Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (1,002)

Search Parameters:
Keywords = Quasi-3D

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
30 pages, 4682 KiB  
Article
Novel Nonlinear Suspension Based on Concept of Origami Metastructures: Theoretical and Experimental Investigations
by Antonio Zippo, Giovanni Iarriccio, Moslem Molaie and Francesco Pellicano
Vibration 2024, 7(4), 1126-1155; https://doi.org/10.3390/vibration7040058 - 22 Nov 2024
Viewed by 317
Abstract
This study presents a comprehensive investigation of an innovative mechanical system inspired by recent advancements in metamaterials; more specifically, the work is focused on origami-type structures due to their intriguing mechanical properties. Originating from specific fields such as aerospace for their lightweight and [...] Read more.
This study presents a comprehensive investigation of an innovative mechanical system inspired by recent advancements in metamaterials; more specifically, the work is focused on origami-type structures due to their intriguing mechanical properties. Originating from specific fields such as aerospace for their lightweight and foldable characteristics, origami mechanical devices exhibit unique nonlinear stiffness; in particular, when suitably designed, they show Quasi-Zero Stiffness (QZS) characteristics within a specific working range. The QZS property, aligned with the High Static Low Dynamic (HSLD) stiffness concept, suggests promising applications such as a low-frequency mechanical passive vibration isolator. The study explores the vibration isolation characteristics of origami-type suspensions, with a particular emphasis on their potential application as low-frequency passive vibration isolators. The Kresling Origami Module (KOM) has been selected for its compactness and compatibility with 3D printers. A detailed analysis using 3D CAD, Finite Element Analysis, and experimental testing has been carried out. The investigation includes the analysis of the influence of geometric parameters on the nonlinear force–displacement curve. Multibody simulations validate the low-frequency isolation properties within the QZS region, as well as disparities in dynamic properties beyond the QZS range. The study underscores the transformative potential of origami-type metamaterials in enhancing low-frequency vibration isolation technology. It also highlights challenges related to material properties and loading mass variations, providing valuable insights for future developments in this promising field. Full article
19 pages, 6484 KiB  
Article
Simulated Impacts of Thundercloud Charge Distributions on Sprite Halos Using a 3D Quasi-Electrostatic Field Model
by Jinbo Zhang, Jiawei Niu, Zhibin Xie, Yajun Wang, Xiaolong Li and Qilin Zhang
Atmosphere 2024, 15(11), 1395; https://doi.org/10.3390/atmos15111395 - 19 Nov 2024
Viewed by 309
Abstract
Sprite halos are transient luminous phenomena in the lower ionosphere triggered by tropospheric lightning. The effect of removed charge distributions on sprite halos has not been sufficiently discussed. A three-dimensional (3D) quasi-electrostatic (QES) field model was developed in this paper, including the ionospheric [...] Read more.
Sprite halos are transient luminous phenomena in the lower ionosphere triggered by tropospheric lightning. The effect of removed charge distributions on sprite halos has not been sufficiently discussed. A three-dimensional (3D) quasi-electrostatic (QES) field model was developed in this paper, including the ionospheric nonlinear effect and optical emissions. Simulation results show that, for a total charge of 150 C removed within 1 ms with different spatial distributions, higher altitudes of charge removal lead to stronger electric fields and increase sprite halos’ emission intensities. The non-axisymmetric horizontal distribution of charge affects mesospheric electric fields, and the corresponding scales and intensities of emissions vary with observation orientations. Considering the tilted dipole charge structure due to wind shear, the generated electric field and the corresponding position of sprite halos shift accordingly with the tropospheric removed charge, providing an explanation for the horizontal displacement between sprite halos and the parent lightning. Full article
(This article belongs to the Special Issue Impact of Thunderstorms on the Upper Atmosphere)
Show Figures

Figure 1

Figure 1
<p>Computational domain of the 3D QES heating model in the Cartesian coordinate system. By default, the positive and negative charges are distributed in a Gaussian sphere, with the centre point situated at 10 km and 5 km, respectively. The electron density demonstrates an exponential increase above 60 km, and the D-region ionosphere of the simulated space is represented in blue.</p>
Full article ">Figure 2
<p>The thundercloud charge geometry. The horizontal dimensions of the charge distribution are determined by a and b, while the vertical dimension is controlled by c.</p>
Full article ">Figure 3
<p>Altitude profiles of (<b>a</b>) electron density <span class="html-italic">N</span><sub>e</sub> and neutral density <span class="html-italic">N</span>, (<b>b</b>) electron mobility <span class="html-italic">μ</span><sub>e</sub>, (<b>c</b>) ionization rate <span class="html-italic">ν</span><sub>i</sub> and attachment rate <span class="html-italic">ν<sub>a</sub></span>, and (<b>d</b>) optical excitation rate <span class="html-italic">ν<sub>k</sub></span> for different bands.</p>
Full article ">Figure 4
<p>The spatial distribution of the charge density <span class="html-italic">ρ</span> at <span class="html-italic">t</span> = 1 ms, for the case of a 200 C charge removed from 10 km altitude within 1 ms. Results obtained by (<b>a</b>) Pasko [<a href="#B8-atmosphere-15-01395" class="html-bibr">8</a>] and (<b>b</b>) the model proposed in this study.</p>
Full article ">Figure 5
<p>(<b>a</b>,<b>b</b>) Altitude profiles of instantaneous optical emission intensities (N<sub>2</sub> 1P) directly above the charge centre at <span class="html-italic">t</span> = 1 ms for different removed charges. (<b>c</b>,<b>d</b>) Altitude profiles of optical emission intensities corresponding to different optical bands for <span class="html-italic">Q</span> = 200 C. (<b>a</b>,<b>c</b>) are the results of Pasko [<a href="#B8-atmosphere-15-01395" class="html-bibr">8</a>], and (<b>b</b>,<b>c</b>) are the results of our 3D model.</p>
Full article ">Figure 6
<p>The vertical profiles of (<b>a</b>) the electric field <span class="html-italic">E</span>, (<b>b</b>) the normalized electric field <span class="html-italic">E</span>/<span class="html-italic">E</span><sub><span class="html-italic">k</span></sub>, (<b>c</b>) the electron density <span class="html-italic">N<sub>e</sub></span>, and (<b>d</b>) the optical emission intensity <span class="html-italic">I</span> of N<sub>2</sub> 1P at <span class="html-italic">t</span> = 1 ms, for a charge of 150 C removed from different altitudes <span class="html-italic">z</span><sub>+</sub> = 5~20 km within 1 ms.</p>
Full article ">Figure 7
<p>The vertical profiles of (<b>a</b>) the electric field <span class="html-italic">E</span>, (<b>b</b>) the normalized electric field <span class="html-italic">E</span>/<span class="html-italic">E</span><sub><span class="html-italic">k</span></sub>, (<b>c</b>) the electron density <span class="html-italic">N<sub>e</sub></span>, and (<b>d</b>) the optical emission intensity <span class="html-italic">I</span> of N<sub>2</sub> 1P at <span class="html-italic">t</span> = 1 ms, for different charges that are removed within 1 ms. The positive charges of 300, 150, 100, and 75 C are centred at 5, 10, 15, and 20 km, respectively.</p>
Full article ">Figure 8
<p>The vertical profiles of (<b>a</b>) the electric field <span class="html-italic">E</span>, (<b>b</b>) the normalized electric field <span class="html-italic">E</span>/<span class="html-italic">E</span><sub><span class="html-italic">k</span></sub>, (<b>c</b>) the electron density <span class="html-italic">N<sub>e</sub></span>, and (<b>d</b>) the optical emission intensity <span class="html-italic">I</span> of N<sub>2</sub> 1P at <span class="html-italic">t</span> = 1 ms, for a Gaussian-distributed charge of 150 C with varying horizontal scales (a = b = 3~40 km), removed from 10 km within 1 ms.</p>
Full article ">Figure 9
<p>Relation between the normalized electric field <span class="html-italic">E</span>/<span class="html-italic">E<sub>k</sub></span> at <span class="html-italic">t</span> = 1 ms and the horizontal scale (a = b) of charge density distributions, for the positive charge <span class="html-italic">Q</span> = 150 C released from 10 km altitude in 1 ms.</p>
Full article ">Figure 10
<p>The vertical profiles of (<b>a</b>) the electric field <span class="html-italic">E</span>, (<b>b</b>) the normalized electric field <span class="html-italic">E</span>/<span class="html-italic">E</span><sub><span class="html-italic">k</span></sub>, (<b>c</b>) the electron density <span class="html-italic">N<sub>e</sub></span>, and (<b>d</b>) the optical emission intensity <span class="html-italic">I</span> of N<sub>2</sub> 1P at <span class="html-italic">t</span> = 1 ms, for a Gaussian-distributed charge of 150 C removed from 10 km within 1 ms. Three geometries are compared: (i) a = b = 3 km, (ii) a = 3 km and b = 30 km, and (iii) a = b = 30 km.</p>
Full article ">Figure 11
<p>Simulation of a +CGs with 150 C removed from 10 km within 1 ms, considering charge geometries with a = 3 km, b = 30 km, and c = 3 km. The vertical distribution of (<b>a</b>,<b>b</b>) the charge density <span class="html-italic">ρ</span> and (<b>c</b>,<b>d</b>) the normalized electric field <span class="html-italic">E</span>/<span class="html-italic">E</span><sub><span class="html-italic">k</span></sub> in a cross-section of the domain at <span class="html-italic">x</span> = 0 km and <span class="html-italic">y</span> = 0 km, the horizontal distribution of (<b>e</b>) the normalized electric field <span class="html-italic">E</span>/<span class="html-italic">E</span><sub><span class="html-italic">k</span></sub> in a cross-section at <span class="html-italic">z</span> = 78 km, and the (<b>f</b>,<b>g</b>) time-averaged optical emission intensity of N<sub>2</sub> 1P over a 2 ms period, viewed along the <span class="html-italic">x</span>-axis and <span class="html-italic">y</span>-axis directions.</p>
Full article ">Figure 12
<p>The tilted dipole charge structure.</p>
Full article ">Figure 13
<p>Simulation of a +CG with 150 C removed from 10 km within 1 ms, considering a 20 km horizontal offset between the positive and negative charge regions. The vertical distribution of (<b>a</b>) the charge density <span class="html-italic">ρ</span> and (<b>b</b>) the normalized electric field <span class="html-italic">E</span>/<span class="html-italic">E</span><sub><span class="html-italic">k</span></sub> in a cross-section of the domain at <span class="html-italic">x</span> = 0 km, the horizontal distribution of (<b>c</b>) the normalized electric field <span class="html-italic">E</span>/<span class="html-italic">E</span><sub><span class="html-italic">k</span></sub> in a cross-section at <span class="html-italic">z</span> = 78 km, and the (<b>d</b>,<b>e</b>) time-averaged optical emission intensity of N<sub>2</sub> 1P over a 2 ms period, viewed along the <span class="html-italic">x</span>-axis and <span class="html-italic">y</span>-axis directions. The red solid line and dotted line mark the centre positions of the negative charge and positive charge, respectively.</p>
Full article ">Figure 14
<p>The time-averaged optical emission intensity of N<sub>2</sub> 1P over a 2 ms period viewed along the <span class="html-italic">x</span>-axis direction, for different horizontal offsets <span class="html-italic">d</span> = 0, 10, 20, and 30 km. The red solid line and dotted line mark the centre positions of the negative charge and positive charge, respectively.</p>
Full article ">
12 pages, 1933 KiB  
Article
Theoretical Study on One- and Two-Photon Absorption Properties of π-Stacked Multimer Models of Phenalenyl Radicals
by Masako Yokoyama, Ryohei Kishi and Yasutaka Kitagawa
Chemistry 2024, 6(6), 1427-1438; https://doi.org/10.3390/chemistry6060085 - 14 Nov 2024
Viewed by 404
Abstract
Effects of the number of monomers (N) on the two-photon absorption (TPA) properties of π-stacked multimer models consisting of phenalenyl radicals were investigated theoretically. We conducted spectral simulations for the π-stacked N-mer models (N = 2, 4, and 6) [...] Read more.
Effects of the number of monomers (N) on the two-photon absorption (TPA) properties of π-stacked multimer models consisting of phenalenyl radicals were investigated theoretically. We conducted spectral simulations for the π-stacked N-mer models (N = 2, 4, and 6) with different stacking distances (d1) and their alternation patterns (d2/d1). Excitation energies and transition dipole moments were calculated at the extended multi-configurational quasi-degenerate second-order perturbation theory (XMC-QDPT2) level based on the complete active space self-consistent field (CASSCF) wavefunctions with the active space orbitals constructed from the singly occupied molecular orbitals (SOMOs) of monomers. The TPA cross-section value per dimer unit at the first peak, originating from the electronic transition along the stacking direction, was predicted to increase significantly as the d2/d1 approaches one, as the d1 decreases, and as the N increases from 2 to 6. These tendencies are similar to the calculation results for the static hyperpolarizabilities. Full article
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Molecular structures of (<b>a</b>) phenalenyl radical <b>1a</b> (R = H), (<b>b</b>) anti-type dimer model <b>1a<sub>2</sub></b>, (<b>c</b>) tetramer model <b>1a<sub>4</sub>(<span class="html-italic">d</span><sub>2</sub>/<span class="html-italic">d</span><sub>1</sub>)</b>, and (<b>d</b>) hexamer model <b>1a<sub>6</sub>(1.0)</b> (<span class="html-italic">d</span><sub>2</sub> = <span class="html-italic">d</span><sub>1</sub>).</p>
Full article ">Figure 2
<p>Calculation results of <span class="html-italic">y</span><sub>av</sub> (<b>a</b>) for the models with the uniform stacking distance <span class="html-italic">d</span><sub>1</sub> for <b>1a<sub>2</sub></b>, <b>1a<sub>4</sub>(1.0)</b>, and <b>1a<sub>6</sub>(1.0)</b>, (<b>b</b>) for <b>1a<sub>4</sub>(<span class="html-italic">d</span><sub>2</sub>/<span class="html-italic">d</span><sub>1</sub>)</b> with different ratios <span class="html-italic">d</span><sub>2</sub>/<span class="html-italic">d</span><sub>1</sub> = 1.0, 1.1, 1.2, 1.3, 1.5, and 2.0, results of <span class="html-italic">y</span><sub>SD</sub>, (<b>c</b>) for <b>1a<sub>4</sub>(1.0)</b> and <b>1a<sub>6</sub>(1.0)</b>, and (<b>d</b>) for <b>1a<sub>4</sub>(<span class="html-italic">d</span><sub>2</sub>/<span class="html-italic">d</span><sub>1</sub>)</b>.</p>
Full article ">Figure 3
<p>Simulated (<b>a</b>) OPA and (<b>b</b>) TPA spectra of <b>1a<sub>2</sub></b> and <b>1a<sub>4</sub>(<span class="html-italic">d</span><sub>2</sub>/<span class="html-italic">d</span><sub>1</sub>)</b> at <span class="html-italic">d</span><sub>1</sub> = 3.0 Å with different ratios <span class="html-italic">d</span><sub>2</sub>/<span class="html-italic">d</span><sub>1</sub> = 1.0, 1.1, 1.2, 1.3, 1.5, and 2.0. The OPA spectra were normalized to that at the first peak of the model <b>1a<sub>4</sub>(1.0)</b>.</p>
Full article ">Figure 4
<p>Calculation results of <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>σ</mi> </mrow> <mrow> <mfenced separators="|"> <mrow> <mn>2</mn> </mrow> </mfenced> </mrow> </msup> <mfenced separators="|"> <mrow> <mi>ω</mi> </mrow> </mfenced> </mrow> </semantics></math> values per dimer for <b>1a<sub>4</sub>(<span class="html-italic">d</span><sub>2</sub>/<span class="html-italic">d</span><sub>1</sub>)</b> with different <span class="html-italic">d</span><sub>1</sub> (2.8 Å ≤ <span class="html-italic">d</span><sub>1</sub> ≤ 4.0 Å) as a function of the ratio <span class="html-italic">d</span><sub>2</sub>/<span class="html-italic">d</span><sub>1</sub>.</p>
Full article ">Figure 5
<p>Calculation results of <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>σ</mi> </mrow> <mrow> <mfenced separators="|"> <mrow> <mn>2</mn> </mrow> </mfenced> </mrow> </msup> </mrow> </semantics></math> value per dimer at the first TPA peak as a function of <span class="html-italic">y</span><sub>av</sub> for the <b>1a<sub>2</sub></b>, <b>1a<sub>4</sub>(1.0)</b>, and <b>1a<sub>6</sub>(1.0)</b>.</p>
Full article ">
18 pages, 8260 KiB  
Article
Role of the Europe–China Pattern Teleconnection in the Interdecadal Autumn Dry–Wet Fluctuations in Central China
by Linwei Jiang, Wenhao Gao, Kexu Zhu, Jianqiu Zheng and Baohua Ren
Atmosphere 2024, 15(11), 1363; https://doi.org/10.3390/atmos15111363 - 13 Nov 2024
Viewed by 256
Abstract
Based on statistical analyses of long-term reanalysis data, we have investigated the interdecadal variations of autumn precipitation in central China (APC-d) and the associated atmospheric teleconnection. It reveals that the increased autumn rainfall in central China during the last decade is a portion [...] Read more.
Based on statistical analyses of long-term reanalysis data, we have investigated the interdecadal variations of autumn precipitation in central China (APC-d) and the associated atmospheric teleconnection. It reveals that the increased autumn rainfall in central China during the last decade is a portion of the APC-d, which exhibits a high correlation coefficient of 0.7 with the interdecadal variations of the Europe–China pattern (EC-d pattern) teleconnection. The EC-d pattern teleconnection presents in a “+-+” structure over Eurasia, putting central China into the periphery of a quasi-barotropic anticyclonic high-pressure anomaly. Driven by positive vorticity advection and the inflow of warmer and moist air from the south, central China experiences enhanced ascending motion and abundant water vapor supply, resulting in increased rainfall. Further analysis suggests that the EC-d pattern originates from the exit of the North Atlantic jet and propagates eastward. It is captured by the Asian westerly jet stream and proceeds towards East Asia through the wave–mean flow interaction. The wave train acquires effective potential energy from the mean flow by the baroclinic energy conversion and simultaneously obtains kinetic energy from the basic westerly jet zones across the North Atlantic and the East Asian coasts. The interdecadal variation of the mid-latitude North Atlantic sea surface temperature (MAT-d) exhibits a significant negative relationship with EC-d, serving as a modulating factor for the EC-d pattern teleconnection. Experiments with CMIP6 models predict that the interdecadal variations in APC-d, EC-d, and MAT-d will maintain stable high correlations for the rest of the 21st century. These findings may contribute to forecasting the interdecadal autumn dry–wet conditions in central China. Full article
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) Climatological autumn precipitation in 1901–2020 [unit: mm/mon]; (<b>b</b>) time series of normalized year-to-year [gray bars] and the interdecadal components [green line] of the autumn precipitation in central China (APC-d index); (<b>c</b>) regression map of precipitation [contours, dotted areas are significant at <span class="html-italic">p</span> &lt; 0.05; unit: mm/mon] on the APC-d index; and (<b>d</b>) same as <a href="#atmosphere-15-01363-f001" class="html-fig">Figure 1</a>c, but for the results in the Global Precipitation Climatology Centre (GPCC) dataset for the same period. The black boxes denote central China (27–37° N, 102–114° E).</p>
Full article ">Figure 2
<p>Regression of the interdecadal components on the APC-d index in autumn during 1905–2011, including (<b>a</b>) zonal wind at 200 hPa [shadings; dotted areas are significant at <span class="html-italic">p</span> &lt; 0.05; unit: m/s], (<b>b</b>) geopotential height at 500 hPa [shadings; dotted areas are significant at <span class="html-italic">p</span> &lt; 0.05; unit: gpm], (<b>c</b>) wind field at 700 hPa [arrows; blue areas are significant at <span class="html-italic">p</span> &lt; 0.05; unit: m/s], and (<b>d</b>) pressure–longitude cross-section of omega averaged between 27° and 37° N [only shadings significant at <span class="html-italic">p</span> &lt; 0.05 are shown; unit: Pa/s]. The black boxes denote the boundaries of central China (27–37° N, 102–114° E), and lines denote 102° E and 114°, respectively.</p>
Full article ">Figure 3
<p>Regression of the interdecadal components on the APC-d index in autumn during 1905–2011, including the meridional wind [only shadings significant at <span class="html-italic">p</span> &lt; 0.05 are shown; unit: m/s] and the geopotential height [contours; interval: 2.5 gpm; solid lines: positive; dashed lines: negative] at 200 hPa. The black box denotes the boundaries of central China (27–37° N, 102°–114° E).</p>
Full article ">Figure 4
<p>Spatial pattern of EOF2 in (<b>a</b>) original meridional wind and (<b>b</b>) interdecadal meridional wind at 200 hPa in 20°–60° N, 0°–150° E in autumn during 1905–2011. (<b>c</b>) Time series of year-to-year [the EC-index; gray bars], the 9 yr-moved [black line] PC2 of EOF2 in original V200. (<b>d</b>) Time series of PC2 of EOF2 in interdecadal V200 [the EC-d index; red line] and the interdecadal autumn precipitation in central China [APC-d index; green line]. (<b>e</b>) Same as <a href="#atmosphere-15-01363-f003" class="html-fig">Figure 3</a>, but for the results with regard to the EC-d index.</p>
Full article ">Figure 5
<p>(<b>a</b>–<b>d</b>) is same as <a href="#atmosphere-15-01363-f002" class="html-fig">Figure 2</a>a–d but for the results with regard to the EC-d index.</p>
Full article ">Figure 6
<p>Regression of the interdecadal components on the EC-d index in autumn during 1905–2011, including (<b>a</b>) water vapor flux [arrows; only arrows significant at <span class="html-italic">p</span> &lt; 0.05 are shown; unit: 10<sup>−2</sup> kg/(m·s)] and its divergence [shadings; dotted areas are significant at <span class="html-italic">p</span> &lt; 0.05; unit: 10<sup>−6</sup> kg/(m<sup>2</sup>·s)] integrated from 1000 hPa to 300 hPa; (<b>b</b>) pressure-longitude cross-section between 27° and 37° N of the horizontal vorticity advection [shadings; dotted areas are significant at <span class="html-italic">p</span> &lt; 0.05, unit: 10<sup>−12</sup> Pa/s<sup>2</sup>]; and (<b>c</b>) pressure-longitude cross-section between 27° and 37° N of the horizontal temperature advection [shadings; dotted areas are significant at <span class="html-italic">p</span> &lt; 0.05, unit: 10<sup>−7</sup> degK/s]. The black box denotes the boundaries of central China (27–37° N, 102–114° E). The black lines denote 102° E and 114° E, respectively.</p>
Full article ">Figure 7
<p>Regression of the interdecadal components on the EC-d index in autumn during 1905–2011, including (<b>a</b>) vorticity source S’ [shadings; unit: 10<sup>−11</sup> s<sup>−2</sup>] with wind anomalies at 200 hPa [purple arrows; only arrows significant at <span class="html-italic">p</span> &lt; 0.05 are shown; unit:20 m/s] and (<b>b</b>) plumb wave activity flux [purple arrows; only arrows significant at <span class="html-italic">p</span> &lt; 0.05 are shown; unit: 1.5 m<sup>2</sup>/s<sup>2</sup>] and its divergence [shadings; unit: m/s<sup>2</sup>] at 200 hPa. The red lines denote the basic westerly flow [U &gt; 20 m/s; interval: 5 m/s]. The black box denotes the boundaries of central China (27–37° N, 102–114° E).</p>
Full article ">Figure 8
<p>(<b>a</b>) CK (conversion of kinetic energy) and (<b>b</b>) CP (conversion of potential energy) of the EC-d pattern teleconnection integrated from 1000 hPa to 10 hPa [shadings; unit: m<sup>2</sup>/s<sup>3</sup>]. The red lines denote the basic westerly flow (U &gt; 20 m/s) [interval: 5]. The black box denotes the boundaries of central China (27–37° N, 102–114° E).</p>
Full article ">Figure 9
<p>Regression of the interdecadal components of sea surface temperature (SST) [shadings; unit: degC] on (<b>a</b>) the EC-d index and (<b>b</b>) the AMO index in autumn during 1905–2011. (<b>c</b>) The time series of the MAT-d index [blue line] and the EC-d index [red line]. The black box denotes mid-latitude North Atlantic (30–60° N, 55–25° W).</p>
Full article ">Figure 10
<p>Regression of the interdecadal components on the negative MAT-d index in autumn during 1905–2011, including (<b>a</b>) SST [shadings; only shadings significant at <span class="html-italic">p</span> &lt; 0.05 are shown; unit: degC] and (<b>b</b>) wind field 700 hPa [arrows; blue areas are significant at <span class="html-italic">p</span> &lt; 0.05; unit: m/s]. (<b>c</b>) Vorticity source S’ [shadings; unit: 10<sup>−10</sup> s<sup>−2</sup>] at 200 hPa of the negative MAT-d. (<b>d</b>) Same as <a href="#atmosphere-15-01363-f003" class="html-fig">Figure 3</a>, but for the results with regard to the MAT-d index. The black box denotes mid-latitude North Atlantic (30–60° N, 55–25° W).</p>
Full article ">Figure 11
<p>The time series of the APC-d index [green bars], the EC-d index [red line], and the MAT index [blue line] during autumn in 2020–2090.</p>
Full article ">
18 pages, 283 KiB  
Article
Short-Term Impact of Digital Mental Health Interventions on Psychological Well-Being and Blood Sugar Control in Type 2 Diabetes Patients in Riyadh
by Abdulaziz M. Alodhialah, Ashwaq A. Almutairi and Mohammed Almutairi
Healthcare 2024, 12(22), 2257; https://doi.org/10.3390/healthcare12222257 - 13 Nov 2024
Viewed by 386
Abstract
Background: Type 2 diabetes (T2D) management is complicated by psychological factors, yet mental health interventions are not routinely integrated into diabetes care. This study investigated the impact of a digital mental health intervention on psychological well-being and glycemic control in T2D patients. Methods: [...] Read more.
Background: Type 2 diabetes (T2D) management is complicated by psychological factors, yet mental health interventions are not routinely integrated into diabetes care. This study investigated the impact of a digital mental health intervention on psychological well-being and glycemic control in T2D patients. Methods: A quasi-experimental study was conducted with 120 T2D patients divided into intervention (n = 60) and control (n = 60) groups. The intervention group received a one-month digital mental health intervention alongside standard care. Psychological well-being (PHQ-9, GAD-7, and DDS) and glycemic control (HbA1c) were assessed at baseline and post-intervention. Results: The intervention group showed significant improvements in HbA1c levels (−0.5%, p = 0.032), PHQ-9 (−3.1, p = 0.001), GAD-7 (−2.8, p = 0.006), and DDS (−7.7, p = 0.012) scores compared to the control group. Strong correlations were observed between psychological improvements and HbA1c reductions. Higher engagement with the digital platform was associated with greater improvements in both psychological and glycemic outcomes. Conclusions: Integrating digital mental health interventions into T2D care can significantly improve both psychological well-being and glycemic control. These findings support a more holistic approach to diabetes management that addresses both mental and physical health aspects. Full article
(This article belongs to the Special Issue Advanced Technological Approaches in Diabetes)
13 pages, 2129 KiB  
Article
Application of the Water-Based Electro-Hydraulic Actuator (EHA) to the Heavy-Duty Collaborative Robot
by Ha-Gwon Song and Dong-Won Lim
Actuators 2024, 13(11), 451; https://doi.org/10.3390/act13110451 - 11 Nov 2024
Viewed by 562
Abstract
In this paper, the design of a driving mechanism for a heavy-duty collaborative robot (cobot) capable of lifting payloads up to 20 kg is presented. This study focuses on an articulated robot utilizing a water-based Electro-Hydraulic Actuator (EHA). The Denavit–Hartenberg (D–H) representation was [...] Read more.
In this paper, the design of a driving mechanism for a heavy-duty collaborative robot (cobot) capable of lifting payloads up to 20 kg is presented. This study focuses on an articulated robot utilizing a water-based Electro-Hydraulic Actuator (EHA). The Denavit–Hartenberg (D–H) representation was employed to relate the rotational angles and the end-effector’s location, facilitating the design of the actuators. The maximum required torques for joints 2 and 3, responsible for lifting for 12 s, were calculated under quasi-static and dynamic loading conditions. The results showed that the maximum required torques were 126.67 Nm and 58.86 Nm for joint 2 and 3, respectively. The maximum torque for joint 2 occurs when the pitch links are fully extended, whereas the maximum torque for joint 3 occurs when the third link is parallel to the ground. The torques, due to the inertia and Coriolis dynamic terms, were also calculated and found to be lower than those required for the gravitational term. Various maneuvering scenarios, along with Ansys Motion simulation, were analyzed for the verification of the results. Based on the calculated maximum torques, the linear actuators of the EHA were designed. The heavy-duty cobot can be built with the developed actuator proposed in this paper. The total weight of the entire frame was measured to be 14.59 kg, resulting in a high Payload/Weight (P/W) ratio of 1.37. In conclusion, the robot was made lighter and can operate more efficiently, particularly for heavy loads up to 20 kg. Full article
Show Figures

Figure 1

Figure 1
<p>Electro-Hydraulic Actuator (EHA). (<b>a</b>) Hydraulic circuit diagram; (<b>b</b>) EHA unit.</p>
Full article ">Figure 2
<p>Robot testbed with 3 degrees of freedom coordinate system for the mechanism design.</p>
Full article ">Figure 3
<p>Calculation results for the required <span class="html-italic">τ</span><sub>2</sub> and <span class="html-italic">τ</span><sub>3</sub> with respect to (<b>a</b>) <span class="html-italic">θ</span><sub>2</sub> and (<b>b</b>) <span class="html-italic">θ</span><sub>3.</sub></p>
Full article ">Figure 4
<p>(<b>a</b>) Numerical analysis prepocessing; (<b>b</b>,<b>c</b>) <span class="html-italic">τ</span><sub>2</sub> and <span class="html-italic">τ</span><sub>3</sub> Ansys measurements with respect to (<b>b</b>) <span class="html-italic">θ</span><sub>2</sub> and (<b>c</b>) <span class="html-italic">θ</span><sub>3.</sub></p>
Full article ">Figure 5
<p>(<b>a</b>) Path planning for cylinder end-point movement (LSPB); (<b>b</b>) time-torque for load movement from low to high position.</p>
Full article ">Figure 6
<p>(<b>a</b>) Maximum torque pose for <span class="html-italic">τ</span><sub>2</sub>; (<b>b</b>) Maximum torque pose for <span class="html-italic">τ</span><sub>3.</sub></p>
Full article ">Figure 7
<p>Torque for various operational durations only required by dynamic terms excluding the gravity effect.</p>
Full article ">
13 pages, 2915 KiB  
Article
Three-Dimensional Flutter Numerical Simulation of Wings in Heavy Gas and Transonic Flutter Similarity Law Correction Method
by Zhe Hu, Bo Lu, Yongping Liu, Li Yu, Xiping Kou and Jun Zha
Aerospace 2024, 11(11), 932; https://doi.org/10.3390/aerospace11110932 - 11 Nov 2024
Viewed by 325
Abstract
Wind tunnel testing is a crucial method for studying aircraft flutter. Using heavy gas as the wind tunnel medium can mitigate the escalating issue of test models being overweight as advanced aircraft develop. This paper employs an analytical method for numerical calculations of [...] Read more.
Wind tunnel testing is a crucial method for studying aircraft flutter. Using heavy gas as the wind tunnel medium can mitigate the escalating issue of test models being overweight as advanced aircraft develop. This paper employs an analytical method for numerical calculations of three-dimensional (3D) wing flutter based on fluid–structure interaction (FSI). Flutter calculations for the Goland wing are conducted, and the results in the air medium are consistent with the literature. In contrast, significant differences in flutter behavior are observed in the heavy gas R134a medium. Compared to air, when the model reaches a critical state in R134a, the incoming flow velocity is lower, the incoming flow density is approximately 3 to 5 times air, and the incoming flow dynamic pressure is about 1.1 to 1.2 times that of air. The correction of heavy gas flutter data is crucial for wind tunnel testing. This paper proposes a correction method based on the unsteady transonic flow similarity law proposed by Bendiksen under quasi-steady conditions. Attempts are made to revise relevant published wind tunnel tests and heavy gas flutter calculation results. The transonic flutter similarity law effectively explains the flutter similarity of rigid models in both heavy gas and air media. Still, it fails in cases with highly reduced frequencies and low mass ratios, such as those encountered with flexible wings. Full article
(This article belongs to the Section Aeronautics)
Show Figures

Figure 1

Figure 1
<p>Goland wing box model, left end fixed.</p>
Full article ">Figure 2
<p>The results of the first four mode shapes.</p>
Full article ">Figure 3
<p>The incoming flow flutter density curve in air.</p>
Full article ">Figure 4
<p>The results of flutter wind tunnel tests and numerical calculation for BSCW.</p>
Full article ">Figure 5
<p>Correction of R12 flutter wind tunnel test data (BSCW).</p>
Full article ">Figure 6
<p>Correction of R134a flutter numerical calculation data (BSCW).</p>
Full article ">Figure 7
<p>Correction of R12 flutter wind tunnel test data (a 45° sweptback wing).</p>
Full article ">Figure 8
<p>Correction of R134a flutter numerical calculation data (Goland Wing).</p>
Full article ">
19 pages, 37808 KiB  
Article
Modified Multiresolution Convolutional Neural Network for Quasi-Periodic Noise Reduction in Phase Shifting Profilometry for 3D Reconstruction
by Osmar Antonio Espinosa-Bernal, Jesús Carlos Pedraza-Ortega, Marco Antonio Aceves-Fernandez, Juan Manuel Ramos-Arreguín, Saul Tovar-Arriaga and Efrén Gorrostieta-Hurtado
Computers 2024, 13(11), 290; https://doi.org/10.3390/computers13110290 - 8 Nov 2024
Viewed by 391
Abstract
Fringe profilometry is a method that obtains the 3D information of objects by projecting a pattern of fringes. The three-step technique uses only three images to acquire the 3D information from an object, and many studies have been conducted to improve this technique. [...] Read more.
Fringe profilometry is a method that obtains the 3D information of objects by projecting a pattern of fringes. The three-step technique uses only three images to acquire the 3D information from an object, and many studies have been conducted to improve this technique. However, there is a problem that is inherent to this technique, and that is the quasi-periodic noise that appears due to this technique and considerably affects the final 3D object reconstructed. Many studies have been carried out to tackle this problem to obtain a 3D object close to the original one. The application of deep learning in many areas of research presents a great opportunity to to reduce or eliminate the quasi-periodic noise that affects images. Therefore, a model of convolutional neural network along with four different patterns of frequencies projected in the three-step technique is researched in this work. The inferences produced by models trained with different frequencies are compared with the original ones both qualitatively and quantitatively. Full article
(This article belongs to the Special Issue Advanced Image Processing and Computer Vision)
Show Figures

Figure 1

Figure 1
<p>Three-dimensional reconstruction of an object, (<b>a</b>) affected by quasi-periodic noise, and (<b>b</b>) original object. The image shows the deformation of the surface caused by the noise present in images acquired by the fringe projection in three steps.</p>
Full article ">Figure 2
<p>Images from the database with quasi-periodic noise at different frequencies: (<b>a</b>) quasi-periodic noise at 4 frequencies, (<b>b</b>) quasi-periodic noise at 8 frequencies, (<b>c</b>) quasi-periodic noise at 16 frequencies, (<b>d</b>) quasi-periodic noise at 32 frequencies.</p>
Full article ">Figure 2 Cont.
<p>Images from the database with quasi-periodic noise at different frequencies: (<b>a</b>) quasi-periodic noise at 4 frequencies, (<b>b</b>) quasi-periodic noise at 8 frequencies, (<b>c</b>) quasi-periodic noise at 16 frequencies, (<b>d</b>) quasi-periodic noise at 32 frequencies.</p>
Full article ">Figure 3
<p>Three-dimensional models acquired from platform Turbosquid.</p>
Full article ">Figure 4
<p>Set of images obtained from a single scene with a 3D model. (<b>a</b>) Ground-truth, (<b>b</b>) original 3D model, (<b>c</b>) region of interest, (<b>d</b>) 3D model with background, (<b>e</b>–<b>g</b>) images with object with 120° shifting pattern projected composed of 4 frequencies, (<b>h</b>–<b>j</b>) reference images with a 4-frequency composite pattern, (<b>k</b>–<b>m</b>) images with object with 120° shifting pattern projected composed of 8 frequencies, (<b>n</b>–<b>p</b>) reference images with a 8-frequency composite pattern, (<b>q</b>–<b>s</b>) images with object with 120° shifting pattern projected composed of 16 frequencies, (<b>t</b>–<b>v</b>) reference images with a 16-frequency composite pattern, (<b>w</b>–<b>y</b>) images with object with 120° shifting pattern projected composed of 32 frequencies, (<b>z</b>,<b>aa</b>,<b>ab</b>) reference images with a 32-frequency composite pattern.</p>
Full article ">Figure 5
<p>The methodology used to generate a database of images with quasi-periodic noise.</p>
Full article ">Figure 6
<p>Images from database created with Blender software: (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) images affected with quasi-periodic noise at different frequencies, (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) ground-truth image.</p>
Full article ">Figure 7
<p>The architecture of convolutional neural network model developed and implemented.</p>
Full article ">Figure 8
<p>Evolution of training and validation loss. Models train with noisy images affected by different frequencies due to different patterns projected. (<b>a</b>) Images with 4 frequencies, (<b>b</b>) images with 8 frequencies, (<b>c</b>) images with 16 frequencies (<b>d</b>) images with 32 frequencies, and (<b>e</b>) images with multifrequencies (4, 8, 16, and 32).</p>
Full article ">Figure 9
<p>Two-dimensional representation of an object Cat. (<b>a</b>) Image with quasi-periodic noise produced by projection of a four-frequency pattern, inference obtained with models trained with (<b>b</b>) four frequencies, (<b>c</b>) 8 frequencies, (<b>d</b>) 16 frequencies, (<b>e</b>) 32 frequencies, and (<b>f</b>) Multifrequencies. (<b>g</b>) ground-truth image, and (<b>h</b>) original object.</p>
Full article ">Figure 10
<p>Three-dimensional representation of an object Cat. (<b>a</b>) Image with quasi-periodic noise produced by projection of a four-frequency pattern, inference obtained with models trained with (<b>b</b>) four frequencies, (<b>c</b>) 8 frequencies, (<b>d</b>) 16 frequencies, (<b>e</b>) 32 frequencies, and (<b>f</b>) Multifrequencies. (<b>g</b>) ground-truth image, and (<b>h</b>) original object.</p>
Full article ">Figure 11
<p>Profile comparison of 3D objects.</p>
Full article ">Figure 12
<p>Two-dimensional representation of an object Cat. (<b>a</b>) Image with quasi-periodic noise produced by projection of an 8-frequency pattern, inference obtained with models trained with (<b>b</b>) four frequencies, (<b>c</b>) 8 frequencies, (<b>d</b>) 16 frequencies, (<b>e</b>) 32 frequencies, and (<b>f</b>) Multifrequencies. (<b>g</b>) ground-truth image, and (<b>h</b>) original object.</p>
Full article ">Figure 13
<p>Three-dimensional representation of an object Cat. (<b>a</b>) Image with quasi-periodic noise produced by projection of an 8-frequency pattern, inference obtained with models trained with (<b>b</b>) four frequencies, (<b>c</b>) 8 frequencies, (<b>d</b>) 16 frequencies, (<b>e</b>) 32 frequencies, and (<b>f</b>) Multifrequencies. (<b>g</b>) ground-truth image, and (<b>h</b>) original object.</p>
Full article ">Figure 14
<p>Profile comparison of 3D objects.</p>
Full article ">Figure 15
<p>Two-dimensional representation of an object Cat. (<b>a</b>) Image with quasi-periodic noise produced by projection of a 16-frequency pattern, inference obtained with models trained with (<b>b</b>) four frequencies, (<b>c</b>) 8 frequencies, (<b>d</b>) 16 frequencies, (<b>e</b>) 32 frequencies, and (<b>f</b>) Multifrequencies. (<b>g</b>) ground-truth image, and (<b>h</b>) original object.</p>
Full article ">Figure 15 Cont.
<p>Two-dimensional representation of an object Cat. (<b>a</b>) Image with quasi-periodic noise produced by projection of a 16-frequency pattern, inference obtained with models trained with (<b>b</b>) four frequencies, (<b>c</b>) 8 frequencies, (<b>d</b>) 16 frequencies, (<b>e</b>) 32 frequencies, and (<b>f</b>) Multifrequencies. (<b>g</b>) ground-truth image, and (<b>h</b>) original object.</p>
Full article ">Figure 16
<p>Three-dimensional representation of an object Cat. (<b>a</b>) Image with quasi-periodic noise produced by projection of a 16-frequency pattern, inference obtained with models trained with (<b>b</b>) four frequencies, (<b>c</b>) 8 frequencies, (<b>d</b>) 16 frequencies, (<b>e</b>) 32 frequencies, and (<b>f</b>) Multifrequencies. (<b>g</b>) ground-truth image, and (<b>h</b>) original object.</p>
Full article ">Figure 17
<p>Profile comparison of 3D objects.</p>
Full article ">Figure 18
<p>Two-dimensional representation of an object Cat. (<b>a</b>) Image with quasi-periodic noise produced by projection of a 32-frequency pattern, inference obtained with models trained with (<b>b</b>) four frequencies, (<b>c</b>) 8 frequencies, (<b>d</b>) 16 frequencies, (<b>e</b>) 32 frequencies, and (<b>f</b>) Multifrequencies. (<b>g</b>) ground-truth image, and (<b>h</b>) original object.</p>
Full article ">Figure 19
<p>Three-dimensional representation of an object Cat. (<b>a</b>) Image with quasi-periodic noise produced by projection of a 32-frequency pattern, inference obtained with models trained with (<b>b</b>) four frequencies, (<b>c</b>) 8 frequencies, (<b>d</b>) 16 frequencies, (<b>e</b>) 32 frequencies, and (<b>f</b>) Multifrequencies. (<b>g</b>) ground-truth image, and (<b>h</b>) original object.</p>
Full article ">Figure 20
<p>Profile comparison of 3D objects.</p>
Full article ">
23 pages, 411 KiB  
Article
Stationary Distribution and Density Function for a High-Dimensional Stochastic SIS Epidemic Model with Mean-Reverting Stochastic Process
by Huina Zhang, Jianguo Sun and Xuhan Wen
Axioms 2024, 13(11), 768; https://doi.org/10.3390/axioms13110768 - 5 Nov 2024
Viewed by 415
Abstract
This paper explores a high-dimensional stochastic SIS epidemic model characterized by a mean-reverting, stochastic process. Firstly, we establish the existence and uniqueness of a global solution to the stochastic system. Additionally, by constructing a series of appropriate Lyapunov functions, we confirm the presence [...] Read more.
This paper explores a high-dimensional stochastic SIS epidemic model characterized by a mean-reverting, stochastic process. Firstly, we establish the existence and uniqueness of a global solution to the stochastic system. Additionally, by constructing a series of appropriate Lyapunov functions, we confirm the presence of a stationary distribution of the solution under R0s>1. Taking 3D as an example, we analyze the local stability of the endemic equilibrium in the stochastic SIS epidemic model. We introduce a quasi-endemic equilibrium associated with the endemic equilibrium of the deterministic system. The exact probability density function around the quasi-stable equilibrium is determined by solving the corresponding Fokker–Planck equation. Finally, we conduct several numerical simulations and parameter analyses to demonstrate the theoretical findings and elucidate the impact of stochastic perturbations on disease transmission. Full article
(This article belongs to the Special Issue Dynamical Systems: Theory and Applications in Mathematical Biology)
Show Figures

Figure 1

Figure 1
<p>Solutions of <math display="inline"><semantics> <mrow> <mo>{</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <msub> <mi>S</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>,</mo> <mi>I</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>}</mo> </mrow> </semantics></math> in a 3D deterministic model (<a href="#FD11-axioms-13-00768" class="html-disp-formula">11</a>) and stochastic model (<a href="#FD49-axioms-13-00768" class="html-disp-formula">49</a>) with an OU process.</p>
Full article ">Figure 2
<p>The frequency histogram and marginal density function curve of <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>S</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.6409</mn> <mo>,</mo> <mrow/> <msub> <mi>S</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.6062</mn> <mo>,</mo> <mi>I</mi> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> <mo>=</mo> <mn>1.0956</mn> <mo>)</mo> </mrow> </semantics></math>.</p>
Full article ">
11 pages, 2945 KiB  
Article
Design of a Broadband and High-Isolation CMOS Active Quasi-Circulator
by Sida Tang, Xiaoqing Liu, Yanfeng Jiang, Jiahui Guan, Peng Li and Jitai Han
Appl. Sci. 2024, 14(22), 10083; https://doi.org/10.3390/app142210083 - 5 Nov 2024
Viewed by 425
Abstract
Using industry standard TSMC 90 nm CMOS technology, a broadband and high-isolation active quasi-circulator MMIC was fabricated to meet the needs of future communication system integration. An active balun and phase inverter stage made of a common-source (CS) field-effect transistor (FET) comprised the [...] Read more.
Using industry standard TSMC 90 nm CMOS technology, a broadband and high-isolation active quasi-circulator MMIC was fabricated to meet the needs of future communication system integration. An active balun and phase inverter stage made of a common-source (CS) field-effect transistor (FET) comprised the quasi-circulator that was suggested. A cascade CS stage was used to produce the active balun, which increased the isolation of |S12| and |S23| through the unilateral characteristic of the FET and allowed for transmission of |S21| and |S32|. Further, a CS FET connected in parallel to ports 1 and 3 for phase cancellation can effectively increase the quasi-circulator’s isolation |S31|. Theoretical analysis for the detailed circuit was shown in the main paper. The results show that all isolations are better than 20 dB over 5–33 GHz, and the measured findings reveal that the suggested quasi-circulator has an insertion loss of less than 10 dB. It is possible to reach an isolation level of 55 dB between ports 1 and 3, which is greater than present research. Full article
Show Figures

Figure 1

Figure 1
<p>Signal flow direction in a quasi-circulator.</p>
Full article ">Figure 2
<p>Flow chart of the proposed broadband and high-isolation quasi-circulator.</p>
Full article ">Figure 3
<p>(<b>a</b>) Simplified FET model. (<b>b</b>) Equivalent network of the quasi-circulator.</p>
Full article ">Figure 4
<p>Simulated S31 as a function of frequency at various widths of transistor M3.</p>
Full article ">Figure 5
<p>Simulated (<b>a</b>) insertion loss (<b>b</b>) isolation of the fabricated quasi-circulator as functions of frequency at 5–35 GHz.</p>
Full article ">Figure 6
<p>Simulated (<b>a</b>) return loss (<b>b</b>) isolation of the fabricated quasi-circulator as functions of frequency at 5–35 GHz.</p>
Full article ">Figure 7
<p>(<b>a</b>) Schematic and (<b>b</b>) photograph of the fabricated broadband and high-isolation quasi-circulator.</p>
Full article ">Figure 8
<p>Measured (<b>a</b>) insertion loss and (<b>b</b>) isolation between port 1 and port 3 of the fabricated quasi-circulator as functions of frequency at 5–35 GHz.</p>
Full article ">Figure 9
<p>Measured and simulated (<b>a</b>) noise figure and (<b>b</b>) P1dB of the quasi-circulator as functions of frequency at 5–35 GHz.</p>
Full article ">
8 pages, 1450 KiB  
Proceeding Paper
Communication System Comparison of IoT Applications Using Custom-Designed Antennas: A Basic Experimental Study
by Marco Vinueza Bustamante, Jordan Guillén Arteaga, Carlos Yépez Vera, Aldrin Reyes Narváez and Hernan Barba Molina
Eng. Proc. 2024, 77(1), 16; https://doi.org/10.3390/engproc2024077016 - 4 Nov 2024
Viewed by 72
Abstract
A comparative study of the performance of a communication system for IoT applications is presented. The experiment is based on the bit error rate, which is obtained by varying the distance between two transceiver modules, each attached to a microcontroller Arduino Uno. Four [...] Read more.
A comparative study of the performance of a communication system for IoT applications is presented. The experiment is based on the bit error rate, which is obtained by varying the distance between two transceiver modules, each attached to a microcontroller Arduino Uno. Four scenarios are considered for our experimentation. Each scenario is mainly characterized by interchanging radiator elements which are attached to the transceiver modules. For this, two antennas are designed and implemented: a modified shape-optimized Landstorfer Yagi-Uda antenna and a printed turnstile antenna. The measurements show good agreement, with simulations having gain values of about 9 dBi and 3 dBi for the quasi Yagi-Uda structure and the turnstile antenna, respectively. System performance tests are conducted to compare the performance of the commercial solution at various distances to custom-designed antennas. These tests aim to evaluate the improvement achieved using a new set of antennas. The key to this solution is the use of a high-directivity antenna for data transmission and a circular polarized omnidirectional antenna for reception, which shows an improvement of around 60% in terms of the bit error rate during data transmission compared to the pair of commercial antennas included in the RF module. Full article
Show Figures

Figure 1

Figure 1
<p>Proposed experimental setup used to evaluate the IoT communication system’s performance by using custom-designed antennas.</p>
Full article ">Figure 2
<p>Antennas under test. (<b>a</b>) Modified shape-optimized Landstorfer Yagi-Uda antenna (LaYUA). (<b>b</b>) Printed turnstile antenna (TuSA) with 90°-phase-shift feeding between U<sub>1</sub> and U<sub>2</sub> sources, realized with a coaxial line.</p>
Full article ">Figure 3
<p>Photographs of the antenna prototypes mounted in an anechoic chamber, along with the definition of their spherical coordinates. (<b>Left</b>) LaYUA. (<b>Right</b>) TuSA.</p>
Full article ">Figure 4
<p>Measured (solid blue and dotted yellow) and simulated (dashed red and dash–dot violet) antenna results. (<b>a</b>) Reflection coefficient magnitude. (<b>b</b>) Normalized radiation pattern on the azimuth plane (ϕ = 90°) at 2.4 GHz. The results are invalid in a sector between 50° and 180° due to the measurement setup.</p>
Full article ">Figure 5
<p>BER obtained from measurements. Scenario 1 (solid blue): commercial dipole (TxU)–commercial dipole (RxU). Scenario 2 (dashed red): LaYUA (TxU)–commercial dipole (RxU). Scenario 3 (dotted yellow): commercial dipole (TxU)–TuSA (RxU). Scenario 4 (dash–dot violet): LaYUA (TxU)-TuSA (RxU).</p>
Full article ">
21 pages, 8446 KiB  
Article
Investigating the Effects of the Height-to-Diameter Ratio and Loading Rate on the Mechanical Properties and Crack Extension Mechanism of Sandstone-Like Materials
by Yunbo Gou, Jianbiao Bai, Yanhui Li, Xiangqian Zhao, Lianhai Tai and Zizhao Fu
Appl. Sci. 2024, 14(21), 10049; https://doi.org/10.3390/app142110049 - 4 Nov 2024
Viewed by 552
Abstract
The causes of the size effect (SE) and loading rate effect (LR) for rocks remain unclear. Based on this, a gypsum-mixed material was used to simulate sandstone, where the dosing ratio was 7.5% river sand, 17.5% quartz, 58.3% α-high-strength gypsum, and 16.7% [...] Read more.
The causes of the size effect (SE) and loading rate effect (LR) for rocks remain unclear. Based on this, a gypsum-mixed material was used to simulate sandstone, where the dosing ratio was 7.5% river sand, 17.5% quartz, 58.3% α-high-strength gypsum, and 16.7% water. The specimens were designed to have a height-to-diameter ratio (HDR) of 0.6~2, and three strain rates (SRs)—static, quasi-dynamic, and dynamic—were used to perform single-factor rotational uniaxial compression experiments. PFC2D was used to numerically simulate the damage pattern of a sandstone-like specimen. The results showed that the physical parameters did not change monotonically, as was previously found. The main reason for this is that the end-face friction effect (EFE) is generated when the dynamic SR or the HDR is 0.6~1, with a damage pattern of “X”. Under mechanical analysis, the power consumed by the EFE was inversely proportional to the HDR and directly proportional to the LR, and it can reduce the actual amount of energy transferred inside the specimen. This paper may provide a foundation for the study of non-linear hazards in coal and rock. Full article
Show Figures

Figure 1

Figure 1
<p>Location of the target coal mine.</p>
Full article ">Figure 2
<p>Flow chart of specimen preparation and testing.</p>
Full article ">Figure 3
<p>Part of the specimens with different HDRs.</p>
Full article ">Figure 4
<p>Stress–strain curves of sandstone and sandstone-like materials.</p>
Full article ">Figure 5
<p>Stress–strain curves of specimens with different HDR. (<b>a</b>) HDRs of 0.6–1; (<b>b</b>) HDRs of 1.2–1.6; (<b>c</b>) HDRs of 1.6–2.</p>
Full article ">Figure 6
<p>The changing trend of mechanical parameters with different HDRs at a LR of 0.8 mm·min<sup>−1</sup>: (<b>a</b>) UCS; (<b>b</b>) axial peak strain.</p>
Full article ">Figure 7
<p>Stress–strain curves of the specimens under different LRs: (<b>a</b>) static (0.1~0.5 mm·min<sup>−1</sup>); (<b>b</b>) quasi-dynamic (1~5 mm·min<sup>−1</sup>); (<b>c</b>) dynamic (10~50 mm·min<sup>−1</sup>).</p>
Full article ">Figure 8
<p>Mechanical parameter trends with same HDR at different LRs: (<b>a</b>) UCS; (<b>b</b>) axial peak strain.</p>
Full article ">Figure 9
<p>Parallel bonding model (PBM) and failure criterion.</p>
Full article ">Figure 10
<p>Comparison of experimental and numerical simulations of stress–strain curves.</p>
Full article ">Figure 11
<p>Crack extension in uniaxial compression of the specimens with different HDRs.</p>
Full article ">Figure 12
<p>The pattern of variation in the number of internal cracks formed when specimens are damaged: (<b>a</b>) different HDRs; (<b>b</b>) different LRs.</p>
Full article ">Figure 13
<p>Crack extension in uniaxial compression of the specimens with different LRs.</p>
Full article ">Figure 14
<p>Schematic diagram of the EFE. (<b>a</b>) Mechanical structural modeling of the end face; (<b>b</b>) conical damage angle for specimens of different heights.</p>
Full article ">Figure 15
<p>Diagram of the actual input power inside the specimen.</p>
Full article ">
27 pages, 18482 KiB  
Article
Current Compensation for Faulted Grid-Connected PV Arrays Using a Modified Voltage-Fed Quasi-Z-Source Inverter
by Abdullah Abdurrahman Al-Saloli and Faris E. Alfaris
Electronics 2024, 13(21), 4312; https://doi.org/10.3390/electronics13214312 - 2 Nov 2024
Viewed by 571
Abstract
Large-scale photovoltaic (PV) systems are being widely deployed to meet global environmental goals and renewable energy targets. Advances in PV technology have driven investment in the electric sector. However, as the size of PV arrays grows, more obstacles and challenges emerge. The primary [...] Read more.
Large-scale photovoltaic (PV) systems are being widely deployed to meet global environmental goals and renewable energy targets. Advances in PV technology have driven investment in the electric sector. However, as the size of PV arrays grows, more obstacles and challenges emerge. The primary obstacles are the occurrence of direct current (DC) faults and shading in a large array of PV panels, where any malfunction in a single panel can have a detrimental impact on the overall output power of the entire series-connected PV string and therefore the PV array. Due to the abrupt and frequent fluctuations in power, beside the low-PV systems’ moment of inertia, various technical problems may arise at the point of common coupling (PCC) of grid-connected PV generations, such as frequency and voltage stability, power efficiency, voltage sag, harmonic distortion, and other power quality factors. The majority of the suggested solutions were deficient in several crucial transient operating features and cost feasibility; therefore, this paper introduces a novel power electronic DC–DC converter that seeks to mitigate these effects by compensating for the decrease in current on the DC side of the system. The suggested solution was derived from the dual-source voltage-fed quasi-Z-source inverter (VF-qZSI), where the PV generation power can be supported by an energy storage element. This paper also presents the system architecture and the corresponding power switching control. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate the feasibility and capability of the proposed scheme, which contributes in suppressing the peak of the transient power-to-time variation (dP/dt) by 72% and reducing its normalized root-mean-square error by about 38%, with an AC current total harmonic distortion (THD) of only 1.04%. Full article
Show Figures

Figure 1

Figure 1
<p>The regular power electronic topology of the unidirectional voltage-fed quasi-Z-source inverter with a (<b>left</b>) single-voltage source; (<b>right</b>) dual-voltage source.</p>
Full article ">Figure 2
<p>A 4 × 7 PV array experiencing different types of faults within each individual string.</p>
Full article ">Figure 3
<p>Equivalent circuit of the PV array during the conditions of (<b>a</b>) an L–G fault; (<b>b</b>) a module fault; (<b>c</b>) partial shading; (<b>d</b>) an L–L fault.</p>
Full article ">Figure 3 Cont.
<p>Equivalent circuit of the PV array during the conditions of (<b>a</b>) an L–G fault; (<b>b</b>) a module fault; (<b>c</b>) partial shading; (<b>d</b>) an L–L fault.</p>
Full article ">Figure 4
<p>I–V characteristic of a 4 × 4 PV array under an L–G fault condition with (<b>left</b>) <span class="html-italic">R<sub>F</sub></span> = 0 Ω; (<b>right</b>) <span class="html-italic">R<sub>F</sub></span> &gt; 0 Ω.</p>
Full article ">Figure 5
<p>I–V characteristic of a 4 × 4 PV array under a (<b>left</b>) module fault condition; (<b>right</b>) 50% partial shading case scenario.</p>
Full article ">Figure 6
<p>I–V characteristic of a 4 × 4 PV array under an (<b>left</b>) open-string fault condition; (<b>right</b>) L–L fault condition.</p>
Full article ">Figure 7
<p>The traditional power conversion system for grid-connected PV applications.</p>
Full article ">Figure 8
<p>Behaviors of the PV and inverter powers, AC current, PV current, and PV and DC bus voltages in response to an L–G fault disturbance at the MPPT level for a 4 × 4 PV array.</p>
Full article ">Figure 9
<p>Behavior of the PV and inverter powers, AC current, PV current, and PV and DC bus voltages in response to a module and partial shading faults at the MPPT level for a 4 × 4 PV array.</p>
Full article ">Figure 10
<p>Behavior of the PV and inverter powers, AC current, PV current, and PV and DC bus voltages in response to open-string and L–L fault disturbances at the MPPT level for a 4 × 4 PV array.</p>
Full article ">Figure 11
<p>The configuration of the proposed modified dual-source voltage-fed quasi-Z-source inverter for grid-connected residential PV applications.</p>
Full article ">Figure 12
<p>System’s power flow during (<b>a</b>) mode 1; (<b>b</b>) mode 2; (<b>c</b>) mode 3; (<b>d</b>) mode 4.</p>
Full article ">Figure 13
<p>Equivalent circuit of the modified dual-source VF-qZSI during mode 2 and the (<b>left</b>) charging period of the PV boost inductor (<span class="html-italic">L</span><sub>3</sub>); (<b>right</b>) discharging period of the PV boost inductor (<span class="html-italic">L</span><sub>3</sub>).</p>
Full article ">Figure 14
<p>Equivalent circuit of the modified dual-source VF-qZSI during mode 3 and the (<b>left</b>) charging period of the inductor (<span class="html-italic">L</span><sub>1</sub>); (<b>right</b>) discharging period of the inductor (<span class="html-italic">L</span><sub>1</sub>).</p>
Full article ">Figure 15
<p>Equivalent circuit of the modified dual-source VF-qZSI during mode 4 and the (<b>left</b>) charging period of the PV boost inductor (<span class="html-italic">L</span><sub>3</sub>); (<b>right</b>) discharging period of the PV boost inductor (<span class="html-italic">L</span><sub>3</sub>).</p>
Full article ">Figure 16
<p>Control scheme of the six-switches inverter bridge.</p>
Full article ">Figure 17
<p>Control scheme of the PV-interfaced DC–DC power converter.</p>
Full article ">Figure 18
<p>Flow chart of the operational process of (<b>left</b>) the P&amp;O control algorithm; (<b>right</b>) the control algorithm for <span class="html-italic">S<sub>7</sub></span> and <span class="html-italic">S<sub>9</sub></span> power switches.</p>
Full article ">Figure 19
<p>Control scheme of <span class="html-italic">S<sub>7</sub></span> and <span class="html-italic">S<sub>9</sub></span> power switches.</p>
Full article ">Figure 20
<p>A laboratory-scale PV array used for investigation analysis.</p>
Full article ">Figure 21
<p>System performance evaluation with the proposed inverter and normal conditions during 27 February.</p>
Full article ">Figure 22
<p>System transient performance with the proposed converter and L–G and module array faults during 27 February.</p>
Full article ">Figure 23
<p>System transient performance with the proposed converter and partial shading, open-string and L–L array faults during 27 February.</p>
Full article ">Figure 24
<p>Switching behavior of <span class="html-italic">S</span><sub>7</sub> and <span class="html-italic">S</span><sub>9</sub> power switches during PV array faults.</p>
Full article ">Figure 25
<p>Solar irradiance data corresponding to different cloud levels during partly cloudy weather conditions during 11 March and 22 May.</p>
Full article ">Figure 26
<p>System performance evaluation with the proposed inverter under 6% partly cloudy weather condition for the entire PV array during 11 March.</p>
Full article ">Figure 27
<p>System performance evaluation with the proposed inverter under a 27% partly cloudy weather condition for the entire PV array during 22 May.</p>
Full article ">
16 pages, 6070 KiB  
Article
Implementation of a Reduced Decoding Algorithm Complexity for Quasi-Cyclic Split-Row Threshold Low-Density Parity-Check Decoders
by Bilal Mejmaa, Chakir Aqil, Ismail Akharraz and Abdelaziz Ahaitouf
Information 2024, 15(11), 684; https://doi.org/10.3390/info15110684 - 1 Nov 2024
Viewed by 407
Abstract
We propose two decoding algorithms for quasi-cyclic LDPC codes (QC-LDPC) and implement the more efficient one in this paper. These algorithms depend on the split row for the layered decoding method applied to the Min-Sum (MS) algorithm. We designate the first algorithm “Split-Row [...] Read more.
We propose two decoding algorithms for quasi-cyclic LDPC codes (QC-LDPC) and implement the more efficient one in this paper. These algorithms depend on the split row for the layered decoding method applied to the Min-Sum (MS) algorithm. We designate the first algorithm “Split-Row Layered Min-Sum” (SRLMS), and the second algorithm “Split-Row Threshold Layered Min-Sum” (SRTLMS). A threshold message passes from one partition to another in SRTLMS, minimizing the gap from the MS and achieving a binary error rate of 3 × 10−5 with Imax = 4 as the maximum number of iterations, resulting in a decrease of 0.25 dB. The simulation’s findings indicate that the SRTLMS is the most efficient variant decoding algorithm for LDPC codes, thanks to its compromise between performance and complexity. This paper presents the two invented algorithms and a comprehensive study of the co-design and implementation of the SRTLMS algorithm. We executed the implementation on a Xilinx Kintex-7 XC7K160 FPGA, achieving a maximum operating frequency of 101 MHz and a throughput of 606 Mbps. Full article
Show Figures

Figure 1

Figure 1
<p>The 802.16e standard’s LDPC parity-check matrix of rate ½.</p>
Full article ">Figure 2
<p>Block diagram for traditional two-phase decoding (<b>a</b>), and (<b>b</b>) split-row decoding; diagram of split-row threshold in (<b>c</b>).</p>
Full article ">Figure 3
<p>Parity-check matrix with layered scheduling.</p>
Full article ">Figure 4
<p>Message-passing flow in horizontal LD.</p>
Full article ">Figure 5
<p>Split-row decoding in a block diagram using two partitions, highlighting sign-passing signals in the layer.</p>
Full article ">Figure 6
<p>Split-row threshold decoding system block diagram with two partitions that emphasize crossing signals for signs and threshold.</p>
Full article ">Figure 7
<p>Comparison of simulation and co-design outputs.</p>
Full article ">Figure 8
<p>Comparison of the total number of consumed LUTs.</p>
Full article ">Figure 9
<p>Comparison of the operating periods.</p>
Full article ">Figure 10
<p>Performance of the BER at the threshold for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi mathvariant="normal">b</mi> </mrow> </msub> <mo>/</mo> <msub> <mrow> <mi mathvariant="normal">N</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> values of 3 dB, 3.5 dB, 3.7 dB, 4 dB, and 4.2 dB presented by the colors pink, red, blue, black, and light blue, respectively.</p>
Full article ">Figure 11
<p>BER performance utilizing MS as a function of the scale factor for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi mathvariant="normal">b</mi> </mrow> </msub> <mo>/</mo> <msub> <mrow> <mi mathvariant="normal">N</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> values of 3.4 dB, 3.5 dB, 3.7 dB, 3.8 dB, and 4.2 dB presented by the colors black (star markers), black (square markers), red, pink, and blue, respectively.</p>
Full article ">Figure 12
<p>BER performance utilizing SRLMS as a function of the scale factor for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi mathvariant="normal">b</mi> </mrow> </msub> <mo>/</mo> <msub> <mrow> <mi mathvariant="normal">N</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> values of 3.5 dB, 3.8 dB, 4 dB, and 4.5 dB presented by the colors blue, pink, black, and red, respectively.</p>
Full article ">Figure 13
<p>BER performance using SRTLMS as a function of the scale factor for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi mathvariant="normal">b</mi> </mrow> </msub> <mo>/</mo> <msub> <mrow> <mi mathvariant="normal">N</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> values of 3 dB, 3.2 dB, 3.5 dB, and 3.7 dB presented by the colors light blue, black, red, and blue, respectively.</p>
Full article ">Figure 14
<p>Comparison of the BER performance of several decoding algorithms.</p>
Full article ">Figure 15
<p>Comparison of the BLER performance of several decoding algorithms.</p>
Full article ">Figure 16
<p>Total power consumption based on Flow_AreaOptimized_high strategy.</p>
Full article ">
22 pages, 25960 KiB  
Article
A New Method for Compression Testing of Reinforced Polymers
by Ciprian Ionuț Morăraș, Dorin Husaru, Viorel Goanță, Paul Doru Bârsănescu, Fabian Cezar Lupu, Corneliu Munteanu, Nicanor Cimpoesu and Elena Roxana Cosau
Polymers 2024, 16(21), 3071; https://doi.org/10.3390/polym16213071 - 31 Oct 2024
Viewed by 492
Abstract
Compressive testing of specimens taken from relatively thin composite plates is difficult, especially due to the occurrence of buckling. To prevent buckling, the central portion of the specimens used for the compression test has smaller dimensions, and the specimens can be guided along [...] Read more.
Compressive testing of specimens taken from relatively thin composite plates is difficult, especially due to the occurrence of buckling. To prevent buckling, the central portion of the specimens used for the compression test has smaller dimensions, and the specimens can be guided along their entire length. For these reasons, optical methods, such as digital image correlation (DIC), cannot be used for the compression test and strain rosettes cannot be glued onto the samples to determine Poisson’s ratio. In this study, compression tests of a glass fiber-reinforced polymer (GFRP) were conducted using both the ASTM D695 (Boeing version) and a newly proposed method. The new method involves using special specimens that allow T-type rosettes to be bonded to determine Poisson’s ratio, whose value of 0.14 was thus determined. SEM images of the failure surfaces were presented and interpreted. A finite element analysis (FEA) of the specimens tested in compression is also presented. The first analyzed case considers the homogeneous and orthotropic composite, loaded with a uniformly distributed force. The normal stress in the central section of the specimen, determined with FEA, has an error of 6.52% compared to that determined experimentally. Additionally, the strain in the center of the strain gauge, determined with FEA, has an error of 4.76% compared to the measured one. In the second case studied with FEA, the sample is loaded with a quasi-concentrated force, which can move in the direction of the symmetry axes of the cross-section, to study the effect of the eccentricity of the compression force on the state of stress. It was shown that the eccentricity of the force has a great influence: the stress distribution in the section of the specimen becomes strongly non-uniform. For a force eccentricity of 0.4 mm in the direction of the OX axis, the minimum stress decreases by 53.7%, and the maximum stress increases by 55.4%. In order to analyze the influence of some manufacturing defects, two other cases were analyzed by FEA, in which it was assumed that the thicknesses of the outer resin layers were modified, making them asymmetrical. For this final FEA, the specimen was considered to be composed of laminates. These results demonstrate the special attention that must be paid to the centric application of force in compression testing. Full article
(This article belongs to the Section Polymer Processing and Engineering)
Show Figures

Figure 1

Figure 1
<p>Compressive failure modes of fiber composites. (<b>a1</b>) Elastic micro buckling—in phase (shear microbuckling); (<b>a2</b>) Elastic microbuckling—out of phase (extensional microbuckling); (<b>b</b>) Fiber kinking; (<b>c</b>) Fiber crushing; (<b>d</b>) Shear band formation; (<b>e</b>) Matrix cracking; (<b>f</b>) Buckle delamination; (<b>g</b>) Contact damage [<a href="#B11-polymers-16-03071" class="html-bibr">11</a>,<a href="#B12-polymers-16-03071" class="html-bibr">12</a>,<a href="#B13-polymers-16-03071" class="html-bibr">13</a>].</p>
Full article ">Figure 2
<p>Fiberglass layers after the application of the complete combustion method (ASTM D 2584).</p>
Full article ">Figure 3
<p>(<b>a</b>) GFRP specimen dimensions (in mm) according to ASTM D 695; (<b>b</b>) compression test device and test specimen set.</p>
Full article ">Figure 4
<p>(<b>a</b>) Specimen for Poisson’s ratio determination (back GFRP sample not shown): 1—front sample; 2—left sample; 3—right sample; 4—Al alloy shell; 5—Al-sheet spacer; 6—T-type T-strain gauge; (<b>b</b>) Section A-A.</p>
Full article ">Figure 5
<p>The compression device mounted on the INSTRON machine.</p>
Full article ">Figure 6
<p>The stress–strain curves in the case of compression tests, analyzed until the samples’ breaking point.</p>
Full article ">Figure 7
<p>Failure types and failure modes for specimen TR of the plate [0°/90°]: (<b>a</b>) Specimen 1; (<b>b</b>) Specimen 2; (<b>c</b>) Specimen 3.</p>
Full article ">Figure 8
<p>Stress variation with longitudinal strain: Approximation lines for the longitudinal elastic modulus at [0°/90°].</p>
Full article ">Figure 9
<p>The stress–strain curve from compression tests at [0°/90°].</p>
Full article ">Figure 10
<p>The Longitudinal strain (εL)–Transversal strain (εT) curve from compression tests at [0°/90°].</p>
Full article ">Figure 11
<p>Longitudinal strain–transversal strain curve at [0°/90°].</p>
Full article ">Figure 12
<p>SEM images of the surface material—GFRP-specimen TR1: (<b>a</b>) 200×; (<b>b</b>) 500×; and (<b>c</b>) 1000×.</p>
Full article ">Figure 13
<p>SEM images of the surface material—GFRP-specimen TR2: (<b>a</b>) 200×; (<b>b</b>) 500×; and (<b>c</b>) 1000×.</p>
Full article ">Figure 14
<p>SEM images of the surface material—GFRP-specimen TR3: (<b>a</b>) 200×; (<b>b</b>) 500×; and (<b>c</b>) 1000×.</p>
Full article ">Figure 15
<p>Cross-section SEM images of GFRP specimen TR1: (<b>a</b>) 200×; (<b>b</b>) 500×; and (<b>c</b>) 1000×.</p>
Full article ">Figure 16
<p>Cross-section SEM images of GFRP specimens TR2: (<b>a</b>) 200×; (<b>b</b>) 500×; and (<b>c</b>) 1000×.</p>
Full article ">Figure 17
<p>Cross-section SEM images of GFRP specimen TR3: (<b>a</b>) 200×; (<b>b</b>) 500×; and (<b>c</b>) 1000×.</p>
Full article ">Figure 18
<p>Mesh (central part of sample).</p>
Full article ">Figure 19
<p>Fixed support and force applied to the specimen.</p>
Full article ">Figure 20
<p>(<b>a</b>) The total displacement of the specimen in the <span class="html-italic">Y</span>-axis direction (without tabs); (<b>b</b>) specific strain distribution on the specimen with tabs removed.</p>
Full article ">Figure 21
<p>Normal stresses σ<sub>y</sub> in the cross-section in the middle of the specimen (loaded with a uniformly distributed force).</p>
Full article ">Figure 22
<p>Normal elastic strain in the cross-section in the middle of the specimen (normal stress is 80 MPa). The strain in the middle of the strain gauge is indicated.</p>
Full article ">Figure 23
<p>(<b>a</b>) Loading the specimen with quasi-concentrated force; (<b>b</b>) loading the specimen with an eccentric force displaced by 0.4 mm in the OZ direction.</p>
Full article ">Figure 24
<p>(<b>a</b>) Minimum principal stresses σ<sub>y</sub> in the cross-section at the middle of the specimen (loaded with a quasi-concentrated force with an eccentricity of 0.2 mm, in the direction of the OZ axis); (<b>b</b>) Minimum principal stresses σ<sub>y</sub> in the cross-section at the middle of the specimen (loading with a quasi-concentrated force with an eccentricity of 0.4 mm in the direction of the OZ axis).</p>
Full article ">Figure 25
<p>(<b>a</b>) Minimum principal stresses σ<sub>y</sub> in the cross-section at the middle of the specimen (loaded with a quasi-concentrated force with an eccentricity of 0.2 mm in the direction of the OX axis); (<b>b</b>) Minimum principal stresses σ<sub>y</sub> in the cross-section at the middle of the specimen (loading with a quasi-concentrated force with an eccentricity of 0.4 mm in the direction of the OX axis).</p>
Full article ">Figure 26
<p>Numbering of resin and fiberglass layers.</p>
Full article ">Figure 27
<p>The discretization network of the stratified test specimen.</p>
Full article ">Figure 28
<p>von Mises stresses in the middle of layer 9 (resin).</p>
Full article ">Figure 29
<p>von Mises stresses in the middle of layer 10 (fiberglass fabric, which contains the plane of symmetry of the specimen).</p>
Full article ">
Back to TopTop