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261 results for “Turbine”
Data from: Empirical determination of severe trauma in seals from collisions with tidal turbine blades
1. Tidal energy converters (turbines) are being developed in many countries as part of attempts to reduce reliance on hydrocarbon fuels. However, the moving blades of tidal turbines pose potential collision risks for marine animals. Accurate assessment of mortality risk as a result of collisions is essential for risk management during planning and consenting processes for marine energy developments. In the absence of information on the physical consequences of such collisions, predicting likely risks relies on theoretical collision risk models. The application of these at a population level usually assumes that all collisions result in mortality. This is unlikely and the approach therefore produces upwardly biased estimates of population consequences. 2. In this study, we estimate the pathological consequences of direct collisions with tidal turbines using seal carcasses and physical models of tidal turbine blades. We quantify severe trauma at a range of impact speeds and to different areas of seal carcasses. A dose-response model was developed with associated uncertainty to determine an impact speed threshold of severe trauma to use in future collision risk models. 3. Results showed that severe trauma was: a. restricted to the thoracic region, with no evidence of injury to the lumbar or cervical spine. b. only observed in collision speeds in excess of 5.6 m.s-1 (95% c.i. 4.4 to 6.6). c. affected by body condition; increasing blubber depth reduced the likelihood of severe trauma 4. Synthesis and applications: This study provides important information for policy makers and regulators looking to predict the potential impacts of tidal turbines on marine mammals. We demonstrate that the probability of severe trauma in seals due to collisions with turbine blades is highly dependent upon collision speed, and that the majority of predicted collisions are unlikely to cause fatal skeletal trauma. We recommend that collision risk models incorporate appropriate mortality assumptions to ensure accurate estimates of the population consequences are produced in risk assessments for tidal turbine deployments.
Site-specific Design Load Cases for floating offshore wind turbine applications I : Historical data
<p>This document includes a brief description of the <a href="https://leopard.tu-braunschweig.de/receive/dbbs_mods_00077703" target="_blank" rel="noopener">first database</a> on the site-specific Design Load Cases (DLCs) based on historical metocean data. The dataset includes metocean data, statistical analysis and site-specific DLCs across the three areas of study defined in the INF4INiTY project: Scottish Sea, Baltic Sea and Adriatic Sea. In addition to the dataset, this deliverable includes a Graphical User Interface (GUI) for the analysis of specific locations within these three areas and the generation of the site-specific DLCs.<br>The aim of this initial version of the database is to provide a first characterisation of the areas of interest in order to use the DLCs on the design of the different innovations planned in various work packages (WPs) INF4INiTY. As the project proceeds, the second database will extend the site-specific DLCs including forecasted data for different horizons and under diverse climate change scenarios.<br>The deliverable is divided into six brief sections describing the (i) the GUI, (ii) characteristics of the data, (iii) the three areas of study and technological requirements, (iv) historical metocean data, (v) site-specific statistical analysis and reporting, and (vi) site-specific DLCs.</p>
Data for "A DNA turbine powered by a transmembrane potential across a nanopore"
<p>Data for "A DNA turbine powered by a transmembrane potential across a nanopore"</p>
Turbine u4
Turbine u4 Created with Polycam Source: Objaverse 1.0 / Sketchfab
Behavioral patterns of bats at a wind turbine events
<p>Bat fatalities at wind energy facilities in North America are predominantly comprised of migratory, tree-dependent species, but it is unclear why these bats are at higher risk. Factors influencing bat susceptibility to wind turbines might be revealed by temporal patterns in their behaviors around these dynamic landscape structures. In northern temperate zones fatalities occur mostly from July through October, but whether this reflects seasonally variable behaviors, passage of migrants, or some combination of factors remains unknown. In this study, we examined video imagery spanning one year in Colorado to characterize patterns of seasonal and nightly variability in bat behavior at a wind turbine. We detected bats on 177 of 306 nights representing approximately 3,800 hours of video and > 2,000 discrete bat events. We observed bats approaching the turbine throughout the night across all months during which bats were observed. Two distinct seasonal peaks of bat activity occurred in July and September, representing 30% and 42% increases in discrete bat events from the preceding months June and August, respectively. Bats exhibited behaviors around the turbine that increased in both diversity and duration in July and September. The peaks in bat events were reflected in chasing and turbine approach behaviors. Many of the bat events involved multiple approaches to the turbine, including when bats were displaced through the air by moving blades. The seasonal and nightly patterns we observed were consistent with the possibility that wind turbines invoke investigative behaviors in bats in late summer and autumn coincident with migration, and that bats may return and fly close to wind turbines even after experiencing potentially disruptive stimuli like moving blades. Our results point to the need for a deeper understanding of the seasonality, drivers, and characteristics of bat movement across spatial scales.</p>
Wind turbine wake flight trials
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Supporting data files for simulations in "Wind fields in Category 1-3 tropical cyclones are not fully represented in wind turbine design standards"
<p>This deposit contains the time-series output from the WRF-LES simulations of three tropical cyclones used in "Wind fields in Category 1-3 tropical cyclones are not fully represented in wind turbine design standards".</p>
First Altitude-Triggered Lightning Experiment Associated with an Elevated Wind Turbine Blade on the Ground
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Experimental Investigation of Surface Roughness Effects and Transition on Wind Turbine Performance
<p>Aerodynamic experiments have been executed in the wind tunnel and on a wind turbine blade to measure the impact of roughness on the airfoil characteristics and the associated effect on rotor performance and to establish the transition location on a rotating blade. The wind tunnel tests have been performed in the low-speed, low-turbulence wind tunnel of TUDelft. The wind turbine tests were carried out at ECN&rsquo;s Wind Turbine Test Site. Roughness simulation material has been installed on the airfoil leading edge to measure the impact on airfoil performance. Microphones were mounted on the airfoil surface to detect the boundary layer laminar to turbulent transition position both on the wind tunnel model and on the wind turbine blade.</p>
Wind turbine wake flight experiment raw data
<p>Raw data from wind turbine wake flight experiments.</p>
Eagles enter rotor-swept zones of wind turbines at rates that vary by turbine
<p>There is increasing pressure on wind energy facilities to manage or mitigate for wildlife collisions. However, little information exists regarding spatial and temporal variation in collision rates, meaning that mitigation is most often a blanket prescription. To address this knowledge gap, we evaluated variation among turbines and months in an aspect of collision risk—probability of entry by an eagle into a rotor swept zone (hereafter, 'probability of entry'). We examined 10,222 eagle flight paths identified and recorded by an automated bird monitoring system at a wind energy facility in Wyoming, USA. Probabilities of entry per turbine-month combination were 4.03 times greater in some months than others, ranging 0.15 to 0.62. The overall probability of entry for the riskiest turbine (i.e. the one with the greatest probability of entry) was 2.39 times greater than the least-risky turbine. Our methodology describes large variation across turbines and months in the probability of entry. If subsequently combined with information on other sources of variation (i.e. weather, topography), this approach can identify risky versus safe situations for eagles under which cost of management, curtailment prescriptions, and collision risk can be simultaneously minimized.</p>
Turbine Upgrade Dataset
<p>This dataset includes three sets, one corresponding to an actual vortex generator installation and two corresponding to an artificial pitch angle adjustment. Two pairs of wind turbines from the same inland wind farm, as used in Chapter 5 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book, are chosen to provide the data, each pair consisting of two wind turbines, together with a nearby met mast. The turbine that undergoes an upgrade in a pair is referred to as the experimental turbine, the reference turbine, or the test turbine, whereas the one that does not have the upgrade is referred to as the control turbine. In both pairs, the test turbine and the control turbine are practically identical and were put into service at the same time. This wind farm is on a reasonably flat terrain.</p> <p>The power output, y, is measured on individual turbines, whereas the environmental variables in x (i.e., the weather covariates) are measured by sensors at the nearby mast. For this dataset, there are five variables in x and they are the same as those in the <a href="https://zenodo.org/record/5516552">Inland Wind Farm Dataset1</a>. For the vortex generator installation pair, there are 14 months' worth of data in the period before the upgrade and around eight weeks of data after the upgrade. For the pitch angle adjustment pair, there are about eight months of data before the upgrade and eight and a half weeks after the upgrade.</p> <p>Note that the pitch angle adjustment is not physically carried out, but rather simulated on the respective test turbine. The following data modification is done to the test turbine data. The actual test turbine data, including both power production data and environmental measurements, are taken from the actual turbine pair operation. Then, the power production from the designated test turbine on the range of wind speed over 9 m/s is increased by 5%, namely multiplied by a factor of 1.05, while all other variables are kept the same. No data modification of any kind is done to the data affiliated with the control turbine in the pitch angle adjustment pair.</p> <p>The third column of a respective dataset is the upgrade status variable, of which a zero means the test turbine is not modified yet, while a one means that the test turbine is modified. The upgrade status has no impact on the control turbine, as the control turbine remains unmodified throughout. The vortex generator installation takes effect on June 20, 2011, and the pitch angle adjustment takes effect on April 25, 2011.</p>
Turbine Bending Moment Dataset
<p>his dataset includes two parts. The first part is three sets of physically measured blade-root flapwise bending moments on three respective turbines, courtesy of Riso-DTU (Technical University of Denmark). The basic characteristics of the three turbines can be found in Table 10.1 of the <a href="https://aml.engr.tamu.edu/book-dswe/">Data Science for Wind Energy</a> book. These datasets include three columns. The first column is the 10-min average wind speed, the second column is the standard deviation of wind speed within a 10-min block, and the third column is the maximum bending moment, in the unit of MN-m, recorded in a 10-min block.</p> <p>The second part of the dataset is the simulated load data used in Section 10.6.5 of the same book. This part has two sets. The first set is the training data that has 1,000 observations and is used to fit an extreme load model. The second set is the test data that consists of 100 subsets, each of which has 100,000 observations. In other words, the second dataset for testing has a total of 10,000,000 observations, which are used to verify the extreme load extrapolation made by a respective model. Both simulated datasets have two columns: the first is the 10-min average wind speed and the second is the maximum bending moment in the corresponding 10-min block. While all other datasets are saved in CSV file format, this simulated test dataset is saved in a text file format, due to its large size. The data simulation procedure is explained in Section 10.6.5.</p>
Operation SCADA Dataset of an Urban Small Wind Turbine in São Paulo, Brazil
<p>The dataset file contains data regarding the electrical and mechanical operational actual quantities and parameters obtained and recorded by the internal inverter controller of a Skystream 3.7 small wind turbine (SWT) installed on the roof of the High Voltage Laboratory at the Institute of Energy and Environment (IEE) of the University of Sao Paulo (USP), Brazil, recorded from 2017 to 2022.</p> <p>The main electrical parameters are the energy, voltages, and currents in the connection grid point and power frequency. Mechanical information can be retrieved, such as the rotation and the wind speed. The temperature, measured in some location points to the nacelle and inverter, is also recorded. Several other parameters concerning the SWT inverter operation, such as the dc voltages on its internal bus, alarms, and flags, are also presented.</p> <p>The files in the dataset are named as "data_swt_iee_usp_YYYY.csv" where YYYY is the referring year. In the files, the semicolon symbol (;) is used as a column separator, while the dot symbol (.) represents the decimal separator. The first row of the CSV file corresponds to the header row to help identify data as described in the file "data_description.txt". Sampling rate one record per minute.</p> <p>The first line on each year-based file represents the header table description of the data columns.</p> <p>The complete and detailed information about the installation, localization,and analysis is in the article published at: <a href="https://doi.org/10.3390/wind2040037">https://doi.org/10.3390/wind2040037</a></p> <h3><strong>LIST OS STATUS CODES - Skystream 3.7</strong></h3> <p><strong>Premise:</strong> Binary logic, where each status is represented by a specific bit within an integer value.</p> <table> <tbody> <tr> <th><strong>Numeric Code</strong></th> <th><strong>Turbine Status</strong></th> <th><strong>Grid Status</strong></th> <th><strong>System Status</strong></th> </tr> </tbody> <tbody> <tr> <td>0</td> <td>Normal. Run with energy generation</td> <td>Normal (No faults detected)</td> <td>Normal (System operating without errors)</td> </tr> <tr> <td>1</td> <td>Low Windspeed</td> <td>L1 Low Voltage</td> <td>HS Backoff</td> </tr> <tr> <td>2</td> <td>Braking</td> <td>L1 High Voltage</td> <td>SIP TX Too Long</td> </tr> <tr> <td>4</td> <td>Overspeed</td> <td>L2 Low Voltage</td> <td>Improper Reset</td> </tr> <tr> <td>8</td> <td>No Stall (Normal Operation)</td> <td>L2 High Voltage</td> <td>Battery Timeout</td> </tr> <tr> <td>16</td> <td>High Wind Test</td> <td>Offset Limit</td> <td>Drive Off</td> </tr> <tr> <td>32</td> <td>Anemometer mode</td> <td>Phase Error</td> <td>Slave Shutdown</td> </tr> <tr> <td>64</td> <td>Ramp</td> <td>Frequency Low</td> <td>Temp Shutdown</td> </tr> <tr> <td>128</td> <td>TSR Incr</td> <td>Frequency High</td> <td>High Temp</td> </tr> <tr> <td>256</td> <td>Power High</td> <td>DPLL Unlock</td> <td>Run (Normal Operation)</td> </tr> <tr> <td>512</td> <td>TSR Limit</td> <td>Grid Disconnect</td> <td>Disabled</td> </tr> <tr> <td>1024</td> <td>Quiet</td> <td>Anti-Islanding</td> <td>Waiting</td> </tr> <tr> <td>2048</td> <td>Incr Delay</td> <td> </td> <td>Temp Backoff</td> </tr> <tr> <td>4096</td> <td>RPM Control</td> <td> </td> <td>Bad Setpoints</td> </tr> <tr> <td>8192</td> <td>Vin High</td> <td> </td> <td>Bad CRC</td> </tr> </tbody> </table> <p> </p> <p><strong>These codes can represent cumulative causes, adding values when multiple causes occur.</strong></p> <p>EXAMPLES:</p> <p>Turbine status = 0 => Generating/Run</p> <p>Turbine status = 1 => Low Windspeed</p> <p>Turbine status = 3 => Low Windspeed and Braking</p> <p>Turbine status = 9 => Low Windspeed and No Stall</p> <p>Turbine status = 33 => Anemometer mode and Low Windspeed</p> <p>System status = 1024 => Waiting</p> <p>System status = 256 => Run</p> <p>Grid status = 512 => Grid Disconect</p>
On the characteristics of the wake of a wind turbine undergoing large motions caused by a floating structure: an insight based on experiments and multi-fidelity simulations from the OC6 Phase III Project - Supplementary material
<p>This link stores the supplemenary material for the paper: "On the characteristics of the wake of a wind turbine undergoing large motions caused by a floating structure: an insight based on experiments and multi-fidelity simulations from the OC6 Phase III Project" published on Wind Energy Science. The pdf file contains the plots for all the investigated metrics analysed in this work, which could not be reported in the manuscript due to space constraints. Further details about these results can be found in the original publication. </p>
Radiofrequency Ablation of Bilateral Inferior Turbinate Followed by Subcutaneous Immunotherapy Trial
ClinicalTrials.gov study NCT05510024. IPD Sharing: NO. Countries: 0. Publications: 8.
The Relationship Between Gastroesophageal Reflux and Pediatric Rhinitis: Significance of Pale/Blue Colored Turbinate
ClinicalTrials.gov study NCT02278081. IPD Sharing: Not stated. Countries: 0. Publications: 13.
Investigation of a Novel Turbine-driven Ventilator for Use in Cardiopulmonary Resuscitation
ClinicalTrials.gov study NCT02743299. IPD Sharing: NO. Countries: 0. Publications: 1.
Inferior Submucosal Turbinectomy Versus Blunt Turbinectomy for Inferior Turbinate Hypertrophy
ClinicalTrials.gov study NCT00651092. IPD Sharing: Not stated. Countries: 0. Publications: 2.
Data from: Empirical determination of severe trauma in seals from collisions with tidal turbine blades
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