A coupled discrete element model for the simulation of soil and water flow through an orifice

Tang, Yao; Chan, Dave; Zhu, David Z.

doi:10.1002/nag.2677

Cited by 50 publications

(14 citation statements)

References 33 publications

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“…DEM is a rapidly developing tool that has largely impacted the particle technology sector (Höhner, Wirtz & Scherer 2013;Vidyapati & Subramaniam 2013;Bertuola et al 2016;Mandal & Khakhar 2016;Zhang et al 2018a,b). Since its first formulation, there has been a great improvement in DEM in solving many micromechanical problems (Chen & Qiu 2012) such as soil consolidation (Cui, Chan, & Nouri 2017), erosion (Tang, Chan, & Zhu 2017), debris flow entrainment (Payne et al 2008), granular flow mobility (Xu, Hu & Gao 2016;Ding & Xu 2018) and soil irregular vibration (Zhang et al 2016;Zhang et al 2018a,b).…”

Section: Governing Equations -Particle Systemmentioning

confidence: 99%

High-resolution fluid–particle interactions: a machine learning approach

Davydzenka

Tahmasebi

2022

J. Fluid Mech.

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Modelling of fluid–particle interactions is a major area of research in many fields of science and engineering. There are several techniques that allow modelling of such interactions, among which the coupling of computational fluid dynamics (CFD) and the discrete element method (DEM) is one of the most convenient solutions due to the balance between accuracy and computational costs. However, the accuracy of this method is largely dependent upon mesh size, where obtaining realistic results always comes with the necessity of using a small mesh and thereby increasing computational intensity. To compensate for the inaccuracies of using a large mesh in such modelling, and still take advantage of rapid computations, we extended the classical modelling by combining it with a machine learning model. We have conducted seven simulations where the first one is a numerical model with a fine mesh (i.e. ground truth) with a very high computational time and accuracy, the next three models are constructed on coarse meshes with considerably less accuracy and computational burden and the last three models are assisted by machine learning, where we can obtain large improvements in terms of observing fine-scale features yet based on a coarse mesh. The results of this study show that there is a great opportunity in machine learning towards improving classical fluid–particle modelling approaches by producing highly accurate models for large-scale systems in a reasonable time.

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Section: Governing Equations -Particle Systemmentioning

confidence: 99%

High-resolution fluid–particle interactions: a machine learning approach

Davydzenka

Tahmasebi

2022

J. Fluid Mech.

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“…In the CFD–DEM method, the Darcy fluid model has been proven to be an effective and efficient method for predicting fluid behaviour using an appropriate permeability (Cui et al, 2016; Goodarzi et al, 2014; Tang et al, 2017). The Darcy fluid model was adopted for the fluid flow simulation in this study.…”

Section: Coupled Cfd–dem Modelmentioning

confidence: 99%

Fluid–solid coupled model for the internal erosion of gap‐graded soil–rock mixtures with different fines contents: Its verification and application

Cao

Sun

Xie

et al. 2022

Hydrological Processes

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Soil-rock mixtures are widely encountered in geotechnical engineering projects.Gap-graded soil-rock mixtures are prone to internal erosion because the fine particles are easily removed by seepage flow within the large pores between the coarse particles. Internal erosion has been the focus of geological disaster research. As concluded from different studies, the fines content (FC) of gap-graded soils is a crucial factor in controlling soil stability. For this reason, a fluid-solid coupled model with the computational fluid dynamics-the discrete element method, with an indoor physical permeability test, was conducted to study the evolution characteristics of internal erosion of gap-graded soil-rock mixtures of different FCs. After the erosion test, the vertical section of the sample was divided into three areas according to changes in fine particles: top, middle uniform, and bottom loss areas. The study showed that a large number of particles in the bottom loss areas are lost at first and the top loss area will enter the middle uniform loss areas. The fine particles in the middle are affected by the fine particle content, from accumulation to equilibrium to loss. Fine particle loss occurred at different sample heights. The top particle loss is the most serious, followed by the bottom and then the middle, and this is consistent with the changes, from a particle-size perspective. An FC of 35% may be the critical value of the coarse-fine particle skeleton structure in the preset working conditions of coarse and fine particle diameters. There are apparent 'island' effects in the sample where FC = 40%. This can be analysed through strong force chain analysis, from the particle-size perspective that 'island' effects continue to disappear under the influence of internal erosion.

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“…Moreover, it is numerically and theoretically challenging to capture the transition between initial seepage flows and potential fluidization that may occur at high inlet pressure. A few studies have attempted to numerically tackle the problem of internal soil fluidization due to upward water jet using various methods such as the coupled Lattice Boltzmann Method-DEM (LBM-DEM) [2,35] or continuum-based models [12], or using simplified analytical techniques [13]. A few issues, however, still need to be addressed in order to be able to practically incorporate these models in real-life prob-lems.…”

Section: Simulation Of Internal Soil Erosion Around Leaking Pipesmentioning

confidence: 99%

Continuum-Based Approach to Model Particulate Soil–Water Interaction: Model Validation and Insight into Internal Erosion

Ibrahim

Meguid

2021

Processes

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Resolving the interaction between soil and water is critical to understanding a wide range of geotechnical applications. In cases when hydrodynamic forces are dominant and soil fluidization is expected, it is necessary to account for the microscale interactions between soil and water. Some of the existing models such as coupled Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) can capture microscale interactions quite accurately. However, it is often computationally expensive and cannot be easily applied at a scale that would aid the design process. Contrastingly, continuum-based models such as the Two-Fluid Model (TFM) can be a computationally feasible and scalable alternative. In this study, we explored the potential of the TFM to simulate granular soil–water interactions. The model was validated by simulating the internal fluidization of a sand bed due to an upward water jet. Analogous to leakage from a pressurized pipe, the simulation was compared with the available experimental data to evaluate the model performance. The numerical results showed decent agreement with the experimental data in terms of excess pore water pressure, fluidization patterns, and physical deformations in violent flow regimes. Moreover, detailed soil characteristics such as particle size distribution could be implemented, which was previously considered a shortcoming of the model. Overall, the model’s performance indicates that TFM is a viable tool for the simulation of particulate soil–water mixtures.

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A coupled discrete element model for the simulation of soil and water flow through an orifice

Cited by 50 publications

References 33 publications

High-resolution fluid–particle interactions: a machine learning approach

High-resolution fluid–particle interactions: a machine learning approach

Fluid–solid coupled model for the internal erosion of gap‐graded soil–rock mixtures with different fines contents: Its verification and application

Continuum-Based Approach to Model Particulate Soil–Water Interaction: Model Validation and Insight into Internal Erosion

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