[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (48)

Search Parameters:
Keywords = viscoplastic flows

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
28 pages, 1249 KiB  
Article
Unified Analysis of Viscoelasticity and Viscoplasticity Using the Onsager Variational Principle
by Kwang Soo Cho
Entropy 2025, 27(1), 55; https://doi.org/10.3390/e27010055 - 10 Jan 2025
Viewed by 361
Abstract
This study is the application of the Onsager variational principle to viscoelasticity and viscoplasticity with the minimization of the assumptions which are popularly used in conventional approaches. The conventional approaches assume Kröner–Lee decomposition, incompressible plastic deformation, flowing rule, stress equation and so on. [...] Read more.
This study is the application of the Onsager variational principle to viscoelasticity and viscoplasticity with the minimization of the assumptions which are popularly used in conventional approaches. The conventional approaches assume Kröner–Lee decomposition, incompressible plastic deformation, flowing rule, stress equation and so on. These assumptions have been accumulated by many researchers for a long time and have shown many successful cases. The large number of successful assumptions leads to the conjecture that the mechanics can be described with a smaller number of assumptions. This paper shows that this conjecture is correct by using the Onsager variational principle. Full article
(This article belongs to the Section Thermodynamics)
Show Figures

Figure 1

Figure 1
<p>The fundamental components for modeling viscoelasticity and viscoplasticity. (<b>a</b>) A 2-element model consisting of a series connection of an elastic spring and an inelastic dashpot, (<b>b</b>) a 3-element model I with an additional elastic element in parallel, (<b>c</b>) a 3-element model II for explaining back stress, (<b>d</b>) a 4-element model incorporating both inelastic behavior and back stress.</p>
Full article ">Figure 2
<p>Classification of materials by cyclic loading and relaxation. The thick curves (red) are the stress-strain curves for cyclic strain and the thin curves (blue) represent the fully relaxed stress which can be obtained from the relaxation at each points of the thick curves. (<b>a</b>) elasticity; (<b>b</b>) rate-independent plasticity; (<b>c</b>) viscoelasticity; (<b>d</b>) viscoplasticity.</p>
Full article ">Figure 3
<p>Extended configurations for viscoplastic modeling. (<b>a</b>) A 4-element model combining viscoelastic and rate-independent behaviors in parallel, (<b>b</b>) a 6-element model integrally describing viscoelastic and viscoplastic behaviors.</p>
Full article ">
19 pages, 1710 KiB  
Article
Predicting the Dynamic of Debris Flow Based on Viscoplastic Theory and Support Vector Regression
by Xinhai Zhang, Hanze Li, Yazhou Fan, Lu Zhang, Shijie Peng, Jie Huang, Jinxin Zhang and Zhenzhu Meng
Water 2025, 17(1), 120; https://doi.org/10.3390/w17010120 - 4 Jan 2025
Viewed by 479
Abstract
The prediction of debris flows is essential for safeguarding infrastructure and minimizing the economic losses associated with the hazards. Traditional empirical and theoretical models, while providing foundational insights, often struggle to capture the complex and nonlinear behaviors inherent in debris flows. This study [...] Read more.
The prediction of debris flows is essential for safeguarding infrastructure and minimizing the economic losses associated with the hazards. Traditional empirical and theoretical models, while providing foundational insights, often struggle to capture the complex and nonlinear behaviors inherent in debris flows. This study aims to enhance debris flow prediction by integrating theoretical modeling with data-driven approaches. We model debris flow as a viscoplastic fluid, employing the Herschel–Bulkley rheological model to describe its behavior. By combining the kinematic wave model with lubrication theory, we develop a comprehensive theoretical framework that encapsulates the mechanical physics of debris flows and identifies key governing parameters. Numerical solutions of this theoretical model are utilized to generate an extensive training dataset, which is subsequently used to train a support vector regression (SVR) model. The SVR model targets slide depth and velocity upon impact, using explanatory variables including yield stress, material density, source area depth and length, and slope length. The model demonstrates high predictive accuracy, achieving coefficients of determination R2 of 0.956 for slide depth and 0.911 for slide velocity at impact. Additionally, the relative residuals σ are primarily distributed within the range of −0.05 to 0.05 for both slide depth and slide velocity upon impact. These results indicate that the proposed hybrid model not only incorporates the fundamental physical mechanisms governing debris flows but also significantly enhances predictive performance through data-driven optimization. This study underscores the critical advantage of merging physical models with machine learning techniques, offering a robust tool for improved debris flow prediction and risk assessment, which can inform the development of more effective early warning systems and mitigation measures. Full article
Show Figures

Figure 1

Figure 1
<p>The simplified physical model of the issue: (<b>a</b>) schematic diagram and (<b>b</b>) physical model.</p>
Full article ">Figure 2
<p>Sketch of the sliding mass (<b>a</b>) at rest and (<b>b</b>) moving along the slope.</p>
Full article ">Figure 3
<p>The flowchart of the SVR model.</p>
Full article ">Figure 4
<p>The structure of SVR [<a href="#B36-water-17-00120" class="html-bibr">36</a>].</p>
Full article ">Figure 5
<p>Numerical solution of slide thickness <math display="inline"><semantics> <mrow> <mi>s</mi> <mo stretchy="false">(</mo> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mo>,</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> along the chute of the example case.</p>
Full article ">Figure 6
<p>Numerical solution of depth-averaged velocity <math display="inline"><semantics> <mrow> <mi>u</mi> <mo stretchy="false">(</mo> <mover accent="true"> <mi>x</mi> <mo stretchy="false">^</mo> </mover> <mo>,</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> along the chute of the example case.</p>
Full article ">Figure 7
<p>Time variation of (<b>a</b>) slide thickness <math display="inline"><semantics> <mrow> <mi>s</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> and (<b>b</b>) depth-averaged velocity <math display="inline"><semantics> <mrow> <mi>u</mi> <mo stretchy="false">(</mo> <mi>t</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> at an indicated position <math display="inline"><semantics> <msub> <mi>l</mi> <mi>s</mi> </msub> </semantics></math> = 0.95 with various initial settings.</p>
Full article ">Figure 8
<p>Three-phase diagram of the rheological parameters yield stress <math display="inline"><semantics> <msub> <mi>τ</mi> <mi>c</mi> </msub> </semantics></math>, consistency <math display="inline"><semantics> <mi>μ</mi> </semantics></math>, and power-law index <span class="html-italic">n</span>.</p>
Full article ">Figure 9
<p>The correlations among the selected input parameters.</p>
Full article ">Figure 10
<p>The modeling process of the SVR prediction.</p>
Full article ">Figure 11
<p>Comparision of the predicted and original (<b>a</b>) <math display="inline"><semantics> <msub> <mi>s</mi> <mn>0</mn> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>u</mi> <mn>0</mn> </msub> </semantics></math>.</p>
Full article ">Figure 12
<p>The error histogram distribution of the (<b>a</b>) <math display="inline"><semantics> <msub> <mi>s</mi> <mn>0</mn> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>u</mi> <mn>0</mn> </msub> </semantics></math>.</p>
Full article ">Figure 13
<p>The PDF and CDF of <math display="inline"><semantics> <mi>σ</mi> </semantics></math> for (<b>a</b>) <math display="inline"><semantics> <msub> <mi>s</mi> <mn>0</mn> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>u</mi> <mn>0</mn> </msub> </semantics></math>.</p>
Full article ">
19 pages, 11272 KiB  
Article
A Chamfered Anchor Impeller Design for Enhanced Efficiency in Agitating Viscoplastic Fluids
by Amine Benmoussa and José C. Páscoa
Fluids 2024, 9(12), 288; https://doi.org/10.3390/fluids9120288 - 5 Dec 2024
Viewed by 587
Abstract
In industrial mixing processes, impeller design, rotational speed, and mixing conditions play a crucial role in determining process efficiency, product quality, and energy consumption. Optimizing the performance of stirring systems for non-Newtonian fluids is essential for achieving better results. This study examines the [...] Read more.
In industrial mixing processes, impeller design, rotational speed, and mixing conditions play a crucial role in determining process efficiency, product quality, and energy consumption. Optimizing the performance of stirring systems for non-Newtonian fluids is essential for achieving better results. This study examines the hydrodynamic and thermal performance of stirring systems for viscoplastic fluids, utilizing close-clearance anchor impellers with chamfered angles of 22.5°, 45°, and 67.5° in cylindrical, flat-bottom and unbaffled vessels. Through a comprehensive comparative analysis between standard and chamfered impeller designs, the study evaluates their efficacy in overcoming yield stress, enhancing flow dynamics, and improving thermal homogeneity. The effects of Reynolds number and yield stress on the hydrodynamic and thermal states are analyzed. The results indicate that the 67.5° chamfered impeller significantly improves flow distribution and minimizes dead zones, particularly in critical areas between the anchor blades and vessel walls, where mixing stagnation typically occurs. It also enhances vertical mixing by promoting a broader shear spread along the vessel height and a more uniform temperature distribution. These insights contribute to the development of more efficient agitation systems, applicable across various industries handling complex fluids. Full article
(This article belongs to the Special Issue Industrial CFD and Fluid Modelling in Engineering, 2nd Edition)
Show Figures

Figure 1

Figure 1
<p>Geometry and presentation of the simulated system using a chamfered anchor impeller.</p>
Full article ">Figure 2
<p>Boundary conditions (<b>a</b>) and generated mesh of the Computational domain (<b>b</b>).</p>
Full article ">Figure 3
<p>Comparison of the tangential velocity on the impeller line (<b>a</b>) and power number (<b>b</b>) of the current CFD model and results of Marouche et al. [<a href="#B34-fluids-09-00288" class="html-bibr">34</a>].</p>
Full article ">Figure 4
<p>Comparison of the tangential velocity on the median line (<b>a</b>) Re = 14; (<b>b</b>) Re = 70 of the current CFD model and results of Anne-Archard et al. [<a href="#B35-fluids-09-00288" class="html-bibr">35</a>].</p>
Full article ">Figure 5
<p>Three-dimensional streamlines for different Reynolds numbers: (<b>a</b>) Re = 13.8; (<b>b</b>) Re = 140; (<b>c</b>) Re = 415 for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>Velocity magnitude contour comparison for different yield stress: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>30</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math> for Re = 13.8.</p>
Full article ">Figure 7
<p>Effect of chamfered angles on the velocity magnitude contour: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">α</mi> <mo>=</mo> <mn>22.5</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> <mo>=</mo> <mn>45</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> <mo>=</mo> <mn>67.5</mn> <mo>°</mo> </mrow> </semantics></math> for Re = 13.8 and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>Effect of blade chamfered angles on the velocity magnitude contour: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> <mo>=</mo> <mn>0</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mtext> </mtext> <mi mathvariant="sans-serif">α</mi> <mo>=</mo> <mn>22.5</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> <mo>=</mo> <mn>45</mn> <mo>°</mo> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">α</mi> <mo>=</mo> <mn>67.5</mn> <mo>°</mo> </mrow> </semantics></math> for Re = 415 and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>Effect of blade chamfered angles on tangential velocity profiles along the impeller line for different Reynolds numbers: (<b>a</b>) Re = 13.8; (<b>b</b>) Re = 140; (<b>c</b>) Re = 415 for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>Effect of blade chamfered angles on tangential velocity profiles along the median line for different Reynolds numbers: (<b>a</b>) Re = 13.8; (<b>b</b>) Re = 140; (<b>c</b>) Re = 415 for <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>Effect of blade chamfered angles on tangential velocity profiles along the impeller line for different yield stress: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>30</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math> for Re = 13.8.</p>
Full article ">Figure 12
<p>Effect of blade chamfered angles on tangential velocity profiles along the median line for different yield stress: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>30</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math> for Re = 13.8.</p>
Full article ">Figure 13
<p>Temperature field distribution for different impeller designs: (<b>a</b>) α = 0°; (<b>b</b>) α = 67° (Re = 13.8, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math> and Pr = 7).</p>
Full article ">Figure 14
<p>Shear rate distribution for different impeller designs: (<b>a</b>) α = 0°; (<b>b</b>) α = 67° (Re = 13.8, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> </mrow> </semantics></math> and Pr = 7).</p>
Full article ">Figure 15
<p>Power number variation for different Reynolds number and different impeller designs (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mtext> </mtext> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">a</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
Full article ">
14 pages, 10182 KiB  
Article
Effect of Ultrasound on Microstructure and Properties of Aluminum–Copper Friction Stir Lap Welding
by Wenzhen Zhao, Yalong Zhu, Zhaoxian Liu, Xiaoyang Yi, Jian Wang, Ao Fu, Fengyi Wang and Huan He
Metals 2024, 14(10), 1162; https://doi.org/10.3390/met14101162 - 11 Oct 2024
Viewed by 962
Abstract
In this paper, the influence mechanism of ultrasound on plastic flow and microstructure features of the aluminum–copper friction stir lap welding (Al/Cu-FSLW) process is systematically investigated by adjusting the welding speed and improving the shear rheology in the plastic stirring zone. Through adjusting [...] Read more.
In this paper, the influence mechanism of ultrasound on plastic flow and microstructure features of the aluminum–copper friction stir lap welding (Al/Cu-FSLW) process is systematically investigated by adjusting the welding speed and improving the shear rheology in the plastic stirring zone. Through adjusting the ultrasonic vibration and welding speed, the directional control of mechanical properties is realized. It is found that increasing the welding speed properly is beneficial to enhance the mechanical shear between the tool and the workpiece, thus forming more staggered layered structures at the copper side and improving the tensile strength of the weld. The acoustic softening enhances the viscoplastic fluid mixing and strengthens the mechanical interlock of the Al/Cu lap interface. As the welding speeds increase or ultrasonic vibration is applied, the thickness of Al/Cu intermetallic compound (IMC) decreases, and the tensile strength and elongation of the Al/Cu joints are enhanced. Compared with adjusting the welding speed, the ultrasonic vibration can further refine the copper particles which are stirred into the plastic zone, and the thinning effect of ultrasound on IMC layers is better than that of increasing welding speed. At the welding speed of 60 mm/min, the IMC layer thickness is reduced by 42% under ultrasonic effect. In three welding speed conditions, the UV reduced the absolute value of the effective heat of formation (EHF) for Al2Cu and Al4Cu9 and suppressed the formation of AlCu phase. Meanwhile, only when the welding speed is increased from 60 mm/min to 100 mm/min can the formation of AlCu be suppressed. Under the ultrasonic optimization, the stable improvement of welding efficiency is ensured. Full article
(This article belongs to the Special Issue Advances in Welding Processes of Metallic Materials)
Show Figures

Figure 1

Figure 1
<p>The schematic diagram of ultrasonic-assisted friction stir lap welding (UFSLW) process.</p>
Full article ">Figure 2
<p>Morphological comparison of weld cross-sections at different welding speeds. (<b>a</b>) 800 rpm, 60 mm/min, FSLW, (<b>b</b>) 800 rpm, 80 mm/min, FSLW, (<b>c</b>) 800 rpm, 100 mm/min, FSLW, (<b>d</b>) 800 rpm, 60 mm/min, UFSLW, (<b>e</b>) 800 rpm, 80 mm/min, UFSLW, (<b>f</b>) 800 rpm, 100 mm/min, UFSLW. Red letters marked the typical positions selected in six groups of cross-sections, and the partially enlarged metallographic diagrams was shown in <a href="#metals-14-01162-f003" class="html-fig">Figure 3</a>.</p>
Full article ">Figure 3
<p>Metallographic microstructures at the Al/Cu interface of the welding parameter at different welding speeds. (<b>A</b>) 800-60-FSLW, (<b>B</b>) 800-80-FSLW, (<b>C</b>) 800-100-FSLW, (<b>D</b>) 800-60-UFSLW, (<b>E</b>) 800-80-UFSLW, (<b>F</b>) 800-100-UFSLW. The serial numbers a~r corresponding to the typical positions selected in <a href="#metals-14-01162-f002" class="html-fig">Figure 2</a>.</p>
Full article ">Figure 4
<p>SEM images showing the morphologies of the hooks at different welding speeds on the Al/Cu lap interfaces. (<b>a</b>) 800 rpm, 60 mm/min, FSLW, (<b>b</b>) 800 rpm, 80 mm/min, FSLW, (<b>c</b>) 800 rpm, 100 mm/min, FSLW, (<b>d</b>) 800 rpm, 60 mm/min, UFSLW, (<b>e</b>) 800 rpm, 80 mm/min, UFSLW, (<b>f</b>) 800 rpm, 100 mm/min, UFSLW. In the red box are copper particles/blocks in the nugget area.</p>
Full article ">Figure 5
<p>SEM image showing the distribution and morphologies of the Al/Cu-IMCs at different welding speeds. (<b>a</b>) 800 rpm, 60 mm/min, FSLW, (<b>b</b>) 800 rpm, 80 mm/min, FSLW, (<b>c</b>) 800 rpm, 100 mm/min, FSLW, (<b>d</b>) 800 rpm, 60 mm/min, UFSLW, (<b>e</b>) 800 rpm, 80 mm/min, UFSLW, (<b>f</b>) 800 rpm, 100 mm/min, UFSLW.</p>
Full article ">Figure 6
<p>Comparison of the IMCs and element distribution on the Al/Cu interfaces at (<b>a</b>) 800 rpm, 60 mm/min of FSLW process, and (<b>b</b>) 800 rpm, 60 mm/min of UFSLW process. The points A, B, C, D and E are the positions for the EDS spot scanning.</p>
Full article ">Figure 7
<p>(<b>a</b>–<b>c</b>) EDS spot scanning results at the marked points A~C in <a href="#metals-14-01162-f006" class="html-fig">Figure 6</a>a for FSLW process at 800 rpm and 60 mm/min, and (<b>d</b>,<b>e</b>) EDS spot scanning results at the marked points D and E in <a href="#metals-14-01162-f006" class="html-fig">Figure 6</a>b for UFSLW process at 800 rpm and 60 mm/min.</p>
Full article ">Figure 8
<p>Comparison of thickness of Al/Cu−IMCs for (<b>a</b>) FSLW process and (<b>b</b>) UFSLW process under different welding speeds.</p>
Full article ">Figure 9
<p>Comparison of the effective Gibbs free energy for the formation of Al/Cu−IMCs phases: (<b>a</b>) Al<sub>65.96</sub>Cu<sub>34.04</sub> at point A in FSLW process under 800 rpm–60 mm/min, (<b>b</b>) Al<sub>36.64</sub>Cu<sub>63.36</sub> at point C in FSLW process under 800 rpm–60 mm/min, (<b>c</b>) Al<sub>68.6</sub>Cu<sub>31.4</sub> at point D in UFSLW process under 800 rpm–60 mm/min, and (<b>d</b>) Al<sub>27.28</sub>Cu<sub>72.72</sub> at point E in UFSLW process under 800 rpm−60 mm/min. Points A, C, D, E correspond to <a href="#metals-14-01162-f006" class="html-fig">Figure 6</a>.</p>
Full article ">Figure 10
<p>Comparison of the tensile strength of the joints under different welding speeds with and without ultrasonic vibration assistance. (<b>a</b>) 800 rpm, 60 mm/min, (<b>b</b>) 800 rpm, 80 mm/min, (<b>c</b>) 800 rpm, 100 mm/min.</p>
Full article ">Figure 11
<p>Comparison of the microhardness of the joints under different welding speeds with and without ultrasonic vibration assistance. (<b>a</b>) 800 rpm, 60 mm/min, (<b>b</b>) 800 rpm, 80 mm/min, (<b>c</b>) 800 rpm, 100 mm/min.</p>
Full article ">
15 pages, 5085 KiB  
Article
Heat Transfer of Crude Waxy Oil with Yield Stress in a Pipe
by Uzak Zhapbasbayev, Timur Bekibayev, Maksim Pakhomov and Gaukhar Ramazanova
Energies 2024, 17(18), 4687; https://doi.org/10.3390/en17184687 - 20 Sep 2024
Viewed by 580
Abstract
This article is devoted to the study of heat exchange of a heated flow of waxy oil in a pipe. Heat exchange between the waxy oil flow and the surrounding environment decreases the oil temperature and sharply increases the rheological properties. The appearance [...] Read more.
This article is devoted to the study of heat exchange of a heated flow of waxy oil in a pipe. Heat exchange between the waxy oil flow and the surrounding environment decreases the oil temperature and sharply increases the rheological properties. The appearance of a solid-like region within the yield-stress fluid flow is a non-trivial problem. This flow property greatly complicates the numerical solution of the system of equations governing the flow and heat transfer of viscoplastic fluids. The Bingham–Papanastasiou model allows one to solve the problem by regularizing the formula for effective molecular viscosity. The novelty of this work lies in establishing the dependence of the Nusselt number on the Reynolds and Bingham numbers for the flow of viscoplastic fluid in a pipe. Via calculations, velocity, temperature, and pressure distributions in the flow were obtained for Bingham numbers ranging from 1.7 to 118.29 and Reynolds numbers ranging from 104 to 2615. The Nusselt number dependence increases with the increase in the Reynolds number and decreases with the decrease in the Bingham number along the pipe length. Full article
(This article belongs to the Special Issue Heat Transfer in Heat Exchangers)
Show Figures

Figure 1

Figure 1
<p>Flow heat exchange diagram: 1—Newtonian fluid flow; 2—Non-Newtonian fluid flow.</p>
Full article ">Figure 2
<p>Calculated data of velocity <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> (<b>a</b>), velocity vector contours (<b>b</b>), temperature <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math> (<b>c</b>), and pressure <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math> (<b>d</b>) at <span class="html-italic">Re</span> = 261, <span class="html-italic">Bn</span> = 118.29.</p>
Full article ">Figure 3
<p>Calculated data of velocity <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> (<b>a</b>), velocity vector contours (<b>b</b>), temperature <math display="inline"><semantics> <mrow> <mi>Θ</mi> </mrow> </semantics></math> (<b>c</b>), and pressure <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math> (<b>d</b>) at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> </mrow> </semantics></math> = 523, and <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> </mrow> </semantics></math> = 59.14.</p>
Full article ">Figure 4
<p>Calculated data of velocity <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> (<b>a</b>), velocity vector contours (<b>b</b>), temperature <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math> (<b>c</b>), and pressure <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math> (<b>d</b>) at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> </mrow> </semantics></math> = 1046, and <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> </mrow> </semantics></math> = 29.57.</p>
Full article ">Figure 4 Cont.
<p>Calculated data of velocity <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> (<b>a</b>), velocity vector contours (<b>b</b>), temperature <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math> (<b>c</b>), and pressure <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math> (<b>d</b>) at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> </mrow> </semantics></math> = 1046, and <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> </mrow> </semantics></math> = 29.57.</p>
Full article ">Figure 5
<p>Calculated data of velocity <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> (<b>a</b>), velocity vector contours (<b>b</b>), temperature <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math> (<b>c</b>), and pressure <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math> (<b>d</b>) at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> </mrow> </semantics></math> = 261, and <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> </mrow> </semantics></math> = 17.01.</p>
Full article ">Figure 5 Cont.
<p>Calculated data of velocity <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> (<b>a</b>), velocity vector contours (<b>b</b>), temperature <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math> (<b>c</b>), and pressure <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math> (<b>d</b>) at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> </mrow> </semantics></math> = 261, and <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> </mrow> </semantics></math> = 17.01.</p>
Full article ">Figure 6
<p>Calculated data of velocity <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> (<b>a</b>), velocity vector contours (<b>b</b>), temperature <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math> (<b>c</b>), and pressure <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math> (<b>d</b>) at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> </mrow> </semantics></math> = 523, and <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> </mrow> </semantics></math> = 8.51.</p>
Full article ">Figure 7
<p>Calculated data of velocity <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> (<b>a</b>), velocity vector contours (<b>b</b>), temperature <math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math> (<b>c</b>), and pressure <math display="inline"><semantics> <mrow> <mi>P</mi> </mrow> </semantics></math> (<b>d</b>) at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> </mrow> </semantics></math> = 1046, and <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> </mrow> </semantics></math> = 4.25.</p>
Full article ">Figure 8
<p>The distribution of the Nusselt number along the pipe length at a wall temperature <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>t</mi> </mrow> <mrow> <mi>w</mi> </mrow> </msub> <mo>=</mo> </mrow> </semantics></math> 10 °C and various Reynolds and Bingham numbers.</p>
Full article ">
20 pages, 4283 KiB  
Article
Numerical Modeling of Non-Isothermal Laminar Flow and Heat Transfer of Paraffinic Oil with Yield Stress in a Pipe
by Uzak Zhapbasbayev, Timur Bekibayev, Maksim Pakhomov and Gaukhar Ramazanova
Energies 2024, 17(9), 2080; https://doi.org/10.3390/en17092080 - 26 Apr 2024
Cited by 1 | Viewed by 931
Abstract
This paper presents the results of a study on the non-isothermal laminar flow and heat transfer of oil with Newtonian and viscoplastic rheologies. Heat exchange with the surrounding environment leads to the formation of a near-wall zone of viscoplastic fluid. As the flow [...] Read more.
This paper presents the results of a study on the non-isothermal laminar flow and heat transfer of oil with Newtonian and viscoplastic rheologies. Heat exchange with the surrounding environment leads to the formation of a near-wall zone of viscoplastic fluid. As the flow proceeds, the transformation of a Newtonian fluid to a viscoplastic state occurs. The rheology of the Shvedoff–Bingham fluid as a function of temperature is represented by the effective molecular viscosity apparatus. A numerical solution to the system of equations of motion and heat transfer was obtained using the Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm. The calculated data are obtained at Reynolds number Re from 523 to 1046, Bingham number Bn from 8.51 to 411.16, and Prandl number Pr = 45. The calculations’ novelty lies in the appearance of a “stagnation zone” in the near-wall zone and the pipe cross-section narrowing. The near-wall “stagnation zone” is along the pipe’s radius from r/R = 0.475 to r/R = 1 at Re = 523, Bn = 411.16, Pr = 45, u1 = 0.10 m/s, t1 = 25 °C, and tw = 0 °C. The influence of the heat of phase transition of paraffinic oil on the development of flow and heat transfer characteristics along the pipe length is demonstrated. Full article
(This article belongs to the Special Issue Fluid Mechanics and Turbulence)
Show Figures

Figure 1

Figure 1
<p>Flow configuration scheme: 1—Newtonian fluid flow; 2—non-Newtonian fluid flow.</p>
Full article ">Figure 2
<p>Profiles of dimensionless axial velocity (<b>a</b>) and dynamic viscosity (<b>b</b>) across the pipe section. The points are the calculation data from [<a href="#B30-energies-17-02080" class="html-bibr">30</a>]; lines are the authors’ calculation: <span class="html-italic">Re</span> = <span class="html-italic">ρ</span><sub>1</sub><span class="html-italic">Ru</span><sub>1</sub>/<span class="html-italic">μ</span><sub>1</sub> = 1000; <span class="html-italic">Sc</span> = 10; <span class="html-italic">Bn</span> = 5; <span class="html-italic">R</span><sub>1</sub>/<span class="html-italic">R</span> = 0.55; <span class="html-italic">q</span>/<span class="html-italic">R</span> = 0.1; <span class="html-italic">μ</span><sub>P</sub>/<span class="html-italic">μ</span><sub>1</sub> = 10; 1—<span class="html-italic">Bn</span> = 0; 2—<span class="html-italic">Bn</span> = 5.</p>
Full article ">Figure 3
<p>Radial profiles of dimensionless longitudinal velocity (<b>a</b>) and temperature (<b>b</b>) across the pipe section. The points are the calculation data from [<a href="#B14-energies-17-02080" class="html-bibr">14</a>], lines are the authors’ calculation: 1 − <span class="html-italic">Y</span> = 0 (Newtonian fluid), 2 − <span class="html-italic">Y</span> = 1.99.</p>
Full article ">Figure 4
<p>Profiles of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), and pressure distribution <span class="html-italic">P</span> (<b>c</b>) at the regime parameters: <span class="html-italic">u</span><sub>1</sub> = 0.10 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 0°C; <span class="html-italic">Re</span> = 523; <span class="html-italic">Bn</span> = 411.16; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.15.</p>
Full article ">Figure 5
<p>Contour plots of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), pressure <span class="html-italic">P</span> (<b>c</b>) at operating parameters <span class="html-italic">u</span><sub>1</sub> = 0.10 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 0 °C; <span class="html-italic">Re</span> = 523; <span class="html-italic">Bn</span> = 411.16; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.15.</p>
Full article ">Figure 6
<p>Profiles of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), and pressure <span class="html-italic">P</span> distribution (<b>c</b>) at the regime parameters: <span class="html-italic">u</span><sub>1</sub> = 0.10 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 5 °C; <span class="html-italic">Re</span> = 523; <span class="html-italic">Bn</span> = 59.14; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.15.</p>
Full article ">Figure 7
<p>Contour plots of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), pressure <span class="html-italic">P</span> (<b>c</b>) at operating parameters u<sub>1</sub> = 0.10 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 5 °C; <span class="html-italic">Re</span> = 523; <span class="html-italic">Bn</span> = 50.14; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.15.</p>
Full article ">Figure 8
<p>Profiles of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), and pressure distribution <span class="html-italic">P</span> (<b>c</b>) at the regime parameters: <span class="html-italic">u</span><sub>1</sub> = 0.10 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 10 °C; <span class="html-italic">Re</span> = 523; <span class="html-italic">Bn</span> = 8.51; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.15.</p>
Full article ">Figure 9
<p>Contour plots of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), pressure <span class="html-italic">P</span> (<b>c</b>) at operating parameters <span class="html-italic">u</span><sub>1</sub> = 0.10 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 10 °C; <span class="html-italic">Re</span> = 523; <span class="html-italic">Bn</span> = 8.51; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.15.</p>
Full article ">Figure 10
<p>Profiles of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), and pressure distribution <span class="html-italic">P</span> (<b>c</b>) at the regime parameters <span class="html-italic">u</span><sub>1</sub> = 0.15 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 0 °C; <span class="html-italic">Re</span> = 785; <span class="html-italic">Bn</span> = 411.16; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.15.</p>
Full article ">Figure 11
<p>Contour plots of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), pressure <span class="html-italic">P</span> (<b>c</b>) at operating parameters <span class="html-italic">u</span><sub>1</sub> = 0.15 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 0 °C; <span class="html-italic">Re</span> = 785; <span class="html-italic">Bn</span> = 411.16; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.15.</p>
Full article ">Figure 12
<p>Profiles of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), and pressure distribution <span class="html-italic">P</span> (<b>c</b>) at the regime parameters <span class="html-italic">u</span><sub>1</sub> = 0.10 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>W</sub> = 0 °C; <span class="html-italic">Re</span> = 523; <span class="html-italic">Bn</span> = 411.16; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> <mn>0.30</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 13
<p>Contour plots of axial velocity <span class="html-italic">U</span> (<b>a</b>), temperature <span class="html-italic">θ</span> (<b>b</b>), pressure <span class="html-italic">P</span> (<b>c</b>) at operating parameters <span class="html-italic">u</span><sub>1</sub> = 0.10 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 0 °C; <span class="html-italic">Re</span> = 523; <span class="html-italic">Bn</span> = 411.16; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.30.</p>
Full article ">Figure 14
<p>Contour plots of effective viscosity at the following regime parameters: <span class="html-italic">u</span><sub>1</sub> = 0.10 m/s; <span class="html-italic">t</span><sub>1</sub> = 25 °C; <span class="html-italic">t</span><sub>w</sub> = 0 °C; <span class="html-italic">Re</span> = 523; <span class="html-italic">Bn</span> = 411.16; <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.15 (<b>a</b>); <math display="inline"><semantics> <mrow> <mi>χ</mi> <mo>=</mo> </mrow> </semantics></math> 0.30 (<b>b</b>).</p>
Full article ">
30 pages, 9533 KiB  
Article
Numerical Investigation of the Damage Effect on the Evolution of Adiabatic Shear Banding and Its Transition to Fracture during High-Speed Blanking of 304 Stainless Steel Sheets
by Konstantina D. Karantza, Spyros A. Papaefthymiou, Nikolaos M. Vaxevanidis and Dimitrios E. Manolakos
Materials 2024, 17(7), 1471; https://doi.org/10.3390/ma17071471 - 23 Mar 2024
Cited by 2 | Viewed by 1076
Abstract
This paper investigates numerically the effect of damage evolution on adiabatic shear banding (ASB) formation and its transition to fracture during high-speed blanking of 304 stainless steel sheets. A structural-thermal-damage-coupled finite element (FE) analysis is developed in LS-DYNA considering the modified Johnson–Cook thermo-viscoplastic [...] Read more.
This paper investigates numerically the effect of damage evolution on adiabatic shear banding (ASB) formation and its transition to fracture during high-speed blanking of 304 stainless steel sheets. A structural-thermal-damage-coupled finite element (FE) analysis is developed in LS-DYNA considering the modified Johnson–Cook thermo-viscoplastic model for both plasticity flow rule and damage law, while further, a temperature-dependent fracture criterion is implemented by introducing a critical temperature. The modeling approach is initially validated against experimental data regarding the fracture profile and ASB width. Next, FE simulations are conducted to examine the effect of strain rate and temperature dependence on damage law, while the effect of damage coupling is also evaluated, aiming to highlight the connection between thermal and damage softening and attribute them a specific role regarding ASB formation and transition to fracture. Also, the influence of dynamic recrystallization (DRX) softening is studied macroscopically, while further, a parametric analysis of the Taylor–Quinney coefficient is conducted to highlight the effect of plastic work-to-internal heat conversion efficiency on ASB formation. The results revealed that the implementation of damage coupling reacts to reduced ASB width and provides an S-shaped fracture profile, while it also decreases the peak force and results in an earlier fracture. Both findings are enhanced when accounting further for DRX softening and a higher value of the Taylor–Quinney coefficient. Finally, the simulations indicated that thermal softening precedes damage softening, showing that the temperature rise is responsible for ASB initiation, while instead, damage evolution drives ASB propagation and fracture. Full article
(This article belongs to the Special Issue Advanced Computational Methods in Manufacturing Processes)
Show Figures

Figure 1

Figure 1
<p>Examined blanking configuration.</p>
Full article ">Figure 2
<p>FE variable mesh with refinement in ASB expected region.</p>
Full article ">Figure 3
<p>Blanked edge profile: (<b>a</b>) C75S steel; (<b>b</b>) 20MnB5 steel.</p>
Full article ">Figure 4
<p>Stress (solid line) and effective plastic strain (dotted line) fluctuation inside ASB core: (<b>a</b>) C75S steel; (<b>b</b>) 20MnB5 steel.</p>
Full article ">Figure 5
<p>Transverse distribution of effective plastic strain: (<b>a</b>) C75S steel; (<b>b</b>) 20MnB5 steel.</p>
Full article ">Figure 6
<p>Damage and shear strain fields before final fracture: (<b>a</b>) Full MJC-DUc-<span class="html-italic">χ</span>09 at t = 222 μs; (<b>b</b>) SH-DUc-<span class="html-italic">χ</span>09 at t = 292 μs; (<b>c</b>) SH/SRH-DUc-<span class="html-italic">χ</span>09 at t = 302 μs.</p>
Full article ">Figure 6 Cont.
<p>Damage and shear strain fields before final fracture: (<b>a</b>) Full MJC-DUc-<span class="html-italic">χ</span>09 at t = 222 μs; (<b>b</b>) SH-DUc-<span class="html-italic">χ</span>09 at t = 292 μs; (<b>c</b>) SH/SRH-DUc-<span class="html-italic">χ</span>09 at t = 302 μs.</p>
Full article ">Figure 7
<p>Effect of strain rate and temperature on the distribution of effective plastic strain transversely to ASB center before fracture.</p>
Full article ">Figure 8
<p>Effect of strain rate and temperature on damage evolution and flow stress inside ASB core: (<b>a</b>) damage evolution versus time; (<b>b</b>) flow stress versus time.</p>
Full article ">Figure 9
<p>Effect of strain rate and temperature on blanking force fluctuation and cracking length: (<b>a</b>) force versus time; (<b>b</b>) cracking lengths (upper and lower).</p>
Full article ">Figure 10
<p>Effect of strain rate and temperature on the length of the district zones of blanked surface.</p>
Full article ">Figure 11
<p>Damage, temperature, and shear strain fields before final fracture: (<b>a</b>) Full MJC-DC-<span class="html-italic">χ</span>09 at t = 166 μs; (<b>b</b>) Full MJC-DUc-<span class="html-italic">χ</span>09 at t = 222 μs.</p>
Full article ">Figure 11 Cont.
<p>Damage, temperature, and shear strain fields before final fracture: (<b>a</b>) Full MJC-DC-<span class="html-italic">χ</span>09 at t = 166 μs; (<b>b</b>) Full MJC-DUc-<span class="html-italic">χ</span>09 at t = 222 μs.</p>
Full article ">Figure 12
<p>Effect of damage coupling on strain and temperature distributions transversely to ASB center before fracture: (<b>a</b>) effective plastic strain; (<b>b</b>) temperature.</p>
Full article ">Figure 13
<p>Effect of damage coupling on damage and temperature evolution and flow stress inside ASB core: (<b>a</b>) damage (solid line) and temperature (dotted line) evolution vs. time; (<b>b</b>) flow stress vs. time.</p>
Full article ">Figure 14
<p>Effect of damage coupling on blanking force fluctuation and cracking length: (<b>a</b>) force versus time; (<b>b</b>) cracking lengths (upper and lower).</p>
Full article ">Figure 15
<p>Damage, temperature, and shear strain fields before final fracture: (<b>a</b>) Full MJC-DC-<span class="html-italic">χ</span>09 at t = 166 μs; (<b>b</b>) Full MJC-DC-DRX-<span class="html-italic">χ</span>09 at t = 138 μs.</p>
Full article ">Figure 16
<p>DRX effect on strain and temperature distributions transversely to ASB center before fracture: (<b>a</b>) effective plastic strain; (<b>b</b>) temperature.</p>
Full article ">Figure 17
<p>DRX effect on damage and temperature evolution and flow stress inside ASB core: (<b>a</b>) damage (solid line) and temperature (dotted line) evolution vs. time; (<b>b</b>) flow stress vs. time.</p>
Full article ">Figure 18
<p>DRX effect on blanking force fluctuation and cracking length: (<b>a</b>) force vs. time; (<b>b</b>) cracking lengths (upper and lower).</p>
Full article ">Figure 19
<p>Damage, temperature, and shear strain fields before final fracture: (<b>a</b>) Full MJC-DC-<span class="html-italic">χ</span>09 at t = 166 μs; (<b>b</b>) Full MJC-DC-<span class="html-italic">χ</span>07 at t = 180 μs; (<b>c</b>) Full MJC-DC-<span class="html-italic">χ</span>05 at t = 180 μs.</p>
Full article ">Figure 20
<p>Effect of Taylor–Quinney coefficient on strain and temperature distributions transversely to ASB center before fracture: (<b>a</b>) effective plastic strain; (<b>b</b>) temperature.</p>
Full article ">Figure 21
<p>Effect of Taylor–Quinney coefficient on damage and temperature evolution and flow stress inside ASB core: (<b>a</b>) damage (solid line) and temperature (dotted line) evolution vs. time; (<b>b</b>) flow stress vs. time.</p>
Full article ">Figure 22
<p>Effect of Taylor–Quinney coefficient on blanking force fluctuation and cracking length: (<b>a</b>) force versus time; (<b>b</b>) cracking lengths (upper and lower).</p>
Full article ">Scheme 1
<p>Structural-thermal-damage double coupling.</p>
Full article ">Scheme 2
<p>Flowchart of computational algorithm (blue boxes: structural part, red boxes: thermal part, grey boxes: damage part).</p>
Full article ">
11 pages, 3500 KiB  
Article
Non-Newtonian Pressure-Governed Rivulet Flows on Inclined Surface
by Sergey V. Ershkov and Dmytro D. Leshchenko
Mathematics 2024, 12(5), 779; https://doi.org/10.3390/math12050779 - 6 Mar 2024
Cited by 3 | Viewed by 1012
Abstract
We have generalized, in the current study, the results of research presented earlier with the aim of obtaining an approximate solution for the creeping, plane-parallel flow of viscoplastic non-Newtonian fluid where the focus is on the study of rivulet fluid flows on an [...] Read more.
We have generalized, in the current study, the results of research presented earlier with the aim of obtaining an approximate solution for the creeping, plane-parallel flow of viscoplastic non-Newtonian fluid where the focus is on the study of rivulet fluid flows on an inclined surface. Namely, profiles of velocity of flow have been considered to be given in the same form as previously (i.e., Gaussian-like, non-stationary solutions) but with a novel type of pressure field p. The latter has been chosen for solutions correlated explicitly with the critical maximal non-zero level of stress τs in the shared plane layer of rivulet flow, when it begins to move as viscous flow (therefore, we have considered here the purely non-Newtonian case of viscoplastic flow). Correlating phenomena such as the above stem from the equations of motion of viscoplastic non-Newtonian fluid considered along with the continuity equation. We have obtained a governing sub-system of two partial differential equations of the first order for two functions, p and τs. As a result, a set of new semi-analytical solutions are presented and graphically plotted. Full article
(This article belongs to the Section E2: Control Theory and Mechanics)
Show Figures

Figure 1

Figure 1
<p>A <span class="html-italic">schematic</span> plot for pressure <span class="html-italic">p</span> = 4<span class="html-italic">xy</span> + ρ<span class="html-italic">gx</span>sinα (meanings on vertical axis); meanings of {<span class="html-italic">x</span>,<span class="html-italic">y</span>} are scaled for each coordinate with respect to the appropriate horizontal axis.</p>
Full article ">Figure 2
<p>A <span class="html-italic">schematic</span> plot for pressure <span class="html-italic">p</span> = −2cos<span class="html-italic">x</span> cos<span class="html-italic">y</span> + ρ<span class="html-italic">gx</span>sinα (meanings on vertical axis); meanings of {<span class="html-italic">x</span>,<span class="html-italic">y</span>} are scaled for each coordinate with respect to the appropriate horizontal axis.</p>
Full article ">Figure 3
<p>A <span class="html-italic">schematic</span> plot for pressure <span class="html-italic">p</span> = −2sin<span class="html-italic">x</span> sin<span class="html-italic">y</span> + ρ<span class="html-italic">gx</span>sinα (meanings on vertical axis); meanings of {<span class="html-italic">x</span>,<span class="html-italic">y</span>} are scaled for each coordinate with respect to the appropriate horizontal axis.</p>
Full article ">Figure 4
<p>A <span class="html-italic">schematic</span> plot for the critical maximal level of stress τ<sub>s</sub> = <span class="html-italic">x</span><sup>2</sup> + <span class="html-italic">y</span><sup>2</sup> (meanings on the vertical axis); meanings of {<span class="html-italic">x</span>,<span class="html-italic">y</span>} are scaled for each coordinate with respect to the appropriate horizontal axis.</p>
Full article ">Figure 5
<p>A <span class="html-italic">schematic</span> plot for the critical maximal level of stress τ<sub>s</sub> = sin<span class="html-italic">x</span> sin<span class="html-italic">y</span> (meanings on the vertical axis); meanings of {<span class="html-italic">x</span>,<span class="html-italic">y</span>} are scaled for each coordinate with respect to the appropriate horizontal axis.</p>
Full article ">Figure 6
<p>A <span class="html-italic">schematic</span> plot for the critical maximal level of stress τ<sub>s</sub> = cos<span class="html-italic">x</span> cos<span class="html-italic">y</span> (meanings on the vertical axis); meanings of {<span class="html-italic">x</span>,<span class="html-italic">y</span>} are scaled for each coordinate with respect to the appropriate horizontal axis.</p>
Full article ">
26 pages, 7430 KiB  
Article
Rheological Characterization of a Thixotropic Semisolid Slurry by Means of Numerical Simulations of Squeeze-Flow Experiments
by Georgios C. Florides, Georgios C. Georgiou, Michael Modigell and Eugenio José Zoqui
Fluids 2024, 9(2), 36; https://doi.org/10.3390/fluids9020036 - 31 Jan 2024
Viewed by 1539
Abstract
We propose a methodology for the rheological characterization of a semisolid metal slurry using experimental squeeze-flow data. The slurry is modeled as a structural thixotropic viscoplastic material, obeying the regularized Herschel–Bulkley constitutive equation. All rheological parameters are assumed to vary with the structure [...] Read more.
We propose a methodology for the rheological characterization of a semisolid metal slurry using experimental squeeze-flow data. The slurry is modeled as a structural thixotropic viscoplastic material, obeying the regularized Herschel–Bulkley constitutive equation. All rheological parameters are assumed to vary with the structure parameter that is governed by first-order kinetics accounting for the material structure breakdown and build-up. The squeeze flow is simulated using finite elements in a Lagrangian framework. The evolution of the sample height has been studied for wide ranges of the Bingham and Reynolds numbers, the power-law exponent as well as the kinetics parameters of the structure parameter. Systematic comparisons have been carried out with available experimental data on a semisolid aluminum alloy (A356), where the sample is compressed from its top side under a specified strain of 80% at a temperature of 582 °C, while the bottom side remains fixed. Excellent agreement with the experimental data could be achieved provided that at the initial instances (up to 0.01 s) of the experiment, the applied load is much higher than the nominal experimental load and that the yield stress and the power-law exponent vary linearly with the structure parameter. The first assumption implies that a different model, such as an elastoviscoplastic one, needs to be employed during the initial stages of the experiment. As for the second one, the evolution of the sample height can be reproduced allowing the yield stress to vary from 0 (no structure) to a maximum nominal value (full structure) and the power-law exponent from 0.2 to 1.4, i.e., from the shear-thinning to the shear-thickening regime. These variations are consistent with the internal microstructure variation pattern known to be exhibited by semisolid slurries. Full article
(This article belongs to the Collection Complex Fluids)
Show Figures

Figure 1

Figure 1
<p>Experimental data on an A356 alloy at 582 °C (semisolid stage): (<b>a</b>) applied load; (<b>b</b>) evolution of the sample height.</p>
Full article ">Figure 2
<p>Geometry and boundary conditions of squeeze flow when a load is applied on the top plate.</p>
Full article ">Figure 3
<p>Load distributions employed in the numerical simulations: (<b>a</b>) distribution following the experimental data; (<b>b</b>) two-step load with a high value for the first 0.01 s and a lower constant value afterwards equal to nominal maximum experimental load.</p>
Full article ">Figure 4
<p>Effect of the Bingham number for different power-law indices when <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>. The experimental load distribution is imposed.</p>
Full article ">Figure 5
<p>Effect of the Bingham number for different power-law indices when <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>1.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>. The experimental load distribution is imposed.</p>
Full article ">Figure 6
<p>Effect of the Bingham number for different power-law indices when <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>. The experimental load distribution is imposed.</p>
Full article ">Figure 7
<p>Effect of the Reynolds number for different power-law indices when <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.001</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1.2</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math>. The experimental load distribution is imposed.</p>
Full article ">Figure 8
<p>Effect of the power-law index when <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.001</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <mo> </mo> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.001</mn> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.0015</mn> </mrow> </semantics></math>. The experimental load distribution is imposed.</p>
Full article ">Figure 9
<p>Results with variable power-law index for <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.001</mn> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.00025</mn> </mrow> </semantics></math>. The experimental load distribution is imposed.</p>
Full article ">Figure 10
<p>Results for <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.001</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, and various Reynolds numbers when the power-law index varies from 0.6 to 1.4. The experimental load distribution is imposed.</p>
Full article ">Figure 11
<p>Results for <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.00025</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, and various Reynolds numbers when the power-law index varies from 0.2 to 1.4. The experimental load distribution is imposed.</p>
Full article ">Figure 12
<p>Effect of the kinetic parameters <math display="inline"><semantics> <mi>a</mi> </semantics></math> and <math display="inline"><semantics> <mi>b</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.00025</mn> </mrow> </semantics></math>, and the power-law index varies from 0.2 to 1.4. The experimental load distribution is imposed.</p>
Full article ">Figure 13
<p>Experimental data are reproduced when the load in the initial 0.01 s of the experiment is 10 times the nominal maximum experimental load <math display="inline"><semantics> <mrow> <msubsup> <mi>F</mi> <mo>∞</mo> <mo>*</mo> </msubsup> <mrow> <mo>=</mo> <mn>9</mn> <mo> </mo> <mi>kN</mi> </mrow> </mrow> </semantics></math> and the power-law index varies from 0.2 to 1.4. The optimal values of the other parameters are <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.000235</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 14
<p>Effects of (<b>a</b>) the load ratio when <math display="inline"><semantics> <mrow> <msubsup> <mi>t</mi> <mi>c</mi> <mo>*</mo> </msubsup> <mo>=</mo> <mn>0.01</mn> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msubsup> <mi>t</mi> <mi>c</mi> <mo>*</mo> </msubsup> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> and the other parameters are optimal: <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.000235</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math>, and the power-law index varies from 0.2 to 1.4.</p>
Full article ">Figure 15
<p>Effects of (<b>a</b>) the Bingham number when <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math> and (<b>b</b>) the Reynolds number when <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.000235</mn> </mrow> </semantics></math> and the other parameters are optimal: <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mi>b</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math>, the initial load for the first 0.01 s is 90 kN, and the power-law index varies from 0.2 to 1.4.</p>
Full article ">Figure 16
<p>Effects of (<b>a</b>) <math display="inline"><semantics> <mi>a</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mi>b</mi> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math> and the other parameters are optimal: <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.000235</mn> </mrow> </semantics></math>, the power-law index varies from 0.2 to 1.4, and the initial load for the first 0.01 s is 9 kN.</p>
Full article ">Figure 17
<p>Evolution of the structure parameter during compression using the optimal parameters. <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.000235</mn> </mrow> </semantics></math>, the power-law index varies from 0.2 to 1.4, and the initial load for the first 0.01 s is 90 kN.</p>
Full article ">Figure 18
<p>Evolution of the power-law index <span class="html-italic">n</span> during compression using the optimal parameters. <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>0.95</mn> <mo>,</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.000235</mn> </mrow> </semantics></math>, the power-law index varies from 0.2 to 1.4, and the initial load for the first 0.01 s is 90 kN.</p>
Full article ">Figure 19
<p>Evolutions of the mean values of the structure parameter (<math display="inline"><semantics> <mover accent="true"> <mi>λ</mi> <mo>¯</mo> </mover> </semantics></math>) and the power-law index (<math display="inline"><semantics> <mover accent="true"> <mi>n</mi> <mo>¯</mo> </mover> </semantics></math>) during compression using the optimal parameters. <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>0.95</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0.000235</mn> </mrow> </semantics></math>, the power-law index varies from 0.2 to 1.4, and the initial load for the first 0.01 s is 90 kN.</p>
Full article ">Figure 20
<p>Constant structure flow curves for <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> (no structure), 0.25, 0.5, 2/3, and 1 (full structure) and equilibrium flow curve (dotted line) with the estimated material parameters: <math display="inline"><semantics> <mrow> <msubsup> <mi>τ</mi> <mn>0</mn> <mo>*</mo> </msubsup> </mrow> </semantics></math> = 2.27 kPa, <math display="inline"><semantics> <mrow> <msup> <mi>k</mi> <mo>*</mo> </msup> </mrow> </semantics></math> = 230 kg/m/s<sup>0.6</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mrow> <mi>min</mi> </mrow> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mrow> <mi>max</mi> </mrow> </msub> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mi>a</mi> <mo>*</mo> </msup> <mo>=</mo> <mn>1.91</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>3</mn> </msup> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mi>b</mi> </semantics></math> = 0.95, and <math display="inline"><semantics> <mrow> <msup> <mi>c</mi> <mo>*</mo> </msup> <mo>=</mo> <mn>0.20</mn> <mo> </mo> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> </semantics></math>.</p>
Full article ">Figure 21
<p>Variation in (<b>a</b>) the yield stress and (<b>b</b>) the power-law exponent with the shear rate at equilibrium calculated using the estimated material parameters, i.e., <math display="inline"><semantics> <mrow> <msubsup> <mi>τ</mi> <mn>0</mn> <mo>*</mo> </msubsup> </mrow> </semantics></math> = 2.27 kPa, <math display="inline"><semantics> <mrow> <msup> <mi>k</mi> <mo>*</mo> </msup> </mrow> </semantics></math> = 230 kg/m/s<sup>0.6</sup>, <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mrow> <mi>min</mi> </mrow> </msub> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mrow> <mi>max</mi> </mrow> </msub> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mi>a</mi> <mo>*</mo> </msup> <mo>=</mo> <mn>1.91</mn> <mo>×</mo> <msup> <mrow> <mn>10</mn> </mrow> <mn>3</mn> </msup> <msup> <mrow> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mi>b</mi> </semantics></math> = 0.95, and <math display="inline"><semantics> <mrow> <msup> <mi>c</mi> <mo>*</mo> </msup> <mo>=</mo> <mn>0.20</mn> <mo> </mo> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>.</p>
Full article ">
27 pages, 13432 KiB  
Article
Prediction of Plastic Shrinkage Cracking of Supplementary Cementitious Material-Modified Shotcrete Using Rheological and Mechanical Indicators
by Kyong-Ku Yun, Valerii Panov and Seungyeon Han
Materials 2023, 16(24), 7645; https://doi.org/10.3390/ma16247645 - 14 Dec 2023
Cited by 1 | Viewed by 1205
Abstract
Plastic shrinkage cracking is a complex and multifaceted process that occurs in the period between placement and the final setting. During this period, the mixture is viscoplastic in nature and therefore possesses rheological properties. The investigation of the relationship between rheological behavior and [...] Read more.
Plastic shrinkage cracking is a complex and multifaceted process that occurs in the period between placement and the final setting. During this period, the mixture is viscoplastic in nature and therefore possesses rheological properties. The investigation of the relationship between rheological behavior and its propensity to undergo cracking during the plastic phase presents an intriguing subject of study. However, many factors influence plastic cracking, and the corresponding interaction of its effects is complex in nature. This study aimed to evaluate the impact of rheological and physicomechanical properties on the occurrence of plastic cracking in high-performance shotcrete containing various supplementary cementitious materials. To achieve this, plastic cracking was evaluated employing the ASTM C 1579 standard and a smart crack viewer FCV-30, and the rheological parameters were controlled using an ICAR rheometer. In addition, a study was conducted to assess the strength development and fresh properties. Further, a relationship was established via statistical evaluation, and the best predicting models were selected. According to the study results, it can be concluded that high-yield stress and low plastic viscosity for colloidal silica mixtures are indicators of plastic cracking resistance owing to improved fresh microstructure and accelerated hydration reaction. However, earlier strength development and the presence of a water-reducing admixture allowed mixtures containing silica fume to achieve crack reduction. A higher indicator of yield stress is an indicator of the capillary pressure development of these mixtures. In addition, a series containing ultrafine fly ash (having high flow resistance and torque viscosity) exhibited a risk of early capillary pressure build-up and a decrease in strength characteristics, which could be stabilized with the addition of colloidal silica. Consequently, the mixture containing both silica fume and colloidal silica exhibited the best performance. Thus, the results indicated that rheological characteristics, compressive strength, and water-reducer content can be used to control the plastic shrinkage cracking of shotcrete. Full article
(This article belongs to the Section Construction and Building Materials)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Particle size distribution curve of combined aggregates used for shotcrete mixture production.</p>
Full article ">Figure 2
<p>Test plate configuration according to ASTM C 1579.</p>
Full article ">Figure 3
<p>Climate control chamber configuration for creating conditions causing shrinkage cracking.</p>
Full article ">Figure 4
<p>Cracking evaluation. (<b>a</b>) crack measuring procedure, (<b>b</b>) crack viewer FCV-30, and (<b>c</b>) image data after measurement.</p>
Full article ">Figure 4 Cont.
<p>Cracking evaluation. (<b>a</b>) crack measuring procedure, (<b>b</b>) crack viewer FCV-30, and (<b>c</b>) image data after measurement.</p>
Full article ">Figure 5
<p>ICAR Rheometer.</p>
Full article ">Figure 6
<p>AEWR content, resulting slump, and air content for different mixture combinations.</p>
Full article ">Figure 7
<p>Obtained image examples of shotcrete specimens.</p>
Full article ">Figure 8
<p>Resulting average crack widths and <span class="html-italic">CRR</span> coefficients of colloidal silica specimens.</p>
Full article ">Figure 9
<p>Average values of plastic shrinkage cracking and <span class="html-italic">CRR</span> indicators of colloidal silica specimens of various particle dimensions.</p>
Full article ">Figure 10
<p>Average crack width and <span class="html-italic">CRR</span> of silica fume samples.</p>
Full article ">Figure 11
<p>Average width of plastic shrinkage cracks and <span class="html-italic">CRR</span> of mixtures with a combination of SCMs.</p>
Full article ">Figure 12
<p>Compressive and flexural strength development of diverse shotcrete mixtures.</p>
Full article ">Figure 13
<p>Pearson correlation coefficients for all experimentally determined factors and mixtures.</p>
Full article ">Figure 14
<p>Pearson correlation coefficients for explanatory variables used. (<b>a</b>) Model I, (<b>b</b>) Model II, and (<b>c</b>) Model III.</p>
Full article ">Figure 15
<p>Second-order polynomial regression curve of AEWR versus average crack width.</p>
Full article ">Figure 16
<p>Linear regression curve explaining the relationship between flow resistance and average crack width.</p>
Full article ">Figure 17
<p>Logarithmic regression curve explaining the relationship between torque viscosity and average crack width.</p>
Full article ">Figure 18
<p>Second-order polynomial regression curve of compressive strength versus average crack width.</p>
Full article ">Figure 19
<p>Shotcrete yield stress development as a result of spraying and pumpability compaction. Figure adapted from Beupre’s doctoral thesis [<a href="#B69-materials-16-07645" class="html-bibr">69</a>].</p>
Full article ">
19 pages, 5490 KiB  
Article
Compression Behavior and Textures of Ti57-Nb43 Alloy at High Temperatures
by Máté Szűcs, Viktor Kárpáti, Tamás Mikó and László S. Tóth
Materials 2023, 16(22), 7116; https://doi.org/10.3390/ma16227116 - 10 Nov 2023
Cited by 1 | Viewed by 813
Abstract
The mechanical behavior, microstructures, as well as the crystallographic textures of the Ti57-Nb43 alloy were investigated on cylindrical specimens compressed at high temperatures, in the range of 700–1000 °C, and strain rates between 0.001 and 1.0 s−1. Hardening, followed by softening [...] Read more.
The mechanical behavior, microstructures, as well as the crystallographic textures of the Ti57-Nb43 alloy were investigated on cylindrical specimens compressed at high temperatures, in the range of 700–1000 °C, and strain rates between 0.001 and 1.0 s−1. Hardening, followed by softening behaviors, were observed as a function of strain due to the occurrence of dynamic recrystallization/recovery in hot deformation. The modified five-parameter Voce-type equation described well the stress–strain curves, but, for the present alloy, it was also possible with only four parameters. A new two-variables polynomial function was employed on the four parameters that described well the flow curves as a direct function of temperature and strain rate. It permitted the reduction in the number of parameters and had the predictive capacity for the flow stress at any temperature, strain, and strain rate in the investigated range. The crystallographic textures were similar at all temperatures, with an increase in intensity from 900 °C. The textures could be characterized by a double <100> and <111> fiber and a unique component of (001) <110>, the latter inherited from the initial hot-rolling texture. Viscoplastic polycrystal self-consistent deformation modeling reproduced the measured textures showing that dynamic recrystallization did not alter the development of the deformation texture, only increased its intensity. Full article
(This article belongs to the Special Issue Plastic Deformation and Mechanical Behavior of Metallic Materials)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>(<b>a</b>) Heating of the specimen up to the forming temperature. (<b>b</b>) Top view of the compressed material.</p>
Full article ">Figure 2
<p>Geometrical parameters of the specimen after compression.</p>
Full article ">Figure 3
<p>Micrographs of two specimens deformed at 700 °C (<b>a</b>), 800 °C (<b>b</b>), 900 °C (<b>c</b>), and 1000 °C (<b>d</b>), with a strain rate of 0.01 s<sup>−1</sup> to a height reduction of 50%.</p>
Full article ">Figure 4
<p>Measured and fitted flow stress–strain curves of Ti57-Nb43: (<b>a</b>) 0.001 s<sup>−1</sup>, (<b>b</b>) 0.01 s<sup>−1</sup>, (<b>c</b>) 0.1 s<sup>−1</sup>, and (<b>d</b>) 1.0 s<sup>−1</sup>. The fitted curves were derived from Equation (13).</p>
Full article ">Figure 5
<p>(<b>a</b>) Strain rate sensitivity diagram at zero plastic strain. (<b>b</b>) Variation of activation energy as a function of the equivalent plastic strain and strain rate.</p>
Full article ">Figure 6
<p>The measured and their fitted curves obtained using Equation (13) at the compression strain rate of 0.001 s<sup>−1</sup>, at four temperatures. (<b>a</b>) Separate fit for each curve, and (<b>b</b>) fit by using polynomial coefficients for the four parameters, expressed directly as a function of temperature.</p>
Full article ">Figure 7
<p>Dependence of the yield stress, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, and the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>c</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>c</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> parameters in Equation (13) on temperature, for testing at 0.001 s<sup>−1</sup>, at four temperatures.</p>
Full article ">Figure 8
<p>Measured and polynomial-predicted stress–strain curves using the function defined in Equation (15) for the four strain rates and four temperatures.</p>
Full article ">Figure 9
<p>(<b>a</b>–<b>d</b>) Fitted surfaces on the data points of the material parameters as a function of the strain rate and temperature.</p>
Full article ">Figure 10
<p>Measured textures shown in (110) and (100) pole figures. The ideal fibers of b.c.c. rolling textures are also shown with the identification of three major ideal orientations [<a href="#B23-materials-16-07116" class="html-bibr">23</a>].</p>
Full article ">Figure 11
<p>Measured and simulated textures in hot compression of Ti-Nb in three pole figures ((110), (100), and (112)).</p>
Full article ">Figure 12
<p>Simulated texture for an axisymmetric compression of −0.5 using a random initial texture represented by 5000 grain orientations, displayed in (111), (100), and (110) pole figures.</p>
Full article ">
18 pages, 18435 KiB  
Article
The Momentum Transfer Mechanism of a Landslide Intruding a Body of Water
by Zhenzhu Meng, Jianyong Hu, Jinxin Zhang, Lijuan Zhang and Zhenxia Yuan
Sustainability 2023, 15(18), 13940; https://doi.org/10.3390/su151813940 - 20 Sep 2023
Cited by 1 | Viewed by 1127
Abstract
Landslide-generated waves occur as a result of the intrusion of landslides such as mud flows and debris flows into bodies of water such as lakes and reservoirs. The objective of this study was to determine how the momentum is transferred from the sliding [...] Read more.
Landslide-generated waves occur as a result of the intrusion of landslides such as mud flows and debris flows into bodies of water such as lakes and reservoirs. The objective of this study was to determine how the momentum is transferred from the sliding mass to the body of water on the basis of theoretical analysis and physical model experiments. Considering the viscoplastic idealization of natural landslides, the theoretical model was established based on the momentum and mass conservation of a two-phase flow in a control volume. To close the theoretical equations, slide thickness and velocity passing through the left boundary of the control volume were estimated by lubrication theory, and the interaction forces between the slide phase and water phase, including hydrostatic force and drag force, were given by semiempirical equations fitted with experimental data obtained using the particle image velocimetry (PIV) technique. The near-field velocity fields of both the sliding mass and the body of water, as well as the air–water–slide interfaces, were determined from the experiments. The theoretical model was validated by comparing the theoretical and experimental data of the slide thickness and slide velocity, as well as the momentum variations of the two phases in the control volume. Full article
(This article belongs to the Special Issue Slope Stability Analysis and Landslide Disaster Prevention)
Show Figures

Figure 1

Figure 1
<p>Sketch of the physical model: (<b>a</b>) still landslide, (<b>b</b>) moving sliding mass on the slope, (<b>c</b>) landslide entering water.</p>
Full article ">Figure 2
<p>(<b>a</b>) Design of the experimental system, (<b>b</b>) position of the lens and laser, (<b>c</b>) optical design, (<b>d</b>) photo of the experimental set up, and (<b>e</b>) the flume illuminated by laser sheet.</p>
Full article ">Figure 3
<p>The principle of the PIV system.</p>
Full article ">Figure 4
<p>Photos of the (<b>a</b>) transparent Carbopol, (<b>b</b>) colored Carbopol, (<b>c</b>) seeded Carbpol recorded by PIV, and (<b>d</b>) seeded water recorded by PIV system.</p>
Full article ">Figure 5
<p>Evolution of (<b>a</b>) free water surface and (<b>b</b>) slide water interface from <span class="html-italic">t</span> = 0.1 s to 1.0 s.</p>
Full article ">Figure 6
<p>Time variation of velocity field of the submerged landslide: (<b>a</b>) t = 0.1 s, (<b>b</b>) t = 0.2 s, (<b>c</b>) t = 0.3 s, (<b>d</b>) t = 0.4 s, (<b>e</b>) t = 0.5 s, and (<b>f</b>) t = 0.6 s.</p>
Full article ">Figure 7
<p>Time variation of velocity field of the water body in the control volume: (<b>a</b>) t = 0.1 s, (<b>b</b>) t = 0.2 s, (<b>c</b>) t = 0.3 s, (<b>d</b>) t = 0.4 s, (<b>e</b>) t = 0.5 s, and (<b>f</b>) t = 0.6 s.</p>
Full article ">Figure 8
<p>Time variation of the hydrostatic force <b>F</b><math display="inline"><semantics> <msub> <mrow/> <mi>P</mi> </msub> </semantics></math> acting on the slide phase: (<b>a</b>) horizontal projection <math display="inline"><semantics> <msub> <mi>F</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> </semantics></math> and (<b>b</b>) vertical projection <math display="inline"><semantics> <msub> <mi>F</mi> <mrow> <mi>p</mi> <mo>,</mo> <mi>y</mi> </mrow> </msub> </semantics></math>.</p>
Full article ">Figure 9
<p>The comparison of the measured and predicted (<b>a</b>) <math display="inline"><semantics> <msub> <mi>F</mi> <mrow> <mi>p</mi> <mi>m</mi> <mi>x</mi> </mrow> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>t</mi> <mi>a</mi> </msub> </semantics></math>.</p>
Full article ">Figure 10
<p>Time variation of the horizontal and vertical projections of the slide’s center of mass (<b>a</b>) <math display="inline"><semantics> <msub> <mi>c</mi> <mi>x</mi> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>c</mi> <mi>y</mi> </msub> </semantics></math>, the frontal area (<b>c</b>) <math display="inline"><semantics> <msub> <mi>A</mi> <mi>x</mi> </msub> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <msub> <mi>A</mi> <mi>y</mi> </msub> </semantics></math>, and the mean velocities of submerged mass (<b>e</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>u</mi> <mo stretchy="false">¯</mo> </mover> <mi>x</mi> </msub> </semantics></math> and (<b>f</b>) <math display="inline"><semantics> <msub> <mover accent="true"> <mi>u</mi> <mo stretchy="false">¯</mo> </mover> <mi>x</mi> </msub> </semantics></math>.</p>
Full article ">Figure 11
<p>Comparison of the measured and predicted <math display="inline"><semantics> <msub> <mi>A</mi> <mrow> <mi>f</mi> <mi>x</mi> <mi>m</mi> </mrow> </msub> </semantics></math>.</p>
Full article ">Figure 12
<p>Time variations of the momentum of the (<b>a</b>) slide phase <math display="inline"><semantics> <msub> <mi>p</mi> <mi>s</mi> </msub> </semantics></math> and the (<b>b</b>) water phase <math display="inline"><semantics> <msub> <mi>p</mi> <mi>f</mi> </msub> </semantics></math> in the observation window.</p>
Full article ">Figure 13
<p>Theoretical and experimental comparison of (<b>a</b>) <math display="inline"><semantics> <msub> <mi>s</mi> <mn>0</mn> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>u</mi> <mn>0</mn> </msub> </semantics></math> with initial parameters varied randomly in the range of 0.85 m <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msub> <mi>l</mi> <mi>s</mi> </msub> <mo>&lt;</mo> </mrow> </semantics></math> 1.05 m, 0.15 m <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msub> <mi>s</mi> <mi>g</mi> </msub> <mo>&lt;</mo> </mrow> </semantics></math> 0.40 m, 0.2 m <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msub> <mi>l</mi> <mn>0</mn> </msub> <mo>&lt;</mo> </mrow> </semantics></math> 0.4 m, and 60 Pa <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msub> <mi>τ</mi> <mi>c</mi> </msub> <mo>&lt;</mo> </mrow> </semantics></math> 90 Pa.</p>
Full article ">Figure 14
<p>Comparison of (<b>a</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>s</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>f</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> </semantics></math> obtained from experiments with the theoretical estimation (Test 42). Theoretical estimations of (<b>c</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>s</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> </semantics></math> and (<b>d</b>) <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>f</mi> <mo>,</mo> <mi>x</mi> </mrow> </msub> </semantics></math> with the initial settings 0.85 <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msub> <mi>l</mi> <mi>s</mi> </msub> <mo>&lt;</mo> </mrow> </semantics></math> 1.05 m, 0.15 <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msub> <mi>s</mi> <mi>g</mi> </msub> <mo>&lt;</mo> </mrow> </semantics></math> 0.40 m, 0.2 <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msub> <mi>l</mi> <mn>0</mn> </msub> <mo>&lt;</mo> </mrow> </semantics></math> 0.4 m, and 60 <math display="inline"><semantics> <mrow> <mo>&lt;</mo> <msub> <mi>τ</mi> <mi>c</mi> </msub> <mo>&lt;</mo> </mrow> </semantics></math> 90 Pa.</p>
Full article ">Figure 15
<p>Time variation of (<b>a</b>) wave amplitude <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> and (<b>b</b>) wave height <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>.</p>
Full article ">Figure 16
<p>The material and nonmaterial interfaces during the momentum transfer process.</p>
Full article ">
15 pages, 5072 KiB  
Article
Simulation of Mechanical Response in Machining of Ti-6Al-4V Based on Finite Element Model and Visco-Plastic Self-Consistent Model
by Qingqing Wang, Chengli Yang, Haifeng Yang and Yibo He
Metals 2023, 13(8), 1362; https://doi.org/10.3390/met13081362 - 28 Jul 2023
Cited by 1 | Viewed by 1351
Abstract
The predictions of mechanical responses (stress–strain variations) in the machining of Ti-6Al-4V alloy are important to analyze the deformation conditions of machining to optimize the machining parameters and investigate the generation of a machined surface. The selection of a constitutive model is an [...] Read more.
The predictions of mechanical responses (stress–strain variations) in the machining of Ti-6Al-4V alloy are important to analyze the deformation conditions of machining to optimize the machining parameters and investigate the generation of a machined surface. The selection of a constitutive model is an essential factor that determines the deformation behavior in the machining simulation model. In this paper, two constitutive models of a modified Johnson–Cook (JC) equation and visco-plastic self-consistent (VPSC) model were used to investigate the stress–strain evolutions in the machining process of Ti-6Al-4V. A finite element (FE) machining model was established, considering the influences of grain refinement and deformation twins, based on a modified JC equation. The VPSC model was fitted based on the macro-strain rate sensitivity of the JC equation. The prediction results of the stress–strain curves of two models were compared, and their validities were further proved. The results show that flow stress hardening and inhomogeneities are caused by multi-scale grain refinement during the machining process of Ti-6Al-4V. Five slip deformation modes and one compressive twinning mode were activated in the VPSC model to be consistent with the macro-deformation behavior predicted with the FE model. The validations show the effectiveness of the modified JC equation, considering microstructural changes and the fitted VPSC model, in predicting dynamic behavior in the machining process of Ti-6Al-4V. The results provide two aspects of macro-deformation and polycrystal plasticity to elucidate the stress variations that occur during the machining of Ti-6Al-4V. Full article
(This article belongs to the Special Issue High Performance Machining of Difficult-to-Process Metals)
Show Figures

Figure 1

Figure 1
<p>Established machining prediction model.</p>
Full article ">Figure 2
<p>Flowchart of Vuhard.</p>
Full article ">Figure 3
<p>Variations in <math display="inline"><semantics><mrow><mi>k</mi><mrow><mo>(</mo><mover accent="true"><mi>ε</mi><mo>˙</mo></mover><mo>)</mo></mrow></mrow></semantics></math> in the VPSC model with <span class="html-italic">n<sub>s</sub></span> of about 41, <math display="inline"><semantics><mrow><msup><mi>k</mi><mo>′</mo></msup><mrow><mo>(</mo><mover accent="true"><mi>ε</mi><mo>˙</mo></mover><mo>)</mo></mrow></mrow></semantics></math> in the modified JC equation with <span class="html-italic">C</span> of 0.042, and <math display="inline"><semantics><mrow><msup><mi>k</mi><mo>′</mo></msup><mrow><mo>(</mo><mover accent="true"><mi>ε</mi><mo>˙</mo></mover><mo>)</mo></mrow></mrow></semantics></math> in the original JC equation with <span class="html-italic">C</span> of 0.042 (under certain strain of 0.5 and temperature of 400 °C).</p>
Full article ">Figure 4
<p>Threshold stress evolutions of different deformation modes (Color online).</p>
Full article ">Figure 5
<p>Experimental setup of high-speed imaging. (<b>a</b>) Photo of experimental setup; (<b>b</b>) photo of primary shear zone.</p>
Full article ">Figure 6
<p>Prediction results of stress map for Ti-6Al-4V in the machining process. (<b>a</b>) At cutting speed of 100 m/min; (<b>b</b>) at cutting speed of 200 m/min; (<b>c</b>) at cutting speed of 400 m/min; (<b>d</b>) at cutting speed of 500 m/min.</p>
Full article ">Figure 7
<p>Stress–strain curves of defined paths at different cutting speeds.</p>
Full article ">Figure 8
<p>Stress maps predicted with the modified JC equation without considering microstructural changes. (<b>a</b>) At cutting speed of 100 m/min; (<b>b</b>) at cutting speed of 400 m/min.</p>
Full article ">Figure 9
<p>Stress–strain curves predicted with VPSC model. (<b>a</b>) At cutting speed of 100 m/min; (<b>b</b>) at cutting speed of 400 m/min.</p>
Full article ">Figure 10
<p>Comparisons of stress–strain variations predicted with FE model and VPSC model. (<b>a</b>) At cutting speed of 100 m/min; (<b>b</b>) at cutting speed of 400 m/min.</p>
Full article ">Figure 11
<p>Comparison of plastic strain between FE simulation result and DIC measurement at cutting speed of 60 m/min as well as a large feed rate of 0.3 mm/r. (<b>a</b>) FE simulation result; (<b>b</b>) DIC analysis result.</p>
Full article ">Figure 12
<p>Comparison of the SHPB test and the VPSC simulation at certain strain rate of 6000 s<sup>−1</sup>.</p>
Full article ">Figure 13
<p>Crystallographic texture variation in Ti-6Al-4V chip root. (<b>a</b>) IPF map of Ti-6Al-4V chip root; (<b>b</b>) measured PFs; (<b>c</b>) predicted PFs.</p>
Full article ">
21 pages, 2080 KiB  
Article
Pipe Flow of Viscoplastic Fluids and Analytical Predictions of Concrete Pumping Based on the Shear-Stress-Dependent Parabolic Model
by Balnur Zhaidarbek, Kristina Savitskaya and Yanwei Wang
Processes 2023, 11(6), 1745; https://doi.org/10.3390/pr11061745 - 7 Jun 2023
Viewed by 2753
Abstract
This study investigates the Hagen–Poiseuille pipe flow of viscoplastic fluids, focusing on analytical predictions of concrete pumping using the shear-stress-dependent parabolic model, extending analytical studies to a nonlinear rheological model with easily accessible experimental parameters. Research novelty and highlights encompass solving the steady [...] Read more.
This study investigates the Hagen–Poiseuille pipe flow of viscoplastic fluids, focusing on analytical predictions of concrete pumping using the shear-stress-dependent parabolic model, extending analytical studies to a nonlinear rheological model with easily accessible experimental parameters. Research novelty and highlights encompass solving the steady laminar pipe flow for viscoplastic fluids described by the parabolic model, presenting detailed results for the two-fluid parabolic model, and introducing a computational app implementing theoretical findings. The parabolic model outperforms linear models, such as the Bingham model, in accuracy by accounting for the nonlinearity in the flow curves (i.e., shear stress and shear rate relations) of pumped concrete. The influence of rheological parameters on these relations is analyzed, and their versatility is demonstrated by a Wolfram Mathematica-based application program. The analytical approach developed in this work is adaptable for other models with shear stress as the independent variable, offering valuable insights into viscoplastic fluid flows. Full article
(This article belongs to the Section Materials Processes)
Show Figures

Figure 1

Figure 1
<p>Graphical representation of the different scenarios described by the parabolic model given in Equation (<a href="#FD7-processes-11-01745" class="html-disp-formula">7</a>).</p>
Full article ">Figure 2
<p>Relationship between the true yield stress of the parabolic model <math display="inline"><semantics> <mfenced separators="" open="(" close=")"> <msub> <mi>τ</mi> <mn>0</mn> </msub> <mo>/</mo> <msubsup> <mi>τ</mi> <mrow> <mn>0</mn> </mrow> <mi mathvariant="normal">B</mi> </msubsup> </mfenced> </semantics></math> and the dimensionless number, <math display="inline"><semantics> <mi>α</mi> </semantics></math>.</p>
Full article ">Figure 3
<p>The Poiseuille number (Po) is shown as a function of the Bingham number (Bi) for different dimensionless <math display="inline"><semantics> <mi>β</mi> </semantics></math> values (see Equation (<a href="#FD45-processes-11-01745" class="html-disp-formula">45</a>)) for the Hagen–Poiseuille pipe flow of a single fluid that follows the Parabolic model.</p>
Full article ">Figure 4
<p>Influence of <math display="inline"><semantics> <msub> <mi>c</mi> <mi>C</mi> </msub> </semantics></math>-parameter of the bulk concrete: (<b>a</b>) on shear rate vs. shear stress curves predicted by the Parabolic model; (<b>b</b>) on shear stress vs. shear rate curves predicted by the Parabolic model. Unless otherwise specified in the figure, the following values were used: <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>0.6</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>C</mi> </msub> <mo>=</mo> <mn>0.02</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>62.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi mathvariant="script">P</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>60</mn> <mspace width="0.166667em"/> <mi>kPa</mi> <mo>/</mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Shear rate vs. shear stress curves predicted by the Parabolic model: (<b>a</b>) influence of <math display="inline"><semantics> <msub> <mi>a</mi> <mi>C</mi> </msub> </semantics></math>-parameter of the bulk concrete; (<b>b</b>) influence of <math display="inline"><semantics> <msub> <mi>b</mi> <mi>C</mi> </msub> </semantics></math>-parameter of the bulk concrete. Unless otherwise specified in the figure, the following values were used: <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>0.6</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>C</mi> </msub> <mo>=</mo> <mn>0.02</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>62.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi mathvariant="script">P</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>60</mn> <mspace width="0.166667em"/> <mi>kPa</mi> <mo>/</mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>Influence of <math display="inline"><semantics> <msub> <mi>c</mi> <mi>C</mi> </msub> </semantics></math>-parameter of the bulk concrete: (<b>a</b>) on the velocity distribution; (<b>b</b>) on the shear rate distribution predicted by the Parabolic model. Unless otherwise specified in the figure, the following values were used: <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>0.6</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>C</mi> </msub> <mo>=</mo> <mn>0.02</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>62.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi mathvariant="script">P</mi> <mo>/</mo> <mi>L</mi> <mo>=</mo> <mn>60</mn> <mspace width="0.166667em"/> <mi>kPa</mi> <mo>/</mo> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 7
<p>(<b>a</b>) Influence of <math display="inline"><semantics> <msub> <mi>c</mi> <mi>C</mi> </msub> </semantics></math>-parameter on volume flow rate vs. pressure loss per unit pipe for the single fluid flow predicted by the Parabolic model; (<b>b</b>) contour plot of the volumetric flow rate <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Q</mi> <mo>)</mo> </mrow> </semantics></math> as a function of the pressure loss per unit pipe length, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi mathvariant="script">P</mi> <mo>/</mo> <mi>L</mi> </mrow> </semantics></math>, as the x-axis and <math display="inline"><semantics> <msub> <mi>c</mi> <mi>C</mi> </msub> </semantics></math>-parameter on the y-axis. Unless otherwise specified in the figure, the following values were used: <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>0.6</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>C</mi> </msub> <mo>=</mo> <mn>0.02</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>62.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>Volume flow rate vs. pressure loss per unit pipe length for the dual fluid flow predicted by the Parabolic model: (<b>a</b>) influence of <math display="inline"><semantics> <msub> <mi>c</mi> <mi>C</mi> </msub> </semantics></math>-parameter of the bulk concrete with <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> <mspace width="0.166667em"/> <mo>·</mo> <mspace width="0.166667em"/> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> for the fluid in the lubrication layer; (<b>b</b>) influence of <math display="inline"><semantics> <msub> <mi>c</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> </semantics></math>-parameter of the fluid in the lubrication layer with <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> for the bulk concrete. Unless otherwise specified in the figure, the following values were used: <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>0.6</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>C</mi> </msub> <mo>=</mo> <mn>0.02</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mn>3.5</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mn>0.2</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> <mspace width="0.166667em"/> <mo>·</mo> <mspace width="0.166667em"/> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>62.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>ℓ</mo> <mo>=</mo> <mn>1.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>Contour plots of the volumetric flow rate <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Q</mi> <mo>)</mo> </mrow> </semantics></math> as a function of the pressure loss per unit pipe length, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi mathvariant="script">P</mi> <mo>/</mo> <mi>L</mi> </mrow> </semantics></math>, as the x-axis and <span class="html-italic">a</span>-parameter on the y-axis: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>a</mi> <mi>C</mi> </msub> </semantics></math>-parameter of the bulk concrete on the y-axis; (<b>b</b>) <math display="inline"><semantics> <msub> <mi>a</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> </semantics></math>-parameter of the fluid in the lubrication layer on the y-axis. Unless otherwise specified in the figure, the following values were used: <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>0.6</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>C</mi> </msub> <mo>=</mo> <mn>0.02</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mn>3.5</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mn>0.2</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> <mspace width="0.166667em"/> <mo>·</mo> <mspace width="0.166667em"/> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>62.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>ℓ</mo> <mo>=</mo> <mn>1.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>Contour plots of the volumetric flow rate <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>Q</mi> <mo>)</mo> </mrow> </semantics></math> as a function of the pressure loss per unit pipe length, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi mathvariant="script">P</mi> <mo>/</mo> <mi>L</mi> </mrow> </semantics></math>, as the x-axis and <span class="html-italic">b</span>-parameter on the y-axis: (<b>a</b>) <math display="inline"><semantics> <msub> <mi>b</mi> <mi>C</mi> </msub> </semantics></math>-parameter of the bulk concrete on the y-axis; (<b>b</b>) <math display="inline"><semantics> <msub> <mi>b</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> </semantics></math>-parameter of the fluid in the lubrication layer on the y-axis. Unless otherwise specified in the figure, the following values were used: <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>0.6</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>C</mi> </msub> <mo>=</mo> <mn>0.02</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mn>3.5</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mn>0.2</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> <mspace width="0.166667em"/> <mo>·</mo> <mspace width="0.166667em"/> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>62.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>ℓ</mo> <mo>=</mo> <mn>1.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>The graphical user interface of a Wolfram-based demonstration App that presents the volume flow rate vs. pressure loss per unit pipe length curve for the dual-fluid pipe flow of the Parabolic model (See Equation (<a href="#FD53-processes-11-01745" class="html-disp-formula">53</a>)). The following values were used for the computational app in the figure: <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>0.6</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mi>C</mi> </msub> <mo>=</mo> <mn>0.02</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>C</mi> </msub> <mo>=</mo> <mo>−</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>a</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>6</mn> <mspace width="0.166667em"/> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>b</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mn>0.2</mn> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mrow> <mi mathvariant="normal">L</mi> <mi mathvariant="normal">L</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1.5</mn> <mspace width="0.166667em"/> <mo>·</mo> <mspace width="0.166667em"/> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </msup> <mspace width="0.166667em"/> <msup> <mi>Pa</mi> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <msup> <mi mathvariant="normal">s</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>62.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>ℓ</mo> <mo>=</mo> <mn>1.5</mn> <mspace width="0.166667em"/> <mi>mm</mi> </mrow> </semantics></math>.</p>
Full article ">
22 pages, 3150 KiB  
Article
Numerical Study of Viscoplastic Flows Using a Multigrid Initialization Algorithm
by Souhail Maazioui, Imad Kissami, Fayssal Benkhaldoun and Driss Ouazar
Algorithms 2023, 16(1), 50; https://doi.org/10.3390/a16010050 - 11 Jan 2023
Cited by 3 | Viewed by 2047
Abstract
In this paper, an innovative methodology to handle the numerical simulation of viscoplastic flows is proposed based on a multigrid initialization algorithm in conjunction with the SIMPLE procedure. The governing equations for incompressible flow, which consist of continuity and momentum equations, are solved [...] Read more.
In this paper, an innovative methodology to handle the numerical simulation of viscoplastic flows is proposed based on a multigrid initialization algorithm in conjunction with the SIMPLE procedure. The governing equations for incompressible flow, which consist of continuity and momentum equations, are solved on a collocated grid by combining the finite volume discretization and Rhie and chow interpolation for pressure–velocity coupling. Using the proposed solver in combination with the regularization scheme of Papanastasiou, we chose the square lid-driven cavity flow and pipe flow as test cases for validation and discussion. In doing so, we study the influence of the Bingham number and the Reynolds number on the development of rigid areas and the features of the vortices within the flow domain. Pipe flow results illustrate the flow’s response to the stress growth parameter values. We show that the representation of the yield surface and the plug zone is influenced by the chosen value. Regarding viscoplastic flows, our experiments demonstrate that our approach based on using the multigrid method as an initialization procedure makes a significant contribution by outperforming the classic single grid method. A computation speed-up ratio of 6.45 was achieved for the finest grid size (320 × 320). Full article
(This article belongs to the Topic Advances in Computational Materials Sciences)
Show Figures

Figure 1

Figure 1
<p>Papanastasiou regularization for the Bingham model: stress magnitude (<b>left</b>) and apparent viscosity (<b>right</b>) as functions of the magnitude of the shear rate tensor <math display="inline"><semantics> <mover accent="true"> <mi>γ</mi> <mo>˙</mo> </mover> </semantics></math>.</p>
Full article ">Figure 2
<p>Two-dimensional orthogonal grid. N, S, E and W correspond to neighbor cells of cell P; n, s, e and w denote cell P faces; <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>x</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>y</mi> </mrow> </semantics></math> are cell P dimensions in the x and y spatial coordinates; <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>y</mi> <mi>n</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>y</mi> <mi>s</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>x</mi> <mi>e</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>δ</mi> <msub> <mi>x</mi> <mi>w</mi> </msub> </mrow> </semantics></math> correspond to cell-center to cell-center distances from cell P to neighbor cells.</p>
Full article ">Figure 3
<p>Illustration of different multigrid cycle strategies: (<b>a</b>) V-cycle, (<b>b</b>) W-cycle.</p>
Full article ">Figure 4
<p>Geometry of a cavity with moving lid.</p>
Full article ">Figure 5
<p>Geometry of a two-dimensional pipe of circular cross-section. Dimensions are height <span class="html-italic">H</span> and length <span class="html-italic">L</span>.</p>
Full article ">Figure 6
<p>Sections of velocity along the vertical (<b>a</b>) and horizontal (<b>b</b>) mid-planes, <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics></math> [<a href="#B36-algorithms-16-00050" class="html-bibr">36</a>].</p>
Full article ">Figure 7
<p>Principal vortex position for various <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> </mrow> </semantics></math> numbers at <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>3</mn> </msup> </mrow> </semantics></math>. Reference results retrieved from Vola et al. [<a href="#B37-algorithms-16-00050" class="html-bibr">37</a>] and Prashant et al. [<a href="#B38-algorithms-16-00050" class="html-bibr">38</a>].</p>
Full article ">Figure 8
<p>Streamline contours in Bingham flow for <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 9
<p>Streamline contours in Bingham flow for <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 10
<p>Streamline contours in Bingham flow for <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>Numerical results: the developing velocity profile of a Bingham fluid entering the pipe, <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>2</mn> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 12
<p>Velocity profiles at <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>0.5</mn> <mo> </mo> <mi mathvariant="normal">L</mi> </mrow> </semantics></math> calculated with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> (solid lines), <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> (dashed lines), <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>400</mn> </mrow> </semantics></math> (dotted lines), and <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>800</mn> </mrow> </semantics></math> (red dotted lines). For <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>2</mn> </msup> </mrow> </semantics></math>, (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, and (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 13
<p>Apparent viscosity profiles along the vertical section <math display="inline"><semantics> <mrow> <mi>x</mi> <mo>=</mo> <mn>0.5</mn> <mo> </mo> <mi mathvariant="normal">L</mi> </mrow> </semantics></math> calculated with <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> (solid lines) and <math display="inline"><semantics> <mrow> <mi>m</mi> <mo>=</mo> <mn>800</mn> </mrow> </semantics></math> (red dotted lines), <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>2</mn> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 14
<p>The <math display="inline"><semantics> <msup> <mi>L</mi> <mo>∞</mo> </msup> </semantics></math> norm of <span class="html-italic">u</span> and <span class="html-italic">v</span> residuals as a function of the number of SIMPLE iterations on the fine grid, for <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>n</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, 1, and 10 (<math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>2</mn> </msup> </mrow> </semantics></math>). Results are shown for simulations with single-grid (SG) in red lines and multi-grid (MG) initialization in blue lines.</p>
Full article ">
Back to TopTop