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16 results for “power dynamics”
Double-Versus Triple-Potential Well Energy Harvesters: Dynamics and Power Output
<div>The present datasets and figures focus on the analysis of BEH and TEH systems where the corresponding depth of the potential well and the width of their characteristics are the same. The efficiency of energy harvesting for TEH and BEH systems assuming similar potential parameters is provided. The basic types of multistable energy harvesters are bistable energy harvesting systems (BEH) and tristable energy harvesting systems (TEH). Providing such parameters allows for reliable formulation of conclusions about the efficiency in both types of systems. These energy harvesting systems are based on permanent magnets and a cantilever beam designed to obtain energy from vibrations. Starting from the bond graphs, we derived the nonlinear equations of motion. Then we followed the bifurcations along the increasing frequency for both configurations. To identify the character of particular solutions, we estimated their corresponding phase portraits, Poincare sections, and Lyapunov exponents. The selected solutions are associated with their voltage output. The results in this numerical study show clearly that the bistable potential is more efficient for energy harvesting provided the corresponding excitation amplitude is large enough. However, the tristable one could work better in the limits of low-level and low-frequency excitations. </div> <div> <h2>Series information</h2> <p>Potential characteristics of energy harvesting systems caused by magnetic field of distributed permanent magnets:</p> <ul> <li>Fig4a_b_V_y1.txt</li> <li>Fig4a_r_V_y1.txt</li> </ul> <p>Potential characteristics of energy harvesting systems caused by magnetic field effect as in the previous case and an additional change in the stiffness of the flexible cantilever beam:</p> <ul> <li>Fig4b_b_V_y1.txt</li> <li>Fig4b_r_V_y1.txt</li> </ul> <p>Series of steady states of the system against frequency for two potential wells. </p> <ul> <li>Fig6a_p005.txt</li> <li>Fig6c_p025.txt</li> <li>Fig6e_p05.txt</li> <li>Fig6g_p085.txt</li> </ul> <p>Series of steady states of the system against frequency for three potential wells</p> <ul> <li>Fig6b_p005.txt</li> <li>Fig6d_p025.txt</li> <li>Fig6f_p05.txt</li> <li>Fig6h_p085.txt</li> </ul> <p>The excitation amplitude increases downwards from 0.05 to 0.85 and its values are listed in the corresponding description. The results were obtained for zero initial conditions. ω and x are dimensionless.</p> </div> <div><strong>Figures:</strong></div> <div> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%202.jpg/content" target="_blank" rel="noopener"><br>ig 2.jpg</a> - A graph of bonds representing the dynamics of the tested design solutions of energy harvesting systems.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%203.jpg/content" target="_blank" rel="noopener">Fig 3.jpg</a> - A Lagrangian bond graph, with causality conflicts intentionally introduced.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%204.jpg/content" target="_blank" rel="noopener">Fig 4.jpg</a> - Potential characteristics of energy harvesting systems caused by: (<strong>a</strong>) magnetic field of distributed permanent magnets (Fig. 1); (<strong>b</strong>) magnetic field effect as in previous case and an additional change in stiffness of the flexible cantilever beam (to satisfy equal potential barriers <em>V</em><sub>2</sub> = <em>V</em><sub>3</sub> ) used in further calculations.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%206.jpg/content" target="_blank" rel="noopener">Fig 6.jpg</a> - Bifurcation diagrams (stroboscopic) of steady states of the system against frequency for: (<strong>a</strong>) Two potential wells; (<strong>b</strong>) three potential wells. The excitation amplitude increases downwards from 0.05 to 0.85 and its values are listed in the corresponding sub-figures. The results were obtained for zero initial conditions. <em>ω</em> and <em>x</em> are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%207.jpg/content" target="_blank" rel="noopener">Fig 7.jpg</a> - Exemplary solutions showing the geometrical structures of chaotic phase flows and the corresponding Poincaré cross-sections of a BEH. <em>Dc</em> denotes the corresponding correlation dimension. <em>ω</em>, <em>p</em>, <em>x, </em>and x' are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%208.jpg/content" target="_blank" rel="noopener">Fig 8.jpg</a> - Examples of periodic responses of a BEH system identified for dimensionless mechanical vibration amplitudes: (<strong>a</strong>) <em>p</em> = 0.05; (<strong>b</strong>) <em>p</em> = 0.25; (<strong>c</strong>) <em>p</em> = 0.5; (<strong>d</strong>) <em>p</em> = 0.85. <em>ω</em>, <em>p</em>, <em>x, </em>and<em> x'</em> are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%209.jpg/content" target="_blank" rel="noopener">Fig 9.jpg</a> - Example solutions showing geometric structures of chaotic phase flows and corresponding Poincaré cross-sections, which were identified for a system with three potential wells (TEH). <em>Dc</em> denotes the corresponding correlation dimension. <em>ω</em>, <em>p</em>, <em>x, </em>and<em> x'</em> are dimensionless.</p> <p>Fig 10.jpg – Influence of external load characteristics on periodic induced solutions in a TEH. Trajectory shapes are plotted for selected frequencies ω. <em>ω</em>, <em>p</em>, <em>x, </em>and<em> x'</em> are dimensionless.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%2011.jpg/content" target="_blank" rel="noopener">Fig 11.jpg</a> - Multicolored maps of the values of effective energy harvesting systems (RMS voltage outputs) with the potential: (<strong>a</strong>) two-well (BEH); (<strong>b</strong>) three-well (TEH) for zero initial conditions. <em>ω</em> and <em>p</em> are dimensionless, while <em>U<sub>RMS</sub></em> is expressed in Volts.</p> <p><a href="https://zenodo.org/api/records/14176152/draft/files/Fig%2012.jpg/content" target="_blank" rel="noopener">Fig 12.jpg</a> - Comparison of RMS<strong> </strong>voltage outputs for two and three wells potential systems versus amplitude and frequency. ω is dimensionless while <em>U<sub>RMS</sub></em> is expressed in Volts.</p> </div>
Open data and power dynamics
<p>Podcast with an expert -Mor Rubenstein- about the management of open data and the power dynamics that are at play in society. In particular we are referring mainly to western Europe as the societal context.</p>
Illustrative dataset for the article: Vieira, R., McDonald, S., Araujo-Soares, V., Sniehotta, F., Henderson, R. (2017) "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies"
<p>This dataset is supplementary material of the manuscript "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies. McDonald et al. (2016) presents a series of novel n-of-1 studies that intended to explore the relationship between physical activity change during the retirement transition. The file contains the data of one participant. The column names correspond to the following variables:</p> <p>time: duration of follow-up (minutes);<br> minute: time of day (hours and minutes);<br> day_num: day since beginning of follow-up (the first two days were considered as adaptation phase and therefore removed); <br> PAscore: accelerometer raw score; <br> startBout: 1 (a bout of PA was initiated in this minute) or 0 (a bout of PA wasn't <br> initiated in this minute); <br> nPAbouts_day: number of PA bouts per day; <br> nPAbouts_day.l1: number of PA bouts in previous day (lag 1); <br> nPAbouts_day.l2: number of PA bouts two day before (lag 2); <br> nBoutsLast2hours: number of PA bouts in previous 2 hours; <br> retirement: 0 (before retirement) or 1 (after retirement)<br> weekday: 0 (workday) or 1 (weekend)<br> sleepLength: number of hours of sleep last night<br> sleepLength.l1: number of hours of sleep the night before<br> sleepLength.l2: number of hours of sleep two nights before<br> pers: personalised measure of partner's influence (scale 0-1)<br> periodDay: morning, evening or afternoon</p> <p>McDonald, S., Vieira, R., O'Brien, N., White, M., & Sniehotta, F. F. (2016). Does physical activity and sedentary behavior change during the retirement transition? Findings from a series of novel n-of-1 natural experiments. <em>International Journal of Behavioral Medicine, 23</em>, S261-S261.</p> <p> </p>
Dataset for publication "Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications"
<p>This is dataset related to paper published in 2020 IEEE I2MTC Conference proceedings:</p> <p>Crotti G., Giordano D., Letizia P. S., Delle Femine A., Gallo D., Landi C., Luiso M., Barbieri L., Mazza P., Pallidini D., (2020, October 29). Measurement of Dynamic Voltage Variation Effect on Instrument Transformers for Power Grid Applications. https://doi.org/10.5281/zenodo.4154617</p> <p>Excel file contains the data for Figure 10 and following parameter evaluation.</p>
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
<p><span><span>The selective catalytic reduction (SCR) de</span><span>-</span><span>NO<sub>x</sub> </span><span>process in coal-fired power plants not only displays nonlinearity, large inertia, and time variation but also a lag in NO<sub>x</sub> analysis; </span><span>hence,</span><span> it is difficult to obtain an accurate model </span><span>that </span><span>can be used to control NH<sub>3</sub> injection </span><span>during changes in the </span><span>operating state. </span><span>In this work,</span><span> a novel dynamic inferential model with delay estimation was proposed for NO<sub>x</sub> emission prediction. First, k-nearest neighbour mutual information (knnMI) was used to estimate the time-delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square (mwKPLS) was</span><span> used</span><span> to improve the prediction ability, </span><span>and this was followed by verification using </span><span>benchmark dataset experiments. Finally, the delay-time difference (DTD) method and feedback correction strategy </span><span>were </span><span>proposed to deal with the time variation of the SCR de</span><span>-</span><span>NO<sub>x</sub> process.</span> <span>Through the analysis of the </span><span>experimental field data </span><span>in the</span> <span>steady state, </span><span>the variable</span><span> state and </span><span>the </span>NO<sub>x</sub> analyser blowback process<span>, the results proved that</span><span> this dynamic model has </span><span>high prediction accuracy</span><span> during</span><span> state changes and can </span><span>realize</span><span> advance prediction of the NO<sub>x</sub> emission. </span></span></p>
Great Power Dynamics and International Economic Cooperation: Experimental Evidence from Parallel Surveys in China and the United States
Open the record for dataset details and reuse information.
Dynamic stability of synthetic power grid models
<p>This repository contains three datasets of synthetic power grids and their dynamic stability.</p> <p>1. 10,000 grids of size 20 (ds20) stored in dataset020.zip</p> <p>2. 10,000 grids of size 100 (ds100) stored in dataset100.zip</p> <p>3. 1 Texan power grid model with 1,910 nodes (texas) stored intexas.zip</p> <p> </p> <p>There are three tasks SNBS (regression) and the identification of troublemakers using regression and thresholding based on the maximum frequency deviation or classification based on binary targets.</p>
Diffusion power spectra as a window into dynamic materials architecture
Open the record for dataset details and reuse information.
Hovering flight in hummingbird hawkmoths: Kinematics, wake dynamics and aerodynamic power
Open the record for dataset details and reuse information.
Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
Open the record for dataset details and reuse information.
Boosting large scale capacitive harvesting of osmotic power by dynamical matching of ion exchange kinetics
<p>Raw data for the article "Boosting large scale capacitive harvesting of osmotic power by dynamical matching of ion exchange kinetics" by N. Chapuis and L.Bocquet.</p>
Comparison of Dynamic Mechanical Power Formula With Geometric Method in Pressure- and Volume-Controlled Ventilation: A Validation Study
ClinicalTrials.gov study NCT07099729. IPD Sharing: NO. Countries: 1. Publications: 14.
Acute Impact of Static, Dynamic, and Proprioceptive Exercises on Proprioception, Strength, Balance, and Explosive Power
ClinicalTrials.gov study NCT07320027. IPD Sharing: NO. Countries: 1. Publications: 1.
Intermediate results of simulation study of the article "Modified Conditional Borrowing-by-part Power Prior for dynamic and parameter-specific information borrowing of the Gaussian endpoint".
<p>This file contains the intermediate results of the simulation study of the article "Modified Conditional Borrowing-by-part Power Prior for dynamic and parameter-specific information borrowing of the Gaussian endpoint".</p>
Source code and data for Ou et al. 2021 (US state-level capacity expansion pathways with improved modeling of the power sector dynamics within a multisector model)
<p>For details, please check the "readme" file.</p>
Clinical Investigation (Beta) of Power Knee Mainstream - Dynamic
ClinicalTrials.gov study NCT04787627. IPD Sharing: NO. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.