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22 results for “operative temperature”
Challenges for room temperature operation of electrically pumped GeSn lasers
<p><strong>This data is accessible for bona-fide users upon request to the point of contact. </strong></p> <p>Data for the paper with the following abstract:</p> <div> <p>Recent demonstrations of room‐temperature lasing in optically pumped GeSn show promise for future CMOS compatible lasers for Si‐photonics applications. However, challenges remain for electrically pumped devices. Investigation of the processes that limit device performance is therefore vital in aiding the production of future commercial devices. In this work, a combined experimental and modelling approach is utilised to explore the dominant loss processes in current devices. By manipulating the band structure of functioning devices using high hydrostatic pressure techniques at low temperature, the dominant carrier recombination pathways are identified. This reveals that 93±5% of the threshold current is attributable to defect‐related recombination at a temperature,</p> <p>T = 85 K. Furthermore, carrier occupation of L‐valley states (carrier leakage) is responsible for 1.1± 0.3% of the threshold current, but this sharply increases to 50% with a decrease of just 30 meV in the L‐Ŵ separation energy. This indicates that thermal broadening of a similar order may reproduce these adverse effects, limiting device performance at higher temperatures. Temperature dependent calculations show that carrier occupation of indirect valley L‐states strongly affects the transparency carrier density and is therefore very sensitive to the Sn composition, leading to an effective operational temperature range for given Sn compositions and strain values. Recommendations for future device designs are proposed based on band structure and growth optimisations.</p> <p>Paper at: https://doi.org/10.1038/s41598-024-60686-3 </p> </div>
Dataset of Vibration, Temperature and Speed Measurements for Multiple Types of Localized Defects on Spherical Roller Bearings across Multiple Operating Conditions
<p><strong>Description:</strong></p> <p>This dataset has been created by the ISED group of Politecnico di Torino to address the lack of data available for developing fault detection models and/or predictive maintenance systems for medium/large-sized spherical roller bearings, commonly used in industrial settings.</p> <p>The data were collected using <a href="https://www.skf.com/it/products/rolling-bearings/roller-bearings/spherical-roller-bearings/productid-22240%20CCK%2FW33">SKF 22240 CCK/W33 spherical roller bearings</a> through an extensive experimental campaign on the medium/large-scale bearing test rig (capable of testing bearings with an outer diameter up to 420 mm), developed by the ISED research group at Politecnico di Torino (<a href="https://www.dimeas.polito.it/la_ricerca/gruppi/ised_industrial_systems_engineering_and_design">ISED Research Group</a>). The technical details of the test rig can be found in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>.</p> <p>The dataset contains data for individual localized defects applied to one of the four bearings tested simultaneously on the rig, which is capable of independently applying both axial and radial loads.</p> <p><strong>Dataset Structure:</strong></p> <p>The dataset is organized into four folders:</p> <ul> <li><code>Undamaged</code></li> <li><code>InnerRaceDamage</code></li> <li><code>OuterRaceDamage</code></li> <li><code>RollerDamage</code></li> </ul> <p>The <code>Undamaged</code> folder contains data for all bearings in healthy condition. The other folders contain data from tests where one of the bearings presents a localized defect. The defects, introduced by chip removal, have a diameter of 2 mm and a depth of 0.5 mm, affecting either the <strong>inner race</strong> (IR), <strong>outer race</strong> (OR), or <strong>roller elements</strong> (B). More detailed information about the defect geometry and location can be found in the following publications:</p> <ul> <li><a href="https://www.mdpi.com/1424-8220/23/1/211">Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/4/2038">Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/22/12458">Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks</a></li> </ul> <p>Each folder contains <code>.mat</code> files named according to the following format:</p> <p><code>(<em>Nominal_Rotation_Speed</em>)rpm_(<em>Radial_Force</em>)kN_(<em>Axial_Force</em>)kN.mat</code></p> <p>Where:</p> <ul> <li><strong>Nominal_Rotation_Speed</strong> is the machine's nominal rotational speed</li> <li><strong>Radial_Force</strong> is the radial force applied to the bearing</li> <li><strong>Axial_Force</strong> is the axial force applied to the bearing</li> </ul> <p>The dataset includes measurements from 10 different nominal rotation speeds and four load conditions, one of which includes an axial load. In some cases, "ramp" files are included, containing data where the rotational speed was linearly varied during the test.</p> <p>Each <code>.mat</code> file contains multiple structures, depending on the type of test. Each structure is labeled as <code>Signal_</code> followed by a number (0, 1, 2, 3, 4), where each represents a specific signal extracted during the test. There is no fixed correspondence between the signal number and the type of measurement. For example, <code>Signal_2</code> does not always represent the accelerometer signal. Users are encouraged to inspect the <code>y_values.quantity</code> field to identify the signal's unit and nature. For instance, if <code>y_values.quantity.label</code> shows "g", the signal corresponds to an accelerometric measurement.</p> <p>All signals have been exported using the <strong>MKS system</strong>, so <code>y_values.values</code> contains data in units of m/s² for acceleration signals. To convert the values to the unit indicated in <code>y_values.quantity.label</code>, users can apply the multiplication factor and offset provided in <code>y_values.quantity.unit_transformation</code>. In the case of accelerometric data, the multiplication factor is 0.1020, converting <code>y_values.values</code> from m/s² to g.</p> <p>A more detailed description of the data structure can be found in the Test.Lab documentation.</p> <p>In addition to acceleration data, the files include temperature signals, tacho sensor signals measuring shaft speed, and tacho impulse signals. In some cases, there is also a signal measured in "N", representing the frictional force generated by the bearings (as detailed in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>). This friction force signal is not always present, as the load cell could go into overload during certain tests.</p> <p><strong>Sensor Data Organization:</strong></p> <p>Acceleration and temperature data are presented in tables with four columns, each corresponding to one of the four bearings. In the damaged condition tests, the damaged bearing is always the one corresponding to sensor 4 (i.e., <code>y_values.values(:, 4)</code>).</p>
ScienceDex guides
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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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International Brain Laboratory public data
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OpenNeuro
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