Two-dimensional numerical simulation of flow around three-stranded rope

Wang, Xinxin; Wu, Rong; Huang, Liuyi; Zhao, Fenfang; Peng, Sun

doi:10.1007/s11802-016-2888-x

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…It demonstrates that when the resolution reaches 60 ppi, c v is 0.00236, 0.00061, and 0.00028, respectively, and R = 60 ppi can be thought of as the optimal resolution. Secondly, in Section 3.2.2, 225 kinds of seepage test simulations were carried out for the SRM model under the four segmentation types, and it was found that the model permeability obtained by the center segmentation type under the same model feature size was the least discrete, which was also consistent with other relevant research conclusions [10]. Thirdly, center segmentation type was used in Section 3.2.3 to create a model with resolutions of R = 60 ppi and S = 100 mm.…”

Section: Discussionsupporting

confidence: 80%

“…This can also be indicated by the flow velocity cloud map in Figure 9. Furthermore, the dispersion degree of model permeability obtained by the four segmentation types under the same S is in order: center < random < equal < top (using SRM-1, S = 50 mm as an example, cv-R = 0.1684, cv-C = 0, cv-T = 0.4365, and cv-E = 0.3729), which is consistent with other relevant research conclusions [10]. Although the random segmentation type has good stability under certain conditions and is used by many researchers, it inevitably has great uncertainty and requires a large number of model data as support to produce the most stable and accurate permeability results.…”

Section: Segmentation Typesupporting

confidence: 87%

“…According to the analysis by Chen et al [9] about the causes of the pore size effect in low permeability clay seepage, a microscale seepage theory model of the pore size effect was proposed. Wang [10] examined the statistical characteristics and size effect of the permeability coefficient of samples with different rock content and tested the ratio of sample side length to the maximum particle size of block stone (millimeter scale) to determine the effect of rock content on the permeability coefficient and its REV. A rock mass seepage test was performed by Liu et al [11] by using the boundary element method after nine two-dimensional (2D) rock mass networks of various sizes were built using the Monte Carlo method.…”

Section: Introductionmentioning

confidence: 99%

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Lattice Boltzmann Numerical Study on Mesoscopic Seepage Characteristics of Soil–Rock Mixture Considering Size Effect

Cai

Mao

et al. 2023

Mathematics

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One of the hot topics in the study of rock and soil hydraulics is the size effect of a soil–rock mixture’s (SRM) seepage characteristics. The seepage process of the SRM was simulated from the pore scale through the lattice Boltzmann method (LBM) in this paper to explore the internal influence mechanism of sample size effect on the SRM seepage characteristics. SRM samples were generated using the improved Monte Carlo method (IMCM), and through 342 simulation test conditions the influence of size feature parameters such as resolution (R), segmentation type, model feature size (S), feature length ratio (F), and soil/rock particle size feature ratio (P) was examined. The study demonstrated that as R increases, the permeability of the SRM gradually rises and tends to stabilize when R reaches 60 ppi. At the same S, the dispersion degree of model permeability obtained by the four segmentation types is in the order of center < random < equal < top. With an increase in S, the permeability (k) of the SRM gradually decreases, conforming to the dimensionless mathematical model, k=a0·S−b0, and tends to stabilize at S = 80 mm. With an increase in F and an increase in S, the permeability of the SRM exhibits a linear “zonal” distribution that declines in order. When F is greater than 12, the dispersion of the permeability value distribution is especially small. With an increase in P, the permeability of the SRM decreases gradually before rising abruptly. P is crucial for the grading and structural makeup of the SRM. Overall, this paper concludes that the conditions of R = 60 ppi, center segmentation type, S = 80 mm, F ≥ 12, and P set by demand can be used to select and generate the size of the SRM optimal representative elementary volume (REV) numerical calculation model. The SRM can serve as a general reference for test and engineering construction as a common geotechnical engineering material.

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Section: Discussionsupporting

confidence: 80%

Section: Segmentation Typesupporting

confidence: 87%