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9 results for “Lamb wave”
COQTEL dataset: Corrosion Quantification Through Extended use of Lamb waves
<p><span>Corrosion is a major threat in the aeronautic industry, both in terms of safety and cost. Ultrasonic Lamb Waves (LW) appear to be very efficient for corrosion monitoring and can be made cost effective and versatile when emitted and received by a sparse array of piezoelectric elements (PZT). A LW solution relying on a sparse PZT array and allowing to monitor corrosion pit growth on stainless 316L grade steel plate is here used to collect data during a controlled corrosion experiment. Experimentally, the corrosion pit size is electrochemically controlled by both the imposed electrical potential and the injection of a corrosive NaCl solution through a capillary located at the desired pit location. In parallel, the corrosion pit growth is monitored in-situ every 10 seconds by sending and measuring LW using a sparse array of 4 PZTs bonded to the back of the steel plate enduring corrosion. Two independent experiments were achieved in order to assess the repeatability of the proposed approach. If embedded in aeronautical structure, such an approach could be a versatile and cost-effective alternative to actual non-destructive maintenance procedures that are time and manpower consuming. This dataset can thus ease the development of associated SHM algorithms and methodologies and help filling the gap actually existing between research and industry in that domain.</span> This dataset has been used for the article "<span>In-situ monitoring of µm-sized electrochemically generated corrosion pits using Lamb Waves managed by a sparse array of piezoelectric transducers" published in open access in the "Ultrasonics" peer reviewed journal by the same authors as the dataset.<br></span></p>
Himawari 8 band 8 derived product for Lamb wave analysis
<p>Data from geostationary satellite Himawari 8 are processed for the analysis of Lamb waves that were generated by the eruption of Hunga Tonga-Hunga Haʻapai in Tonga on 15 January 2022. Himawari 8/9 gridded data are distributed by the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The second time derivatives of band 8 thermal infrared images are stored. The used band was changed from version 1.</p> <p>The data format is NetCDF. The file name represents the date of the middle image used to generate each file (changed from version 2).</p>
Temperature-dependent Lamb wave signals in highly anisotropic CFRP
<p>The dataset contains signals of propagating Lamb waves in highly anisotropic carbon fibre reinforced polymer (CFRP). The reinforcement is unidirectional along 0 deg. A detailed description of the material and its parameters is given in [1]. The arrangement of piezoelectric actuator A and sensors S1-S7 is shown in figure "plate_angular_pzt_arrangement_50x50.png". Sensors are placed at propagation angles from 0 deg to 90 deg with a step of 15 deg. It should be noted that two piezoelectric transducers bonded to both sides of the plate were used as the actuator. It allowed for exciting Lamb waves with dominant A0 and S0 modes, respectively. Hence, there are two respective zip files with data.</p> <p>The following parameters were used during measurements:</p> <ul> <li>Temperatures: T=[50,40,30,20,10,0,-10,-20,-30,-40,-50];</li> <li>Number of cycles in Hann windowed signals: no_of_cycles=[2,2.5,3];</li> <li>Carrier frequencies of excitation signals [kHz]: frequencies=[20:10:250];</li> <li>Number of averages: 50;</li> <li>Sampling frequency: 10 MHz.</li> </ul> <p>The following equipment was used in the experiment:</p> <ul> <li>Environmental chamber by Angelantoni Test Technologies, model MyDiscovery 600 C;</li> <li>National Instruments waveform generator PXIe-5413;</li> <li>Krohn-Hite voltage amplifier model 7500;</li> <li>Cedrat Technologies LWDS amplifier (used as a charge amplifier);</li> <li>National Instruments oscilloscope PXIe-5105.</li> </ul> <p>Files in CSV format contain environmental chamber data (temperature programme, actual temperature and humidity over time, etc.). This can be read and plotted in Matlab by running the script “Read_plot_environmental_chamber.m”. There is another file “Read_plot_environmental_chamber_plus_DS18B20_RH20_KROHN_A0.m” in which temperature was registered also by DS18B20 digital sensor. It loops over all measurements so that it can be used also for reading signals from “niscope_avg_waveform.mat” in respective subfolders. In particular, sensor signals are stored in the “niscope_avg_waveform” variable, a matrix of dimensions 8192x7.</p>
Ultrasonic guided-wave experiment data for manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textilecomposites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Experimental data in support of manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Synthetic dataset of a full wavefield representing the propagation of Lamb waves and their interactions with delaminations
<p>The dataset contains 475 simulated cases of full wavefield of Lamb waves propagation in a plate made of carbon fibre reinforced plastic (CFRP). The simulated 475 cases represent delaminations with different locations, shapes, and sizes. The following random factors were simulated in each case:</p> <ul> <li>delamination geometrical size (ellipse minor and major axis randomly selected from the interval [10 mm, 40 mm],</li> <li>delamination angle (randomly selected from the interval [0◦ , 180◦]),</li> <li>coordinates of the centre of delamination (randomly selected from the interval [0 mm, 250 mm − δ] and [250 mm + δ, 500 mm], where δ = 10 mm).</li> </ul> <p>The guided waves were excited at the centre of the plate by applying equivalent piezoelectric forces. The excitation was in the form of toneburst sine signal modulated by the Hann window. The carrier frequency is assumed 50 kHz, and the modulation frequency is 10 kHz. The total wave propagation time was set to 0.75 ms so that the guided wave can propagate to plate edges and back to the actuator twice. The number of time integration steps was 150000 which was selected for the stability of the central difference scheme.</p> <p>The material is a typical cross-ply CFRP laminate. The stacking sequence [0/90]<sub>4</sub> was used in the model. The properties of a single-ply were as follows [GPa]: C<sub>11</sub> = 52.55, C<sub>12</sub> = 6.51, C<sub>22</sub> = 51.83, C<sub>44</sub> = 2.93, C<sub>55</sub> = 2.92, C<sub>66</sub> = 3.81. The assumed mass density was 1522.4 kg/m3. These properties were selected so that simulated numerically wave front patterns and wavelengths are similar to the wavefields measured by SLDV on CFRP specimens used later on for testing the developed methods for delamination identification. The shortest wavelength of propagating A0 Lamb wave mode was 21.2 mm for numerical simulations and 19.5 mm for experimental measurements.</p> <p><br> In each delamination case, 512 frames were generated to visualise the propagation of Lamb waves and their interactions and reflections from the delamination and edges.<br> The numerically generated dataset resembles the velocity measurements acquired by the scanning laser Doppler vibrometer (SLDV) at the bottom surface of the plate of dimensions 500×500 mm.</p> <p>The uploaded dataset contains two ZIP files:</p> <ol> <li> The first file contains 475 folders regarding all cases of different delaminations and their interaction with Lamb waves. In each case, there are 512 images in PNG format representing the propagation of guided waves.</li> <li> The second file contains: <ul> <li>475 images in PNG format representing the ground truth of the delaminations.</li> <li>CSV file contains all info regarding delaminations.</li> </ul> </li> </ol> <p> </p>
Composite geostationary weather satellite images (second time derivative of water vapor channel) for visualizing Lamb waves
<p>Second time derivative of water vapor channel (6.2 micrometer) brightness temperature from geostationary weather satellites (Units: K s<sup>-2</sup>)</p> <p>Himawari-8 (original data obtained from NICT Science Cloud)</p> <p>GOES-16/17 (original data obtained from Amazon AWS)</p> <p>Meteosat-8/9/10/11 (original data obtained from EUMETSAT)</p> <p> </p> <p>Time interval of the files: 5 minutes</p> <p> </p> <p>Time interval of each satellite, dt for time derivative:</p> <p>Himaawri-8, GOES-16/17: 10 minutes, 10 minutes</p> <p>Meteosat-8/9/11: 15 minutes, 15 minutes</p> <p>Meteosat-10: 5 minutes, 10 minutes</p> <p> </p> <p>Each file contains the latest images from those satellites at that time. The time stamp for each satellite represents the beginning of each full-disk scan.</p> <p> </p> <p>Bias correction:</p> <p>Himawari-8: bias removal for each swath</p> <p>GOES-16/17, Meteosat-11: bias removal for each east-west line</p> <p>Meteosat-8/9/10: bias removal for each east-west line (note: satellite attitude was not stable)</p> <p> </p> <p>Smoothing:</p> <p>Band-pass filter for each full-disk image separately: 2-40 degrees on lat-lon coordinate</p> <p>Stronger smoothing at latitudes higher than 60 degrees north/south</p> <p> </p> <p>Down-sampling:</p> <p>Full-disk images were mapped to a 0.04-degree lat-lon coordinate.</p> <p>Then, composite images were produced at the 0.2-degree resolution.</p> <p> </p> <p>Version 2:</p> <p>Improved interpolation algorithm</p> <p>Himawari-8: improved geolocation</p> <p>Meteosat-8/9: improved treatment of noise near the edge of full disk images</p>
Numerical simulation "Numerical simulation of atmospheric Lamb waves generated by the 2022 Hunga-Tonga volcanic eruption"
<p>Numerical simulation results for the atmospheric Lamb waves generated by the Hunga-Tonga volcano explosion on January 15th 2022.</p>
A multi-feature integration method for fatigue crack detection and crack length estimation in riveted lap joints using Lamb waves
This paper presents an experimental study of damage detection and quantification in riveted lap joints. Embedded lead zirconate titanate piezoelectric (PZT) ceramic wafer-type sensors are employed to perform in situ non-destructive evaluation (NDE) during fatigue cyclical loading. PZT wafers are used to monitor the wave reflection from the boundaries of the fatigue crack at the edge of bolt joints. The group velocity of the guided wave is calculated to select a proper time window in which the received signal contains the damage information. It is found that the fatigue crack lengths are correlated with three main features of the signal, i.e., correlation coefficient, amplitude change, and phase change. It was also observed that a single feature cannot be used to quantify the damage among different specimens since a considerable variability was observed in the response from different specimens. A multi-feature integration method based on a second-order multivariate regression analysis is proposed for the prediction of fatigue crack lengths using sensor measurements. The model parameters are obtained using training datasets from five specimens. The effectiveness of the proposed methodology is demonstrated using several lap joint specimens from different manufactures and under different loading conditions.
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