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19 pages, 6251 KiB  
Article
Cooling Effectiveness of the Sustainable Cooling Solution for Cattle: Case Study in Poland
by Jagoda Błotny, Anna Szczepanowska-Białek, Robert Kupczyński, Anna Budny-Walczak and Sabina Rosiek
Appl. Sci. 2024, 14(21), 9678; https://doi.org/10.3390/app14219678 - 23 Oct 2024
Viewed by 543
Abstract
Recently, the dairy sector has been ever more affected by global warming. This study aimed to test a novel conductive cooling system for cattle that was successfully implemented and evaluated under summer thermally challenging weather conditions in Poland. The system consists mainly of [...] Read more.
Recently, the dairy sector has been ever more affected by global warming. This study aimed to test a novel conductive cooling system for cattle that was successfully implemented and evaluated under summer thermally challenging weather conditions in Poland. The system consists mainly of the chiller, tank, and chilled water-driven mattress, designed to prioritize animal well-being. The experimental evaluation was carried out on three Friesian dry cows, housed on different types of bedding—commercial water mattress, straw, and cooling water mattress—and supplied with water at 10 °C (day) and 16 °C (night). The cooling water mattress’ surface temperature was twice as low as that of the commercial water mattress. The animal’s thermal comfort was assessed with physiological and behavioral reactions. The cooling effect on animals’ bodies was demonstrated with a lower reticulorumen temperature of the cooled cow (p < 0.05) than the reference ones. The local effect of cooling was proved with an 8 °C-lower skin temperature after the cow’s resting period. The presented study opens a new research direction toward dairy cattle’s welfare, sustainability, and the food–energy–water nexus, based on potential energy and water savings. Full article
(This article belongs to the Section Applied Biosciences and Bioengineering)
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<p>Geometry of the innovative cooling water mattress [<a href="#B24-applsci-14-09678" class="html-bibr">24</a>].</p>
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<p>Innovative cooling water mattress and its supplying cooling system.</p>
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<p>Hydraulic scheme of the cooling water production and distribution system (RadMAT system) and its connection with an innovative cooling water mattress.</p>
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<p>Three types of beddings that were used during the experiment: the innovative cooling water mattress (CM), the commercial water mattress (M), and the conventional straw bedding (S). The last two are the referenced ones.</p>
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<p>Outdoor temperature measured from 6:00 at 14.08.2022 to 6:00 at 21.08.2022 in the experimental period by the meteorological station located near the selected barn.</p>
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<p>Experimental barn’s air temperature, humidity, and Temperature Humidity Index (THI) for the experimental period.</p>
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<p>Hourly average Temperature Humidity Index (THI) level distribution for each experimental day.</p>
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<p>Water temperature measured at the inlet and outlet to the cooling water mattress and the temperature increment between them.</p>
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<p>The surface temperature of the cooling water mattress and the commercial mattress for all experimental days.</p>
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<p>Thermograms taken 1 min after the animals got up from their bedding areas (temperature scale in °C).</p>
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<p>Cows’ skin temperature in the thigh area within 7 min after animals got up.</p>
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<p>Cows’ skin temperature in the abdomen area within 7 min after animals got up.</p>
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<p>Changes in rumen temperature of each cow during the experimental days.</p>
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17 pages, 8968 KiB  
Article
Improvement of Optical-Induced Thermography Defect Detectability by Equivalent Heating and Non-Uniformity Compensation in Polyetheretherketone
by Yoonjae Chung, Chunyoung Kim, Seungju Lee, Hyunkyu Suh and Wontae Kim
Appl. Sci. 2024, 14(19), 8720; https://doi.org/10.3390/app14198720 - 27 Sep 2024
Viewed by 512
Abstract
This paper deals with the experimental procedures of lock-in thermography (LIT) for polyetheretherketone (PEEK), which is used as a lightweight material in various industrial fields. The LIT has limitations due to non-uniform heating by external optic sources and the non-uniformity correction (NUC) of [...] Read more.
This paper deals with the experimental procedures of lock-in thermography (LIT) for polyetheretherketone (PEEK), which is used as a lightweight material in various industrial fields. The LIT has limitations due to non-uniform heating by external optic sources and the non-uniformity correction (NUC) of the infrared (IR) camera. It is generating unintended contrast in the IR image in thermal imaging inspection, reducing detection performance. In this study, the non-uniformity effect was primarily improved by producing an equivalent array halogen lamp. Then, we presented absolute temperature compensation (ATC) and temperature ratio compensation (TRC) techniques, which can equalize the thermal contrast of the test samples by compensating for them using reference samples. By applying compensation techniques to data acquired from the test samples, defect detectability improvement was quantitatively presented. In addition, binarization was performed and detection performance was verified by evaluating the roundness of the detected defects. As a result, the contrast of the IR image was greatly improved by applying the compensation technique. In particular, raw data were enhanced by up to 54% using the ATC compensation technique. Additionally, due to improved contrast, the signal-to-noise ratio (SNR) was improved by 7.93%, and the R2 value of the linear trend equation exceeded 0.99, demonstrating improved proportionality between the defect condition and SNR. Full article
(This article belongs to the Section Optics and Lasers)
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<p>Principle of radiation emission from surrounding environment and heat source in thermographic testing.</p>
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<p>The principle of the four-point phase shifting method for thermal waves demodulated on the object surface.</p>
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<p>Geometric information of PEEK test sample.</p>
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<p>The experimental configuration for LIT testing with reflection mode.</p>
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<p>Information on HA lamp and parabolic lamp: (<b>a</b>) geometric details of HA lamp, (<b>b</b>) HA lamp picture, and (<b>c</b>) conventional parabolic lamp picture.</p>
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<p>The LIT testing and analysis flow chart of study.</p>
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<p>IR Images at 50 s of modulation frequency 0.01 Hz along each lamp type: (<b>a</b>) parabolic type, (<b>b</b>) HA type.</p>
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<p>Sample surface temperature distribution according to lamp type: (<b>a</b>) horizontal direction, (<b>b</b>) vertical direction.</p>
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<p>Images along the data type at modulation frequency 0.01 Hz: (<b>a</b>) raw (temperature), (<b>b</b>) amplitude, and (<b>c</b>) phase.</p>
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<p>Images along the data type at modulation frequency 0.01 Hz with the ATC compensation method applied: (<b>a</b>) raw (temperature), (<b>b</b>) amplitude, and (<b>c</b>) phase.</p>
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<p>Images along the data type at modulation frequency 0.01 Hz with the TRC compensation method applied: (<b>a</b>) raw (temperature), (<b>b</b>) amplitude, and (<b>c</b>) phase.</p>
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<p>Amplitude binarization image by Otsu algorithm: (<b>a</b>) not ATC applied, (<b>b</b>) ATC applied.</p>
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<p>Phase binarization image by Otsu algorithm: (<b>a</b>) not ATC applied, (<b>b</b>) ATC applied.</p>
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<p>SNR trend along the compensation method applied: (<b>a</b>) according to defect depth, (<b>b</b>) according to defect diameter.</p>
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12 pages, 1611 KiB  
Article
Application of High-Resolution Infrared Thermography to Study the Effects of Technologically Processed Antibodies on the Near-Surface Layer of Aqueous Solutions
by Elena Don, Evgenii Zubkov, Ekaterina Moroshkina, Irina Molodtsova, Anastasia Petrova and Sergey Tarasov
Molecules 2024, 29(18), 4309; https://doi.org/10.3390/molecules29184309 - 11 Sep 2024
Viewed by 514
Abstract
A new class of biologics is obtained using the technologically processed of antibodies (TPA), which are used as the initial substance, and their dilution at each stage is accompanied by a controlled external vibrational (mechanical) treatment. This article focuses on the development and [...] Read more.
A new class of biologics is obtained using the technologically processed of antibodies (TPA), which are used as the initial substance, and their dilution at each stage is accompanied by a controlled external vibrational (mechanical) treatment. This article focuses on the development and validation of a novel technique that can be applied for assessing the identity of TPA-based drugs. It has previously been found that after such treatment, the resulting solution either acquired new properties that were not present in the initial substance or a quantitative change in properties compared to the initial substance was observed. The use of mechanical treatment during the manufacture of the TPA-based drugs can cause the formation of new bonds between the solvent and antibody molecules. These changes manifest themselves in altered adsorption at the surface of the test solutions, which results in the formation of a near-surface film. One of the indicators of such events is the change in the surface temperature of the solution, which can be analyzed using high-resolution thermography. Unlike other methods, the high-resolution thermography allows the near-surface layer of a heterogeneous aqueous solution to be clearly visualized and quantified. A number of experiments were performed: seven replicates of sample preparations were tested; the influence of factors “day” or “operator” was investigated during 12 days of testing by two operators. The method also allowed us to distinguish between technologically processed antibodies and samples containing technologically processed buffer. The thermographic analysis has proven to be a simple, specific, and reproducible technique that can be used to analyze the identity of TPA-based drugs, regardless of the dosage form tested. Full article
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<p>The mean surface area free of film for samples of one batch of the test sample and placebo. (*—<span class="html-italic">p</span> &lt; 0.1 vs. placebo, all q &gt; 17.8, qcrit = 3.98, Tukey test).</p>
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<p>An example of a scatter plot with straight line approximation and its confidence interval.</p>
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<p>Confidence intervals which show the difference in the mean surface areas free of film for the compared groups of samples.</p>
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<p>Mean surface area free of film (in relative units) for samples of TPAs to various molecules, and control (*—<span class="html-italic">p</span> &lt; 0.1 vs. placebo sample, q1 = 36.5, q2 = −25.5, qcrit = 2.95, Tukey test).</p>
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<p>A representative image of a Petri dish with a surface film formed during cooling. The film is the dark part of the image. The lighter part of the image shows the area free of film.</p>
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13 pages, 3715 KiB  
Article
Thermal Reading of Texts Buried in Historical Bookbindings
by Stefano Paoloni, Giovanni Caruso, Noemi Orazi, Ugo Zammit and Fulvio Mercuri
Sensors 2024, 24(17), 5493; https://doi.org/10.3390/s24175493 - 24 Aug 2024
Viewed by 513
Abstract
In the manufacture of ancient books, it was quite common to insert written scraps belonging to earlier library material into bookbindings. For scholars like codicologists and paleographers, it is extremely important to have the possibility of reading the text lying on such scraps [...] Read more.
In the manufacture of ancient books, it was quite common to insert written scraps belonging to earlier library material into bookbindings. For scholars like codicologists and paleographers, it is extremely important to have the possibility of reading the text lying on such scraps without dismantling the book. In this regard, in this paper, we report on the detection of these texts by means of infrared (IR) pulsed thermography (PT), which, in recent years, has been specifically proven to be an effective tool for the investigation of Cultural Heritage. In particular, we present a quantitative analysis based, for the first time, on PT images obtained from books of historical relevance preserved at the Biblioteca Angelica in Rome. The analysis has been carried out by means of a theoretical model for the PT signal, which makes use of two image parameters, namely, the distortion and the contrast, related to the IR readability of the buried texts. As shown in this paper, the good agreement between the experimental data obtained in the historical books and the theoretical analysis proved that the capability of the adopted PT method could be fruitfully applied, in real case studies, to the detection of buried texts and to the quantitative characterization of the parameters affecting their thermal readability. Full article
(This article belongs to the Section Remote Sensors)
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<p>A book printed in 1758 (ʘ.2.16) from the Biblioteca Angelica of Rome: (<b>a</b>) a picture of the back endleaf; (<b>b</b>) a thermogram recorded 0.02 s after the light pulse, showing the text buried at a depth of 95 μm; (<b>c</b>) a thermogram recorded 0.30 s after the light pulse, also showing the text buried at a depth of 155 μm.</p>
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<p>A book printed in 1592 (f.9.31) from the Biblioteca Angelica of Rome: (<b>a</b>) a picture of the back endleaf; thermograms of the black framed part (area III) recorded 0.02 s (<b>b</b>), 0.05 s (<b>c</b>) and 0.30 s (<b>d</b>) after the light pulse, showing the text buried at a depth of 110 μm.</p>
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<p>A sketch of the specimen considered in the model.</p>
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<p>A sketch of the PT signal profiles over the edge at <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>x</mi> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math> = 0 of a subsurface ink feature, where 1D (black dotted line) and 3D (continuous gray line) heat diffusion regimes are considered. Also represented is the distortion index ∆ (see text).</p>
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<p>The theoretical delay-time dependence of the contrast <span class="html-italic">C</span>(<span class="html-italic">t</span>) (<b>a</b>) and the distortion index ∆(<span class="html-italic">t</span>) (<b>b</b>) over the edge of graphical features buried at different depths in a paper layer.</p>
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<p>Sketches of the bookbinding cross-sections of (<b>a</b>) book ʘ.2.16 and (<b>b</b>) book f.9.31.</p>
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<p>Thermograms of text I buried just beneath the endpaper in the area framed in blue previously shown in <a href="#sensors-24-05493-f001" class="html-fig">Figure 1</a>b, obtained for increasing delay times of 0.02 s (<b>a</b>), 0.05 s (<b>b</b>) and 0.30 s (<b>c</b>) after the heating light pulse. (<b>d</b>) The PT signal profiles obtained over one of the letters.</p>
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<p>Thermograms of text II buried just beneath the endpaper in the area framed in red (previously shown in <a href="#sensors-24-05493-f001" class="html-fig">Figure 1</a>c) obtained for increasing delay times of 0.02 s (<b>a</b>), 0.05 s (<b>b</b>) and 0.30 s (<b>c</b>) after the heating light pulse. (<b>d</b>) The PT signal profiles obtained over one of the letters.</p>
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<p>Thermograms of text III buried just beneath the area framed in black of the endpaper previously shown in <a href="#sensors-24-05493-f002" class="html-fig">Figure 2</a>a, obtained for increasing delay times of 0.02 s (<b>a</b>), 0.05 s (<b>b</b>) and 0.30 s (<b>c</b>) after the heating light pulse. (<b>d</b>) The PT signal profiles obtained over one of the letters.</p>
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<p>The time dependence of (<b>a</b>) the contrast <span class="html-italic">C</span>(<span class="html-italic">t</span>) and (<b>b</b>) distortion ∆ of the texts buried at different depths. The continuous lines represent the theoretical prediction, while the symbols correspond to the experimental data obtained according to the procedure described in the text.</p>
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23 pages, 9322 KiB  
Article
Defect Detection of GFRP Composites through Long Pulse Thermography Using an Uncooled Microbolometer Infrared Camera
by Murniwati Anwar, Faizal Mustapha, Mohd Na’im Abdullah, Mazli Mustapha, Nabihah Sallih, Azlan Ahmad and Siti Zubaidah Mat Daud
Sensors 2024, 24(16), 5225; https://doi.org/10.3390/s24165225 - 12 Aug 2024
Viewed by 907
Abstract
The detection of impact and depth defects in Glass Fiber Reinforced Polymer (GFRP) composites has been extensively studied to develop effective, reliable, and cost-efficient assessment methods through various Non-Destructive Testing (NDT) techniques. Challenges in detecting these defects arise from varying responses based on [...] Read more.
The detection of impact and depth defects in Glass Fiber Reinforced Polymer (GFRP) composites has been extensively studied to develop effective, reliable, and cost-efficient assessment methods through various Non-Destructive Testing (NDT) techniques. Challenges in detecting these defects arise from varying responses based on the geometrical shape, thickness, and defect types. Long Pulse Thermography (LPT), utilizing an uncooled microbolometer and a low-resolution infrared (IR) camera, presents a promising solution for detecting both depth and impact defects in GFRP materials with a single setup and minimal tools at an economical cost. Despite its potential, the application of LPT has been limited due to susceptibility to noise from environmental radiation and reflections, leading to blurry images. This study focuses on optimizing LPT parameters to achieve accurate defect detection. Specifically, we investigated 11 flat-bottom hole (FBH) depth defects and impact defects ranging from 8 J to 15 J in GFRP materials. The key parameters examined include the environmental temperature, background reflection, background color reflection, and surface emissivity. Additionally, we employed image processing techniques to classify composite defects and automatically highlight defective areas. The Tanimoto Criterion (TC) was used to evaluate the accuracy of LPT both for raw images and post-processed images. The results demonstrate that through parameter optimization, the depth defects in GFRP materials were successfully detected. The TC success rate reached 0.91 for detecting FBH depth defects in raw images, which improved significantly after post-processing using Canny edge detection and Hough circle detection algorithms. This study underscores the potential of optimized LPT as a cost-effective and reliable method for detecting defects in GFRP composites. Full article
(This article belongs to the Section Sensor Materials)
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Graphical abstract

Graphical abstract
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<p>Radiation captured during data measurement using an IR camera [<a href="#B33-sensors-24-05225" class="html-bibr">33</a>].</p>
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<p>Front rear of the eleven flat bottom holes (FBH) of the GFRP sample.</p>
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<p>Impact defect of the GFRP sample: (<b>a</b>) sample IM1 and (<b>b</b>) sample IM2.</p>
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<p>LPT setup using (<b>a</b>) reflex configurations and (<b>b</b>) an enclosure.</p>
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<p>Example of black-colored paper used in the experiment.</p>
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<p>Close setup of the enclosure using hard cardboard.</p>
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<p>Surface material covered with color tape.</p>
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<p>Image-segmentation method for automatic defect detection.</p>
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<p>Edge detection flowchart.</p>
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<p>Circle detection algorithm flowchart.</p>
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<p>Outdoor output image at temperatures above 35 °C for 10–40 s of heating duration.</p>
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<p>Indoor output image at room temperature (23–25 °C) for 10–40 s of heating duration.</p>
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<p>Indoor output image at low temperatures (16–18 °C) for 10–40 s of heating duration.</p>
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<p>Temperature bar for one of the images captured outdoors at temperatures above 35 °C.</p>
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<p>Output image for the black-colored background of the internal wall for 20–40 s of heating.</p>
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<p>Output image for the white-colored background of the internal wall for 20–40 s of heating.</p>
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<p>Output image for the yellow-colored background of the internal wall for 20–40 s of heating.</p>
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<p>Result for indoors, without an enclosure at temperatures from 16 °C to 18 °C from 20 s (first row) to 40 s (last row) of heating.</p>
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<p>Result for indoors, using an enclosure at temperatures from 16 °C to 18 °C from 20 s (first row) to 40 s (last row) of heating.</p>
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<p>Without tape covered on the surface material at low temperatures (16° C to 18 °C) from 10 s (first row) to 40 s (last row) of heating.</p>
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<p>Yellow tape covered on top of the surface sample at low temperatures (16 °C to 18 °C) from 10 s (first row) to 40 s (last row) of heating.</p>
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<p>With black tape on the surface material at low temperatures (16 °C to 18 °C) from 10 s (first row) to 40 s (last row) of heating.</p>
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<p>Optimized FBH defect of the GFRP detected.</p>
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<p>Impact defect detection results using optimized parameters for samples (<b>a</b>) B1 and (<b>b</b>) B2.</p>
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<p>FBH depth defect detection process using Canny edge detection segmentation.</p>
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<p>FBH depth defect detection process using Sobel edge detection segmentation.</p>
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<p>Edge image segmentation for GFRP impact defect detection using the Canny edge detection method for (<b>a</b>) defect IM1 and (<b>b</b>) defect IM2.</p>
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<p>FBH depth defect detection using histogram threshold.</p>
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<p>Histogram threshold segmentation method result for (<b>a</b>) defect IM1 and (<b>b</b>) defect IM2.</p>
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<p>FBH defect detection using the circle segmentation method.</p>
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24 pages, 9047 KiB  
Article
Integrated Investigations to Study the Materials and Degradation Issues of the Urban Mural Painting Ama Il Tuo Sogno by Jorit Agoch
by Giulia Germinario, Andrea Luigia Logiodice, Paola Mezzadri, Giorgia Di Fusco, Roberto Ciabattoni, Davide Melica and Angela Calia
Sustainability 2024, 16(12), 5069; https://doi.org/10.3390/su16125069 - 14 Jun 2024
Viewed by 1041
Abstract
This paper focuses on an integrated approach to study the materials and the degradation issues in the urban mural painting Ama Il Tuo Sogno, painted by the famous street artist Jorit Agoch in Matera (Italy). The study was conducted in the framework of [...] Read more.
This paper focuses on an integrated approach to study the materials and the degradation issues in the urban mural painting Ama Il Tuo Sogno, painted by the famous street artist Jorit Agoch in Matera (Italy). The study was conducted in the framework of a conservation project, aiming to contrast a progressive decay affecting the artifact that started a few months after its creation. Multi-analytical techniques were used to investigate the stratigraphy and chemical composition of the pictorial film within a low-impact analytical protocol for sustainable diagnostics. They included polarized light microscopy in UV and VIS reflected light, FTIR spectroscopy, Py-GC-HRAMS, and SEM-EDS. The mineralogical–petrographic composition of the mortar employed in the pictorial support was also studied with optical microscopy of thin sections and X-ray diffractometry. To know the mechanism underlying the degradation, IR thermography was performed in situ to establish the waterways and the distribution of the humidity in the mural painting. In addition, ion chromatography and X-ray diffractometry were used to identify and quantify the soluble salts and to understand their sources. The overall results allowed us to determine the chemical composition of the binder and pigments within the pictorial layers, the mineralogical–petrographic characteristics of the mortar of the support, and the execution technique of the painting. They also highlighted a correlation between the presence of humidity in the painted mural and the salt damage. The mineralogical phases were detected in the mural materials by XRD, and the results of ion chromatographic analyses suggested a supply of soluble salts mainly from the mortar of the support. Finally, the study provided basic knowledge for planning appropriate sustainable conservation measures. Full article
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<p>The mural painting <span class="html-italic">Ama Il Tuo Sogno</span> (6.45 × 2.10 m<sup>2</sup>) by Jorit Agoch and the sampling points.</p>
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<p>Decay affecting the mural painting: (<b>a</b>) lack of adhesion and lifting of pictorial film; (<b>b</b>) lacuna on painting layers; (<b>c</b>) white veils due to efflorescence; (<b>d</b>) crystal salt aggregates on the surface, as observed with a Dino Lite video microscope (50× magnification).</p>
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<p>Cross-section stratigraphy under reflected VIS light: (<b>a</b>) JA1 and (<b>b</b>) JA2 samples, where layer a is the mortar support; (<b>c</b>) JA9 sample, where layer a is the stone support.</p>
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<p>Cross-section stratigraphy under reflected VIS (on the <b>right</b>) and UV light (on the <b>left</b>) in the JA4 sample.</p>
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<p>JA4 sample: backscattered image of the pink pictorial layer (b) and the dark red/orange layer (a), EDS spectra (Sp.) and maps of titanium, silicon, calcium, and iron.</p>
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<p>ATR-FTIR spectrum of the JA1 (in bleu), brown JA4 (in brown), and black JA9 (in orange) samples.</p>
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<p>Comparison of two FTIR-ATR spectra relative to the JA4 mural painting sample (in purple) and RV205 spray paint sample (in orange).</p>
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<p>THM-GC-HRAMS chromatograms for RV136 (<b>top</b>), RV97 (<b>middle</b>) and JA4 (<b>bottom</b>) at a pyrolysis temperature of 550 °C. (Key: 1: benzaldehyde; 2: octanoic acid, ME; 3: benzoic acid, ME; 4: decanoic acid; 5: phthalic anhydride; 6: 1(3H)-isobenzofuranone; 7: octanedioic acid, DME; 8:dimethyl phthalate; 9: N-propyl benzamide; 10: nonanedioic acid, DME; 11: decenedioic acid, diethyl ester; 12: decanedioic acid, DME; 13: 1,4-benzene dicarboxylic acid; 14: dimethyl phthalate; 15: hexanoic acid, ME; 16: isopropyl phthalate; 17: dibutyl phthalate; 18: oleic acid, ME; 19: octadecanoic acid, ME; 20: docosanoic acid, ME; 21: linoleic acid, ME; 22: oxiraneoctanoic acid, 3-octyl, ME; 23: phthalic acid, methyl phenyl ester; 24: phthalic acid, methyl 2-pentyl ester).</p>
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<p>Thin-section micrographs (transmitted light, cross-nicols) of (<b>a</b>) top mortar layer; (<b>b</b>) bottom mortar layer.</p>
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<p>XRD spectra: (<b>a</b>) IG21 mortar used for the top plaster layer; (<b>b</b>) KD2 mortar used in the bottom plaster layer. (Key: A: albite; M: calcite magnesian; P: portlandite; Q: quartz; C<sub>2</sub>S: belite; C<sub>3</sub>S: alite; C<sub>3</sub>A: celite).</p>
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<p>Thermographic images of the mural painting: (<b>a</b>) spring season; (<b>b</b>) autumn season; (<b>c</b>) overlapping of the thermographic image in spring season with the graphic map of the decay.</p>
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<p>Weight percentages of anions (<b>a</b>) and cations (<b>b</b>) in the samples from the mural paintings, in the raw mortar materials of the preparatory layers, and in the samples from the masonry wall. (Sample key: IG21, raw mortar used for the top layer in the paint support; KD2, raw mortar used for the bottom layer in the paint support; JA3, efflorescence on the painting; JA5, mortar from the top layer under the painting; JA6, efflorescence on the mortar joint of the wall; JA7, joint mortar powder; JA8, white veil on the painting; JA10, white veil on the stone ashlar of the wall; JA11, stone ashlar of the wall).</p>
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<p>XRD spectra: (<b>a</b>) efflorescence on the painted surface (JA3); (<b>b</b>) white veil on the painted surface (JA8); (<b>c</b>) efflorescence on the wall mortar joint (JA6). (Key: E: epsomite; G: gypsum; M: calcite magnesian; MPh: magnesium phosphate; Q: quartz; Th: thenardite).</p>
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16 pages, 2291 KiB  
Review
Beyond the Spectrum: Unleashing the Potential of Infrared Radiation in Poultry Industry Advancements
by Khawar Hayat, Zunzhong Ye, Hongjian Lin and Jinming Pan
Animals 2024, 14(10), 1431; https://doi.org/10.3390/ani14101431 - 10 May 2024
Viewed by 1501
Abstract
The poultry industry is dynamically advancing production by focusing on nutrition, management practices, and technology to enhance productivity by improving feed conversion ratios, disease control, lighting management, and exploring antibiotic alternatives. Infrared (IR) radiation is utilized to improve the well-being of humans, animals, [...] Read more.
The poultry industry is dynamically advancing production by focusing on nutrition, management practices, and technology to enhance productivity by improving feed conversion ratios, disease control, lighting management, and exploring antibiotic alternatives. Infrared (IR) radiation is utilized to improve the well-being of humans, animals, and poultry through various operations. IR radiation occurs via electromagnetic waves with wavelengths ranging from 760 to 10,000 nm. The biological applications of IR radiation are gaining significant attention and its utilization is expanding rapidly across multiple sectors. Various IR applications, such as IR heating, IR spectroscopy, IR thermography, IR beak trimming, and IR in computer vision, have proven to be beneficial in enhancing the well-being of humans, animals, and birds within mechanical systems. IR radiation offers a wide array of health benefits, including improved skin health, therapeutic effects, anticancer properties, wound healing capabilities, enhanced digestive and endothelial function, and improved mitochondrial function and gene expression. In the realm of poultry production, IR radiation has demonstrated numerous positive impacts, including enhanced growth performance, gut health, blood profiles, immunological response, food safety measures, economic advantages, the mitigation of hazardous gases, and improved heating systems. Despite the exceptional benefits of IR radiation, its applications in poultry production are still limited. This comprehensive review provides compelling evidence supporting the advantages of IR radiation and advocates for its wider adoption in poultry production practices. Full article
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<p>Different forms of infrared radiation and their uses in different sectors.</p>
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<p>Mechanism of action of IR at the cellular level. IR generates ROS and increases intracellular Ca<sup>2+</sup>. Changes in water dynamics affect membrane and mitochondrial function, releasing Ca<sup>2+</sup> into the cytosol. Elevated Ca<sup>2+</sup> activates enzymes in cellular respiration, producing ATP via TCA and ETC, essential for cellular processes, while ROS and Ca<sup>2+</sup> regulate cellular signaling.</p>
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<p>Overall impact of infrared radiation on poultry practices.</p>
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<p>Integrating infrared technology for optimizing poultry farming: (<b>a</b>) infrared heating system to improve heating control and preserve energy; (<b>b</b>) infrared beak trimming improves bird welfare; (<b>c</b>) infrared thermography to detect bird body temperature; and (<b>d</b>) infrared spectroscopy to evaluate poultry feed quality.</p>
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<p>Physical appearance of beak after trimming: (1–2) beak trimming using a heat blade showing crack in beak and abnormal shape; and (3–4) infrared beak trimming showing normal beak appearance.</p>
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<p>Infrared in computer vision for poultry monitoring.</p>
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<p>Effect of IR radiation on poultry growth parameters.</p>
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27 pages, 8579 KiB  
Review
LED Junction Temperature Measurement: From Steady State to Transient State
by Xinyu Zhao, Honglin Gong, Lihong Zhu, Zhenyao Zheng and Yijun Lu
Sensors 2024, 24(10), 2974; https://doi.org/10.3390/s24102974 - 8 May 2024
Cited by 1 | Viewed by 1937
Abstract
In this review, we meticulously analyze and consolidate various techniques used for measuring the junction temperature of light-emitting diodes (LEDs) by examining recent advancements in the field as reported in the literature. We initiate our exploration by delineating the evolution of LED technology [...] Read more.
In this review, we meticulously analyze and consolidate various techniques used for measuring the junction temperature of light-emitting diodes (LEDs) by examining recent advancements in the field as reported in the literature. We initiate our exploration by delineating the evolution of LED technology and underscore the criticality of junction temperature detection. Subsequently, we delve into two key facets of LED junction temperature assessment: steady-state and transient measurements. Beginning with an examination of innovations in steady-state junction temperature detection, we cover a spectrum of approaches ranging from traditional one-dimensional methods to more advanced three-dimensional techniques. These include micro-thermocouple, liquid crystal thermography (LCT), temperature sensitive optical parameters (TSOPs), and infrared (IR) thermography methods. We provide a comprehensive summary of the contributions made by researchers in this domain, while also elucidating the merits and demerits of each method. Transitioning to transient detection, we offer a detailed overview of various techniques such as the improved T3ster method, an enhanced one-dimensional continuous rectangular wave method (CRWM), and thermal reflection imaging. Additionally, we introduce novel methods leveraging high-speed camera technology and reflected light intensity (h-SCRLI), as well as micro high-speed transient imaging based on reflected light (μ_HSTI). Finally, we provide a critical appraisal of the advantages and limitations inherent in several transient detection methods and offer prognostications on future developments in this burgeoning field. Full article
(This article belongs to the Special Issue Advanced Optical and Optomechanical Sensors)
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<p>Schematic diagram of the structure of the article.</p>
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<p>Schematic diagram of the experimental arrangement of liquid crystal thermal imaging technology. The experimental setup usually consists of a polarized laser beam, a charge-coupled camera with a color filter, and a liquid crystal covering the surface of the LED [<a href="#B56-sensors-24-02974" class="html-bibr">56</a>].</p>
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<p>Lateral infrared distribution image of flip LEDs prepared by photolithography and dry reaction etching techniques. The schematic of the chip is shown on the left, while the captured infrared radiation is shown on the right [<a href="#B66-sensors-24-02974" class="html-bibr">66</a>].</p>
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<p>μ-HSI was utilized to measure the two-dimensional temperature distribution of blue (<b>a</b>), green (<b>b</b>), and red (<b>c</b>) LEDs at a heat sink temperature of 75 °C (348.15 K). Information regarding the color, material system, and driving current of each LED is provided at the top, while the junction temperature (T<sub>j</sub>) measured by both micro-thermocouple and μ-HSI is presented at the bottom. The average standard deviation of T<sub>j</sub> (μ-TC) and T<sub>j</sub> (μ-HSI) was recorded as 0.9 °C (0.9 K) [<a href="#B84-sensors-24-02974" class="html-bibr">84</a>].</p>
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<p>Frequency domain thermoreflectance imaging of a 40 μm heater with ‘cyclic phase lag’ heterodyne locking (the frequency shown is the thermal frequency) [<a href="#B89-sensors-24-02974" class="html-bibr">89</a>].</p>
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<p>T3ster junction temperature measurement system.</p>
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<p>Simulation using Flotherm to drive dynamic junction temperature peaks of LEDs with pulse trains.</p>
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<p>(<b>a</b>) Continuous rectangular-wave current. (<b>b</b>) Different voltage response waveforms of LED [<a href="#B39-sensors-24-02974" class="html-bibr">39</a>].</p>
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<p>Voltage waveform of the LED driven by rectangular-wave current [<a href="#B94-sensors-24-02974" class="html-bibr">94</a>].</p>
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<p>Schematic diagram of time-resolved micro-Raman thermal imaging experimental device [<a href="#B99-sensors-24-02974" class="html-bibr">99</a>].</p>
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<p>The variation of the center temperature of the mesa isolated AlGaN/GaN device grown on (<b>a</b>) SiC substrate and (<b>b</b>) sapphire substrate with time [<a href="#B99-sensors-24-02974" class="html-bibr">99</a>].</p>
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<p>(<b>a</b>) The variation of the center temperature of a 20 μm wide ungated AlGaN/GaN device on SiC substrate with time, and the simulated temperature evolution at different depths (z) in the device and SiC substrate. (<b>b</b>) The measured temperature evolution at different depths in the SiC substrate. The device operates at 25 μs long 40 V (159 mA) square bias pulse and a 50% duty cycle [<a href="#B99-sensors-24-02974" class="html-bibr">99</a>].</p>
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<p>Schematic diagram of the heat reflection imaging experimental device [<a href="#B104-sensors-24-02974" class="html-bibr">104</a>].</p>
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<p>(<b>a</b>) |μ R/R| is a function of time, pixel offset to 12 V 10 ms; (<b>b</b>) Timing diagram: the timing of the LED pulse relative to the pixel pulse changes; four function generators are used to generate various pulses, with LED pulses of 1 ms [<a href="#B104-sensors-24-02974" class="html-bibr">104</a>].</p>
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<p>(<b>a</b>) The setup used in the experiment to measure the dynamic two-dimensional temperature distribution. The measurement setup mainly contains highspeed camera (2), microscope with a high-pass filter (3), blue LUT (5), incident red LED (7), Heat sink (6) with temperature controller (8) (9), electrical source meter (10) (11) for blue LUT and incident red LED. (<b>b</b>) An illustration detailing the drive current waveform of the LUT, the acquisition waveform of the camera, and the processed transient temperature waveform [<a href="#B17-sensors-24-02974" class="html-bibr">17</a>].</p>
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<p>Transient two-dimensional temperature distribution of the rising edge of the blue LUT at 300 mA at (<b>a</b>) 0 ms, (<b>b</b>) 75 ms, (<b>c</b>) 95 ms, and (<b>d</b>) 125 ms, and (<b>e</b>) the comparison of the transient response of the h-SCRLI method and the thermal reflection imaging method [<a href="#B17-sensors-24-02974" class="html-bibr">17</a>].</p>
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<p>The evolving two-dimensional temperature profile of the blue LUT, propelled by a 300 mA current, is depicted at the descent of (<b>a</b>) 0 ms, (<b>b</b>) 5 ms, (<b>c</b>) 10 ms, and (<b>d</b>) 145 ms. Additionally, (<b>e</b>) presents a comparative analysis of the transient responses between the h-SCRLI method and the thermal reflection imaging method [<a href="#B17-sensors-24-02974" class="html-bibr">17</a>].</p>
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<p>(<b>a</b>) The pulse signal diagram of driving the LUT (heating process); (<b>b</b>) Dropping sampling pulse setting (cooling process) [<a href="#B11-sensors-24-02974" class="html-bibr">11</a>].</p>
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<p>Schematic diagram of the experimental device [<a href="#B11-sensors-24-02974" class="html-bibr">11</a>].</p>
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<p>The transient two-dimensional temperature distribution of LUT driven by different pulse widths during the heating process of (<b>a</b>) 10 ns, (<b>b</b>) 500 ns, (<b>c</b>) 100 μs, and (<b>d</b>) 1 ms. (<b>e</b>) The time–response curve in the logarithmic coordinate of the average transient junction temperature in the red box [<a href="#B11-sensors-24-02974" class="html-bibr">11</a>].</p>
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<p>The transient two-dimensional temperature distribution of LUT driven by different pulse widths during the cooling process of (<b>a</b>) 0 μs, (<b>b</b>) 50 μs, (<b>c</b>) 100 μs, and (<b>d</b>) 200 μs. (<b>e</b>) The logarithmic coordinate time–response curve of the average transient junction temperature in the red box [<a href="#B11-sensors-24-02974" class="html-bibr">11</a>].</p>
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26 pages, 5801 KiB  
Article
Impact of Solar Shading on Façades’ Surface Temperatures under Summer and Winter Conditions by IR Thermography
by María del Mar Barbero-Barrera, Ricardo Tendero-Caballero and María García de Viedma-Santoro
Architecture 2024, 4(2), 221-246; https://doi.org/10.3390/architecture4020014 - 29 Apr 2024
Cited by 1 | Viewed by 1808
Abstract
In warm climates with high levels of solar irradiation, solar shading plays a determinant role on buildings’ envelope performance, both during summer and winter conditions. In this research, an evaluation of the solar shading effect on sunny façades through IR thermography non-destructive testing [...] Read more.
In warm climates with high levels of solar irradiation, solar shading plays a determinant role on buildings’ envelope performance, both during summer and winter conditions. In this research, an evaluation of the solar shading effect on sunny façades through IR thermography non-destructive testing was performed. Sunny and shaded areas revealed temperature differences of 7.4 °C in summer conditions and up to 1.2 °C in wintertime. Moreover, solar shading was shown to be beneficial not only for decreasing surface temperature in summertime but also for reducing convective air flow in wintertime. In addition, it was found that the prevalence of dense shadows, especially with non-reflective materials in louvres, is favorable. External Thermal Insulation Constructive Systems (ETICS) must be shadowed and the use of clear colors is recommended to reinforce homogeneity in the surface in wintertime and reduce solar absorptance in summertime. Under steady-state calculations, thermal losses can be reduced up to 30% at night in wintertime and up to 50–60% at daytime in summertime because of the shadowing. However, another important finding lied in the confirmation of the performance gap that arises between using air temperature, sol-air temperature and the actual surface temperature data, in such a way that the two former implied high levels of inaccuracy and overestimated the performance of the buildings compared to the actual behavior. Some of the main conclusions can be extrapolated to other circumstances. Full article
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<p>Location of case study in the historical layout. At the bottom, solar obstruction occurring with the maximum inclination of solar beams in summer (<b>a</b>) and at winter (<b>b</b>), at noon. In both cases, a square frame indicates the studied façade.</p>
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<p>Façade louvres and solar incidence angle: in dashed line for summertime and continuous line for wintertime.</p>
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<p>Example of on-site hourly air temperature by the weather station in the building (winter and summer).</p>
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<p>Photographic image, IR thermography and temperature gradient (from top to bottom), in summer. L1, L2 and L3 indicate different readings along the façade (see IR thermography image). The red lines show the placement of the louvres.</p>
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<p>(<b>a</b>) Photographic, IR image and temperatures in summer at 16:30 h of the whole façade; (<b>b</b>) detail of IR thermography of the façade closed to the pavement. Tm pavement = 56.4 °C (emissivity: 0.9); T façade = 46.7 °C (emissivity: 0.95).</p>
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<p>Photographic image, IR thermography and temperature gradient (from top to bottom), in winter. The red line indicates the placement of the louvres.</p>
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<p>Photographic image, IR thermography and temperature gradient (from top to bottom), in winter. The red line indicates the placement of the louvres.</p>
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<p>Details of IR thermography (<b>a</b>) during the blower door test performed on the dwellings (<b>b</b>) IR image and temperatures in winter at 22:00 h.</p>
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<p>Differences between sol-air temperature and real temperatures; (<b>a</b>) as a function of the outside in summer and (<b>b</b>) in winter. Differences among sol-air and real surface temperatures together with air temperatures in (<b>c</b>) summer and (<b>d</b>) winter.</p>
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<p>(<b>a</b>) Thermal fluxes according to the construction system throughout a summer day; (<b>b</b>) energy losses Q(W/m<sup>2</sup>) through the opaque façade exposed to the sun compared to the area under the louvres for the type 1 construction system, in summertime.</p>
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<p>Thermal fluxes based on air temperature compared to those based on surface temperatures. (<b>a</b>) For types 1 and 4 walls; (<b>b</b>) detail of type 1 (T1) and (<b>c</b>) type 4 walls (T4).</p>
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<p>Thermal fluxes based on air temperatures compared to those based on surface temperatures and compared to sol-air temperatures commonly used in dynamic simulation for T1 types (<b>a</b>) and T4 types (<b>b</b>).</p>
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<p>(<b>a</b>) Thermal flow in winter on sunny and shaded surfaces of the façade for the different construction systems; (<b>b</b>) energy losses Q(W/m<sup>2</sup>) through the opaque façade exposed to the sun compared to the area under the louvres for type 1 construction system, in wintertime.</p>
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<p>Thermal fluxes based on air temperature data compared to those obtained based on surface temperature data for types 1 and 4. (<b>a</b>) Comparison of thermal fluxes between all T1 and T4; (<b>b</b>) Thermal fluxes for the T1 types; (<b>c</b>) Thermal fluxes for the T4 types.</p>
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<p>Thermal fluxes based on air temperature data compared to those obtained based on surface temperature data for types 1 and 4. (<b>a</b>) Comparison of thermal fluxes between all T1 and T4; (<b>b</b>) Thermal fluxes for the T1 types; (<b>c</b>) Thermal fluxes for the T4 types.</p>
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<p>Thermal fluxes based on air temperatures compared to those based on surface temperatures and comparing to sol-air temperatures commonly used in dynamic simulation (<b>a</b>) for of T1 types; (<b>b</b>) for the T4 types.</p>
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17 pages, 12556 KiB  
Article
Lateral Heat Distribution Characteristics of CLP S275 Using Gaussian FFT Algorithm in Optical Thermographic Testing
by Seungju Lee, Yoonjae Chung, Wontae Kim and Hyunkyu Suh
Appl. Sci. 2024, 14(9), 3776; https://doi.org/10.3390/app14093776 - 28 Apr 2024
Viewed by 918
Abstract
In general, when using infrared thermography (IRT) techniques to excite a heat source on the surface of an inspection object, the heat source is focused on the center of the image of the infrared (IR) camera. If the object to be inspected is [...] Read more.
In general, when using infrared thermography (IRT) techniques to excite a heat source on the surface of an inspection object, the heat source is focused on the center of the image of the infrared (IR) camera. If the object to be inspected is small, uniform excitation of the heat source is possible, but if the area is large, the heat source is concentrated locally, resulting in uneven heat distribution. Therefore, in this study, heat distribution was analyzed after inducing a non-uniform heat source by exciting the heat source at different locations. Additionally, the fast Fourier transform (FFT) algorithm with Gaussian filtering was applied to resolve the non-uniform distribution of the heat sources. Excellent results were obtained from the amplitude image, and the effectiveness of the FFT algorithm was verified using the Otsu algorithm. Finally, the signal-to-noise ratio (SNR) was calculated, and the detection ability according to each thinning rate was analyzed. Full article
(This article belongs to the Special Issue Progress in Nondestructive Testing and Evaluation (NDT&E))
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<p>The principle of Gaussian filtering-based FFT algorithm.</p>
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<p>Configuration of LIT experimental devices.</p>
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<p>Two types of S275 specimen shapes. The A–type specimen has a flat surface without defects, and the B–type specimen has 9 artificial defects. (<b>a</b>) A–type specimen; (<b>b</b>) B–type specimen.</p>
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<p>Dimensions of S275 specimen with 9 artificial thinning defects.</p>
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<p>Three arrangements of halogen lamps to excite non-uniform heat sources. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Thermal wave form of raw (temperature distribution) and lock-in algorithm. (<b>a</b>) Long pulse; (<b>b</b>) sine wave.</p>
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<p>Thermal image of A–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Thermal image of B–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Gaussian–filtered image with FFT algorithm applied to type A specimen. Gaussian weights are set to 2.0. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Gaussian–filtered image with FFT algorithm applied to type B specimen. Gaussian weights are set to 2.0. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Profile graph of Line 2 with FFT applied for A– and B–type specimens. (<b>a</b>) A–type, center; (<b>b</b>) A–type, left side; (<b>c</b>) A–type, top left; (<b>d</b>) B–type, center; (<b>e</b>) B–type, left side; (<b>f</b>) B–type, top left.</p>
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<p>Phase image of A–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Phase image of B–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Phase image with FFT applied of A–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Phase image with FFT applied of B–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Line 2 phase profile graph using FFT for B–type specimen. (<b>a</b>) B–type, center; (<b>b</b>) B–type, left side; (<b>c</b>) B–type, top left.</p>
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<p>Amplitude images of A–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Amplitude images of B–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Amplitude image with FFT applied of A–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Amplitude image with FFT applied of B–type specimen. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Amplitude profile graph of Line 2 to which FFT of B–type specimen was applied. (<b>a</b>) A–type, center; (<b>b</b>) A–type, left side; (<b>c</b>) A–type, top left; (<b>d</b>) B–type, center; (<b>e</b>) B–type, left side; (<b>f</b>) B–type, top left.</p>
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<p>Binarization images of B–type specimen without FFT algorithm applied. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>Binarization images of B–type specimen with FFT algorithm applied. (<b>a</b>) center; (<b>b</b>) left side; (<b>c</b>) top left.</p>
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<p>SNR graph of amplitude image based on FFT algorithm applying center thermal excitation. (<b>a</b>) center; (<b>b</b>) 1.0; (<b>c</b>) 1.5; (<b>d</b>) 2.5; (<b>e</b>) 3.0; (<b>b</b>–<b>e</b>) refer to Gaussian weights.</p>
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<p>SNR graph of amplitude image based on FFT algorithm applying left side thermal excitation. (<b>a</b>) left side; (<b>b</b>) 1.0; (<b>c</b>) 3.0; (<b>b</b>,<b>c</b>) refer to Gaussian weights.</p>
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<p>SNR graph of amplitude image based on FFT algorithm applying top left thermal excitation. (<b>a</b>) top left; (<b>b</b>) 1.0; (<b>c</b>) 3.0; (<b>b</b>,<b>c</b>) refer to Gaussian weights.</p>
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15 pages, 5256 KiB  
Article
Modelling and Thermographic Measurements of LED Optical Power
by Maria Strąkowska, Sebastian Urbaś, Mariusz Felczak, Błażej Torzyk, Iyad S. M. Shatarah, Rafał Kasikowski, Przemysław Tabaka and Bogusław Więcek
Sensors 2024, 24(5), 1471; https://doi.org/10.3390/s24051471 - 24 Feb 2024
Cited by 1 | Viewed by 1012
Abstract
This paper presents a simple engineering method for evaluating the optical power emitted by light-emitting diodes (LEDs) using infrared thermography. The method is based on the simultaneous measurement of the electrical power and temperature of an LED and a heat source (resistor) that [...] Read more.
This paper presents a simple engineering method for evaluating the optical power emitted by light-emitting diodes (LEDs) using infrared thermography. The method is based on the simultaneous measurement of the electrical power and temperature of an LED and a heat source (resistor) that are enclosed in the same plastic packaging under the same cooling conditions. This ensures the calculation of the optical power emitted by the LED regardless of the value of the heat transfer coefficient. The obtained result was confirmed by comparing it with the standard direct measurement method using an integrated sphere. The values of the estimated optical power using the proposed method and the integrated sphere equipped with a spectrometer were consistent with each other. The tested LED exhibited a high optical energy efficiency, reaching approximately η ≈ 30%. In addition, an uncertainty analysis of the obtained results was performed. Compact modelling based on a thermal resistor network (Rth) and a 3D-FEM analysis were performed to confirm the experimental results. Full article
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<p>Cross-section of a high-efficiency LED.</p>
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<p>Scheme of thermal model of a diode (<b>a</b>) and R<sub>th</sub>-network compact thermal model of an LED (<b>b</b>).</p>
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<p>Connection wire model.</p>
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<p>The concept of thermovision measurements of the optical power of LEDs.</p>
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<p>Block diagram of the proposed measurement method for LED optical power.</p>
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<p>Determination of the thermal power dissipated to the environment through the wires supplying the resistor and the diode.</p>
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<p>Experimental setup and the tray with the measured elements: resistor R (1) and an LED (2).</p>
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<p>Scheme of the measurement system.</p>
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<p>Integrating sphere with a mounted LED.</p>
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<p>Temperature distribution inside the LED enclosure in a steady state.</p>
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<p>Thermogram of a series connection of the diode (on the left) and the resistor (on the right).</p>
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<p>Power density of emitted LED radiation vs. wavelength for <span class="html-italic">I</span> = 20 mA.</p>
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<p>Optical power of a diode as a function of the current.</p>
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17 pages, 4717 KiB  
Article
Experimental Investigations on the Thermal Characteristics of Domestic Convectors
by Duncan Gibb, Jack Oliphant, Ross Gary McIntosh, Taimoor Asim and Aditya Karnik
Energies 2024, 17(5), 1017; https://doi.org/10.3390/en17051017 - 21 Feb 2024
Viewed by 794
Abstract
Better understanding of local thermal characteristics of domestic convectors could play a crucial role in reducing energy consumption for space heating and decarbonizing the economy. The current study evaluates the impact of varying water inlet temperature and flowrate on the local surface temperature [...] Read more.
Better understanding of local thermal characteristics of domestic convectors could play a crucial role in reducing energy consumption for space heating and decarbonizing the economy. The current study evaluates the impact of varying water inlet temperature and flowrate on the local surface temperature of domestic convectors through extensive empirical investigations. Experiments are performed using a custom-made test-rig featuring a 400 mm × 600 mm Type 11 convector within a large and well-ventilated environment, minimizing the thermal influence of the surrounding space on the thermal behavior of the convector. Infrared thermography (IR) is used to acquire local surface temperature data for further analysis. Based on the results obtained, it has been observed that the inlet water temperature has a negligible effect on thermal characteristics of the convector while increasing the flowrate substantially decreases the time required for the convector to reach maximum surface temperature. Based on the numerical data, an analytical model for average surface temperature has been developed using multiple variable regression analysis, demonstrating a prediction accuracy of >90% compared with the experimental data. A detailed understanding of the heating behavior exhibited by domestic convectors has led to a better understanding of the local thermal characteristics, while the prediction model can be used to develop machine learning algorithms to install better flow control techniques for efficient space heating. Full article
(This article belongs to the Section J: Thermal Management)
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<p>Type 11 domestic panel convector.</p>
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<p>Domestic convector test rig (<b>a</b>) schematic and (<b>b</b>) experimental setup [(1) Cold water inlet (2) Water Tank (3) Thermal Camera (4) Power Supply (5) Domestic Convector (type 11) (6) Drain (7) PC with Software (8) Water Pump].</p>
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<p>Domestic convector test rig (<b>a</b>) schematic and (<b>b</b>) experimental setup [(1) Cold water inlet (2) Water Tank (3) Thermal Camera (4) Power Supply (5) Domestic Convector (type 11) (6) Drain (7) PC with Software (8) Water Pump].</p>
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<p>Local surface temperature data recording points on the domestic convector.</p>
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<p>Variations in the local temperature on the front surface of the domestic convector at T<sub>set</sub> = 70 °C and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 0.5 lpm after (<b>a</b>) 1.3 min (<b>b</b>) 2.5 min (<b>c</b>) 6.4 min and (<b>d</b>) 17.8 min.</p>
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<p>Variations in the local temperature on the front surface of the domestic convector at T<sub>set</sub> = 70 °C and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 0.5 lpm after (<b>a</b>) 1.3 min (<b>b</b>) 2.5 min (<b>c</b>) 6.4 min and (<b>d</b>) 17.8 min.</p>
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<p>Variations in the local temperature on the front surface of the domestic convector at T<sub>set</sub> = 70 °C and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 0.5 lpm.</p>
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<p>Variations in the local temperature on the front surface of the domestic convector at T<sub>set</sub> = 50 °C and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 0.5 lpm after (<b>a</b>) 2.2 min (<b>b</b>) 2.9 min (<b>c</b>) 5.1 min and (<b>d</b>) 10.3 min.</p>
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<p>(<b>a</b>) Variations in the local temperature on the front surface of the domestic convector at T<sub>set</sub> = 50 °C and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 0.5 lpm and (<b>b</b>) difference in the normalized temperature between T<sub>set</sub> = 50 °C and T<sub>set</sub> = 70 °C.</p>
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<p>Effect of varying T<sub>set</sub> on the average surface temperature of the domestic convector at <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 0.5 lpm.</p>
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<p>Variations in the local temperature on the front surface of the domestic convector at T<sub>set</sub> = 70 °C and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 2.4 lpm after (<b>a</b>) 0.3 min, (<b>b</b>) 1.4 min, (<b>c</b>) 2.2 min, and (<b>d</b>) 3.9 min.</p>
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<p>(<b>a</b>) Variations in the local temperature on the front surface of the domestic convector at T<sub>set</sub> = 70 °C and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 2.4 lpm. (<b>b</b>) Difference in the normalized temperature between <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 2.4 lpm and <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> = 0.5 lpm.</p>
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<p>Effect of varying <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> </semantics></math> on the average surface temperature of the domestic convector at T<sub>set</sub> = 70 °C.</p>
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<p>(<b>a</b>) Comparison of T<sub>avg</sub> between the recorded and predicted values. (<b>b</b>) Difference in the predicted and calculated T<sub>avg</sub> values.</p>
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20 pages, 9871 KiB  
Article
Nondestructive Testing (NDT) for Damage Detection in Concrete Elements with Externally Bonded Fiber-Reinforced Polymer
by Jesús D. Ortiz, Seyed Saman Khedmatgozar Dolati, Pranit Malla, Armin Mehrabi and Antonio Nanni
Buildings 2024, 14(1), 246; https://doi.org/10.3390/buildings14010246 - 16 Jan 2024
Cited by 6 | Viewed by 1885
Abstract
Fiber-reinforced polymer (FRP) composites offer a corrosion-resistant, lightweight, and durable alternative to traditional steel material in concrete structures. However, the lack of established inspection methods for assessing reinforced concrete elements with externally bonded FRP (EB-FRP) composites hinders industry-wide confidence in their adoption. This [...] Read more.
Fiber-reinforced polymer (FRP) composites offer a corrosion-resistant, lightweight, and durable alternative to traditional steel material in concrete structures. However, the lack of established inspection methods for assessing reinforced concrete elements with externally bonded FRP (EB-FRP) composites hinders industry-wide confidence in their adoption. This study addresses this gap by investigating non-destructive testing (NDT) techniques for detecting damage and defects in EB-FRP concrete elements. As such, this study first identified and categorized potential damage in EB-FRP concrete elements considering where and why they occur. The most promising NDT methods for detecting this damage were then analyzed. And lastly, experiments were carried out to assess the feasibility of the selected NDT methods for detecting these defects. The result of this study introduces infrared thermography (IR) as a proper method for identifying defects underneath the FRP system (wet lay-up). The IR was capable of highlighting defects as small as 625 mm2 (1 in.2) whether between layers (debonding) or between the substrate and FRP (delamination). It also indicates the inability of GPR to detect damage below the FRP laminates, while indicating the capability of PAU to detect concrete delamination and qualitatively identify bond damage in the FRP system. The outcome of this research can be used to provide guidance for choosing effective on-site NDT techniques, saving considerable time and cost for inspection. Importantly, this study also paves the way for further innovation in damage detection techniques addressing the current limitations. Full article
(This article belongs to the Special Issue Fiber Reinforced Polymer (FRP) Composites for Construction)
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<p>Application of an Externally Bonded FRP system [<a href="#B28-buildings-14-00246" class="html-bibr">28</a>]; (<b>a</b>) surface preparation; (<b>b</b>) application of resin and FRP sheets; (<b>c</b>) coating and finishes.</p>
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<p>Defects in EB-FRP concrete elements [<a href="#B15-buildings-14-00246" class="html-bibr">15</a>].</p>
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<p>Source of damage in EB-FRP concrete elements. Note: colors are related to location given in <a href="#buildings-14-00246-f001" class="html-fig">Figure 1</a> and source given in <a href="#buildings-14-00246-t004" class="html-table">Table 4</a>.</p>
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<p>Slab M’, specimen with steel rebars and damage in the EB-FRP system. 1 in. = 25.4 mm.</p>
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<p>Slab Q’, specimen defects within concrete and in the EB-FRP system. 1 in. = 25.4 mm. (<b>a</b>) Before EB-FRP system installation. (<b>b</b>) After EB-FRP system installation.</p>
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<p>Defects generation in EB-FRP concrete elements. (<b>a</b>) Unidirectional CFRP sheet, (<b>b</b>) mixing of the resin, (<b>c</b>) adhesive–concrete debonding, (<b>d</b>) surface impregnation, (<b>e</b>) first layer of CFRP, (<b>f</b>) strengthened slab.</p>
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<p>Visual inspection of slabs: (<b>a</b>) Slab M’, (<b>b</b>) Slab Q’.</p>
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<p>Tap Testing on Slab Q’.</p>
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<p>Infrared image of slabs: (<b>a</b>) Slab M’, (<b>b</b>) Slab Q’.</p>
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<p>GPR line scans of Slab M’ and Q’. (<b>a</b>) Slab M’ before strengthening, (<b>b</b>) Slab M’ after strengthening, (<b>c</b>) Slab Q’ before strengthening, (<b>d</b>) Slab Q’ after strengthening.</p>
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<p>PAU stripe scan of Slab Q’. (<b>i</b>) 300 mm, (<b>ii</b>) 570 mm and (<b>iii</b>) 800 mm from face 2.</p>
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16 pages, 14510 KiB  
Article
Hand Neuropathies and Musculoskeletal Disorders: Complementary Diagnosis Using IR Thermography
by Joana Cerqueira, Catarina Aguiar Branco, Adélio Vilaça and Joaquim Mendes
Appl. Sci. 2024, 14(1), 70; https://doi.org/10.3390/app14010070 - 20 Dec 2023
Viewed by 1532
Abstract
Hand neuropathies and musculoskeletal disorders represent significant health concerns, often requiring accurate and non-invasive diagnostic methods. Current diagnostic approaches may have limitations in terms of accuracy and patient comfort. This study addresses the need for an improved complementary diagnostic tool for these conditions [...] Read more.
Hand neuropathies and musculoskeletal disorders represent significant health concerns, often requiring accurate and non-invasive diagnostic methods. Current diagnostic approaches may have limitations in terms of accuracy and patient comfort. This study addresses the need for an improved complementary diagnostic tool for these conditions by investigating the potential of infrared thermography for identifying thermal patterns associated with these pathologies. Thermal images were acquired from both control participants with healthy hands and patients with hand neuropathies and/or musculoskeletal disorders. The mean temperatures of various regions of interest (ROIs) were analysed, and statistical tests were conducted to determine if there were significant temperature differences between the control and injury groups. The analysis consistently revealed higher mean temperatures in the injury group across multiple ROIs on both the dorsal and palmar aspects of the hand. Levene’s test confirmed the equality of variances between the groups, supporting the validity of the statistical comparisons. The observed thermal differences between the control and injury groups underscore the potential of IR thermography for enhancing diagnostic precision of hand pathologies. Its integration into clinical practice could lead to early detection, personalised treatment, and improved patient care in the future. Full article
(This article belongs to the Section Applied Physics General)
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<p>Distinct sensory areas supplied by the hand’s nerves.</p>
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<p>Participant selection process flowchart.</p>
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<p>Setup of thermal data acquisition in two hand regions: dorsal and palmar.</p>
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<p>Thermal image with the regions of interest of the dorsal area (<b>a</b>) and palmar area (<b>b</b>). A—Arm; TM—Thumb metacarpophalangeal; TP—Thumb proximal; FM—Forefinger metacarpophalangeal; FP—Forefinger proximal; FD—Forefinger distal; MM—Middle metacarpophalangeal; MP—Middle proximal; MD—Middle distal; RM—Ring metacarpophalangeal; RP—Ring proximal; RD—Ring distal; LM—Little metacarpophalangeal; LP—Little proximal; LD—Little distal; RB—Radial branch; MB—Median branch; UB—Ulnar branch.</p>
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<p>Box plot comparing the mean dorsum hand temperature between the control group and the injured group. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Box plot illustrating the comparison of mean palm temperature between the control group and the group with injuries. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Thermal patterns associated with healthy control (left hand) and with pathology (right hand) in dorsal (<b>a</b>) and palmar views (<b>b</b>) of the same subject.</p>
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<p>Thermal differences between hands with pathology and control healthy hands segmented based on pathology: CTS, Dupuytren’s contracture, Osteoarthritis (<b>a</b>); CuTS, Tenosynovitis of Quervain, Distal radius fracture (<b>b</b>); Hemiparesis, Rhizarthrosis, and Rupture of ulnar collateral ligament (<b>c</b>).</p>
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5 pages, 2023 KiB  
Proceeding Paper
Monitoring Moisture Diffusion after Contact Sponge Application
by Paolo Bison, Gianluca Cadelano, Giovanni Ferrarini, Mario Girotto, Erika Guolo, Fabio Peron and Monica Volinia
Eng. Proc. 2023, 51(1), 41; https://doi.org/10.3390/engproc2023051041 - 12 Dec 2023
Cited by 1 | Viewed by 1000
Abstract
The contact sponge method is applied on a piece of clay brick. According to the standard, the sponge is moistened with water, applied on the surface of the material by means of a cup, and weighted before and after the application. It allows [...] Read more.
The contact sponge method is applied on a piece of clay brick. According to the standard, the sponge is moistened with water, applied on the surface of the material by means of a cup, and weighted before and after the application. It allows us to determine the amount of water absorbed by the porous material by unit area and unit time. After the application, the moistened area begins to evaporate and cool down. The IR camera is used to monitor the temperature variation of the imprint of the sponge. Meanwhile, moisture diffuses on the material as well. The IR camera is used to monitor the in-plane diffusion of moisture by following the imprint of the sponge that enlarges with time. A suitable model is used to evaluate the shape of the imprint that varies with time. Full article
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<p>The application of the <span class="html-italic">contact sponge</span> (top-left) and some IR images at successive time intervals.</p>
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<p>On the left, the fitting of Equation (<a href="#FD2-engproc-51-00041" class="html-disp-formula">2</a>) with the temperature data; on the right, the fitting of the square of the radius of the humid zone imprint, increasing with time. The slope is <math display="inline"><semantics> <msub> <mi>α</mi> <mi>W</mi> </msub> </semantics></math>.</p>
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