Predicting the permeability coefficient of polydispersed sand via coupled CFD–DEM simulations

Xu, Shanlin; Zhu, Yanzhen; Cai, Yuanqiang; Sun, Honglei; Cao, Hongtao; Shi, Jun-Qiang

doi:10.1016/j.compgeo.2022.104634

Cited by 30 publications

(4 citation statements)

References 43 publications

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“…Our results further the research conducted by other scholars through seepage tests [58,59] and discrete element simulations [60][61][62][63], microscopically confirming, based on CT images, that the hydraulic conductivity of coarse-grained soil primarily depends on the characteristic particle size d 20 , porosity, and aspect ratio of the particles. Moreover, our proposed formula predicting the hydraulic conductivity of coarse-grained soil based on CT image analysis is more precise, demonstrating a higher value for engineering applications.…”

Section: Discussionsupporting

confidence: 90%

Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils

Peng

Shen

Zhang

et al. 2023

Water

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Permeability characteristics in coarse-grained soil is pivotal for enhancing the understanding of its seepage behavior and effectively managing it, directly impacting the design, construction, and operational safety of embankment dams. Furthermore, these insights bridge diverse disciplines, including hydrogeology, civil engineering, and environmental science, broadening their application and relevance. In this novel research, we leverage a Convolutional Neural Network (CNN) model to achieve the accurate segmentation of coarse-grained soil CT images, surpassing traditional methods in precision and opening new avenues in soil granulometric analysis. The three-dimensional (3D) models reconstructed from the segmented images attest to the effectiveness of our CNN model, highlighting its potential for automation and precision in soil-particle analysis. Our study uncovers and validates new empirical formulae for the ideal particle size and the discount factor in coarse-grained soils. The robust linear correlation underlying these formulae deepens our understanding of soil granulometric characteristics and predicts their hydraulic behavior under varying gradients. This advancement holds immense value for soil-related engineering and hydraulic applications. Furthermore, our findings underscore the significant influence of granular composition, particularly the concentration of fine particles, on the tortuosity of water-flow paths and the discount factor. The practical implications extend to multiple fields, including water conservancy and geotechnical engineering. Altogether, our research represents a significant step in soil hydrodynamics research, where the CNN model’s application unveils key insights into soil granulometry and hydraulic conductivity, laying a strong foundation for future research and applications.

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Section: Discussionsupporting

confidence: 90%

Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils

Peng

Shen

Zhang

et al. 2023

Water

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“…In this study, dense specimens with different aspect ratios were generated, with their homogeneity being established by Xu et al [61]. Subsequently, isotropic consolidation was carried out on these specimens, utilizing a six servo-wall apparatus.…”

Section: Simulation Programmentioning

confidence: 99%

Investigation on the mechanism of size effect on suffusion via CFD-DEM simulations

Zhu,

Hu,

et al. 2023

Preprint

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Suffusion is a critical issue in geotechnical engineering. Despite extensive studies, the effect of soil specimen dimensions on suffusion remains unclear. In this paper, a coupled computational fluid dynamics and discrete element method (CFD-DEM) approach is employed to study the suffusion of gap-graded soils with varying aspect ratios, and the underlying physical mechanisms are discussed. The results indicate that as the aspect ratio increases, erosion degree, mechanical coordination numbers, and unevenness in the fines distribution decrease, while the likelihood of fine particles integrating into the soil skeleton rises. Before suffusion, specimens with lower aspect ratios show higher peak strengths. After suffusion, peak strength decreases with erosion degree. However, all specimens exhibit comparable residual strengths. The mechanism behind different suffusion behaviors in specimens with varying aspect ratios is primarily governed by their unique suffusion boundary conditions. Accounting for suffusion boundaries significantly modify erosion laws and eroded soil mechanics behaviors. A standardized specimen size is proposed to account for suffusion boundary effects, thereby minimizing errors attributed to variations in outlet sieve aperture sizes and inconsistencies in specimen dimensions. The results obtained highlight the influence of specimen size on suffusion, advancing our precise understanding of eroded soil behavior and furthering the development of phenomenological constitutive models.

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“…The permeability coefficient is a comprehensive index of the permeability of a dam and is generally related to the particle size, shape, and porosity of the dam material [41,42]. As shown in Figure 6, there is a significant decrease in the permeability coefficient of the dams at the beginning of the experiments and it varies with time [43,44].…”

Section: Permeable Coefficientmentioning

confidence: 99%

Experimental Study on the Influence of Different Dam Body on the Sediment Interception and Discharge Capacity of the Cascade Permeable Dams

Liu,

Zhou,

Pan

et al. 2023

Applied Sciences

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Sediment deposition is an ecological and environmental problem faced by most water bodies. In view of the poor structural stability and unrepeatable use of existing permeable structures, this paper proposes a cascade permeable dam, which consists of four dam bodies. As the composition of the dam material is the key to sediment interception and discharge capacity, this study sets up two groups of dam material particle sizes for experiments. The results show that the sediment interception performance of the cascade permeable dam is good. When the dam material with a small particle size is selected, the percentage of intercepted sediment mass inside the four dam bodies is 75–89%. The interception sediment rate is much greater than that of the dam material with a large particle size, which tends to decline one by one along the flow direction. The discharge capacity of the dam gradually decreases with time, and there is an obvious decrease in the permeability coefficient of 1# dam. The results of this study provide programmatic support for reducing sediment entering shallow lakes and rivers, which can further optimize the structure design of permeable dams.

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Predicting the permeability coefficient of polydispersed sand via coupled CFD–DEM simulations

Cited by 30 publications

References 43 publications

Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils

Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils

Investigation on the mechanism of size effect on suffusion via CFD-DEM simulations

Experimental Study on the Influence of Different Dam Body on the Sediment Interception and Discharge Capacity of the Cascade Permeable Dams

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