase boundary to the measured conditions. The discrepancy may reflect a small shift in the phase envelope due to silica dissolution in addition to any uncertainty in the <i>pT</i> measurements.</p>
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
<p>Energy harvesting from flow induced vibrations (FIV) in flexible bodies offer opportunities for power generation in biomimicking robotic devices and is an active area of research. The focus of this study is on investigating the underlying physics and qualitatively analysing the energy extraction scenarios in similar structural systems, comprising of a flexible piezoelectric flapper in a low Reynolds number flow regime. A high-fidelity three-way fully coupled fluid-structure-electric energy solver is developed in-house to study the energy harvesting capabilities of such a flapper, its hydrodynamic characteristics and the associated unsteady flow-field. The results indicate that the flapper deformation profiles at the most efficient harvesting regimes, resemble the propulsion gaits of natural swimmers. Investigations on the effects of a sinusoidal heaving actuation reveal no significant impact on the harvested power at the high yield (high power output) regime, identified under the passive condition showing biomimetic gait. This study provides mechanics based insights that is expected to be useful for bio-inspired designs of FIV based harvesters.</p>
Production Line energy and material flow dataset
<p>File dumpXXXXXX-1122 recounts production of 16 pieces, with<br> workplan "production with no fuse".<br> File dumpXXXXXX-1302 recounts production of 16 pieces, with workplan :production with left<br> fuse"</p>
Fig. 4 in Modeling energy flow in a large Neotropical reservoir: a tool do evaluate fishing and stability
Fig. 4. Simulated Total catch (solid line) and catch values observed (triangles). Simulations performed on ITAIPU-2 model, under constant fishing effort (values were close to 1998). Simulations made in Ecopath with Ecosim (Subroutine: Run Ecossim, module: Results).
Fig. 1 in Modeling energy flow in a large Neotropical reservoir: a tool do evaluate fishing and stability
Fig. 1. Itaipu Reservoir, its tributaries and the upper Paraná River Floodplain upstream (spawning areas for the reservoir migratory fish species).
Data from: Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits
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Data for: Kelp forest loss and emergence of turf algae reshapes energy flow to predators in a rapidly warming ecosystem
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Renormalisation of the energy-momentum tensor in three-dimensional scalar SU(N) theories using the Wilson flow -- data release
<p>This repository contains the lattice two-point function measurements required to reproduce the results of the paper "Renormalisation of the energy-momentum tensor in three-dimensional scalar SU(N) theories using the Wilson flow" (<a href="https://arxiv.org/abs/2009.14767">https://arxiv.org/abs/2009.14767</a>).</p> <p>The code required to perform the data analysis can be found in <a href="https://github.com/josephleekl/scalar_emt_analysis">https://github.com/josephleekl/scalar_emt_analysis</a>.</p> <p>For any questions please get in touch: joseph.lee@ed.ac.uk </p>
Elastic energy storage in seahorses leads to a unique suction flow dynamics compared to other actinopterygian
<p></p><p>Suction feeding is a dominant prey-capture strategy across actinopterygians, consisting of a rapid expansion of the mouth cavity that drives a flow of water containing the prey into the mouth. Suction feeding is a power-hungry behavior, involving the actuation of cranial muscles as well as the anterior third of the fish's swimming muscles. Seahorses, which have reduced swimming muscles, evolved a unique mechanism for elastic energy storage that powers their suction flows. This mechanism allows seahorses to achieve head rotation speeds that are 50 times faster than fish lacking such a mechanism. However, it is unclear how the dynamics of suction flows in seahorses differ from the conserved pattern observed across other actinopterygians, nor how differenced in snout length across seahorses affect these flows. Using flow visualization experiments, we show that seahorses generate suction flows that are 8 times faster than similar-sized fish, and that the temporal patterns of cranial kinematics and suction flows in seahorses differs from the conserved pattern observed across other actinopterygians. However, the spatial patterns retain the conserved actinopterygian characteristics, where suction flows impact a radially symmetric region of ∼1 gape diameter outside the mouth. Within seahorses, increases in snout length were associated with slower suction flows and faster head rotation speeds, resulting in a trade-off between pivot feeding and suction feeding. Overall, this study shows how the unique cranial kinematics in seahorses are manifested in their suction feeding performance, and highlights the trade-offs associated with their unique morphology and mechanics.</p><p></p>
Auto Continuous Positive Airway Pressure (CPAP) Based Energy Spectrum Analysis of Flow for Treatment of Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS)
ClinicalTrials.gov study NCT00750165. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Elastic energy storage in seahorses leads to a unique suction flow dynamics compared to other actinopterygian
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CoUDlabs_WP8_T811_UOS_001. Investigating geometrical effects on hydraulic energy losses during sewer to surface flow interactions during urban floods
<p>This document describes the dataset used in CO UD-labs JRA3 (WP 8) Task 8.1.1. This considers the hydraulic exchange (surcharge) from a piped drainage system to surface flood flow through a manhole. The dataset includes measurements of pressure, flow rate and depth from a physical scale model. The effect of changing the manhole lid properties on flow exchange (surcharge) and pressure in the experimental system is quantified over a range of flow rates. </p>
Data file for paper: Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018</p> <p>DOI: 10.1088/2515-7655/aaee17 </p> <p>Please cite the above reference if you wish to use this data</p>
Dataset for the paper "Electrochemical and Spectroscopic Characterisation of Organic Molecules with High Positive Redox Potentials for Energy Storage in Aqueous Flow Cells", DOI:10.1039/d4ya00366g
<table> <tbody> <tr> <td>The data in this spreadsheet was used to produce the figures in the paper</td> </tr> <tr> <td>Authors:</td> <td>Christopher G. Cannon, Peter A. A. Klusener, Nigel P. Brandon, and Anthony R. J. Kucernak</td> </tr> <tr> <td>Title:</td> <td>Electrochemical and Spectroscopic Characterisation of Organic Molecules with High Positive Redox Potentials for Energy Storage in Aqueous Flow Cells</td> </tr> <tr> <td>Journal:</td> <td>Energy Advances</td> </tr> <tr> <td>DOI:</td> <td>DOI:10.1039/d4ya00366g</td> </tr> <tr> <td>Please cite the above reference if you wish to use this data</td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td>DOI of data:</td> <td><span>10.5281/zenodo.13712672</span></td> </tr> </tbody> </table>
The Effects of Differing Cognitive Task Demands on Whole-body Energy Metabolism and Cerebral Blood-flow: Modulation by Multivitamins/Minerals and Coenzyme Q10
ClinicalTrials.gov study NCT02381964. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Codes and datasets associated with the paper "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows"
<p>Here, you will find some of the codes and datasets utilized in the article: "Parameterizing the Energy Dissipation Rate in Stably Stratified Flows". (<a href="https://arxiv.org/abs/2001.02255">https://arxiv.org/abs/2001.02255</a>)</p> <p>The direct numerical simulations were performed using the HERCULES code (<a href="https://github.com/friedenhe/HERCULES">https://github.com/friedenhe/HERCULES</a>). For all the simulations, the bulk Reynolds number is kept at 20,000. The bulk Richardson number is varied from 0.1 to 0.5. The zip files Re20000RiXX.zip contains the various turbulence statistics. </p> <p>Re20000Ri10: Ri_b = 0.1</p> <p>Re20000Ri20: Ri_b = 0.2</p> <p>Re20000Ri30: Ri_b = 0.3</p> <p>Re20000Ri40: Ri_b = 0.4</p> <p>Re20000Ri50: Ri_b = 0.5</p> <p>Every dat file (within the zip archives) is in ascii format and has 10 rows and 286 columns. Each row corresponds to normalized time (ranging from 1 to 10). Each column represents vertical model levels. </p> <p> </p>
Grains-energy release governs debris flow high mobility
<p>These files include the data to generate Figs. 1-2 and 4 and Figs. S7-S13 in supporting information</p>
Data and MATLAB code for the publication entitled "Intensification of Submesoscale Frontogenesis and Forward Energy Cascade Driven by Upper-Ocean Convergent Flows"
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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)
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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.