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21 results for “thermal power”
Mars Express thermal power dataset
<p>This dataset contains several features related to the power resources and general situation of <a href="https://www.esa.int/Science_Exploration/Space_Science/Mars_Express">ESA's Mars Express</a> spacecraft. Its purpose is the design of predictive models related to thermal power consumption. Having an efficient planning on the allocation of power is critical, as it allows to maximize the scientific gain and extend the life-time of the overall mission.</p> <p>The data has been split into two sets:</p> <ul> <li>MEX1 containing 5795 orbits from 01.01.2015 to 18.08.2019</li> <li>MEX2 containing 1507 orbits from 08.08.2019 to 31.10.2020</li> </ul> <p>MEX1 can be seen as a training set. It was used to fit a reference model (THP 2.0 - Thermal Power Model) which is available in the <strong>thp2 </strong>column. MEX2 can be seen as a test set, providing previously unseen orbits.</p> <p>All the features are aggregated for each full orbit of the spacecraft. They include:</p> <ul> <li><strong>timestamp</strong>: time [unix timestamp]</li> <li><strong>sme</strong>: the value of Sun-Mars-Earth angle [deg]</li> <li><strong>s_m_distance</strong>: Sun-Mars distance in [km]</li> <li><strong>orbital_p</strong>: length of orbital period [s]</li> <li><strong>average_power</strong>: measured average power consumption [W]</li> <li><strong>right_flag</strong>: Guidance flag, North/South flag, determines whether LVA is exposed to sun, 1 = yes, 0 = no </li> <li><strong>eclipse_l</strong>: length of the eclipse in orbit [s]</li> <li><strong>full_off</strong>: whether full off or drive off, full off = 1, only drive off = 0</li> <li><strong>average_xtx</strong>: average current drawn by transmitter component [A] (multiply by 28V to get W)</li> <li><strong>gsep_dur</strong>: time spent in GSEP [s]</li> <li><strong>lvah</strong>: Heating of the LVA (launch vehicle adapter) [combined feature]</li> <li><strong>sh</strong>: Solar heating [combined feature]</li> <li><strong>thp2</strong>: output of the reference model THP2 model [W]</li> </ul> <p><strong>lvah </strong>is a heating related to the orientation of the spacecraft and is computed by </p> <p><span class="math-tex">\(LVAH = SME \cdot \text{right_flag} \)</span></p> <p><strong>sh</strong> is another aggregated feature representing the total solar power influx. It is computed by:</p> <p><span class="math-tex">\(SH = \left( 1 - \frac{\text{eclipse_d}}{\text{orbital_p}}\right) \frac{3.846 \cdot 10^{26} W}{4 \cdot \pi \cdot (\text{s_m_distance} \cdot 1000)^2} \)</span></p> <p><strong>average_power</strong> is the target of prediction and thus of interest to estimate from other parameters.</p> <p><strong>thp2 </strong>provides a reference model that has been developed by ESA engineers to predict <strong>average_power.</strong></p>
Measurement Dataset of Thermal Fault Emulation of a 46Ah High-Power Kokam Nano Pouch Cell via Uniform and Local Heating
<h1>Preface</h1> <p>This dataset contains experimental data that supplement the article <em>Thermal fault detection by changes in electrical behaviour in lithium-ion cells </em>(<a href="https://doi.org/10.1016/j.jpowsour.2021.229572" target="_blank" rel="noopener">10.1016/j.jpowsour.2021.229572</a>) in the Journal of Power Sources. This dataset extends the already published cell characteristics (see <a href="https://doi.org/10.17632/g443f7cn7p.2" target="_blank" rel="noopener">10.17632/g443f7cn7p.2</a>) by all measured quantities associated with the conducted study. Therefore, the dataset includes sensor readings that have not been described in the before mentioned documents due to space limitations. <em><br></em></p> <p>The published data belongs to the master thesis <em>Development of a model-based method for the early detection of safety-critical heating of lithium-ion cells (transl.), Klink</em> <em>(2020), TU Clausthal</em> that is connected to a study thankfully funded by the European Automobile Manufacturers' Association (ACEA).</p> <h1>Structure</h1> <p>The repository is subdivided in four directories (.zip) based on the content. Within these directories, the individual datasets can be found. While every dataset contains three different file types, the corresponding files can be identified based on the identical filenames. The following file types are provided:</p> <table> <tbody> <tr> <td><strong>File type</strong></td> <td><strong>Content</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>*.png</td> <td>Simple graph of the provided data.</td> <td>Missing values are interpolated.</td> </tr> <tr> <td>*.csv</td> <td>Tabular data of the dataset.</td> <td>Columns are separated by ";", the decimal point is ".".</td> </tr> <tr> <td>*.pickle</td> <td>Pickled object of a <a href="https://pandas.pydata.org/docs/index.html" target="_blank" rel="noopener">pandas</a> dataframe (Python) of the data. Preserve index and data types.</td> <td>Pickled with pandas version 2.2.2 using the pickle protocol 5</td> </tr> </tbody> </table> <p>The index and column names of the tabular time series have the following name scheme: X_Y_Z </p> <table> <tbody> <tr> <td><strong>Placeholder</strong></td> <td><strong>Description</strong></td> <td><strong>Example</strong></td> </tr> <tr> <td>X</td> <td>Quantity symbol</td> <td>U for voltage, I for current</td> </tr> <tr> <td>Y</td> <td>[optional] Additional index</td> <td><em>meas </em>for measured quantities</td> </tr> <tr> <td>Z</td> <td>Unit</td> <td>s for seconds, V for volt</td> </tr> </tbody> </table> <h1>Content</h1> <p>The dataset contains the data of both experiments for validation and for investigation of the fault characteristics of the conducted thermal abuse test. While the electrical quantities have been recorded using a battery test stand from Keysight/Scienlab (SL60/200/12BT4C) the temperature readings have been measured by type K thermocouples and recorded with data logger from PCE instruments. For all tests, the temperature sample rate has been set to 1 Hz. Please refer to the attached schematics in <em>SensorPositions.zip</em> for the placement of the individual thermocouples. In addition, T_5 represents the surrounding and T_2 is on the backside of T_1. The sensor positions T_7 and T_8 are added only for the uniform heating where T_7 is located between heating element and cell and T_8 central at the heating plate. Within the referenced article, only T_1 has been used. </p> <p>For details on the experimental setup, please refer to the method section of the linked article. </p> <h2>1. Validation</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>The data contains the electrical load of the cell with an extended WLTC driving cycle that has been scaled to approx. 400 A as well as the corresponding temperature at T_1. The test was conducted within a climatic chamber at 20°C. This data can be used to either parameterize a model of the cell or to validate a model based on other parameter such as the linked parameter set.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current for WLTC emulation</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_meas_C</td> <td>Cell surface temperature</td> </tr> </tbody> </table> <h2>2. ThermalCalibration</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>For each heating setup (uniform, local) this directory contains one data set. Within this experiment, the cell was pulsed with short high current (150 A) pulses to achieve a constant thermal heating power without changing the SOC. Based on the temperature response, a thermal model can be parameterized for both heating setups. Please note, that the electrical sample rate was higher and no interpolation was conducted. </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>3. UniformThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, the cell went into thermal runaway during a charging procedure. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. Temperature readings of 9999°C (Upper range) due to sensor failure have been replaced by NaN. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>4. LocalThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td> <p>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, a charging process and observation, no thermal runaway occurred. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. It seems that the heat transfer into the cell could have been optimized, as shown by the relatively low cell temperature despite the hot heating element. Nevertheless, this experiment can be used to investigate online detection of small cell changes due to local heating - even without thermal runaway. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</p> </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table>
A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors (dataset)
<p>This repository contains the software and datasets needed to reproduce the results presented in the article "<a href="https://doi.org/10.1016/j.anucene.2022.109674">A non-intrusive reduced order model for the characterisation of the spatial power distribution in large thermal reactors</a>", published in Annals of Nuclear Energy.</p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
Fig. 1 in Opisthobranch mollusks (Gastropoda: Opisthobranchia) of cooling system of the Vladivostok Thermal Power Station 2
Fig. 1. Scheme of water-intake artificial inlet and water-pumping station (after Zvyagintzev [2005], with additions).
Рис. 2. А – сбросный канал (6 июнЯ 2008 г.); В – Cuthonella soboli (особь с поврежденными папиллами и ее кладка); C, D – Coryphella athodona (D – кладки); E–G – Catriona columbiana (F – кладка, G – радула). МасШтаб: B–F – 5 мм, G – 20 мкм. in Opisthobranch mollusks (Gastropoda: Opisthobranchia) of cooling system of the Vladivostok Thermal Power Station 2
Рис. 2. А – сбросный канал (6 июнЯ 2008 г.); В – Cuthonella soboli (особь с поврежденными папиллами и ее кладка); C, D – Coryphella athodona (D – кладки); E–G – Catriona columbiana (F – кладка, G – радула). МасШтаб: B–F – 5 мм, G – 20 мкм.
Fig. 2. A in Opisthobranch mollusks (Gastropoda: Opisthobranchia) of cooling system of the Vladivostok Thermal Power Station 2
Fig. 2. A – discharging canal (6th June, 2008); В – Cuthonella soboli (injured specimen and egg mass); C, D – Coryphella athodona (D – egg mass); E–G – Catriona columbiana (F – egg mass, G – radula). Scale bar: B–F – 5 mm, G – 20 μm.
GCNT-Plume: Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning
<p>This repository contains the relevant code and data for the paper <strong>Long-term observation of global nuclear power plants thermal plumes using Landsat images and deep learning</strong><strong> </strong>(Wei et al, 2023, <em>Remote Sensing of Environment</em>).</p> <p>Specifically, the <strong>U-Net.zip</strong> file includes the associated codes for segmenting surface thermal plumes from nuclear power plants along the global coasts and the Great Lakes by using the U-Net model integrated with prior knowledge. The <strong>GCNT-Plume.zip</strong> file includes the occurrence footprints of core area plumes (the <strong>occurrence_all </strong>folder), raw water temperature increment (WST) images (the <strong>delta </strong>folder), mixed area plumes and annotations (the <strong>extractWithLocation </strong>folder), model-predicted core area plumes (the <strong>prediction*_*</strong> folders), the mixed/core area plumes and background areas in shapefile format (the <strong>sampleAnnotation* </strong>folders), and location information (the <strong>location.xlsx </strong>table). Please refer to the <strong>README.md </strong>file in the <strong>U-Net.zip</strong> file for more detailed information.</p>
Data from: Population genomics of rapid evolution in natural populations: polygenic selection in response to power station thermal effluents
Background: Examples of rapid evolution are common in nature but difficult to account for with the standard population genetic model of adaptation. Instead, selection from the standing genetic variation permits rapid adaptation via soft sweeps or polygenic adaptation. Empirical evidence of this process in nature is currently limited but accumulating. Results: We provide genome-wide analyses of rapid evolution in two Fundulus heteroclitus populations subjected to recently elevated temperatures due to coastal power station thermal effluents. Bayesian and multivariate analyses of population genomic structure reveal a substantial portion of genetic variation that is most parsimoniously explained by selection at the site of thermal effluents. An FST outlier approach in conjunction with additional conservative requirements identify significant allele frequency differentiation that exceeds neutral expectations among exposed and closely related reference populations. Genomic variation patterns near these candidate loci reveal that individuals living near thermal effluents have rapidly evolved from the standing genetic variation through small allele frequency changes at many loci in a pattern consistent with polygenic selection on the standing genetic variation. Conclusions: While the ultimate trajectory of selection in these populations is unknown, our findings suggest that polygenic models of adaptation may play important roles in large, natural populations experiencing recent selection due to environmental changes that cause broad physiological impacts.
Data for coherence measurements of polaritons in thermal equilibrium reveal a power law for two-dimensional condensates
<p>All the raw data sets collected for this project are included in this submission. The code for the numerics is also included. 'Readme.text' files are included with the data sets explaining what the data sets are and how to read them. </p>
Рис. 1. Схема водоЗаборного ковШа и насосной станции (по: ЗвЯгинцев [2005], с иЗменениЯми). in Opisthobranch mollusks (Gastropoda: Opisthobranchia) of cooling system of the Vladivostok Thermal Power Station 2
Рис. 1. Схема водоЗаборного ковШа и насосной станции (по: ЗвЯгинцев [2005], с иЗменениЯми).
Data from: Population genomics of rapid evolution in natural populations: polygenic selection in response to power station thermal effluents
Open the record for dataset details and reuse information.
Prevalence and Predictors of Esophageal Thermal Lesions in High-power-Short-duration Ablation of Atrial Fibrillation
ClinicalTrials.gov study NCT05709756. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Thermal Power Prediction Data set
<p>Haoning Jia 's graduation thesis chapter three raw data.</p>
Artificial Intelligence (AI)-Powered Thermal Imaging for Gingival Inflammation Detection
ClinicalTrials.gov study NCT06830161. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Time series dataset of fish assemblages near thermal discharges at nuclear power plants in northern Taiwan
Open the record for dataset details and reuse information.
Increasing the Power: Absorption Bleach, Thermal Quenching, and Auger Quenching of the Red-Emitting Phosphor K2TiF6:Mn4+
<p>Main-text figure data</p>
Prognostics Of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven And Model-Based Methodologies
An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die-attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
Prognostics of Power MOSFETs under Thermal Stress Accelerated Aging using Data-Driven and Model-Based Methodologies
An approach for predicting remaining useful life of power MOSFETs (metal oxide field effect transistor) devices has been developed. Power MOSFETs are semiconductor switching devices that are instrumental in electronics equipment such as those used in operation and control of modern aircraft and spacecraft. The MOSFETs examined here were aged under thermal overstress in a controlled experiment and continuous performance degradation data were collected from the accelerated aging experiment. Die- attach degradation was determined to be the primary failure mode. The collected run-to-failure data were analyzed and it was revealed that ON-state resistance increased as die-attach degraded under high thermal stresses. Results from finite element simulation analysis support the observations from the experimental data. Data-driven and model based prognostics algorithms were investigated where ON-state resistance was used as the primary precursor of failure feature. A Gaussian process regression algorithm was explored as an example for a data-driven technique and an extended Kalman filter and a particle filter were used as examples for model-based techniques. Both methods were able to provide valid results. Prognostic performance metrics were employed to evaluate and compare the algorithms.
Prognostics Approach For Power Mosfet Under Thermal-Stress Aging
The prognostic technique for a power MOSFET presented in this paper is based on accelerated aging of MOSFET IRF520Npbf in a TO-220 package. The methodology utilizes thermal and power cycling to accelerate the life of the devices. The major failure mechanism for the stress conditions is die attachment degradation, typical for discrete devices with lead free solder die attachment. It has been determined that die attach degradation results in an increase in ON-state resistance due to its dependence on junction temperature. Increasing resistance, thus, can be used as a precursor of failure for the die-attach failure mechanism under thermal stress. A feature based on normalized ON-resistance is computed from in-situ measurements of the electro-thermal response. An Extended Kalman filter is used as a model-based prognostics techniques based on the Bayesian tracking framework.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.