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12 pages, 1919 KiB  
Article
Sensitivity Analysis of Numerical Coherency Model for Rock Sites
by Dongyeon Lee, Yonghee Lee, Hak-Sung Kim, Jeong-Seon Park and Duhee Park
Appl. Sci. 2025, 15(6), 2925; https://doi.org/10.3390/app15062925 - 7 Mar 2025
Abstract
Characterization of ground motion incoherency can significantly reduce the seismic load imposed on large scale infrastructures. Because of difficulties in developing an empirical coherency function from a site-specific dense array, it is seldom used in practice. A number of studies used numerical simulations [...] Read more.
Characterization of ground motion incoherency can significantly reduce the seismic load imposed on large scale infrastructures. Because of difficulties in developing an empirical coherency function from a site-specific dense array, it is seldom used in practice. A number of studies used numerical simulations to develop generic coherency models. However, they have only been developed for idealized profiles. A comprehensive parametric study evaluating the effect of various parameters influencing the calculated coherency function has not yet been performed. We utilized the measured shear wave velocity (Vs) profile at Pinyon Flat, located in California, to perform a suite of time history analyses. This site was selected because the empirical coherency function developed here has been used as a reference model for rock sites. We performed several sensitivity studies investigating the effect of both the site spatial variability and numerical analysis parameters in order to provide a guideline for developing a coherency model from numerical simulations. The outputs were compared against the empirical coherency model to better illustrate the importance of the parameters. The coefficient of variation (CV) of Vs was revealed to be the primary parameter influencing the calculated plane-wave coherency, whereas the correlation length (CL) has a secondary influence. Site-specific convergence analyses should be performed to determine the optimum numerical parameter, including the number of analyses and depth of numerical model. Considering the importance of CV and Vs, it is recommended to perform field tests to determine site-specific values to derive numerical coherency functions. Full article
(This article belongs to the Section Civil Engineering)
19 pages, 28982 KiB  
Article
Low-Velocity Impact Response of Sandwich Structure with Triply Periodic Minimal Surface Cores
by Dong Wei, Shaoan Li, Laiyu Liang, Longfei Sun and Yaozhong Wu
Polymers 2025, 17(6), 712; https://doi.org/10.3390/polym17060712 - 7 Mar 2025
Abstract
Triply periodic minimal surface (TPMS) sandwich structures were proposed based on the TPMSs. The test samples for the TPMS sandwich were prepared using Multi Jet Fusion (MJF) with PA12 as the base material. Their low-velocity impact responses were investigated using experimental tests and [...] Read more.
Triply periodic minimal surface (TPMS) sandwich structures were proposed based on the TPMSs. The test samples for the TPMS sandwich were prepared using Multi Jet Fusion (MJF) with PA12 as the base material. Their low-velocity impact responses were investigated using experimental tests and numerical simulation. The effect of structural parameters (relative density, panel thickness, impact energy, and TPMS core) on the impact performance of the sandwich structures was analyzed through parameter studies. The results indicate that the peak load and stiffness of the sandwich structure increase with the increase in relative density, panel thickness, and impact energy. Among three types of TPMS core sandwich structures, the Diamond sandwich structure exhibits the biggest peak load and best impact resistance. Full article
(This article belongs to the Section Polymer Physics and Theory)
Show Figures

Figure 1

Figure 1
<p>Construction of the TPMS sandwich structures: (<b>a</b>) P-TPMS sandwich, (<b>b</b>) G-TPMS sandwich, (<b>c</b>) D-TPMS sandwich.</p>
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<p>Low-velocity impact test setup.</p>
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<p>FE model of impact for the TPMS sandwich.</p>
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<p>True stress–strain curves of 3D-printed PA12 material.</p>
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<p>Convergence of the peak loads under different element sizes.</p>
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<p>Comparison of force–time curves between test and simulation.</p>
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<p>Experimental and simulation force–time curves of TPMS sandwich with different <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>D</mi> </mrow> </semantics></math>.</p>
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<p>Comparison of energy–time curves between test and simulation of TPMS sandwich with different <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>D</mi> </mrow> </semantics></math>.</p>
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<p>Comparison of the experimental and simulation impact damage of TPMS sandwich with different <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>D</mi> </mrow> </semantics></math>: (<b>a</b>) D-0.2-1.5-10; (<b>b</b>) D-0.25-1.5-10; (<b>c</b>) D-0.3-1.5-10.</p>
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<p>Comparison of the experimental and simulation pit depth of TPMS sandwich with different <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>D</mi> </mrow> </semantics></math>.</p>
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<p>Experimental and simulation force–time curves of TPMS sandwich with different <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>f</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Comparison of energy–time curves between test and simulation of TPMS sandwich with different <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>f</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Comparison of the experimental and simulation impact damage of TPMS sandwich with different <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>f</mi> </msub> </mrow> </semantics></math>: (<b>a</b>) D-0.25-1.0-10; (<b>b</b>) D-0.25-1.5-10; (<b>c</b>) D-0.25-2.0-10.</p>
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<p>Comparison of the experimental and simulation pit depth of TPMS sandwich with different <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>f</mi> </msub> </mrow> </semantics></math>.</p>
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<p>Experimental and simulation force–time curves of TPMS sandwich with different <math display="inline"><semantics> <mi>E</mi> </semantics></math>.</p>
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<p>Comparison of energy–time curves between test and simulation of TPMS sandwich with different <math display="inline"><semantics> <mi>E</mi> </semantics></math>.</p>
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<p>Comparison of the experimental and simulation impact damage of TPMS sandwich with different <math display="inline"><semantics> <mi>E</mi> </semantics></math>: (<b>a</b>) D-0.25-1.5-5; (<b>b</b>) D-0.25-1.5-10; (<b>c</b>) D-0.25-1.5-15.</p>
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<p>Experimental and simulation pit depths of TPMS sandwich with different <math display="inline"><semantics> <mi>E</mi> </semantics></math>.</p>
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<p>Experimental and simulation force–time curves of TPMS sandwich with different TPMS cores.</p>
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<p>Comparison of energy–time curves between test and simulation of TPMS sandwich with different TPMS cores.</p>
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<p>Comparison of the experimental and simulation impact damage of TPMS sandwich with different TPMS cores: (<b>a</b>) P-0.25-1.5-10; (<b>b</b>) G-0.25-1.5-10; (<b>c</b>) D-0.25-1.5-10.</p>
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<p>Comparison of the experimental and simulation pit depth of TPMS sandwich with different TPMS cores.</p>
Full article ">
13 pages, 2383 KiB  
Article
Quarter-Wave Plate Meta-Atom Metasurfaces for Continuous Longitudinal Polarization Modulation of Hybrid Poincaré Sphere Beams
by Yunxiao Li, Quanhong Feng, Gongzheng Fang, Haonan Sun, Xingyi Fan, Zhenghao Liu, Hao Wang, Yuexu Si, Shuhao Si, Xuran Li and Chen Cheng
Photonics 2025, 12(3), 242; https://doi.org/10.3390/photonics12030242 - 7 Mar 2025
Abstract
Quarter-wave plate (QWP) metasurfaces provide a novel approach for generating three-dimensional (3D) hybrid-order Poincaré sphere (HyOPS) beams and enabling longitudinal polarization modulation, owing to their unique spin-decoupling properties. In this work, we designed a set of QWP meta-atom metasurfaces that generate 3D HyOPS [...] Read more.
Quarter-wave plate (QWP) metasurfaces provide a novel approach for generating three-dimensional (3D) hybrid-order Poincaré sphere (HyOPS) beams and enabling longitudinal polarization modulation, owing to their unique spin-decoupling properties. In this work, we designed a set of QWP meta-atom metasurfaces that generate 3D HyOPS beams with continuously varying polarization states along the propagation direction. The third-, fourth- and fifth-order HyOPS beams are generated by three metasurface devices, respectively. The HyOPS beams exhibit a focal depth of 30 μm, a stable longitudinal propagation, and a continuously evolving polarization state. Notably, complete polarization evolution along the equator of the HyOPS occurs within a depth of 20 μm. Numerical calculations in MATLAB R2022b validated the feasibility of the designed QWP metasurfaces. The finite-difference time-domain (FDTD) simulations further confirmed the stable propagation and continuous polarization evolution of the longitudinal light field. Additionally, the concentric arrangement of the QWP meta-atoms on the metasurface effectively mitigates scattering crosstalk caused by abrupt edge phase variations. This work offers new insights into the generation and control of HyOPS light fields and contributes significantly to the development of miniaturized, functionally integrated high-performance nanophotonics. Full article
20 pages, 576 KiB  
Article
Model-Free Adaptive Control for Attitude Stabilization of Earth-Pointing Spacecraft Using Magnetorquers
by Fabio Celani, Mohsen Heydari and Alireza Basohbat Novinzadeh
Aerospace 2025, 12(3), 219; https://doi.org/10.3390/aerospace12030219 - 7 Mar 2025
Abstract
This paper presents an attitude stabilization algorithm for a Low Earth Orbit (LEO) Earth-pointing spacecraft using magnetorquers as the only torque actuators and employing Model-Free Adaptive Control (MFAC) as the control algorithm. MFAC is a data-driven control algorithm that relies solely on input–output [...] Read more.
This paper presents an attitude stabilization algorithm for a Low Earth Orbit (LEO) Earth-pointing spacecraft using magnetorquers as the only torque actuators and employing Model-Free Adaptive Control (MFAC) as the control algorithm. MFAC is a data-driven control algorithm that relies solely on input–output data from the plant. This paper validates the effectiveness of the proposed approach through numerical simulations in a specific case study. The simulations show that the proposed algorithm drives the spacecraft’s attitude to three-axis stabilization in the orbital frame from arbitrary initial tumbling conditions. The numerical study also shows that the proposed control algorithm outperforms a model-based Proportional–Derivative (PD) control in terms of pointing accuracy at the expense of higher energy consumption. Full article
(This article belongs to the Special Issue Spacecraft Dynamics and Control (2nd Edition))
19 pages, 4669 KiB  
Article
Investigation on the Bending Mechanism of Single-Crystal Copper Under High Bending Rates via Molecular Dynamics
by Peng Wu, Pengyue Zhao, Zhengkun Li, Jianwei Wu and Jiubin Tan
Micromachines 2025, 16(3), 314; https://doi.org/10.3390/mi16030314 - 7 Mar 2025
Abstract
Leaf spring-type flexible hinges serve as critical transmission components in kilogram quantization energy balance systems. Investigating their bending behavior is crucial for enhancing measurement accuracy and ensuring structural reliability. This work employs molecular dynamics simulations to analyze the mechanical properties and deformation characteristics [...] Read more.
Leaf spring-type flexible hinges serve as critical transmission components in kilogram quantization energy balance systems. Investigating their bending behavior is crucial for enhancing measurement accuracy and ensuring structural reliability. This work employs molecular dynamics simulations to analyze the mechanical properties and deformation characteristics of such hinges under varying bending rates. The findings reveal a significant correlation between the bending rate and the hinges’ plastic deformation and microstructural evolution, indicating the presence of a critical bending rate. When the bending rate is below the critical threshold, the hinges exhibit excellent structural stability, characterized by low dislocation density, reduced von Mises stress, and limited temperature rise, making them suitable for long-term use. Conversely, when the bending rate exceeds the critical threshold, the hinges undergo significant plastic deformation, including notable increases in stress and temperature concentration, as well as microstructural alterations. Specifically, the initially stable crystal structure is disrupted, leading to the formation of numerous defect structures. These changes result in localized instability and elevate the risk of fatigue damage. This work comprehensively elucidates the mechanical responses and failure mechanisms of flexible hinges, providing valuable data and guidance for their optimized design and application. Full article
24 pages, 4934 KiB  
Article
Numerical and Experimental Investigation of the Ultra-Low Head Bidirectional Shaft Extension Pump Under Near-Zero Head Conditions
by Fulin Zhang, Yuan Zheng, Gaohui Li and Jing Dai
Machines 2025, 13(3), 220; https://doi.org/10.3390/machines13030220 - 7 Mar 2025
Abstract
Theoretical analysis, numerical simulation, and experimental study are used to investigate the ultra-low head bidirectional shaft extension pump, especially near-zero head conditions. The results show that under forward operation, at low flow and design flow conditions, the closer to the shroud, the closer [...] Read more.
Theoretical analysis, numerical simulation, and experimental study are used to investigate the ultra-low head bidirectional shaft extension pump, especially near-zero head conditions. The results show that under forward operation, at low flow and design flow conditions, the closer to the shroud, the closer the vortex is to the back of the guide vanes, and the vortex area is becoming smaller. The hydraulic loss of the outlet passage is 15% of the operating head at the minimum flow and 170% of the operating head under near-zero head condition. The peak-to-peak (PTP) value of pressure fluctuation increases with the increase in flow rate. The primary frequency (PF) of vibration is strongly related to the primary and secondary frequencies (PSFs) of pressure fluctuation. Under reverse operation, when the flow rate is less than 0.83Qr0, the uniformity of axial velocity distribution Vu and the velocity-weighted average angle θ show an approximately exponential declining pattern. The hydraulic loss of the outlet passage at the minimum flow rate is 61% of the operating head and 350% of the operating head under near-zero head condition. The exponential fitting can better describe the relationship between circulation and hydraulic loss. As the flow rate decreases, the PF of vibration decreases to rotational frequency. Full article
(This article belongs to the Section Turbomachinery)
14 pages, 1247 KiB  
Article
Effects of Discretization of Smagorinsky–Lilly Subgrid Scale Model on Large-Eddy Simulation of Stable Boundary Layers
by Jonas Banhos and Georgios Matheou
Atmosphere 2025, 16(3), 310; https://doi.org/10.3390/atmos16030310 - 7 Mar 2025
Abstract
Large-eddy simulation (LES) models are sensitive to numerical discretization because of the large fraction of resolved turbulent energy (>80%) and the strong non-linear interactions between resolved-scale fields with the turbulence subgrid scale (SGS) model. The effects of the Smagorinsky–Lilly [...] Read more.
Large-eddy simulation (LES) models are sensitive to numerical discretization because of the large fraction of resolved turbulent energy (>80%) and the strong non-linear interactions between resolved-scale fields with the turbulence subgrid scale (SGS) model. The effects of the Smagorinsky–Lilly SGS model discretization are investigated. Three finite difference schemes are compared. Second-, fourth-, and sixth-order centered difference schemes are used to approximate the spatial derivatives of the SGS model. In the LES of homogeneous isotropic turbulence (HIT), including (non-isotropic) turbulent mixing of a passive scalar, no differences are observed with respect to the SGS model discretization. The HIT LES results are validated against a direct numerical simulation, which resolves all flow scales and does not include an SGS model. In the LES of a moderately stable atmospheric boundary layer, the LES results depend on the SGS discretization for coarse grid resolutions. The second-order scheme performs better at coarse resolutions compared to higher-order schemes. Overall, it is found that higher-order discretizations of the Smagorinsky–Lilly model are not beneficial compared to the second-order scheme. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
Show Figures

Figure 1

Figure 1
<p>Vertical planes of the LES initial scalar fluctuations fields. The LES initial condition is constructed using coarsened DNS data. The panels show four LES grid resolutions: (<b>a</b>) <math display="inline"><semantics> <msup> <mn>32</mn> <mn>3</mn> </msup> </semantics></math> grid, (<b>b</b>) <math display="inline"><semantics> <msup> <mn>64</mn> <mn>3</mn> </msup> </semantics></math> grid, (<b>c</b>) <math display="inline"><semantics> <msup> <mn>128</mn> <mn>3</mn> </msup> </semantics></math> grid, and (<b>d</b>) <math display="inline"><semantics> <msup> <mn>256</mn> <mn>3</mn> </msup> </semantics></math> grid. The DNS grid size is <math display="inline"><semantics> <msup> <mn>1024</mn> <mn>3</mn> </msup> </semantics></math>, which is 4 times finer than the field in panel (<b>d</b>).</p>
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<p>LES model grid configuration. A three-dimensional grid cell schematic is shown on the left. The corresponding two-dimensional configuration is shown on the right. Scalar variables and the diagonal elements of the stress tensor <math display="inline"><semantics> <mi>τ</mi> </semantics></math> are located at the grid cell centers. The velocity components (<span class="html-italic">u</span>, <span class="html-italic">v</span>, and <span class="html-italic">w</span>) and components of the SGS scalar flux (<math display="inline"><semantics> <mi>σ</mi> </semantics></math>) are located at the middle of the cell faces. The off-diagonal elements of the stress tensor are located at the middle grid cell edges in three dimensions (<b>left</b>) and at the grid cell corners in two dimensions (<b>right</b>).</p>
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<p>Comparison of resolved-scale TKE and scalar variance between the DNS and LES. (<b>Top row</b>) Panels (<b>a</b>–<b>d</b>) correspond to TKE and (<b>bottom row</b>) (<b>e</b>–<b>h</b>) correspond to scalar variance. Each column corresponds to a different LES grid resolution: (<b>a</b>,<b>e</b>) <math display="inline"><semantics> <msup> <mn>32</mn> <mn>3</mn> </msup> </semantics></math> grid, (<b>b</b>,<b>f</b>) <math display="inline"><semantics> <msup> <mn>64</mn> <mn>3</mn> </msup> </semantics></math> grid, (<b>c</b>,<b>g</b>) <math display="inline"><semantics> <msup> <mn>128</mn> <mn>3</mn> </msup> </semantics></math> grid, and (<b>d</b>,<b>h</b>) <math display="inline"><semantics> <msup> <mn>256</mn> <mn>3</mn> </msup> </semantics></math> grid.</p>
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<p>Stable boundary layer zonal velocity profiles at different LES grid resolutions and SGS term discretizations at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>9</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>. Each panel corresponds to a different grid resolution. Lines correspond to different orders of SGS term discretizations.</p>
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<p>Stable boundary layer potential temperature profiles at different LES grid resolutions and SGS term discretizations at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>9</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>. Each panel corresponds to a different grid resolution: (<b>a</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>8</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>4</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>2</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>. Lines correspond to different orders of SGS term discretization.</p>
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<p>Stable boundary layer turbulent heat flux at different LES grid resolutions and SGS term discretizations at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>9</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>. Each panel corresponds to a different grid resolution: (<b>a</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>8</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>4</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>2</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>. Lines correspond to different orders of SGS term discretization.</p>
Full article ">Figure 7
<p>Stable boundary layer time traces of vertically integrated TKE at different LES grid resolutions and SGS term discretizations. Each panel corresponds to a different grid resolution: (<b>a</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>8</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>4</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>, and (<b>c</b>) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>2</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>. Lines correspond to different orders of SGS term discretization.</p>
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<p>One-dimensional compensated spectra of zonal wind for the stable boundary layer at grid resolution <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>8</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>9</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>. Each panel corresponds to a different height. Color lines correspond to different orders of the SGS term discretization, as in <a href="#atmosphere-16-00310-f007" class="html-fig">Figure 7</a>. Black line denotes slope <math display="inline"><semantics> <msup> <mi>k</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> for reference.</p>
Full article ">Figure 9
<p>One-dimensional compensated spectra of zonal wind for the stable boundary layer at grid resolution <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> <mo>=</mo> <mn>4</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>=</mo> <mn>9</mn> <mspace width="0.277778em"/> <mi mathvariant="normal">h</mi> </mrow> </semantics></math>. Each panel corresponds to a different height. Color lines correspond to different orders of the SGS term discretization, as in <a href="#atmosphere-16-00310-f007" class="html-fig">Figure 7</a>. Black line denotes slope <math display="inline"><semantics> <msup> <mi>k</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> for reference.</p>
Full article ">
23 pages, 20698 KiB  
Article
Numerical Study on the Bending Performance of Steel-Ribbed Composite Slabs for Substations
by Lin Li, Yong Liu, Zhenzhong Wei, Yunan Jiang, Haomiao Chen, Yu Zhang, Chen Liu, Kunjie Rong and Li Tian
Appl. Sci. 2025, 15(6), 2876; https://doi.org/10.3390/app15062876 - 7 Mar 2025
Abstract
This study investigates the bending behavior of steel-ribbed composite slabs for a 500 kV substation project in China through numerical simulation. The unidirectional bending performance of the slab was first analyzed and validated against theoretical calculations. After that, the bidirectional bending performance of [...] Read more.
This study investigates the bending behavior of steel-ribbed composite slabs for a 500 kV substation project in China through numerical simulation. The unidirectional bending performance of the slab was first analyzed and validated against theoretical calculations. After that, the bidirectional bending performance of double-spliced and triple-spliced composite slabs were evaluated against the monolithic slab, followed by a parametric analysis to identify the influence of key factors. The results indicate that the steel-ribbed composite slabs feature high cracking strength, post-crack stiffness, bearing capacity, and commendable ductility under both unidirectional and bidirectional loading conditions. Under unidirectional loading, the ultimate capacity of the slab reaches 57–58 kN/m2. Under bidirectional loading, the cracking load and bearing capacity of the dense-splicing composite slabs increase by more than 60% compared with unidirectional loading. Composite and monolithic slabs exhibit similar crack patterns and ultimate capacities under bidirectional loading; however, the presence of splicing joints results in a slight increase in the ultimate deflection of the double-spliced and triple-spliced composite slabs by 7.53% and 7.75% compared with that of the monolithic slab. The ratio of prestressing steel is identified as the most critical parameter for failure control, followed by the concrete strength. When the strength of the joint-connecting rebars exceeds 235 MPa and the diameter is greater than 4 mm, transversal force transfer across the joints is reliable. This paper provides valuable insights and practical guidance for the prefabricated construction of substations. Full article
Show Figures

Figure 1

Figure 1
<p>Daocheng (Huangbuling) 500 kV substation. (<b>a</b>) Aerial view; (<b>b</b>) Layout plan for the 8.800 m floor (mm).</p>
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<p>Illustration of the steel-ribbed composite slab (mm).</p>
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<p>Detailed drawing of precast base slabs A and B (mm).</p>
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<p>Double-spliced, triple-spliced, and cast-in-place monolithic slab (mm).</p>
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<p>Constitutive models: (<b>a</b>) Response of concrete in compression; (<b>b</b>) Concrete compressive damage; (<b>c</b>) Response of concrete in tension; (<b>d</b>) Concrete tensile damage; (<b>e</b>) Response of rebar in tension; (<b>f</b>) Response of prestressing steel in tension.</p>
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<p>FE model of steel-ribbed composite slab A.</p>
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<p>FE model of steel-ribbed composite slab B.</p>
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<p>FE model of the double-spliced composite slab: (<b>a</b>) After composition; (<b>b</b>) Transverse joint-connecting rebars before composition.</p>
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<p>FE model of the triple-spliced composite slab: (<b>a</b>) After composition; (<b>b</b>) Transverse joint-connecting rebars before composition.</p>
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<p>FE model of the cast-in-place monolithic slab: (<b>a</b>) After construction; (<b>b</b>) Reinforcement.</p>
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<p>Comparison of load–deflection curves between test and FE results: (<b>a</b>) Uniform loading of the precast base slab [<a href="#B24-applsci-15-02876" class="html-bibr">24</a>]; (<b>b</b>) Four-point loading of the composite slab [<a href="#B14-applsci-15-02876" class="html-bibr">14</a>].</p>
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<p>Analysis results of composite slab A under unidirectional loading: (<b>a</b>) Deflection at cracking; (<b>b</b>) Deflection at failure; (<b>c</b>) Initial bottom cracks at cracking; (<b>d</b>) Distribution of bottom cracks at failure; (<b>e</b>) Stress in prestressing steel at cracking; (<b>f</b>) Stress in prestressing steel at failure.</p>
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<p>Analysis results of composite slab B under unidirectional loading: (<b>a</b>) Deflection at cracking; (<b>b</b>) Deflection at failure; (<b>c</b>) Initial bottom cracks at cracking; (<b>d</b>) Distribution of bottom cracks at failure; (<b>e</b>) Stress in prestressing steel at cracking; (<b>f</b>) Stress in prestressing steel at failure.</p>
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<p>Load–deflection curves for steel-ribbed composite slabs at mid-span.</p>
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<p>Analysis results of double-spliced composite bidirectional slab: (<b>a</b>) Deflection at cracking; (<b>b</b>) Deflection at failure; (<b>c</b>) Initial bottom cracks at cracking; (<b>d</b>) Distribution of bottom cracks at failure; (<b>e</b>) Stress in prestressing steel at cracking; (<b>f</b>) Stress in prestressing steel at failure; (<b>g</b>) Stress of transverse rebar stress at cracking; (<b>h</b>) Stress of transverse rebar stress at failure.</p>
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<p>Analysis results of double-spliced composite bidirectional slab: (<b>a</b>) Deflection at cracking; (<b>b</b>) Deflection at failure; (<b>c</b>) Initial bottom cracks at cracking; (<b>d</b>) Distribution of bottom cracks at failure; (<b>e</b>) Stress in prestressing steel at cracking; (<b>f</b>) Stress in prestressing steel at failure; (<b>g</b>) Stress of transverse rebar stress at cracking; (<b>h</b>) Stress of transverse rebar stress at failure.</p>
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<p>Load–stress curves for reinforcement at the center of the slab: (<b>a</b>) Prestressing steel; (<b>b</b>) Joint-connecting rebar.</p>
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<p>Analysis results of triple-spliced composite bidirectional slab: (<b>a</b>) Deflection at cracking; (<b>b</b>) Deflection at failure; (<b>c</b>) Initial bottom cracks at cracking; (<b>d</b>) Distribution of bottom cracks at failure; (<b>e</b>) Stress in prestressing steel at cracking; (<b>f</b>) Stress in prestressing steel at failure; (<b>g</b>) Stress of transverse rebar stress at cracking; (<b>h</b>) Stress of transverse rebar stress at failure.</p>
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<p>Analysis results of triple-spliced composite bidirectional slab: (<b>a</b>) Deflection at cracking; (<b>b</b>) Deflection at failure; (<b>c</b>) Initial bottom cracks at cracking; (<b>d</b>) Distribution of bottom cracks at failure; (<b>e</b>) Stress in prestressing steel at cracking; (<b>f</b>) Stress in prestressing steel at failure; (<b>g</b>) Stress of transverse rebar stress at cracking; (<b>h</b>) Stress of transverse rebar stress at failure.</p>
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<p>Load–stress curves for reinforcement at the center of the slab: (<b>a</b>) Prestressing steel; (<b>b</b>) Joint-connecting rebar.</p>
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<p>Analysis results of the cast-in-place bidirectional slab: (<b>a</b>) Deflection at cracking; (<b>b</b>) Deflection at failure; (<b>c</b>) Initial bottom cracks at cracking; (<b>d</b>) Distribution of bottom cracks at failure; (<b>e</b>) Stress in prestressing steel at cracking; (<b>f</b>) Stress in prestressing steel at failure; (<b>g</b>) Stress of transverse rebar stress at cracking; (<b>h</b>) Stress of transverse rebar stress at failure.</p>
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<p>Load–stress curves for reinforcement at the center of the slab: (<b>a</b>) Prestressing steel; (<b>b</b>) Joint-connecting rebar.</p>
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<p>Load–deflection curves for bidirectional slabs.</p>
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<p>Failure modes of dense splicing slabs: (<b>a</b>) Yielding of the longitudinal prestressing steel; (<b>b</b>) Concrete crushing; (<b>c</b>) Yielding of the transverse joint-connecting steel.</p>
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<p>Parametric analysis results of the triple-spliced composite slab: (<b>a</b>) Concrete strength; (<b>b</b>) Ratio of the longitudinal prestressing steel; (<b>c</b>) Strength of the joint-connecting rebar; (<b>d</b>) Diameter of the joint-connecting rebar.</p>
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12 pages, 7869 KiB  
Article
Design of an E-Band Multiplexer Based on Turnstile Junction
by Shaohang Li, Yuan Yao, Xiaohe Cheng and Junsheng Yu
Electronics 2025, 14(6), 1072; https://doi.org/10.3390/electronics14061072 - 7 Mar 2025
Abstract
This paper presents an E-band four-channel multiplexer based on a turnstile junction. The proposed multiplexer consists of a power distribution unit featuring a turnstile junction topology and four Chebyshev bandpass filters. Thanks to the implementation of a rotating gate connection structure as the [...] Read more.
This paper presents an E-band four-channel multiplexer based on a turnstile junction. The proposed multiplexer consists of a power distribution unit featuring a turnstile junction topology and four Chebyshev bandpass filters. Thanks to the implementation of a rotating gate connection structure as the distribution unit, the overall compactness was enhanced, and the complexity of optimization was significantly reduced. Furthermore, this configuration offers a well-organized spatial port distribution, facilitating scalability for additional channels. According to the frequency band planning and design requirements of the communication system, an E-band four-channel multiplexer was designed and manufactured using high-precision computer numerical control (CNC) milling technology, achieving an error margin of ±5 μm. The experimental results indicate that the passbands are 70.6–73.07 GHz, 73.7–76.07 GHz, 82.55–82.9 GHz, and 83.4–85.9 GHz. The in-band insertion loss of each channel is below 1.7 dB, while the return loss at the common port exceeds 12 dB. The measured results align closely with simulations, demonstrating promising potential for practical applications. Full article
(This article belongs to the Section Microwave and Wireless Communications)
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<p>Physical structure and dimensions of the turnstile junction.</p>
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<p>Electric field distributions and schematic diagram of the turnstile junction.</p>
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<p>Schematic diagram of energy transmission in the turnstile junction. The <span class="html-italic">a</span><sub>Ei</sub><sup>+</sup> and <span class="html-italic">a</span><sub>Ei</sub><sup>−</sup> represent the input E-field intensity and the output E-field intensity of Port 1, respectively. (<span class="html-italic">i</span> = 1, 2). The <span class="html-italic">a</span><sub>j</sub><sup>+</sup> and <span class="html-italic">a</span><sub>j</sub><sup>−</sup> represent the output E-field intensity and the input E-field intensity of Port j, respectively. (<span class="html-italic">j</span> = 2, 3, 4, 5).</p>
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<p>Simulated S-parameter results of the turnstile junction.</p>
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<p>Physical structure and dimensions of the bandpass filter.</p>
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<p>(<b>a</b>) Effects of <span class="html-italic">w</span><sub>12</sub> on the coupling coefficient; (<b>b</b>) effects of <span class="html-italic">w</span><sub>i</sub> on <span class="html-italic">Q</span><sub>ex</sub>.</p>
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<p>Simulation results of each bandpass filter.</p>
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<p>Simulation results of each bandpass filter.</p>
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<p>(<b>a</b>) Distributed model and (<b>b</b>) physical structure and dimensions of the turnstile junction multiplexer.</p>
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<p>Simulated S-parameter results of the turnstile junction multiplexer.</p>
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<p>The electric field distribution at different frequencies.</p>
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<p>The fabrication model of the turnstile junction multiplexer.</p>
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<p>The fabricated prototype and test scenario.</p>
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<p>The simulated and measured results of the turnstile junction multiplexer.</p>
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30 pages, 17191 KiB  
Review
Review of the Near-Water Effect of Rotors in Cross-Media Vehicles
by Xingzhi Bai, Mingqing Lu, Qi Zhan, Yu Wang, Daixian Zhang, Xiao Wang and Wenhua Wu
Drones 2025, 9(3), 195; https://doi.org/10.3390/drones9030195 - 7 Mar 2025
Abstract
Cross-media vehicles, which combine the advantages of airplanes and submarines, are capable of performing complex tasks in different media and have attracted significant interest in recent years. In practice, however, cross-media rotorcrafts face numerous challenges during the cross-media transition, one of which is [...] Read more.
Cross-media vehicles, which combine the advantages of airplanes and submarines, are capable of performing complex tasks in different media and have attracted significant interest in recent years. In practice, however, cross-media rotorcrafts face numerous challenges during the cross-media transition, one of which is the complex mixed air–water flows induced by their rotors operating in close proximity to the water surface. These flows can result in aerodynamic penalties and structural damage to the rotors. The interactions between a water surface and a rotor wake bring about potential risks of cross-media locomotion, which is known as the near-water effect of rotors. Given that the distinctions between the near-water effect and the ground effect of rotors are not yet widely understood, this study details the discovery of the near-water effect and provides a comprehensive review of the evolutionary development of the near-water effect, tracing its understanding from the ground effect to the influence of droplets through aerodynamic modeling, numerical simulations, and near-water experimental studies. Furthermore, open problems and challenges associated with the near-water effect are discussed, including flow field measurements and numerical simulation approaches. Additionally, potential applications of the near-water effect for the development of cross-media rotorcraft are also described, which are valuable for aerodynamic design and cross-media control. Full article
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<p>Representative cross-media vehicles (CMVs): (<b>a</b>) multi-rotored [<a href="#B12-drones-09-00195" class="html-bibr">12</a>]; (<b>b</b>) fixed-winged [<a href="#B7-drones-09-00195" class="html-bibr">7</a>]; (<b>c</b>) hybrid-winged [<a href="#B9-drones-09-00195" class="html-bibr">9</a>]; (<b>d</b>) bioinspired [<a href="#B11-drones-09-00195" class="html-bibr">11</a>]; and (<b>e</b>) hydrofoil [<a href="#B13-drones-09-00195" class="html-bibr">13</a>] vehicles.</p>
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<p>Typical water exit modes for multi-rotor and fixed-wing CMVs [<a href="#B16-drones-09-00195" class="html-bibr">16</a>].</p>
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<p>(<b>a</b>) Schematic of near–water effect; (<b>b</b>) mixed air–water flows induced by rotor [<a href="#B17-drones-09-00195" class="html-bibr">17</a>].</p>
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<p>Image sequence showing the mini-CMV breaching the water surface [<a href="#B27-drones-09-00195" class="html-bibr">27</a>].</p>
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<p>(<b>a</b>) Tested <span class="html-italic">D</span> = 0.56 m and <span class="html-italic">D</span> = 0.25 m commercial rotor blades; (<b>b</b>) mixed air–water flows induced by rotor at <span class="html-italic">z</span>/<span class="html-italic">R</span> = 0.4 [<a href="#B17-drones-09-00195" class="html-bibr">17</a>].</p>
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<p>Aerodynamic performance affected by near-water effect [<a href="#B17-drones-09-00195" class="html-bibr">17</a>]: (<b>a</b>) thrust coefficient of <span class="html-italic">D</span> = 0.56 m blade; (<b>b</b>) torque coefficient of <span class="html-italic">D</span> = 0.56 m blade; (<b>c</b>) thrust coefficient of <span class="html-italic">D</span> = 0.25 m blade; (<b>d</b>) thrust versus power of <span class="html-italic">D</span> = 0.56 m blade under NEW, IGE, and OGE states at <span class="html-italic">z</span>/<span class="html-italic">R</span> = 0.1; (<b>e</b>) thrust fluctuation caused by droplets of <span class="html-italic">D</span> = 0.56 m blade at <span class="html-italic">z</span>/<span class="html-italic">R</span> = 0.3; (<b>f</b>) structural damage caused by droplets on lower wing of <span class="html-italic">D</span> = 0.56 m blade.</p>
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<p>(<b>a</b>) Test platform; (<b>b</b>) rotor speed characteristic with height under the NWE at different throttle settings; (<b>c</b>) thrust characteristic with height under the NWE at different throttle settings; (<b>d</b>) comparison of rotor thrust under IGE vs. NWE [<a href="#B29-drones-09-00195" class="html-bibr">29</a>].</p>
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<p>(<b>a</b>) Image of rotor in transition (top: at low throttle, bottom: at high throttle); (<b>b</b>) the variance of the Transition Index as the rotor enters or exits the water at various throttle settings; (<b>c</b>) RPM versus throttle at various heights between fully-in-air and fully-in-water results [<a href="#B30-drones-09-00195" class="html-bibr">30</a>].</p>
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<p>Sequence diagram of cross-media locomotion [<a href="#B31-drones-09-00195" class="html-bibr">31</a>]. (<b>a</b>) t<sub>1</sub>=1.82 s; (<b>b</b>) t<sub>2</sub>=3.47 s; (<b>c</b>) t<sub>c</sub>=4.84 s; (<b>d</b>) t<sub>4</sub>=5.82 s; (<b>e</b>) t<sub>6</sub>=7.00 s.</p>
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<p>(<b>a</b>) Tilting ducted fan CMV; (<b>b</b>) rotor thrust characteristics under NWE and OGE conditions [<a href="#B32-drones-09-00195" class="html-bibr">32</a>].</p>
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<p>Comparison of experimental results (OGE vs. NWE) [<a href="#B34-drones-09-00195" class="html-bibr">34</a>]: (<b>a</b>) thrust; (<b>b</b>) power.</p>
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<p>Schematic of typical depression modes [<a href="#B35-drones-09-00195" class="html-bibr">35</a>] (the green arrows show the approximate trajectory of air, the red curve shows the approximate trajectory of the droplets entering the rotor disk): (<b>a</b>) dimpling; (<b>b</b>) splashing; (<b>c</b>) penetrating.</p>
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<p>Comparison of experimental results (OGE vs. NWE): (<b>a</b>) thrust characteristic of <span class="html-italic">D</span> = 1.3 m ducted fan under OGE, IGE, and NWE states [<a href="#B44-drones-09-00195" class="html-bibr">44</a>]; (<b>b</b>) spatial streamlines under NWE state [<a href="#B44-drones-09-00195" class="html-bibr">44</a>]; (<b>c</b>) thrust characteristic of <span class="html-italic">D</span> = 0.15 m ducted fan [<a href="#B45-drones-09-00195" class="html-bibr">45</a>]; (<b>d</b>) diagram of velocity vector [<a href="#B45-drones-09-00195" class="html-bibr">45</a>].</p>
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<p>Water surface shape: (<b>a</b>) simulation result for <span class="html-italic">D</span> = 1.3 m ducted fan [<a href="#B44-drones-09-00195" class="html-bibr">44</a>]; (<b>b</b>) simulation result for 0.15 m diameter ducted fan [<a href="#B45-drones-09-00195" class="html-bibr">45</a>]; (<b>c</b>) experimental result for <span class="html-italic">D</span> = 0.07 m ducted fan [<a href="#B35-drones-09-00195" class="html-bibr">35</a>].</p>
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<p>Velocity contour map and aerodynamic characteristics [<a href="#B47-drones-09-00195" class="html-bibr">47</a>]: (<b>a</b>) thrust coefficient at different rotor heights; (<b>b</b>) torque coefficient at different rotor heights; (<b>c</b>) the velocity magnitude field under the IGE state; (<b>d</b>) the velocity magnitude field under the NWE state.</p>
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<p>Time-averaged velocity field measured via PIV at different rotor heights off the water surface [<a href="#B35-drones-09-00195" class="html-bibr">35</a>] (arrows represent streamlines): (<b>a</b>) <span class="html-italic">z</span>/<span class="html-italic">R</span> = 0.5; (<b>b</b>) <span class="html-italic">z</span>/<span class="html-italic">R</span> = 0.2; (<b>c</b>) <span class="html-italic">z</span>/<span class="html-italic">R</span> = 0.1.</p>
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<p>Droplets generated by interface instability at low rotor speeds [<a href="#B35-drones-09-00195" class="html-bibr">35</a>]: (<b>a</b>) crown formation; (<b>b</b>) finger structure.</p>
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<p>Flow field through a hovering rotor under IGE (the left column in each subgraph) or FSE (the right column in each subgraph) conditions. Plots show sectional contours of dimensionless vorticity. The dark blue solid line represents the free surface at the end, while the black dashed line represents the free surface at the initial state [<a href="#B49-drones-09-00195" class="html-bibr">49</a>].</p>
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<p>Comparison of the normalized rotor thrust vs. dimensionless rotor-plane distance between the two proximity conditions. The black solid line represents the fitted curve under the IGE, while the red one represents the fitted curve under the FSE. <span class="html-italic">γ</span> represents dimensionless rotor height [<a href="#B49-drones-09-00195" class="html-bibr">49</a>].</p>
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<p>Experimental results [<a href="#B50-drones-09-00195" class="html-bibr">50</a>]: (<b>a</b>) thrust curve; (<b>b</b>) change in water surface.</p>
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<p>Simulation results [<a href="#B51-drones-09-00195" class="html-bibr">51</a>]: (<b>a</b>) trajectory tracking in the second simulation; (<b>b</b>) forces involved in the vehicle displacement.</p>
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<p>Optical interference caused by droplets and splashing.</p>
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<p>The effect of splash-reflected light outside the laser sheet on the cross-correlation calculations: (<b>a</b>) raw image; (<b>b</b>) cross-correlation results.</p>
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<p>Schematic of potential fountain effect as part of near-water effect for multi-rotor CMVs.</p>
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<p>Mixed air–water flows induced by multi-rotor system and thrust characteristics at <span class="html-italic">n</span> = 6600 r/min and <span class="html-italic">z/R</span> = 0.5.</p>
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<p>The water film remaining on the blade.</p>
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17 pages, 7744 KiB  
Article
An Equivalent Modeling Method for Electromagnetic Radiation of PWM Fans with Multiple Radiation Sources
by Jinsheng Yang, Xuan Zhao, Jingxuan Xia, Wei Zhang, Pingan Du and Baolin Nie
Appl. Sci. 2025, 15(6), 2887; https://doi.org/10.3390/app15062887 - 7 Mar 2025
Abstract
Axial flow fans, used for heat dissipation in electronic equipment, may generate significant electromagnetic interference during PWM speed regulation. Due to its multiple radiation sources and relatively smaller size compared to the equipment, the radiation prediction model for equipment-level EMC analysis often involves [...] Read more.
Axial flow fans, used for heat dissipation in electronic equipment, may generate significant electromagnetic interference during PWM speed regulation. Due to its multiple radiation sources and relatively smaller size compared to the equipment, the radiation prediction model for equipment-level EMC analysis often involves a huge number of grids, which leads to computational difficulties and inefficiencies, and thus an equivalent modeling method for the electromagnetic radiation of PWM fan is presented. First, a detailed field-circuit coupling model of the radiation from winding and driving circuits is established using the time-domain finite-integral method with non-uniform grids. Then, a near-field hexahedron is defined to surround the fan, and the electromagnetic field of all its surfaces is derived based on the Huygens principle and calculated. Finally, the hexahedron encapsulating all radiation sources within the fan can be used in a higher level simulation as replicable and reusable equivalent sources. The proposed method is validated by a numerical example and actual measurements and applied to predict the radiation emissions within an electronic enclosure. The results show that the equivalent model can reduce 81.4% computation time and maintain good consistency in comparison to the detailed field-circuit coupling model. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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<p>Electronic enclosure with axial flow fans for heat dissipation.</p>
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<p>Paths of conducted interference.</p>
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<p>Multiple electromagnetic radiation sources inside the fan.</p>
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<p>Three-dimensional detailed model of fan radiation calculation.</p>
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<p>PCB 3D model. (<b>a</b>) Power input network; (<b>b</b>) inverter circuit output network.</p>
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<p>Three-dimensional detailed model of fan radiation calculation.</p>
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<p>Modeling of axial flow fan external drive circuit.</p>
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<p>Detailed model using non-uniform grid technique.</p>
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<p>Schematic diagram of equivalent modeling of near-far field of fan radiation.</p>
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<p>Computational domain of the equivalent model.</p>
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<p>Electric field distribution for two models. (<b>a</b>) Calculated by detailed model; (<b>b</b>) calculated by equivalent model.</p>
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<p>Comparison of electric field intensity at the monitoring point.</p>
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<p>Measurement configuration.</p>
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<p>Layout for radiation emission measurements.</p>
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<p>Detailed model of the radiation emission of the fan.</p>
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<p>Conduction current measurement for DC lines. (<b>a</b>) Conduction current measurement; (<b>b</b>) current measurement results.</p>
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<p>Comparison of the measured results with the results of the two computational models.</p>
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<p>Axial flow fan installed inside the enclosure.</p>
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<p>Comparison of electric field distribution inside an electronic enclosure in top view. (<b>a</b>) Calculated by detailed model; (<b>b</b>) calculated by equivalent model.</p>
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<p>Comparison of electric field intensity at the monitoring point.</p>
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15 pages, 2665 KiB  
Article
Fluid Dynamics Analysis of Coherent Jet with a Mixed Shrouding H2-CO2/N2 for EAF Steelmaking
by Songtao Yan, Fuhai Liu, Rong Zhu, Guangsheng Wei and Kai Dong
Metals 2025, 15(3), 291; https://doi.org/10.3390/met15030291 - 7 Mar 2025
Viewed by 94
Abstract
In order to suppress the rapid combustion effect and consumption rate of pure hydrogen gas, N2 or CO2 at flow rates of 0, 80, and 240 Nm3/h was pre-mixed with shrouding H2 at flow rates of 800, 720, [...] Read more.
In order to suppress the rapid combustion effect and consumption rate of pure hydrogen gas, N2 or CO2 at flow rates of 0, 80, and 240 Nm3/h was pre-mixed with shrouding H2 at flow rates of 800, 720, and 560 Nm3/h at room temperature, and the behaviors of the main oxygen jet and shrouding flame were analyzed by both numerical simulation and combustion experiments. The results showed that, because of the participation of CO2 in the H2 combustion reaction, the length of the axial velocity potential core was reduced using the CO2 shrouding mixed injection method, compared to the same mixed rate of N2. This trend would be further enhanced as N2 and CO2 mixing ratio increased. Meanwhile, when the shrouding mixed rate is 30%, the maximum axial and radial expansion rate generated by N2-H2 shrouding method is 1.28 and 1.04 times longer than that by the CO2-H2 shrouding method. The Fo-a, theoretical impaction depth and area generated by the 10% N2 shrouding mixed rate was 84.0, 95.5 and 86.4% of those generated by the traditional coherent jet, respectively, which indicated that the 10% N2 shrouding mixed rate method might lead to comparable production indexes in the EAF steelmaking process. Full article
(This article belongs to the Special Issue Advanced Metal Smelting Technology and Prospects)
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<p>Cross view (<b>a</b>) and front view (<b>b</b>) of the coherent lance.</p>
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<p>The (<b>a</b>) physical diagram, (<b>b</b>) front view and (<b>c</b>) cross view of the high temperature combustion furnace. (Unit: mm).</p>
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<p>(<b>a</b>) Geometric construction of the numerical model. (<b>b</b>) Mesh profile of the numerical model with boundary conditions.</p>
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<p>The axial velocity profiles of the main oxygen jet using different fuel mixed injection methods at the centerline of the Laval nozzle: (<b>a</b>) 10% shrouding fuel mixed method. (<b>b</b>) 30% shrouding fuel mixed method.</p>
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<p>The total temperature of main oxygen profiles using different fuel mixed injection methods at centerline of the Laval nozzle: (<b>a</b>) 10% shrouding fuel mixed method. (<b>b</b>) 30% shrouding fuel mixed method.</p>
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<p>The total temperature of coherent jet profiles using different fuel mixed injection methods.</p>
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<p>The theoretical impaction depth and area generated by different fuel mixed injection methods.</p>
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<p>The effective oxygen flow rate through the theoretical impaction area using different fuel mixed injection methods.</p>
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20 pages, 2732 KiB  
Article
Throughput of Buffer with Dependent Service Times
by Andrzej Chydzinski
Appl. Syst. Innov. 2025, 8(2), 34; https://doi.org/10.3390/asi8020034 - 7 Mar 2025
Viewed by 18
Abstract
We study the throughput and losses of a buffer with stochastically dependent service times. Such dependence occurs not only in packet buffers within TCP/IP networks but also in many other queuing systems. We conduct a comprehensive, time-dependent analysis, which includes deriving formulae for [...] Read more.
We study the throughput and losses of a buffer with stochastically dependent service times. Such dependence occurs not only in packet buffers within TCP/IP networks but also in many other queuing systems. We conduct a comprehensive, time-dependent analysis, which includes deriving formulae for the count of packets processed and lost over an arbitrary period, the temporary intensity of output traffic, the temporary intensity of packet losses, buffer throughput, and loss probability. The model considered enables mimicking any packet interarrival time distribution, service time distribution, and correlation between service times. The analytical findings are accompanied by numerical computations that demonstrate the influence of various factors on buffer throughput and losses. These results are also verified through simulations. Full article
(This article belongs to the Section Applied Mathematics)
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<p>Packet buffer with stochastically dependent packet sizes and service times.</p>
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<p>Correlation of two consecutive transmission times, <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </semantics></math>, versus parameter <span class="html-italic">a</span>.</p>
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<p>Coefficient of variation, <span class="html-italic">C</span>, for the time between packets versus parameter <span class="html-italic">b</span>.</p>
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<p>Output traffic intensity in time for four different values of <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </semantics></math>. <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> in every case.</p>
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<p>Output traffic intensity in time for four different values of <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </semantics></math>. <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> <mi>K</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> in every case.</p>
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<p>Output traffic intensity over a long time for four different values of <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </semantics></math>. <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> <mi>K</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> in every case.</p>
Full article ">Figure 7
<p>Output traffic intensity in time for four different values of <span class="html-italic">C</span>. <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math> in every case.</p>
Full article ">Figure 8
<p>Output traffic intensity in time for four different values of <span class="html-italic">C</span>. <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> <mi>K</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math> in every case.</p>
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<p>Output traffic intensity over a long time for four different values of <span class="html-italic">C</span>. <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>(</mo> <mn>0</mn> <mo>)</mo> <mo>=</mo> <mi>K</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math> in every case.</p>
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<p>Loss intensity in time for three different initial buffer occupancies, <span class="html-italic">n</span>. <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> in every case.</p>
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<p>Loss intensity in time for three different initial buffer occupancies, <span class="html-italic">n</span>. <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> in every case.</p>
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<p>Stationary throughput versus <span class="html-italic">C</span> and <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </semantics></math>.</p>
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<p>Stationary throughput versus <span class="html-italic">C</span> for four different values of <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </semantics></math>.</p>
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<p>Stationary throughput versus <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </semantics></math> for four different values of <span class="html-italic">C</span>.</p>
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<p>Stationary loss probability versus <span class="html-italic">C</span> and <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </semantics></math>.</p>
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<p>Stationary throughput <span class="html-italic">T</span> versus buffer size <span class="html-italic">K</span> for five different values of <math display="inline"><semantics> <msub> <mi>ρ</mi> <mn>1</mn> </msub> </semantics></math>. <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>.</p>
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<p>Stationary throughput <span class="html-italic">T</span> versus buffer size <span class="html-italic">K</span>, for four different values of traffic intensity. <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>.</p>
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20 pages, 3687 KiB  
Article
Towards a Comprehensive Framework for Made-to-Measure Alginate Scaffolds for Tissue Engineering Using Numerical Simulation
by Alexander Bäumchen, Johnn Majd Balsters, Beate-Sophie Nenninger, Stefan Diebels, Heiko Zimmermann, Michael Roland and Michael M. Gepp
Gels 2025, 11(3), 185; https://doi.org/10.3390/gels11030185 - 7 Mar 2025
Viewed by 141
Abstract
Alginate hydrogels are integral to many cell-based models in tissue engineering and regenerative medicine. As a natural biomaterial, the properties of alginates can vary and be widely adjusted through the gelation process, making them versatile additives or bulk materials for scaffolds, microcarriers or [...] Read more.
Alginate hydrogels are integral to many cell-based models in tissue engineering and regenerative medicine. As a natural biomaterial, the properties of alginates can vary and be widely adjusted through the gelation process, making them versatile additives or bulk materials for scaffolds, microcarriers or encapsulation matrices in tissue engineering and regenerative medicine. The requirements for alginates used in biomedical applications differ significantly from those for technical applications. Particularly, the generation of novel niches for stem cells requires reliable and predictable properties of the resulting hydrogel. Ultra-high viscosity (UHV) alginates possess alginates with special physicochemical properties, and thus far, numerical simulations for the gelation process are currently lacking but highly relevant for future designs of stem cell niches and cell-based models. In this article, the gelation of UHV alginates is studied using a microscopic approach for disc- and sphere-shaped hydrogels. Based on the collected data, a multiphase continuum model was implemented to describe the cross-linking process of UHV alginate polysaccharides. The model utilizes four coupled kinetic equations based on mixture theory, which are solved using finite element software. A good agreement between simulation results and experimental data was found, establishing a foundation for future refinements in the development of an interactive tool for cell biologists and material scientists. Full article
(This article belongs to the Special Issue Recent Research on Alginate Hydrogels in Bioengineering Applications)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Time-lapse sequence of alginate gelation with different concentrations of cross-linking agents. (<b>a</b>) 10 mM BaCl<sub>2</sub> solution, (<b>b</b>) 20 mM BaCl<sub>2</sub> solution and (<b>c</b>) 40 mM BaCl<sub>2</sub> solution. The gelation kinetics of the alginate are derived from the course of the traveling gelled/liquid interface. Due to low contrast, dashed white lines are used to indicate segments of the gelled/liquid interface. Scale bar indicates 1000 μm. Images are enhanced using a bandpass filter in ImageJ.</p>
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<p>Analysis of the gelation process of alginate discs. (<b>a</b>) Gelation kinetics analyzed by the decreasing diameter of the gelation front. The kinetics of gelation depend strongly on the applied cross-linker concentration: the higher the BaCl<sub>2</sub> concentration, the faster the overall gelation of the alginate droplet. (<b>b</b>) Velocity of the gelation front of alginates. Doubling the cross-linker concentration leads to a linear increase in gelation velocity. The velocity of gelation in this work is defined as the reduction of the ungelled core and is negative. Data are expressed as mean value ± standard deviation (n = 5 gelation experiments). Standard deviation in (<b>a</b>) is shown as a ribbon for visualization purposes.</p>
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<p>Analysis of the alginate gelation process of alginate spheres (beads, microcarriers). Gelation kinetics were analyzed by the decreasing diameter of the gelation front. The kinetics of gelation depend strongly on the applied cross-linker concentration: the higher the BaCl<sub>2</sub> concentration, the faster the overall gelation of the alginate droplet. (<b>a</b>) Single gelation experiments using 10 mM BaCl<sub>2</sub> solution; (<b>b</b>) single gelation experiments using 20 mM BaCl<sub>2</sub> solution; (<b>c</b>) single gelation experiments using 40 mM BaCl<sub>2</sub> solution; (<b>d</b>) the velocity of gelation front of alginates from (<b>a</b>) to (<b>c</b>) extracted by linear curve fitting. The velocity of gelation in this work is defined as the reduction of the ungelled core and is negative. Doubling the cross-linker concentration leads to a linear increase in gelation velocity. Data colors in (<b>a</b>–<b>c</b>) refer to different gelation experiments. Data in (<b>d</b>) are expressed as mean values ± standard deviation (n = 5 gelation experiments).</p>
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<p>Alginate micro-layer formation during gelation. (<b>a</b>) <b>Top</b>: Microscopic image of the formed layer at the outer border of the alginate disc; scale bar: 200 µm. Inset: Lower magnification of the area indicated by the black dashed line. Black arrow: Line scan of intensity in the graph. <b>Bottom</b>: The graph illustrates the data from the line scan of intensity. (<b>b</b>) Schematic illustration of layer formation in alginate disc-like hydrogels (adapted from [<a href="#B52-gels-11-00185" class="html-bibr">52</a>]; created with BioRender.com).</p>
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<p>Time-lapse of alginate gelation simulation with different concentration boundary conditions of the cross-linking agent. The left half of each time point shows the visualization of the numerical model, while the right half shows the microscopic image of one experimental replicate. (<b>a</b>) 10 mM BaCl<sub>2</sub> solution, (<b>b</b>) 20 mM BaCl<sub>2</sub> solution and (<b>c</b>) 40 mM BaCl<sub>2</sub> solution. Brighter areas indicate a higher amount of the ongoing gelling reaction. Scale bar indicates 1000 µm.</p>
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<p>Comparison of experimental data (solid line) and numerical modeling (dotted lines).</p>
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<p>Setup and principle of observing the gelation process. (<b>a</b>) A thin disc-like volume of alginate is poured into a dish and covered by a thin silicone spacer for gelation with different BaCl<sub>2</sub> solutions. This process can be observed using phase contrast microscopy, and a concentric decrease in the traveling liquid/gelled interface can be tracked and used for the quantification of the gelation process. (<b>b</b>) Schematic drawing at two different time points of alginate gelation. The disc-like volume of alginate is surrounded by the BaCl<sub>2</sub> cross-linker solutions and, consequently, barium (and chloride) ions diffuse into the alginate sol, triggering the gelation that can be tracked by the traveling liquid/gelled interface over time. The diameters of the circular interfaces decrease over time and disappear after the complete gelation of the alginate discs. (<b>b</b>) generated with BioRender.com.</p>
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<p>(<b>a</b>) Representative volume filled with the free polymer, barium ions and cross-linked polymer (and water). (<b>b</b>) Macroscopic domain and RVE as a magnification of a spatial point. The mass of constituent φ<sup>α</sup> inside the RVE changes due to the flux over the boundary and the mass exchange. Created with BioRender.com.</p>
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10 pages, 252 KiB  
Article
Generalized Local Charge Conservation in Many-Body Quantum Mechanics
by F. Minotti and G. Modanese
Mathematics 2025, 13(5), 892; https://doi.org/10.3390/math13050892 - 6 Mar 2025
Viewed by 51
Abstract
In the framework of the quantum theory of many-particle systems, we study the compatibility of approximated non-equilibrium Green’s functions (NEGFs) and of approximated solutions of the Dyson equation with a modified continuity equation of the form [...] Read more.
In the framework of the quantum theory of many-particle systems, we study the compatibility of approximated non-equilibrium Green’s functions (NEGFs) and of approximated solutions of the Dyson equation with a modified continuity equation of the form tρ+(1γ)·J=0. A continuity equation of this kind allows the e.m. coupling of the system in the extended Aharonov–Bohm electrodynamics, but not in Maxwell electrodynamics. Focusing on the case of molecular junctions simulated numerically with the Density Functional Theory (DFT), we further discuss the re-definition of local current density proposed by Wang et al., which also turns out to be compatible with the extended Aharonov–Bohm electrodynamics. Full article
(This article belongs to the Special Issue Mathematics and Applications)
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