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34 pages, 4698 KiB  
Article
Stat-Space Approach to Three-Dimensional Thermoelastic Half-Space Based on Fractional Order Heat Conduction and Variable Thermal Conductivity Under Moor–Gibson–Thompson Theorem
by Hamdy M. Youssef
Fractal Fract. 2025, 9(3), 145; https://doi.org/10.3390/fractalfract9030145 - 25 Feb 2025
Viewed by 160
Abstract
This study presents a mathematical model of a three-dimensional thermoelastic half-space with variable thermal conductivity under the definition of fractional order heat conduction based on the Moor–Gibson–Thompson theorem. The non-dimensional governing equations using Laplace and double Fourier transform methods have been applied to [...] Read more.
This study presents a mathematical model of a three-dimensional thermoelastic half-space with variable thermal conductivity under the definition of fractional order heat conduction based on the Moor–Gibson–Thompson theorem. The non-dimensional governing equations using Laplace and double Fourier transform methods have been applied to a three-dimensional thermoelastic, isotropic, and homogeneous half-space exposed to a rectangular thermal loading pulse with a traction-free surface. The double Fourier transforms and Laplace transform inversions have been computed numerically. The numerical distributions of temperature increment, invariant stress, and invariant strain have been shown and analysed. The fractional order parameter and the variability of thermal conductivity significantly influence all examined functions and the behaviours of the thermomechanical waves. Classifying thermal conductivity as weak, normal, and strong is crucial and closely corresponds to the actual behaviour of the thermal conductivity of thermoelastic materials. Full article
Show Figures

Figure 1

Figure 1
<p>The homogeneous, isotropic, thermoelastic, and three-dimensional body.</p>
Full article ">Figure 2
<p>The temperature increment distribution based on weak thermal conductivity with different values of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>υ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>The temperature increment distribution based on normal thermal conductivity with different values of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>υ</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 4
<p>The temperature increment distribution based on strong thermal conductivity with different values of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>υ</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>The temperature increment distribution with various values of fractional order parameter and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>The temperature increment distribution with various values of fractional order parameter and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 7
<p>The temperature increment distribution with various values of fractional order parameter and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>The invariant stress distribution based on weak thermal conductivity with different values of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>υ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>The invariant distribution based on normal thermal conductivity with different values of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>υ</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>The invariant stress distribution based on strong thermal conductivity with different values of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>υ</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>The invariant stress distribution with various values of fractional order parameter and different positions <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 12
<p>The invariant stress distribution with various values of fractional order parameter and different positions <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 13
<p>The invariant stress distribution with various values of fractional order parameter and different positions <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 14
<p>The strain distribution based on weak thermal conductivity with different values of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>υ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 15
<p>The strain distribution based on normal thermal conductivity with different values of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>υ</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 16
<p>The strain distribution based on strong thermal conductivity with different values of <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> </mrow> </semantics></math> and different positions of <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>υ</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 17
<p>The strain distribution with various values of fractional order parameter and different positions <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 18
<p>The strain distribution with various values of fractional order parameter and different positions <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.0</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 19
<p>The strain distribution with various values of fractional order parameter and different positions <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>,</mo> <mi>z</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>K</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>.</p>
Full article ">
14 pages, 3637 KiB  
Article
Two-Dimensional Transient Flow in a Confined Aquifer with a Cut-Off Curtain Due to Dewatering
by Guangcheng Li, Huiming Lin, Min Deng, Lu Wang, Jianxiao Wang, Fanshui Kong, Yushan Zhang and Qinggao Feng
Water 2025, 17(4), 601; https://doi.org/10.3390/w17040601 - 19 Feb 2025
Viewed by 256
Abstract
Long, narrow, deep excavations commonly encountered in practice, such as those for subway stations, require effective groundwater management to prevent disasters in water-rich areas. To achieve this, a cut-off curtain and pumping well are typically employed during long, deep foundation pit dewatering. The [...] Read more.
Long, narrow, deep excavations commonly encountered in practice, such as those for subway stations, require effective groundwater management to prevent disasters in water-rich areas. To achieve this, a cut-off curtain and pumping well are typically employed during long, deep foundation pit dewatering. The unsteady groundwater flow behavior in the confined aquifer must consider the influence of the cut-off curtain during dewatering. This paper establishes a two-dimensional analytical model to describe transient groundwater flow in a confined aquifer with a cut-off curtain. Both the dewatering well pumped at a steady discharge inside the pit and the cut-off curtain are partially penetrating in the anisotropic confined aquifer. With the help of the Laplace and Fourier cosine transformations, the semi-analytical drawdown solution for the model is derived and validated against numerical solution and unsteady pumping test data. It is shown that the inserted cut-off curtain depth and the structural parameters of the pumping well significantly affect the drawdown inside the pit. Sensitivity analysis reveals that, regardless of whether the observation is made inside or outside the curtain, the drawdown is very sensitive to the change in pumping rate, aquifer thickness, storage coefficient, and horizontal hydraulic conductivity. Additionally, drawdown near the cut-off curtain outside the pit is sensitive to the vertical hydraulic conductivity of the aquifer, the width of the pit, and the interception depth of the cut-off curtain, while drawdown far from the curtain outside the pit is not sensitive to the location and length of the well screen. Full article
Show Figures

Figure 1

Figure 1
<p>Sketch of the two-dimensional flow in the vertical plane in an infinite confined aquifer with a partially penetrating well and a cut-off curtain.</p>
Full article ">Figure 2
<p>Comparison of drawdown obtained by the present solution (solid curve) and the numerical solution (circle symbols): (<b>a</b>) <span class="html-italic">s</span> versus <span class="html-italic">t</span> for Zone 1 (<span class="html-italic">x</span> = 10 m, 14 m and 18 m) and Zone 2 (<span class="html-italic">x</span> = 22 m, 30 m and 40 m), where <span class="html-italic">z</span> = 18 m; (<b>b</b>) <span class="html-italic">z</span> versus <span class="html-italic">s</span> at <span class="html-italic">t</span> = 3 d, 5 d and 10 d, where <span class="html-italic">x</span> = 18 m and 22 m.</p>
Full article ">Figure 3
<p>Groundwater head: Comparison between observed data and calculated results.</p>
Full article ">Figure 4
<p>Curves of drawdown for various <span class="html-italic">B<sub>a</sub></span> (2 m, 6 m, 10 m, 14 m, 18 m, 20 m): (<b>a</b>) drawdown versus time at <span class="html-italic">z</span> = 18 m with <span class="html-italic">x</span> = 18 m for the Zone 1 and <span class="html-italic">x</span> = 22 m for Zone 2; (<b>b</b>) drawdown versus vertical distance at <span class="html-italic">t</span> = 20 d with <span class="html-italic">x</span> = 18 m for Zone 1 and <span class="html-italic">x</span> = 22 m for Zone 2; (<b>c</b>) drawdown versus horizontal distance at <span class="html-italic">t</span> = 20 d and <span class="html-italic">z</span> = 18 m.</p>
Full article ">Figure 5
<p>Curves of drawdown for various <span class="html-italic">l</span> (20 m, 18 m, 14 m, 10 m, 8 m) with <span class="html-italic">B<sub>a</sub></span> = 10 m: (<b>a</b>) drawdown versus time at <span class="html-italic">z</span> = 18 m with <span class="html-italic">x</span> = 18 m for Zone 1 and <span class="html-italic">x</span> = 22 m for Zone 2; (<b>b</b>) drawdown versus vertical distance at <span class="html-italic">t</span> = 20 d with <span class="html-italic">x</span> = 18 m for Zone 1 and <span class="html-italic">x</span> = 22 m for Zone 2; (<b>c</b>) drawdown versus horizontal distance at <span class="html-italic">t</span> = 20 d and <span class="html-italic">z</span> = 18 m.</p>
Full article ">Figure 6
<p>Curves of drawdown for various <span class="html-italic">d</span> (15 m, 10 m, 5 m, 0 m) with <span class="html-italic">B<sub>a</sub></span> = <span class="html-italic">l</span> = 10 m: (<b>a</b>) drawdown versus time at <span class="html-italic">z</span> = 18 m with <span class="html-italic">x</span> = 18 m for Zone 1 and <span class="html-italic">x</span> = 22 m for Zone 2; (<b>b</b>) drawdown versus vertical distance at <span class="html-italic">t</span> = 20 days with <span class="html-italic">x</span> = 18 m for Zone 1 and <span class="html-italic">x</span> = 22 m for Zone 2.; (<b>c</b>) drawdown versus horizontal distance at <span class="html-italic">t</span> = 20 d and <span class="html-italic">z</span> = 18 m.</p>
Full article ">Figure 7
<p>The NSC analysis of drawdown for the parameters <span class="html-italic">B<sub>a</sub></span>, <span class="html-italic">K<sub>x</sub></span>, <span class="html-italic">K<sub>z</sub></span>, <span class="html-italic">S<sub>s</sub></span>, <span class="html-italic">B</span>, <span class="html-italic">l</span>, <span class="html-italic">d</span>, <span class="html-italic">x</span><sub>0</sub>, and <span class="html-italic">Q</span> inside the cut-off curtain at <span class="html-italic">x</span> = 18 m, <span class="html-italic">z</span> = 18 m.</p>
Full article ">Figure 8
<p>The NSC of drawdown outside the cut-off curtain for the parameters <span class="html-italic">B<sub>a</sub></span>, <span class="html-italic">K<sub>x</sub></span>, <span class="html-italic">K<sub>z</sub></span>, <span class="html-italic">S<sub>s</sub></span>, <span class="html-italic">B</span>, <span class="html-italic">l</span>, <span class="html-italic">d</span>, <span class="html-italic">x</span><sub>0</sub>, and <span class="html-italic">Q</span> (<b>a</b>) at <span class="html-italic">x</span> = 22 m, <span class="html-italic">z</span> = 18 m; (<b>b</b>) at <span class="html-italic">x</span> = 40 m, <span class="html-italic">z</span> = 18 m.</p>
Full article ">
21 pages, 751 KiB  
Article
Operational Calculus of the Quantum Statistical Fermi–Dirac and Bose–Einstein Functions Leading to the Novel Fractional Kinetic Equations
by Asifa Tassaddiq, Carlo Cattani, Rabab Alharbi, Ruhaila Md Kasmani and Sania Qureshi
Fractal Fract. 2024, 8(12), 749; https://doi.org/10.3390/fractalfract8120749 - 19 Dec 2024
Viewed by 597
Abstract
The sun is a fundamental element of the natural environment, and kinetic equations are crucial mathematical models for determining how quickly the chemical composition of a star like the sun is changing. Taking motivation from these facts, we develop and solve a novel [...] Read more.
The sun is a fundamental element of the natural environment, and kinetic equations are crucial mathematical models for determining how quickly the chemical composition of a star like the sun is changing. Taking motivation from these facts, we develop and solve a novel fractional kinetic equation containing Fermi–Dirac (FD) and Bose–Einstein (BE) functions. Several distributional properties of these functions and their proposed new generalizations are investigated in this article. In fact, it is proved that these functions belong to distribution space D while their Fourier transforms belong to Z. Fourier and Laplace transforms of these functions are computed by using their distributional representation. Thanks to them, we can compute various new fractional calculus formulae and a new relation involving the Fox–Wright function. Some fractional kinetic equations containing the FD and BE functions are also formulated and solved. Full article
19 pages, 4814 KiB  
Article
Sustainable Management of Pollutant Transport in Defective Composite Liners of Landfills: A Semi-Analytical Model
by Shan Zhao, Botao Sun and Xinjia Su
Sustainability 2024, 16(24), 10954; https://doi.org/10.3390/su162410954 - 13 Dec 2024
Viewed by 670
Abstract
This study presents an analytical model for two-dimensional pollutant transport within a three-layer composite liner system, which comprises a geomembrane (GM), a geosynthetic clay liner (GCL), and a soil liner (SL), with particular attention to defects in the geomembrane. The model integrates key [...] Read more.
This study presents an analytical model for two-dimensional pollutant transport within a three-layer composite liner system, which comprises a geomembrane (GM), a geosynthetic clay liner (GCL), and a soil liner (SL), with particular attention to defects in the geomembrane. The model integrates key processes such as convection, diffusion, adsorption, and degradation, offering a more accurate prediction of pollutant behavior. Through Laplace and Fourier transforms, pollutant concentration distributions are derived, providing a comprehensive view of pollutant migration in landfill settings. Verification against COMSOL 6.0 simulations underscores the model’s robustness. Results show that there is an optimal thickness for the SL that balances the effectiveness of pollutant containment and material usage, while higher diffusion coefficients and advection velocity accelerate migration. The degradation of organic pollutants reduces concentrations over time, especially with shorter half-lives. These findings not only improve the design of landfill liners but also support more sustainable waste management practices by reducing the risk of environmental contamination. This research contributes to the development of more effective, long-lasting landfill containment systems, enhancing sustainability in waste management infrastructure. Full article
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Figure 1

Figure 1
<p>The migration of leachate through the composite liner system: (<b>a</b>) schematic diagram; (<b>b</b>) mathematical model. <span class="html-italic">C<sub>s</sub></span>: concentration of pollutants in the soil liner; <span class="html-italic">C<sub>G</sub></span>: concentration of pollutants in the GCL; <span class="html-italic">C<sub>in</sub></span>: initial concentration of pollutants entering the system, representing the source term.</p>
Full article ">Figure 2
<p>Comparison of the solution in this study with COMSOL and the existing solution [<a href="#B24-sustainability-16-10954" class="html-bibr">24</a>].</p>
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<p>Transportation process of organic pollutants and heavy metal pollutants.</p>
Full article ">Figure 4
<p>Comparison of breakthrough concentration of Zn<sup>2+</sup> under different time [<a href="#B36-sustainability-16-10954" class="html-bibr">36</a>].</p>
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<p>Comparison of breakthrough concentration of TOL under different time [<a href="#B24-sustainability-16-10954" class="html-bibr">24</a>].</p>
Full article ">Figure 6
<p>The variation in pollutant concentration with the thickness of SL at different depths of co-ordinates.</p>
Full article ">Figure 7
<p>The variation in pollutant concentration with the diffusion coefficient at different depths of co-ordinates: (<b>a</b>) diffusion coefficient of GCL; (<b>b</b>) diffusion coefficient of SL.</p>
Full article ">Figure 8
<p>Spatial distribution of pollutant concentration under diffusion coefficient: (<b>a</b>–<b>c</b>) diffusion coefficient of GCL; (<b>d</b>–<b>f</b>) diffusion coefficient of SL.</p>
Full article ">Figure 9
<p>The variation in pollutant concentration with the advection velocity at different depths of co-ordinates.</p>
Full article ">Figure 10
<p>Spatial distribution of pollutant concentration under advection velocity and adsorption retardation factor: (<b>a</b>–<b>c</b>) advection velocity; (<b>d</b>–<b>f</b>) retardation factor.</p>
Full article ">Figure 11
<p>The variation in pollutant concentration with the adsorption retardation factor at different depths of co-ordinates.</p>
Full article ">Figure 12
<p>The variation in pollutant concentration with the degradation factor at different depths of co-ordinates in the SL.</p>
Full article ">
14 pages, 896 KiB  
Article
Galerkin Finite Element Method for Caputo–Hadamard Time-Space Fractional Diffusion Equation
by Zhengang Zhao and Yunying Zheng
Mathematics 2024, 12(23), 3786; https://doi.org/10.3390/math12233786 - 29 Nov 2024
Viewed by 576
Abstract
In this paper, we study the Caputo–Hadamard time-space fractional diffusion equation, where the Caputo derivative is defined in the temporal direction and the Hadamard derivative is defined in the spatial direction separately. We first use the Laplace transform and the modified Fourier transform [...] Read more.
In this paper, we study the Caputo–Hadamard time-space fractional diffusion equation, where the Caputo derivative is defined in the temporal direction and the Hadamard derivative is defined in the spatial direction separately. We first use the Laplace transform and the modified Fourier transform to study the analytical solution of the Cauchy problem. Then, using the Galerkin finite element method in space, we generate a semi-discrete scheme and study the convergence analysis. Furthermore, using the L1 scheme of the Caputo derivative in time, we construct a fully discrete scheme and then discuss the stability and error estimation in detail. Finally, the numerical experiments are displaced to verify the theoretical results. Full article
Show Figures

Figure 1

Figure 1
<p>The exact solution of Example 1 with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mn>0.9</mn> <mo>,</mo> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>64</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>∈</mo> <mo>[</mo> <mn>1</mn> <mo>,</mo> <mi>e</mi> <mo>]</mo> <mo>,</mo> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 2
<p>The numerical solution of Example 1 with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mn>0.9</mn> <mo>,</mo> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>64</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>∈</mo> <mo>[</mo> <mn>1</mn> <mo>,</mo> <mi>e</mi> <mo>]</mo> <mo>,</mo> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 3
<p>The experimental error results of Example 1 with <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mi>β</mi> <mo>=</mo> <mn>0.9</mn> <mo>,</mo> <mi>h</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>64</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>∈</mo> <mo>[</mo> <mn>1</mn> <mo>,</mo> <mi>e</mi> <mo>]</mo> <mo>,</mo> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
Full article ">
18 pages, 5693 KiB  
Article
A Novel Approach to Transient Fourier Analysis for Electrical Engineering Applications
by Mariana Beňová, Branislav Dobrucký, Jozef Šedo, Michal Praženica, Roman Koňarik, Juraj Šimko and Martin Kuchař
Appl. Sci. 2024, 14(21), 9888; https://doi.org/10.3390/app14219888 - 29 Oct 2024
Cited by 1 | Viewed by 824
Abstract
This paper presents a detailed investigation into the application of transient Fourier analysis in select electrical engineering contexts. Two novel approaches for addressing transient analysis are introduced. The first approach combines the Fourier series with the Laplace–Carson (L-C) transform [...] Read more.
This paper presents a detailed investigation into the application of transient Fourier analysis in select electrical engineering contexts. Two novel approaches for addressing transient analysis are introduced. The first approach combines the Fourier series with the Laplace–Carson (L-C) transform in the complex domain, utilizing complex time vectors to streamline the computation of the original function. The inverse transformation back into the time domain is achieved using the Cauchy-Heaviside (C-H) method. The second approach applies the Fourier transform (F-Τ) for the transient analysis of a power converter circuit with both passive and active loads. The method of complex conjugate amplitudes is employed for steady-state analysis. Both contributions represent innovative approaches within this study. The process begins with Fourier series expansions and the computation of Fourier coefficients, followed by solving the system’s steady-state and transient responses. The transient states are then confirmed using the Fourier transform. To validate these findings, the analytical results are verified through simulations conducted in the Matlab/Simulink R2023b environment. Full article
(This article belongs to the Special Issue Electric Power System Stability and Control)
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<p>Time waveforms of non-harmonic courses used in EE generated by PES compensator (<b>a</b>) and inverter (<b>b</b>).</p>
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<p>Time waveform of considered voltage (<b>a</b>), and electrical circuit (<b>b</b>).</p>
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<p>Principle graphic representation of considered vectors <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">U</mi> <mfenced> <mrow> <mi>t</mi> </mrow> </mfenced> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi mathvariant="bold-italic">U</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mfenced> <mrow> <mi>t</mi> </mrow> </mfenced> <mo>,</mo> <mo> </mo> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">d</mi> <mo> </mo> <msub> <mrow> <mi mathvariant="bold-italic">U</mi> </mrow> <mrow> <mi>ν</mi> </mrow> </msub> <mfenced> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math> in complex domains.</p>
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<p>Steady-state total current waveform decomposed into fundamental and sum of higher harmonics in complex domain.</p>
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<p>Course of higher harmonics in the range <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>−</mo> <mi>π</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>, both in the stationary and rotary coordinate system (<b>a</b>); the graphic representation of the considered vectors <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">I</mi> <mfenced> <mrow> <mi>t</mi> </mrow> </mfenced> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi mathvariant="bold-italic">I</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mfenced> <mrow> <mi>t</mi> </mrow> </mfenced> <mo>,</mo> <mo> </mo> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">d</mi> <mo> </mo> <mi mathvariant="bold-italic">I</mi> <mfenced> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math> in complex domain and rotary coordinate system (<b>b</b>).</p>
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<p>Time waveforms of the transient Fourier analysis in the complex domain.</p>
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<p>Schematics (<b>a</b>) and courses of network and load voltages (<b>b</b>).</p>
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<p>Time waveform of the load current and load and network voltages (<b>a</b>); the decomposition of the total current (<b>b</b>).</p>
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<p>Time waveforms of input voltage and load current.</p>
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<p>Amplitude spectra of steady-state (<b>a</b>) and transient component (<b>b</b>) under <math display="inline"><semantics> <mrow> <mi>R</mi> </mrow> </semantics></math>-<math display="inline"><semantics> <mrow> <mi>L</mi> </mrow> </semantics></math> load in transient phenomenon at switch-on the system.</p>
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<p>Schematic connection of an inductive load with back-emf.</p>
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<p>Time waveform of the load current and load and network voltages (<b>a</b>); the decomposition of the total current (<b>b</b>).</p>
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<p>Created a decreasing cosine function of the load voltage (<b>a</b>) and current (<b>b</b>).</p>
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<p>A decreasing cosine function and its voltage magnitude spectrum <math display="inline"><semantics> <mrow> <mfenced open="|" close="|" separators="|"> <mrow> <msub> <mrow> <mi mathvariant="bold-italic">F</mi> </mrow> <mrow> <mi>u</mi> </mrow> </msub> <mfenced> <mrow> <mi>j</mi> <mi>ω</mi> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>Magnitude spectrum <math display="inline"><semantics> <mrow> <mfenced open="|" close="|" separators="|"> <mrow> <msub> <mrow> <mi mathvariant="bold-italic">F</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mfenced> <mrow> <mi>j</mi> <mi>ω</mi> </mrow> </mfenced> </mrow> </mfenced> </mrow> </semantics></math> of the decreasing sinusoidal function.</p>
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15 pages, 2512 KiB  
Article
Protein Microarrays for High Throughput Hydrogen/Deuterium Exchange Monitored by FTIR Imaging
by Joëlle De Meutter and Erik Goormaghtigh
Int. J. Mol. Sci. 2024, 25(18), 9989; https://doi.org/10.3390/ijms25189989 - 16 Sep 2024
Viewed by 837
Abstract
Proteins form the fastest-growing therapeutic class. Due to their intrinsic instability, loss of native structure is common. Structure alteration must be carefully evaluated as structural changes may jeopardize the efficiency and safety of the protein-based drugs. Hydrogen deuterium exchange (HDX) has long been [...] Read more.
Proteins form the fastest-growing therapeutic class. Due to their intrinsic instability, loss of native structure is common. Structure alteration must be carefully evaluated as structural changes may jeopardize the efficiency and safety of the protein-based drugs. Hydrogen deuterium exchange (HDX) has long been used to evaluate protein structure and dynamics. The rate of exchange constitutes a sensitive marker of the conformational state of the protein and of its stability. It is often monitored by mass spectrometry. Fourier transform infrared (FTIR) spectroscopy is another method with very promising capabilities. Combining protein microarrays with FTIR imaging resulted in high throughput HDX FTIR measurements. BaF2 slides bearing the protein microarrays were covered by another slide separated by a spacer, allowing us to flush the cell continuously with a flow of N2 gas saturated with 2H2O. Exchange occurred simultaneously for all proteins and single images covering ca. 96 spots of proteins that could be recorded on-line at selected time points. Each protein spot contained ca. 5 ng protein, and the entire array covered 2.5 × 2.5 mm2. Furthermore, HDX could be monitored in real time, and the experiment was therefore not subject to back-exchange problems. Analysis of HDX curves by inverse Laplace transform and by fitting exponential curves indicated that quantitative comparison of the samples is feasible. The paper also demonstrates how the whole process of analysis can be automatized to yield fast analyses. Full article
(This article belongs to the Special Issue Protein Structure Research 2024)
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Graphical abstract

Graphical abstract
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<p>A. Schematic description of the device used for recording HDX kinetics. The cell was built from a bottom 40 × 26 × 2 mm<sup>3</sup> BaF<sub>2</sub> slide on which microarrays were deposited. Protein solutions were printed as ca. 100 pl droplets containing each ca. 10 ng protein. On top of a spacer, another BaF<sub>2</sub> slide sealed the exchange chamber. N<sub>2</sub> bubbling in <sup>2</sup>H<sub>2</sub>O arrived through a tubing on one side and was driven out of the microscope-purged chamber by a tubing plugged on the other side. N<sub>2</sub> bubbled in 3 vials containing <sup>2</sup>H<sub>2</sub>O placed in series at a flow rate of 80 mL/min. The scale for drawing the microarray in (<b>A</b>) has been increased for the sake of visibility. In reality, the entire 96-spot microarray occupied an area of 2.5 × 2.5 mm<sup>2</sup>. (<b>B</b>) FTIR images each containing 16,384 spectra were recorded every 3 min at the beginning of the kinetics; time spacing was increased after 1 h. Images in (<b>B</b>) report absorbance at 1654 cm<sup>−1</sup>. (<b>C</b>) Mean spectra were collected for each spot and each deuteration time, as detailed in <a href="#app1-ijms-25-09989" class="html-app">Supplementary Materials</a>. They were finally analyzed in terms of HDX kinetics.</p>
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<p>Absorbance at 1654 cm<sup>−1</sup> of a protein microarray containing 96 protein spots. Pixels with SNR &lt; 35 have been colored in black. Numbers in the left margin identify the 12 proteins of grid #1, drawn in white. Each protein was quadruplicated, resulting in 4 columns. Another grid drawn in magenta identifies another series of 12 samples. Absorbance is color coded, as indicated by the color bar.</p>
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<p>FTIR spectra of (<b>A</b>) myoglobin and (<b>B</b>) metallothionein in the course of HDX. Times go from 0 (blue spectra) to 1400 min (red spectra). Inset: evolution of the fraction of unexchanged amide protons in the course of the first 50 min; data fitted by 3 exponentials.</p>
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<p>Inverse Laplace transform of the exchange curves for (<b>A</b>) myoglobin and (<b>B</b>) metallothionein (see Equation (6) in <a href="#sec4-ijms-25-09989" class="html-sec">Section 4</a>).</p>
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<p>Fraction of the amide protons (%) belonging to 3 classes of exchange rates characterized by time constants of 11.3 min, 68.7 min and 18,506 min. Proteins have been sorted in order of increasing α-helix content, i.e., the order presented in <a href="#app1-ijms-25-09989" class="html-app">Table S1</a>.</p>
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<p>Histogram of the ratio between the area of amide I after 370 min deuteration and its area at t = 0 min for 85 proteins. Inset: picture of 3 protein spots at t = 0 (left column) and y = 370 min (right column) deuteration. Refer to <a href="#ijms-25-09989-f002" class="html-fig">Figure 2</a> for more details on the image.</p>
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20 pages, 868 KiB  
Article
A Double Legendre Polynomial Order N Benchmark Solution for the 1D Monoenergetic Neutron Transport Equation in Plane Geometry
by Barry D. Ganapol
Foundations 2024, 4(3), 422-441; https://doi.org/10.3390/foundations4030027 - 21 Aug 2024
Viewed by 756
Abstract
As more and more numerical and analytical solutions to the linear neutron transport equation become available, verification of the numerical results becomes increasingly important. This presentation concerns the development of another benchmark for the linear neutron transport equation in a benchmark series, each [...] Read more.
As more and more numerical and analytical solutions to the linear neutron transport equation become available, verification of the numerical results becomes increasingly important. This presentation concerns the development of another benchmark for the linear neutron transport equation in a benchmark series, each employing a different method of solution. In 1D, there are numerous ways of analytically solving the monoenergetic transport equation, such as the Wiener–Hopf method, based on the analyticity of the solution, the method of singular eigenfunctions, inversion of the Laplace and Fourier transform solutions, and analytical discrete ordinates in the limit, which is arguably one of the most straightforward, to name a few. Another potential method is the PN (Legendre polynomial order N) method, where one expands the solution in terms of full-range orthogonal Legendre polynomials, and with orthogonality and series truncation, the moments form an open set of first-order ODEs. Because of the half-range boundary conditions for incoming particles, however, full-range Legendre expansions are inaccurate near material discontinuities. For this reason, a double PN (DPN) expansion in half-range Legendre polynomials is more appropriate, where one separately expands incoming and exiting flux distributions to preserve the discontinuity at material interfaces. Here, we propose and demonstrate a new method of solution for the DPN equations for an isotropically scattering medium. In comparison to a well-established fully analytical response matrix/discrete ordinate solution (RM/DOM) benchmark using an entirely different method of solution for a non-absorbing 1 mfp thick slab with both isotropic and beam sources, the DPN algorithm achieves nearly 8- and 7-place precision, respectively. Full article
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<p>Ratio of unaccelerated to accelerated relative errors.</p>
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27 pages, 1806 KiB  
Article
Efficient Gaussian Process Calculations Using Chebyshev Nodes and Fast Fourier Transform
by Adrian Dudek and Jerzy Baranowski
Electronics 2024, 13(11), 2136; https://doi.org/10.3390/electronics13112136 - 30 May 2024
Viewed by 1234
Abstract
Gaussian processes have gained popularity in contemporary solutions for mathematical modeling problems, particularly in cases involving complex and challenging-to-model scenarios or instances with a general lack of data. Therefore, they often serve as generative models for data, for example, in classification problems. However, [...] Read more.
Gaussian processes have gained popularity in contemporary solutions for mathematical modeling problems, particularly in cases involving complex and challenging-to-model scenarios or instances with a general lack of data. Therefore, they often serve as generative models for data, for example, in classification problems. However, a common problem in the application of Gaussian processes is their computational complexity. To address this challenge, sparse methods are frequently employed, involving a reduction in the computational domain. In this study, we propose an innovative computational approach for Gaussian processes. Our method revolves around selecting a computation domain based on Chebyshev nodes, with the optimal number of nodes determined by minimizing the degree of the Chebyshev series, while ensuring meaningful coefficients derived from function values at the Chebyshev nodes with fast Fourier transform. This approach not only facilitates a reduction in computation time but also provides a means to reconstruct the original function using the functional series. We conducted experiments using two computational methods for Gaussian processes: Markov chain Monte Carlo and integrated nested Laplace approximation. The results demonstrate a significant reduction in computation time, thereby motivating further development of the proposed algorithm. Full article
(This article belongs to the Section Systems & Control Engineering)
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<p>The plot presents Chebyshev series coefficient values compared to the order of the series. It illustrates the point at which the series reaches machine precision values with an interpolation algorithm, indicating which series order is required to achieve the desired accuracy of interpolation. For degrees up to approximately k = 80, the Chebyshev series struggles to accurately represent the function, f, as it cannot adequately capture the oscillations occurring at shorter wavelengths. However, beyond this point, there is rapid convergence in the series. By the time the degree reaches k = 150, the accuracy significantly improves to about 15 digits, leading to the truncation of the computed Chebyshev series at this point. This demonstrates the effectiveness of the Chebyshev series in approximating functions at higher degrees, where it becomes more capable of capturing the intricate details of the function.</p>
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<p>The flow chart visually outlines the structure and flow of the proposed algorithm. It begins with data fitting using a Gaussian process model, followed by generating values at Chebyshev points. The next step involves applying the FFT to these values, leading to the generation of Chebyshev series coefficients. The final stage of the algorithm is the optimization of the order of the series, a crucial step to enhance the algorithm’s efficiency and accuracy.</p>
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<p>The provided flowchart effectively illustrates the intricate steps and progression of the designated algorithm. It initiates with the phase of data fitting, which is conducted using a Gaussian process model. This model is executed in two distinct manners: firstly, by employing the integrated nested Laplace approximation (INLA) method, and secondly, through the use of the Stan framework with Markov chain Monte Carlo (MCMC) techniques. The primary objective of these generative models is to facilitate the generation of values at specific Chebyshev points. Following this, the algorithm advances to the implementation of the fast Fourier transform (FFT). This transformative process is integrated into the script and is pivotal in deriving the Chebyshev series coefficients from the aforementioned values. Concluding the algorithm’s sequence is the optimization phase, where the series order is meticulously adjusted. This final step is instrumental in elevating the overall efficacy and precision of the algorithm, ensuring optimal performance and accurate outcomes.</p>
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<p>The plot shows an example of the twice differentiable function (red line) with sampled noisy values (blue) used for model fitting.</p>
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<p>The plot shows an example of the infinitely differentiable function (red line) with sampled noisy values (blue) used for model fitting.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">MCMC sampling</span> method, with a Gaussian process model that employs a <span class="html-italic">Matérn kernel</span>. Experiments use sample data from selected functions and the random variables are set in <span class="html-italic">10 and 30 Chebyshev nodes</span>. The left plots show the mean values of calculated samples as well as the 95% confidence interval received from the sampled distribution. The right plots show the behavior of Chebyshev series coefficient values compared to their order. The red line shows the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). We can deduce that both results are nowhere near machine precision, so we should rely on the selected threshold. In the case of only 10 Chebyshev nodes, we did not even reach the threshold value, whereas with 30 nodes, sample number 18 did. As a result of our optimization, we should take the value around 18 as the order of the Chebyshev series. Moreover, the computation times for the small number of nodes were acceptable for real-life cases, ranging from several seconds to a dozen seconds.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">MCMC sampling</span> method, with a Gaussian process model that employs a <span class="html-italic">Matérn kernel</span>. Experiments use sample data from selected functions and the random variables are set in <span class="html-italic">70 and 200 Chebyshev nodes</span>. The left plots show the mean values of calculated samples as well as the 95% confidence interval derived from the sampled distribution. The right plots illustrate the behavior of Chebyshev series coefficient values relative to their order. A red line indicates the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). We can deduce that oscillation around the threshold starts early, around the order of 30, so our previous assumption of selecting the 18th order should be correct. Moreover, the values decrease only slightly up to the order of <math display="inline"><semantics> <msup> <mn>10</mn> <mn>6</mn> </msup> </semantics></math>, which suggests that they probably won’t reach machine precision very soon. These experiments were conducted primarily to observe the behavior after reaching the threshold and to verify if the previous assumption was correct. Additionally, they allowed us to check the computation times for the number of nodes. Unfortunately, these times were unacceptable, ranging from several minutes to 10 min.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">MCMC sampling</span> method, with a Gaussian process model that employs an <span class="html-italic">RBF kernel</span>. Experiments use sample data from a selected function, with random variables set at <span class="html-italic">10 and 30 Chebyshev nodes</span>. The left plots show the mean values of calculated samples as well as the 95% confidence interval derived from the sampled distribution. The right plots illustrate the behavior of Chebyshev series coefficient values relative to their order. A red line indicates the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). The first 10 coefficients are highly informative; none reached the selected threshold. When we increased the number of selected nodes, it was clear that the 24th coefficient crossed the threshold. Contrary to the previous case, this function required more coefficients to gather sufficient information based on our assumptions. Moreover, as with the other MCMC case, computation times for this number of nodes were acceptable and similar to the Matérn ranging from several seconds to a maximum of a dozen seconds.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">MCMC sampling</span> method, with a Gaussian process model that employs an <span class="html-italic">RBF kernel</span>. Experiments use sample data from a selected function, with random variables set at <span class="html-italic">10 and 30 Chebyshev nodes</span>. The left plots show the mean values of calculated samples as well as the 95% confidence interval derived from the sampled distribution. The right plots illustrate the behavior of Chebyshev series coefficient values relative to their order. A red line indicates the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). The first 10 coefficients are highly informative; none reached the selected threshold. When we increased the number of selected nodes, it was clear that the 24th coefficient crossed the threshold. Contrary to the previous case, this function required more coefficients to gather sufficient information based on our assumptions. Moreover, as with the other MCMC case, computation times for this number of nodes were acceptable and similar to the Matérn ranging from several seconds to a maximum of a dozen seconds.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">MCMC sampling</span> method, with a Gaussian process model that employs an <span class="html-italic">RBF kernel</span>. The experiments use sample data from a selected function, with random variables set at <span class="html-italic">70 and 200 Chebyshev nodes</span>. The left plots show the mean values of calculated samples, as well as the 95% confidence interval derived from the sampled distribution. The right plots illustrate the behavior of Chebyshev series coefficient values relative to their order. A red line indicates the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). We can deduce that adding more nodes to the GP model does not actually help gather more information. Oscillations around the threshold start around the order of 30 and do not seem to reach machine precision. Additionally, the computation times for this number of nodes were unacceptable, ranging from several minutes to a dozen minutes.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">INLA framework</span> method, with a Gaussian process model that employs a <span class="html-italic">Matérn kernel</span>. Experiments utilized sample data from a selected function with random variables set at <span class="html-italic">10 and 30 Chebyshev nodes</span>. The left plots show the mean values of calculated samples as well as the 95% confidence interval derived from the sampled distribution. The right plots illustrate the behavior of Chebyshev series coefficient values in relation to their order. A red line indicates the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). Contrary to MCMC experiments, the INLA framework, in this case, managed to catch the threshold at the 2nd order, highlighting the importance of checking higher orders, especially since no other coefficient reached the selected threshold. For the 30th order, the 5th sample could be considered as the cutoff for optimization, but the next value below this is the 16th coefficient, with a clearly visible decreasing trend between the 6th and 16th coefficients. We can deduce that oscillation slightly starts around the order of 17, but we need to verify this with higher orders. The computation times for the node numbers using INLA were excellent and completed within several seconds; this was predictable due to the INLA approximation properties.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">INLA framework</span> method, with a Gaussian process model that employs a <span class="html-italic">Matérn kernel</span>. Experiments utilize sample data from a selected function, with random variables set at <span class="html-italic">70 and 200 Chebyshev nodes</span>. The left plots show the mean values of calculated samples along with the 95% confidence interval derived from the sampled distribution. The right plots illustrate the behavior of Chebyshev series coefficient values relative to their order. A red line indicates the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). Based on previous experiments with a lesser value or order and the information gathered here, we can deduce that values truly cross the threshold around the order of 20. Moreover, coefficient values are slowly decreasing and do not start to oscillate, but in our case, it makes no sense to aim for values below the threshold. As expected, due to the INLA approximation properties, the computation times for any number of nodes were excellent, conducted within several seconds.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">INLA framework</span> method, with a Gaussian process model that employs a modified Matérn kernel, simulating the <span class="html-italic">RBF</span> one. Experiments utilized sample data from a selected function with random variables set at <span class="html-italic">10 and 30 Chebyshev nodes</span>. The left plots show the mean values of calculated samples along with the 95% confidence interval derived from the sampled distribution. The right plots illustrate the behavior of Chebyshev series coefficient values relative to their order. A red line indicates the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). In this case, the INLA framework mimics the behavior observed in previous cases with low-order coefficients, where values reach the threshold quickly but continue to show a decreasing trend. We can deduce that the required value is likely around the order of 20. The computation times for this number of nodes were excellent, conducted within several seconds.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">INLA framework</span> method, with a Gaussian process model that employs a modified Matérn kernel, simulating the <span class="html-italic">RBF</span> one. Experiments utilized sample data from a selected function with random variables set at <span class="html-italic">10 and 30 Chebyshev nodes</span>. The left plots show the mean values of calculated samples along with the 95% confidence interval derived from the sampled distribution. The right plots illustrate the behavior of Chebyshev series coefficient values relative to their order. A red line indicates the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). In this case, the INLA framework mimics the behavior observed in previous cases with low-order coefficients, where values reach the threshold quickly but continue to show a decreasing trend. We can deduce that the required value is likely around the order of 20. The computation times for this number of nodes were excellent, conducted within several seconds.</p>
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<p>The plots present results for the experiment conducted using the <span class="html-italic">INLA framework</span> method, with a Gaussian process model that employs a modified Matérn kernel, simulating the <span class="html-italic">RBF</span> one. Experiments utilized sample data from selected functions, with random variables set at <span class="html-italic">70 and 200 Chebyshev nodes</span>. The left plots show the mean values of calculated samples along with the 95% confidence interval derived from the sampled distribution. The right plots illustrate the behavior of Chebyshev series coefficient values relative to their order. A red line indicates the selected threshold for optimization (<math display="inline"><semantics> <mrow> <mn>0.1</mn> <mo>·</mo> <msup> <mi>σ</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). We can deduce that all INLA cases behave similarly: initially, the values decrease rapidly, reaching the threshold, and then the downward trend slows down. The computation times for this number of nodes were excellent, conducted within several seconds.</p>
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13 pages, 3331 KiB  
Article
Dynamic Thermal Response of Multiple Interface Cracks between a Half-Plane and a Coating Layer under General Transient Temperature Loading
by Mahsa Nourazar, Weilin Yang and Zengtao Chen
Materials 2024, 17(11), 2478; https://doi.org/10.3390/ma17112478 - 21 May 2024
Cited by 1 | Viewed by 872
Abstract
This paper explores the thermal behavior of multiple interface cracks situated between a half-plane and a thermal coating layer when subjected to transient thermal loading. The temperature distribution is analyzed using the hyperbolic heat conduction theory. In this model, cracks are represented as [...] Read more.
This paper explores the thermal behavior of multiple interface cracks situated between a half-plane and a thermal coating layer when subjected to transient thermal loading. The temperature distribution is analyzed using the hyperbolic heat conduction theory. In this model, cracks are represented as arrays of thermal dislocations, with densities calculated via Fourier and Laplace transformations. The methodology involves determining the temperature gradient within the uncracked region, and these calculations contribute to formulating a singular integral equation specific to the crack problem. This equation is subsequently utilized to ascertain the dislocation densities at the crack surface, which facilitates the estimation of temperature gradient intensity factors for the interface cracks experiencing transient thermal loading. This paper further explores how the relaxation time, loading parameters, and crack dimensions impact the temperature gradient intensity factors. The results can be used in fracture analysis of structures operating at high temperatures and can also assist in the selection and design of coating materials for specific applications, to minimize the damage caused by temperature loading. Full article
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<p>Schematic view of a single dislocation at an interface.</p>
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<p>(<b>a</b>) Temperature variation at the central point on the upper and lower crack faces. (<b>b</b>) Finite element model of the crack problem simulated in Abaqus.</p>
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<p>(<b>a</b>) Temperature gradient intensity factor for a single crack versus time for different values of <math display="inline"><semantics> <mrow> <mi>η</mi> </mrow> </semantics></math> and <span class="html-italic">h</span>. (<b>b</b>) Temperature gradient intensity factor for a single crack versus time (symmetrical, <span class="html-italic">ηL</span> = 1). (<b>c</b>) Temperature gradient intensity factor for a single crack versus time for different values of the coating’s thermal conductivity (symmetrical, <math display="inline"><semantics> <mrow> <mi>η</mi> <mi>L</mi> </mrow> </semantics></math> = 1). (<b>d</b>) Temperature gradient intensity factor for a single crack versus time for different values of the coating’s relaxation time (symmetrical, <math display="inline"><semantics> <mrow> <mi>η</mi> <mi>L</mi> </mrow> </semantics></math> = 1).</p>
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<p>(<b>a</b>) Temperature gradient intensity factor for a single crack versus time for different values of <math display="inline"><semantics> <mrow> <mi>η</mi> </mrow> </semantics></math> and <span class="html-italic">h</span>. (<b>b</b>) Temperature gradient intensity factor for a single crack versus time (symmetrical, <span class="html-italic">ηL</span> = 1). (<b>c</b>) Temperature gradient intensity factor for a single crack versus time for different values of the coating’s thermal conductivity (symmetrical, <math display="inline"><semantics> <mrow> <mi>η</mi> <mi>L</mi> </mrow> </semantics></math> = 1). (<b>d</b>) Temperature gradient intensity factor for a single crack versus time for different values of the coating’s relaxation time (symmetrical, <math display="inline"><semantics> <mrow> <mi>η</mi> <mi>L</mi> </mrow> </semantics></math> = 1).</p>
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<p>(<b>a</b>) Temperature gradient intensity factor for two identical cracks versus time (<math display="inline"><semantics> <mrow> <mi>η</mi> <mi>L</mi> </mrow> </semantics></math> = 0). (<b>b</b>) Temperature gradient intensity factor for two colinear cracks versus time (<math display="inline"><semantics> <mrow> <mi>η</mi> <mi>L</mi> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </semantics></math> = 1).</p>
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<p>(<b>a</b>) Temperature gradient intensity factor for three colinear cracks with different lengths versus time (<span class="html-italic">L</span>(1) = <span class="html-italic">L</span>(2) = <span class="html-italic">L</span>(3) = <span class="html-italic">L</span>). (<b>b</b>) Temperature gradient intensity factor for three colinear cracks with different lengths versus time (<span class="html-italic">L</span>(1) = <span class="html-italic">L</span>(2) = <span class="html-italic">L</span> &lt; <span class="html-italic">L</span>(3)). (<b>c</b>) Temperature gradient intensity factor for three colinear cracks with different lengths versus time (<span class="html-italic">L</span>(1) = <span class="html-italic">L</span>(3) = <span class="html-italic">L</span>, <span class="html-italic">L</span> &lt; <span class="html-italic">L</span>(2)).</p>
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18 pages, 5671 KiB  
Article
A New Method for Anti-Interference Measurement of Capacitance Parameters of Long-Distance Transmission Lines Based on Harmonic Components
by Kaibai Wang, Zihao Zhang, Xingwei Xu, Zhijian Hu, Zhengwei Sun, Jiahao Tan, Xiang Yao and Jingfu Tian
Electronics 2024, 13(10), 1982; https://doi.org/10.3390/electronics13101982 - 18 May 2024
Viewed by 1120
Abstract
In the context of strong electromagnetic interference environments, the measurement accuracy of the capacitance parameters of transmission lines under power frequency measurement methods is not high. In this paper, a capacitance parameter anti-interference measurement method for transmission lines based on harmonic components is [...] Read more.
In the context of strong electromagnetic interference environments, the measurement accuracy of the capacitance parameters of transmission lines under power frequency measurement methods is not high. In this paper, a capacitance parameter anti-interference measurement method for transmission lines based on harmonic components is proposed to overcome the impact of power frequency interference. When applying this method, it is first necessary to open-circuit the end of the line under test. Subsequently, apply voltage to the head end of the tested line through a step-up transformer. Due to the saturation of the transformer during no-load conditions, a large number of harmonics are generated, primarily third harmonic. The third harmonic components of voltage and current on the tested transmission line are extracted using the Fourier transform. The proposed method addresses the influence of line distribution effects by establishing a distributed parameter model for long-distance transmission lines. The relevant transmission matrix for the zero-sequence distributed parameters is obtained by combining Laplace transform and similarity transform to solve the transmission line equations. Using synchronous measurement data from the third harmonic components of voltage and current at both ends of the transmission line, combined with the transmission matrix, this method accurately measures the zero-sequence capacitance parameters. The PSCAD/EMTDC simulation results and field test outcomes have demonstrated the feasibility and accuracy of the proposed method for measuring line capacitance parameters under strong electromagnetic interference. Full article
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<p>Excitation current waveform of saturated transformer.</p>
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<p>Distributed parameter model for double-circuit transmission lines.</p>
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<p>Simulation model of long-distance single-circuit line.</p>
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<p>The tower structure of the single-circuit transmission line.</p>
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<p>Current waveform of C-phase at the head end.</p>
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<p>Voltage waveform of C-phase at the head end.</p>
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<p>Impact of measurement errors at the head end of line on zero sequence capacitance error.</p>
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<p>Simulation model of long-distance double-circuit transmission lines.</p>
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<p>The tower structure of the double-circuit transmission line.</p>
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<p>Measurement errors of zero sequence capacitance using this method under different line lengths.</p>
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<p>Sensitivity analysis of the theoretical values of line parameters by the proposed method.</p>
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<p>Measurement errors of zero-sequence capacitance under different voltage application magnitudes.</p>
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<p>Schematic diagram of the line to be tested.</p>
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<p>Zero-sequence current waveform of Line I.</p>
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<p>Zero-sequence voltage waveform of Line I.</p>
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16 pages, 7184 KiB  
Article
A Study of Singular Similarity Solutions to Laplace’s Equation with Dirichlet Boundary Conditions
by Chao-Kang Feng and Jyh-Haw Tang
AppliedMath 2024, 4(2), 596-611; https://doi.org/10.3390/appliedmath4020033 - 6 May 2024
Viewed by 1062
Abstract
The infinite series solution to the boundary-value problems of Laplace’s equation with discontinuous Dirichlet boundary conditions was found by using the basic method of separation of variables. The merit of this paper is that the closed-form solution, or the singular similarity solution in [...] Read more.
The infinite series solution to the boundary-value problems of Laplace’s equation with discontinuous Dirichlet boundary conditions was found by using the basic method of separation of variables. The merit of this paper is that the closed-form solution, or the singular similarity solution in the semi-infinite strip domain and the first-quadrant domain, can be generated from the basic infinite series solution in the rectangular domain. Moreover, based on the superposition principle, the infinite series solution in the rectangular domain can be related to the singular similarity solution in the semi-infinite strip domain. It is proven that the analytical source-type singular behavior in the infinite series solution near certain singular points in the rectangular domain can be revealed from the singular similarity solution in the semi-infinite strip domain. By extending the boundary of the rectangular domain, the infinite series solution to Laplace’s equation in the first-quadrant domain can be derived to obtain the analytical singular similarity solution in a direct and much easier way than by using the methods of Fourier transform, images, and conformal mapping. Full article
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<p>Laplace’s equation in the rectangular domain.</p>
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<p>The 3D distribution of temperature from Equation (7) by numerical calculation.</p>
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<p>The 2D distribution of temperature from Equation (7) by numerical calculation.</p>
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<p>The 3D distribution of temperature from Equation (9) by numerical calculation.</p>
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<p>The 2D distribution of temperature from Equation (9) by numerical calculation.</p>
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<p>Semi-infinite strip domain.</p>
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<p>The 3D distribution of temperature from Equation (20).</p>
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<p>The 2D distribution of temperature from Equation (20).</p>
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<p>Dimensionless similarity solution <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> <mrow> <mo>∗</mo> </mrow> </msubsup> <mfenced separators="|"> <mrow> <mi>η</mi> </mrow> </mfenced> </mrow> </semantics></math> in Equation (25).</p>
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<p>The 3D distribution of temperature from the closed form of Equation (22).</p>
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<p>The 2D distribution of temperature from the closed form of Equation (22).</p>
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<p>The 3D temperature distribution from the calculation of the right-hand side of Equation (30).</p>
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<p>The 2D temperature distribution from the calculation of the right-hand side of Equation (30).</p>
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<p>Laplace’s equation in the first-quadrant domain.</p>
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<p>Dimensionless singular similarity solution, <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> </mrow> <mrow> <mo>∗</mo> </mrow> </msubsup> <mfenced separators="|"> <mrow> <mi>η</mi> </mrow> </mfenced> </mrow> </semantics></math> in Equation (48).</p>
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<p>The 3D distribution of temperature from Equation (45).</p>
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<p>The 2D distribution of temperature from Equation (45).</p>
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<p>The 2D contour of the temperature distribution in Equation (56).</p>
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16 pages, 1302 KiB  
Article
Fractional Heat Conduction with Heat Absorption in a Solid with a Spherical Cavity under Time-Harmonic Heat Flux
by Yuriy Povstenko, Tamara Kyrylych, Bożena Woźna-Szcześniak and Andrzej Yatsko
Appl. Sci. 2024, 14(4), 1627; https://doi.org/10.3390/app14041627 - 17 Feb 2024
Cited by 2 | Viewed by 1222
Abstract
The central-symmetric time-fractional heat conduction equation with heat absorption is investigated in a solid with a spherical hole under time-harmonic heat flux at the boundary. The problem is solved using the auxiliary function, for which the Robin-type boundary condition with a prescribed value [...] Read more.
The central-symmetric time-fractional heat conduction equation with heat absorption is investigated in a solid with a spherical hole under time-harmonic heat flux at the boundary. The problem is solved using the auxiliary function, for which the Robin-type boundary condition with a prescribed value of a linear combination of a function and its normal derivative is fulfilled. The Laplace and Fourier sine–cosine integral transformations are employed. Graphical representations of numerical simulation results are given for typical values of the parameters. Full article
(This article belongs to the Section Applied Thermal Engineering)
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<p>Dependence of temperature on the radial coordinate <math display="inline"><semantics> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> </semantics></math>. The results of computer simulations for the angular frequency <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ω</mi> <mo>¯</mo> </mover> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math> and the values of parameters: <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>—(<b>a</b>); <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>—(<b>b</b>); <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>—(<b>c</b>); <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>—(<b>d</b>); <span style="color:#EC008C"><b>—–</b></span> <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0; <span style="color: #0000FF"><b>—–</b></span> <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 0.5; <span style="color: #000000"><b>—–</b></span> <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 1; <span style="color:#BF8040"><b>—–</b></span> <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 1.5; <span style="color:#FF8000"><b>—–</b></span> <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 1.75; <span style="color:#800080"><b>—–</b></span> <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 1.95; <span style="color:#008080"><b>—–</b></span> <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 1.99; <span style="color: #FF0000"><b>—–</b></span> <math display="inline"><semantics> <mi>α</mi> </semantics></math> = 2.</p>
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<p>Dependence of temperature <math display="inline"><semantics> <mover> <mi>T</mi> <mo>¯</mo> </mover> </semantics></math> on the radial coordinates and the order of fractional derivative for time <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and the values of the parameter: <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>—(<b>a</b>); <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>—(<b>b</b>).</p>
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<p>Dependence of temperature <math display="inline"><semantics> <mover> <mi>T</mi> <mo>¯</mo> </mover> </semantics></math> on time and the radial coordinates for the angular frequency <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ω</mi> <mo>¯</mo> </mover> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and the order of time-fractional derivative: <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>—(<b>a</b>); <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1.9</mn> </mrow> </semantics></math>—(<b>b</b>).</p>
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<p>Dependence of temperature <math display="inline"><semantics> <mover> <mi>T</mi> <mo>¯</mo> </mover> </semantics></math> on time and the radial coordinates for the angular frequency <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ω</mi> <mo>¯</mo> </mover> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and the order of time-fractional derivative: <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>—(<b>a</b>); <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1.9</mn> </mrow> </semantics></math>—(<b>b</b>).</p>
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<p>Dependence of the solution to the parabolic heat conduction Equation (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>) on the radial coordinates. The results of computer simulations for the angular frequency <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ω</mi> <mo>¯</mo> </mover> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math> and time <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>. <span style="color: #EC008C"><b>—–</b></span> the quasi-steady-state approach for <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; <span style="color: #0000FF"><b>—–</b></span> the general solution describing the transition process for <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>; <span style="color: #000000"><b>—–</b></span> the quasi-steady-state approach for <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; <span style="color: #FF0000"><b>—–</b></span> the general solution describing the transition process for <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
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<p>Dependence of the solution to the hyperbolic Klein–Gordon Equation (<math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>) on the spatial variable <math display="inline"><semantics> <mover accent="true"> <mi>r</mi> <mo>¯</mo> </mover> </semantics></math>. The computer simulation results for the angular frequency <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>ω</mi> <mo>¯</mo> </mover> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>b</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo>¯</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>. <span style="color: #FF0000"><b>—–</b></span> the general solution describing the transition process; <span style="color: #000000"><b>—–</b></span> the quasi-steady-state approach (<a href="#FD71-applsci-14-01627" class="html-disp-formula">71</a>).</p>
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17 pages, 895 KiB  
Article
Analytical Solutions of Systems of Linear Delay Differential Equations by the Laplace Transform: Featuring Limit Cycles
by Gilbert Kerr, Nehemiah Lopez and Gilberto González-Parra
Math. Comput. Appl. 2024, 29(1), 11; https://doi.org/10.3390/mca29010011 - 4 Feb 2024
Viewed by 2303
Abstract
In this paper we develop an approach for obtaining the solutions to systems of linear retarded and neutral delay differential equations. Our analytical approach is based on the Laplace transform, inverse Laplace transform and the Cauchy residue theorem. The obtained solutions have the [...] Read more.
In this paper we develop an approach for obtaining the solutions to systems of linear retarded and neutral delay differential equations. Our analytical approach is based on the Laplace transform, inverse Laplace transform and the Cauchy residue theorem. The obtained solutions have the form of infinite non-harmonic Fourier series. The main advantage of the proposed approach is the closed-form of the solutions, which are capable of accurately evaluating the solution at any time. Moreover, it allows one to study the asymptotic behavior of the solutions. A remarkable discovery, which to the best of our knowledge has never been presented in the literature, is that there are some particular linear systems of both retarded and neutral delay differential equations for which the solution asymptotically approaches a limit cycle. The well-known method of steps in many cases is unable to obtain the asymptotic behavior of the solution and would most likely fail to detect such cycles. Examples illustrating the Laplace transform method for linear systems of DDEs are presented and discussed. These examples are designed to facilitate a discussion on how the spectral properties of the matrices determine the manner in which one proceeds and how they impact the behavior of the solution. Comparisons with the exact solution provided by the method of steps are presented. Finally, we should mention that the solutions generated by the Laplace transform are, in most instances, extremely accurate even when the truncated series is limited to only a handful of terms and in many cases become more accurate as the independent variable increases. Full article
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<p>Solution of the linear system of RDDEs (<a href="#FD24-mca-29-00011" class="html-disp-formula">24</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mi>τ</mi> <mo>]</mo> </mrow> </semantics></math> using the LTM (<b>left</b>). The components of the solution are <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (blue) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (red). Solution in the phase space for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>14</mn> <mo>]</mo> </mrow> </semantics></math> (<b>right</b>).</p>
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<p>Absolute errors of the solution of the linear system of RDDEs (<a href="#FD24-mca-29-00011" class="html-disp-formula">24</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mi>τ</mi> <mo>]</mo> </mrow> </semantics></math> using LTM. Absolute error for <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (<b>right</b>).</p>
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<p>Solution of the linear system of RDDEs (<a href="#FD25-mca-29-00011" class="html-disp-formula">25</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mi>τ</mi> <mo>]</mo> </mrow> </semantics></math> using the LTM (<b>left</b>). The components of the solution are <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (blue) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (red). Solution in the phase space for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>20</mn> <mo>]</mo> </mrow> </semantics></math> (<b>right</b>).</p>
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<p>Absolute errors of the solution of the linear system of RDDEs (<a href="#FD25-mca-29-00011" class="html-disp-formula">25</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mi>τ</mi> <mo>]</mo> </mrow> </semantics></math> using LTM. For <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>left</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>right</b>).</p>
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<p>Sequence of poles in the complex plane for the linear system of RDDEs (<a href="#FD26-mca-29-00011" class="html-disp-formula">26</a>).</p>
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<p>Solution of the linear system of RDDEs (<a href="#FD26-mca-29-00011" class="html-disp-formula">26</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mi>τ</mi> <mo>]</mo> </mrow> </semantics></math> using the LTM (<b>left</b>). The components of the solution are <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (blue) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (red). Solution in the phase space for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>90</mn> <mo>,</mo> <mn>200</mn> <mo>]</mo> </mrow> </semantics></math> (<b>right</b>).</p>
Full article ">Figure 7
<p>Absolute errors of the solution of the linear system of RDDEs (<a href="#FD26-mca-29-00011" class="html-disp-formula">26</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> using LTM. For <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>left</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>right</b>).</p>
Full article ">Figure 8
<p>Solution of the linear system of RDDEs (<a href="#FD27-mca-29-00011" class="html-disp-formula">27</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>12</mn> <mo>]</mo> </mrow> </semantics></math> using both the MoS and the LTM (<b>left</b>). The components of the solution are <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (dashed-blue) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (solid red). Solution (LT) in the phase space for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>36</mn> <mo>]</mo> </mrow> </semantics></math> (<b>middle</b>) and for large <span class="html-italic">t</span> (<b>right</b>).</p>
Full article ">Figure 9
<p>Errors of the truncated LT solution (15 terms) of the linear system of RDDEs (<a href="#FD27-mca-29-00011" class="html-disp-formula">27</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> (<b>left-hand side</b>) and over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>6</mn> <mo>,</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> (<b>middle</b>). Errors of the numerical solution (dde23) of the linear system of RDDEs (<a href="#FD27-mca-29-00011" class="html-disp-formula">27</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> (<b>right-hand side</b>).</p>
Full article ">Figure 10
<p>Sequence of poles in the complex plane for the linear system of RDDEs (<a href="#FD29-mca-29-00011" class="html-disp-formula">29</a>).</p>
Full article ">Figure 11
<p>Solution of the linear system of RDDEs (<a href="#FD29-mca-29-00011" class="html-disp-formula">29</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>16</mn> <mo>]</mo> </mrow> </semantics></math> using the MoS and the LTM (<b>left</b>). The components of the solution are <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (dashed), <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (solid) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (dashed-point). Solution (LT) in the phase space for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>25</mn> <mo>]</mo> </mrow> </semantics></math> (<b>right</b>).</p>
Full article ">Figure 12
<p>Absolute errors of the solution of the linear system of RDDEs (<a href="#FD29-mca-29-00011" class="html-disp-formula">29</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>10</mn> <mo>]</mo> </mrow> </semantics></math> using LTM. For <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>left</b>), <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>middle</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>3</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>right</b>).</p>
Full article ">Figure 13
<p>Sequence of poles in the complex plane for the linear system of NDDEs (<a href="#FD30-mca-29-00011" class="html-disp-formula">30</a>).</p>
Full article ">Figure 14
<p>Solution of the linear system of NDDEs (<a href="#FD30-mca-29-00011" class="html-disp-formula">30</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>50</mn> <mo>]</mo> </mrow> </semantics></math> using the LTM (<b>left</b>). The components of the solution are <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (blue) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (red). Solution in the phase space for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>30</mn> <mo>,</mo> <mn>50</mn> <mo>]</mo> </mrow> </semantics></math> (<b>right</b>).</p>
Full article ">Figure 15
<p>Absolute errors of the solution of the linear system of NDDEs (<a href="#FD30-mca-29-00011" class="html-disp-formula">30</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>50</mn> <mo>]</mo> </mrow> </semantics></math> using LTM. For <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>left</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>right</b>).</p>
Full article ">Figure 16
<p>Solution of the linear system of NDDEs (<a href="#FD33-mca-29-00011" class="html-disp-formula">33</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>224</mn> <mo>,</mo> <mn>232</mn> <mo>]</mo> </mrow> </semantics></math> using the LTM (<b>left</b>). The components of the solution are <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (blue) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (red). Solution in the phase space for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>24</mn> <mo>]</mo> </mrow> </semantics></math> (<b>right</b>).</p>
Full article ">Figure 17
<p>Solution of the linear system of NDDEs (<a href="#FD33-mca-29-00011" class="html-disp-formula">33</a>) in the phase space for <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>120</mn> <mo>,</mo> <mn>150</mn> <mo>]</mo> </mrow> </semantics></math> (<b>left</b>) and <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>329</mn> <mo>,</mo> <mn>339</mn> <mo>]</mo> </mrow> </semantics></math> (<b>right</b>).</p>
Full article ">Figure 18
<p>Relative errors of the solution of the linear system of NDDEs (<a href="#FD33-mca-29-00011" class="html-disp-formula">33</a>) over <math display="inline"><semantics> <mrow> <mi>t</mi> <mo>∈</mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>30</mn> <mo>]</mo> </mrow> </semantics></math> using LTM. For <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>left</b>) and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> on the (<b>right</b>).</p>
Full article ">
14 pages, 294 KiB  
Article
Integral Transforms and the Hyers–Ulam Stability of Linear Differential Equations with Constant Coefficients
by Douglas R. Anderson
Symmetry 2024, 16(2), 135; https://doi.org/10.3390/sym16020135 - 23 Jan 2024
Cited by 1 | Viewed by 1021
Abstract
Integral transform methods are a common tool employed to study the Hyers–Ulam stability of differential equations, including Laplace, Kamal, Tarig, Aboodh, Mahgoub, Sawi, Fourier, Shehu, and Elzaki integral transforms. This work provides improved techniques for integral transforms in relation to establishing the Hyers–Ulam [...] Read more.
Integral transform methods are a common tool employed to study the Hyers–Ulam stability of differential equations, including Laplace, Kamal, Tarig, Aboodh, Mahgoub, Sawi, Fourier, Shehu, and Elzaki integral transforms. This work provides improved techniques for integral transforms in relation to establishing the Hyers–Ulam stability of differential equations with constant coefficients, utilizing the Kamal transform, where we focus on first- and second-order linear equations. In particular, in this work, we employ the Kamal transform to determine the Hyers–Ulam stability and Hyers–Ulam stability constants for first-order complex constant coefficient differential equations and, for second-order real constant coefficient differential equations, improving previous results obtained by using the Kamal transform. In a section of examples, we compare and contrast our results favorably with those established in the literature using means other than the Kamal transform. Full article
(This article belongs to the Special Issue Feature Papers in Mathematics Section)
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