2.1. Granular flow model

Within the framework of continuum mechanics, the governing equations dictating a granular flow include the mass conservation

(2.3)\begin{equation} \frac{{\rm d}\rho}{{\rm d} t}=-\rho\,\boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol u, \end{equation}

and the linear momentum conservation

(2.4)\begin{equation} \frac{{\rm d}\boldsymbol u}{{\rm d} t}=\frac{1}{\rho}\,\boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol\sigma+\boldsymbol g. \end{equation}

Here, ${\rm d}(\ \cdot\ )/{\rm d} t=\partial (\ \cdot\ )/\partial t+\boldsymbol u\boldsymbol {\cdot }\boldsymbol {\nabla }(\ \cdot\ )$ is the material derivative, $\rho$ represents the bulk density; $\boldsymbol u$ is the velocity vector; $\boldsymbol \sigma$ denotes the stress tensor; and $\boldsymbol g$ refers to gravity, which is the most common external body force in granular flows. In this work, the granular mass is assumed incompressible, i.e. the bulk density $\rho$ remains constant, so (2.3) can be deactivated.

Often, granular flow is considered within hydrodynamics by Navier–Stokes equations, in which the non-Newtonian viscosity is deployed to describe the behaviour of granular material (Gilberg & Steiner Reference Gilberg and Steiner2020). However, such a model is incompatible with the solid-like behaviours of the granular mass in the quasi-static state (Yang et al. Reference Yang, Zheng, Bai and Yu2021). Therefore, we use a regularized $\mu (I)$-rheology-based elastoplastic model in this study (Zhu, Peng & Wu Reference Zhu, Peng and Wu2022a). Details will be presented in § 2.3.

2.2. Segregation–diffusion model

To close the governing equations for the segregation dynamics of a bidisperse system, we need the so-called segregation–diffusion equation proposed by Gray & Chugunov (Reference Gray and Chugunov2006), which is formulated initially in the Eulerian framework. In this work, we first propose the following Lagrangian description:

(2.5)\begin{equation} \frac{{\rm d}C^{S}}{{\rm d}t}=-C^{S}\,\boldsymbol{\nabla}\boldsymbol{\cdot}\boldsymbol u-\boldsymbol{\nabla}_{g}Q+\boldsymbol{\nabla}\boldsymbol{\cdot}(D\,\boldsymbol{\nabla} C^{S} ). \end{equation}

In this equation, $\boldsymbol {\nabla }_{g}$ denotes the gradient along the direction of gravity;

(2.6)\begin{equation} Q=VF \end{equation}

denotes the segregation flux, with $V$ the segregation velocity, and $F$ the segregation function, whose specific expression will be given subsequently; and $D\,\boldsymbol {\nabla } C^{S}$ is the diffusion flux, with $D$ the diffusivity. The segregation velocity $V$ and diffusivity $D$ have similar scaling relations with the shear rate magnitude $\dot \gamma$ and average grain diameter $d_{avg}$ such that (Savage & Dai Reference Savage and Dai1993; Hajra & Khakhar Reference Hajra and Khakhar2002)

(2.7a,b)\begin{equation} D=\chi_{D}\dot\gamma d_{avg}^2,\quad V=\chi_{V}\dot\gamma d_{avg}. \end{equation}

Here, $\chi _{D}$ and $\chi _{V}$ are material coefficients; $\dot \gamma =\sqrt {2\dot {\boldsymbol e}:\dot {\boldsymbol e}}$ (with $:$ denoting the tensor inner product), where $\dot {\boldsymbol e}=\dot {\boldsymbol \varepsilon }-\dot \varepsilon _v\boldsymbol{\mathsf{I}}$ represents the deviatoric strain rate, with $\dot {\boldsymbol \varepsilon }=0.5(\boldsymbol u\,\boldsymbol {\nabla }+\boldsymbol {\nabla }\boldsymbol u)$ and $\dot \varepsilon _v=\textrm {tr}(\dot {\boldsymbol \varepsilon })/3$ (with $\textrm {tr}(\ \cdot\ )$ denoting the trace), and $\boldsymbol{\mathsf{I}}$ is the identity tensor: $d_{avg}=d^{S}C^{S}+d^{L}C^{L}$ with $d$ referring to the grain diameter. It should be noted that (2.7a,b) are indeed simplified, as both segregation velocity and diffusivity are found to rely on many other factors, such as frictional coefficient, inertial number, lithostatic pressure and grain radius ratio (Fry et al. Reference Fry, Umbanhowar, Ottino and Lueptow2019; Bancroft & Johnson Reference Bancroft and Johnson2021; Trewhela, Ancey & Gray Reference Trewhela, Ancey and Gray2021a). Also, in some studies, $V$ and $D$ are taken as constants, resulting in consistent agreement with experimental data (Van der Vaart et al. Reference Van der Vaart, Gajjar, Épely-Chauvin, Andreini, Gray and Ancey2015; Yang et al. Reference Yang, Zheng, Bai and Yu2021). Therefore, the effect of using constant or functional forms of $V$ and $D$ will be discussed in § 5. From (2.5), we can identify three factors that influence the temporal evolution of the local concentration of the small-size grain on a material point. The first term on the right-hand side of (2.5) reflects the contribution from the volume change, while the second accounts for the particle-size segregation, and the last for diffusive remixing. As mentioned above, this study is restricted to the incompressible granular flow, so that the first term on the right-hand side in (2.5) can be disregarded. Since the unity condition, i.e. (2.2), holds everywhere, and only $C^{S}$ is utilized in the rest of the paper, the superscript is omitted for brevity.

We proceed to consider the segregation function in (2.5). The following quadratic segregation function is used in a number of studies (Dolgunin & Ukolov Reference Dolgunin and Ukolov1995; Gray & Thornton Reference Gray and Thornton2005; Fan et al. Reference Fan, Schlick, Umbanhowar, Ottino and Lueptow2014):

(2.8)\begin{equation} F=C(1-C). \end{equation}

Equation (2.8) is also named the symmetric flux function as it is symmetric about $C=0.5$, where both large and small grains have equal but opposite segregation flux. Despite its success in modelling segregation, the quadratic form may sometimes deviate from reality. For instance, Golick & Daniels (Reference Golick and Daniels2009) observed that the downward penetration of a small grain through a bidisperse matrix in an annular ring shear cell is faster than the rising of a large grain through the same matrix. Likewise, the percolation rate is observed noticeably lower for a large intruder migrating through a dissimilar granular matrix in a shear box than for a small intruder (Trewhela et al. Reference Trewhela, Ancey and Gray2021a). Based on the experimental observation, Gajjar & Gray (Reference Gajjar and Gray2014) proposed the asymmetric flux model

(2.9)\begin{equation} F=AC(1-C)(1-\kappa C), \end{equation}

where the parameter $\kappa \in [0,1)$ controls the degree of asymmetry. The constant $A$ is chosen to ensure that the maximum flux is the same as for the symmetric flux function. Two sets of parameters $A$ and $\kappa$ (i.e. $A=1.0$, $\kappa =0$ and $A=1.6$, $\kappa =0.89$) are utilized in this study, and the corresponding segregation functions are demonstrated schematically in figure 2. Notice that (2.9) reduces to (2.8) in the case $A=1$ and $\kappa =0$. Also, in the case of asymmetric segregation flux, the cubic relation between $F$ and $C$ can lead to remarkable errors if (2.9) is substituted directly into (2.5) within the SPH framework. This is due to the complexity of high order between the segregation flux $Q$ and coordinates. A detailed explanation is presented in Appendix A. For a better approximation, we modify the segregation–diffusion equation as

(2.10)\begin{equation} \dfrac{{\rm d}C}{{\rm d}t}=-V\mathcal{F}\,\boldsymbol{\nabla}_{g}C-F\,\boldsymbol{\nabla}_{g}V+\boldsymbol{\nabla}\boldsymbol{\cdot}(D\,\boldsymbol{\nabla} C ), \end{equation}

in which $\mathcal {F}={\partial F}/{\partial C}=A( 3\kappa C^2-2( 1+\kappa )C+1 )$.

Figure 2.

Quadratic and cubic segregation functions.

2.3. Regularized $\mu (I)$-rheology-based elastoplastic model

In this subsection, the regularized $\mu (I)$-rheology-based elastoplastic model with Drucker–Prager yield surface, first put forward in Zhu et al. (Reference Zhu, Peng and Wu2022a), is introduced briefly for the sake of self-containment. This model is featured with the pressure-dependent yield function written as

(2.11)\begin{equation} f(\,p,J_2)=\sqrt{J_2}-k_\mu p, \end{equation}

where $p={\rm tr}(\boldsymbol \sigma )/3$ is the hydrostatic pressure; $J_2 = 0.5\boldsymbol s:\boldsymbol s$ represents the second invariant of the deviatoric stress $\boldsymbol s=\boldsymbol \sigma -p\boldsymbol{\mathsf{I}}$; and $k_\mu$ is the constitutive parameter related to the Mohr–Coulomb model through

(2.12)\begin{equation} k_\mu=\frac{3\mu}{\sqrt{9+12\mu^2}}, \end{equation}

in which $\mu$ is the frictional coefficient. In the standard Drucker–Prager model, $\mu$ is constant, which is valid for granular materials in quasi-static state. However, under the fast-shear condition, the frictional coefficient is found to be dependent on the local pressure level and shear rate magnitude with the so-called $\mu (I)$ relation (Jop, Forterre & Pouliquen Reference Jop, Forterre and Pouliquen2006)

(2.13)\begin{equation} \mu(I) =\mu_s + (\mu_d-\mu_s ) /(I_0/I+1). \end{equation}

Here, $I= {\dot \gamma } d_{avg}/\sqrt {p/\rho _s}$ is the so-called inertial number; $\mu _s$ and $\mu _d$ denote the static and dynamic frictional coefficients, bounding the lower and upper limits; and $I_0$ is a material parameter. Equations (2.11)–(2.13) are the major ingredients of the $\mu (I )$ rheology-based elastoplastic model for granular materials.

However, (2.13) contains the potential numerical singularity in the case of zero shear rate, ${\dot \gamma }=0$. Also, the $\mu (I)$ relation is found to be well-posed for intermediate inertial numbers, but ill-posed for both low and high inertial numbers, which could result in a resolution-dependent simulation result or numerical instability (Barker et al. Reference Barker, Schaeffer, Bohorquez and Gray2015). For restoring the well-posedness, the $\mu (I)$ relation should be modified, or some regularization techniques such as non-local theory should be utilized (Dsouza & Nott Reference Dsouza and Nott2020). In this study, the following regularized $\mu (I )$ relation based on the penalty scheme is adopted (Zhu et al. Reference Zhu, Peng and Wu2022a):

(2.14)\begin{equation} \mu(I) =\mu_s + \frac{(\mu_d-\mu_s ) {\dot \gamma}}{I_0\sqrt{p/(\rho_s d_{avg}^2 ) }+\sqrt{ {\dot \gamma}^2+\lambda^2 }}, \end{equation}

where $\lambda$ is the regularization parameter, and $\lambda =0.001$ is taken in this study following the suggestion in Zhu et al. (Reference Zhu, Peng and Wu2022a). The non-associated flow rule is employed in this study with the potential function

(2.15)\begin{equation} g(\,p,J_2 ) =\sqrt{J_2}, \end{equation}

which is consistent with the incompressible flow assumption. As a result, the final stress rate could be given explicitly as

(2.16)\begin{equation} \dot{\boldsymbol{\sigma}} = \dot{\boldsymbol{\omega}}\boldsymbol{\sigma} - \boldsymbol{\sigma}\dot{\boldsymbol{\omega}} + 2G\dot{\boldsymbol{ e}} + K \dot\varepsilon_v\boldsymbol{\mathsf{I}} - \dfrac{\dot{\lambda}G}{\sqrt{J_2}}\,\boldsymbol{ s}. \end{equation}

In this equation, $\dot {\boldsymbol \omega }=0.5(\boldsymbol u\,\boldsymbol {\nabla }-\boldsymbol {\nabla }\boldsymbol u)$ is the spin tensor. The first and second terms in (2.16) come from the Jaumann stress rate, which ensures the objectivity of the constructed constitutive equation; $\dot {\boldsymbol {e}}$ and $\dot \varepsilon _v$ are the deviatoric and volumetric strains, respectively, in which $\dot {\boldsymbol {e}}=\dot {\boldsymbol {\varepsilon }}-\dot \varepsilon _v\boldsymbol{\mathsf{I}}$, with the strain rate $\dot {\boldsymbol {\varepsilon }}=0.5(\boldsymbol u\,\boldsymbol {\nabla }+\boldsymbol {\nabla }\boldsymbol u)$ and $\dot \varepsilon _v = {\rm tr}(\dot {\boldsymbol {\varepsilon }})/3$. Also, $G$ and $K$ are the shear and bulk moduli, respectively; $\boldsymbol {s}=\boldsymbol {\sigma }-p\boldsymbol{\mathsf{I}}$, denoting the deviatoric stress; and $\dot {\lambda }$ is the plastic multiplier, which can be written as

(2.17)\begin{equation} \dot{\lambda}=\frac{3k_{\mu}K\dot{\varepsilon}_v+(G/\sqrt{J_2})\boldsymbol{s}:\dot{\boldsymbol{\varepsilon}}}{G}. \end{equation}

A brief introduction to the derivation of (2.16) within the elasto-perfect plastic theoretical framework is provided in Appendix B, where more details are accessible.

2.4. One-step return-mapping algorithm

With the specific yield criterion and potential function introduced above, and the generic plasticity theory, one can update the stress state provided that the strain rate is given. Here, a variety of schemes exist, but we consider only the so-called one-step return-mapping algorithm due to its accuracy and efficiency (de Borst Reference de Borst1993; Zhu et al. Reference Zhu, Peng and Wu2022a), which originates from the features of the Drucker–Prager-model-based elasto-perfect plasticity (de Borst Reference de Borst1991; Zhu, Peng & Wu Reference Zhu, Peng and Wu2021). Details for the algorithm are presented in Appendix C.

3.1. SPH approximation

Two key steps are involved in the SPH approximation to an arbitrary field function $f(\boldsymbol x)$, i.e. the kernel approximation and the particle approximation. The former step starts from the identity

(3.1)\begin{equation} f(\boldsymbol x)=\int_{\varOmega}f({\boldsymbol x}^\prime)\,\delta(\boldsymbol x-{\boldsymbol x}^\prime)\,{\rm d}\kern0.06em \boldsymbol {x}^\prime, \end{equation}

in which $\varOmega$ is the integral domain containing $\boldsymbol x$, and $\delta (\boldsymbol x-{\boldsymbol x}^\prime )$ represents the Dirac delta function. If we replace $\delta (\boldsymbol x-{\boldsymbol x^\prime })$ with a smoothing kernel function, then (3.1) can be rewritten as

(3.2)\begin{equation} f(\boldsymbol x)\doteq\int_{\varOmega}f({\boldsymbol x}^\prime)\,W(\boldsymbol x-{\boldsymbol x}^\prime,h)\,{\rm d}\kern0.06em \boldsymbol {x}^\prime. \end{equation}

In the kernel function $W(\boldsymbol x-{\boldsymbol x}^\prime,h)$, $h$ is the smoothing length defining the influence domain of a particle. Some requirements or properties for the kernel function should be satisfied for a better performance, including the unity, compact support and positivity (for more details, see Liu & Liu Reference Liu and Liu2003, Reference Liu and Liu2010). Various kernel functions have been used in the literature, such as the Gaussian kernel (Gingold & Monaghan Reference Gingold and Monaghan1977), the cubic B-spline function (Monaghan & Lattanzio Reference Monaghan and Lattanzio1985), the higher-order (quartic and quintic) splines (Morris Reference Morris1996), and the Wenland function class (Wendland Reference Wendland1995). Among them, the Wendland C2 kernel is employed in this study due to its computational convenience and the capability to prevent pairing instability (Dehnen & Hossam Reference Dehnen and Hossam2012), which reads

(3.3)\begin{equation} W=\alpha_d\begin{cases} (1-q/2)^4(2q+1), & 0\leq{q}\leq2,\\ 0, & q>2, \end{cases} \end{equation}

where $\alpha _d$ is a constant, taking the value ${7}/{(4{\rm \pi} h^2)}$ in two dimensions, and ${21}/{(16{\rm \pi} h^3)}$ in three dimensions, and $q=r/h$ is the non-dimensional distance between points $\boldsymbol x$ and $\boldsymbol x^\prime$, with $r=\| \boldsymbol x- \boldsymbol x^\prime \|$.

When the field function in (3.2) is replaced by its spatial derivative, we can get the following expression by using integration by parts:

(3.4)\begin{equation} \boldsymbol{\nabla}{f(\boldsymbol{x})} \doteq\int_{\varOmega}f(\boldsymbol{x}^{\prime})\,\boldsymbol{\nabla}_{\boldsymbol{x}}W(\boldsymbol{x}-\boldsymbol{x}^{\prime},h)\,{\rm d}\kern0.06em \boldsymbol x^\prime, \end{equation}

where $\boldsymbol {\nabla }_{\boldsymbol {x}}$ represents the gradient with respect to $\boldsymbol x$. The kernel approximation of the diffusion term in (2.5) has the expression

(3.5)\begin{align} \boldsymbol{\nabla}(g(\boldsymbol x)\,\boldsymbol{\nabla} f(\boldsymbol x))=\int_{\varOmega}[g(\boldsymbol x)+g(\boldsymbol x^\prime)][\,f(\boldsymbol x)-f(\boldsymbol x^\prime)]\,\frac{\boldsymbol{x}-\boldsymbol{x}^{\prime}}{\| \boldsymbol{x}-\boldsymbol{x}^{\prime}\|^2}\boldsymbol{\cdot} \boldsymbol{\nabla}_{\boldsymbol{x}}W(\boldsymbol{x}-\boldsymbol{x}^{\prime},h)\,{\rm d}\kern0.06em \boldsymbol x^\prime. \end{align}

Details of the mathematical deduction of this equation are presented in Appendix D. Hitherto, we have constructed the integral representations of the field function, its derivative, and the diffusion term in the governing equations. The second step is to conduct the particle approximation, at which stage discrete particles will be used to replace the continuous domain, and the integral representations will be converted as summations over all the particles in the support domain such that

(3.6)$$\begin{gather} f(\boldsymbol{x}_i) \doteq\sum_{j=1}^{n}f(\boldsymbol{x}_j)\,W_{ij}m_j/\rho_j, \end{gather}$$

(3.7)$$\begin{gather}\boldsymbol{\nabla}{f(\boldsymbol{x}_i)} \doteq\sum_{j=1}^{n}f(\boldsymbol{x}_j)\,\boldsymbol{\nabla}_i{W_{ij}m_j}/\rho_j, \end{gather}$$

(3.8)$$\begin{gather}\{ \boldsymbol{\nabla}(g(\boldsymbol x)\,\boldsymbol{\nabla} f(\boldsymbol x))\} _i=\sum_{j=1}^{n}[g(\boldsymbol x_i)+g(\boldsymbol x_j)][\,f(\boldsymbol x_i)-f(\boldsymbol x_j)]\,\frac{\boldsymbol{x}_{ij}}{\| \boldsymbol{x}_{ij}\| ^2+\eta^2}\boldsymbol{\cdot}\boldsymbol{\nabla}_i{ W_{ij}}m_j/\rho_j, \end{gather}$$

where the subscripts $i$ and $j$ denote the target particle and the neighbouring particles in the support domain; $n$ means the total particle number within the support domain of particle $i$; $\boldsymbol x_{ij}=\boldsymbol x_i-\boldsymbol x_j$; $\eta =0.1h$ acts as a clipping constant to keep the denominator non-zero; $W_{ij}$ is short for $W(\boldsymbol x_{ij},h)$; and $\boldsymbol {\nabla }_i{W_{ij}}$ short for $\boldsymbol {\nabla }_{\boldsymbol {x}_i}W(\boldsymbol x_{ij},h)$.

3.2. The SPH discretization of governing equations

By applying (3.6)–(3.8), one can obtain the SPH formulation for the momentum conservation equation as

(3.9)\begin{equation} \frac{{\rm d}\boldsymbol u_i}{{\rm d}t}=\sum_{j=1}^{n}m_j\left( \frac{\boldsymbol\sigma_i}{\rho_i^2}+\frac{\boldsymbol\sigma_j}{\rho_j^2}+\varPi_{ij}\right)\boldsymbol{\nabla}_i W_{ij} +\boldsymbol g. \end{equation}

In (3.9), $\varPi _{ij}$ denotes the artificial viscosity, which is a common stabilization scheme in the SPH theory to damp out unphysical numerical oscillations. In this study, the formulation proposed by Monaghan (Reference Monaghan1992) is adopted, reading

(3.10)\begin{equation} \varPi_{ij}=\begin{cases} \dfrac{\alpha_{\varPi}{c_{s}}h}{{\bar\rho}_{ij}}\,\dfrac{\boldsymbol{u}_{ij}\boldsymbol{\cdot}\boldsymbol{x}_{ij}}{\| \boldsymbol{x}_{ij}\| ^2+0.01h^2}, & \boldsymbol{u}_{ij}\boldsymbol{\cdot}\boldsymbol{x}_{ij}<0,\\ 0, & \boldsymbol{u}_{ij}\boldsymbol{\cdot}\boldsymbol{x}_{ij}\geq0, \end{cases} \end{equation}

in which $\alpha _{\varPi }$ is a constant coefficient, taken as 0.1 in this study (Bui et al. Reference Bui, Fukagawa, Sako and Ohno2008); $c_{s}$ is the sound velocity; $\bar \rho _{ij}=0.5( \rho _i+\rho _j)$ is the average density of particles $i$ and $j$; and $\boldsymbol u_{ij}=\boldsymbol u_i-\boldsymbol u_j$. The specific terms related to stress in (3.9) come from the identity

(3.11)\begin{equation} \frac{\boldsymbol{\nabla}\boldsymbol\sigma}{\rho}=\boldsymbol{\nabla}\boldsymbol{\cdot}\left( \frac{\boldsymbol\sigma}{\rho}\right) +\frac{\boldsymbol\sigma}{\rho^2}\,\boldsymbol{\nabla}\rho. \end{equation}

The pairwise SPH form of the momentum equation, i.e. (3.9), ensures the action–reaction principle and is stable in the calculation, thus gaining high popularity. Note that SPH suffers from the short-length-scale-noise problem, which could lead to unrealistic stress perturbations (Nguyen et al. Reference Nguyen, Nguyen, Bui, Nguyen and Fukagawa2000; Monaghan Reference Monaghan2012). To regularize the stress field, the Shepard filter scheme is adopted every so many steps ($N_{step} = 30$ in this study) to reproduce the linear variation of stress fields, reading

(3.12)\begin{equation} \sigma_i^{new}=\frac{\sum\sigma_jW_{ij}}{\sum W_{ij}}. \end{equation}

Likewise, the SPH formulation for the segregation–diffusion equation should be written as

(3.13)\begin{align} \frac{{\rm d}C}{{\rm d}t}&=-\sum_{j=1}^{n}\frac{m_j}{\rho_j}\,( V_i\mathcal{F}_{i}C_{ij}+F_{i}V_{ij})\,\frac{\boldsymbol g}{\| \boldsymbol g\|}\boldsymbol{\cdot}\boldsymbol{\nabla}_i W_{ij}\nonumber\\ &\quad +\sum_{j=1}^{n}\frac{m_j}{\rho_j}\,(D_i+D_j)C_{ij}\,\frac{\boldsymbol{x}_{ij}}{\| \boldsymbol{x}_{ij}\| ^2+\eta^2}\boldsymbol{\cdot}\boldsymbol{\nabla}_i{ W_{ij}}, \end{align}

where $C_{ij}=C_i-C_j$ and $V_{ij}=V_i-V_j$. A similar process for the strain rate tensor and spin tensor gives

(3.14)$$\begin{gather} \dot{\boldsymbol\varepsilon}=\dfrac{1}{2}\left( \sum_{j=1}^{n}\boldsymbol u_{ij}\otimes\boldsymbol{\nabla}_i{W_{ij}m_j}/\rho_j+\sum_{j=1}^{n}\boldsymbol{\nabla}_i{W_{ij}}\otimes\boldsymbol u_{ij}m_j/\rho_j\right) \!, \end{gather}$$

(3.15)$$\begin{gather}\dot{\boldsymbol \omega}=\dfrac{1}{2}\left( \sum_{j=1}^{n}\boldsymbol u_{ij}\otimes\boldsymbol{\nabla}_i{W_{ij}m_j}/\rho_j-\sum_{j=1}^{n}\boldsymbol{\nabla}_i{W_{ij}}\otimes\boldsymbol u_{ij}m_j/\rho_j\right) \!, \end{gather}$$

where $\boldsymbol u_{ij}=\boldsymbol u_i-\boldsymbol u_j$. The inclusion of $\boldsymbol u_i$ is because of the identity

(3.16)\begin{equation} \sum_{j=1}^{n}\boldsymbol u_{i}\otimes\boldsymbol{\nabla}_i{W_{ij}m_j}/\rho_j=\boldsymbol 0. \end{equation}

One benefit of the adoption of the velocity difference $\boldsymbol u_{ij}$ in (3.14)–(3.15) is the restoration of $O(h)$ convergence on the free surface (Colagrossi, Antuono & Le Touzé Reference Colagrossi, Antuono and Le Touzé2009).

3.3. Time stepping

The SPH forms of the governing equations are solved numerically using explicit time integration schemes. In this work, we employ a second-order predictor–corrector integrator (Zhu et al. Reference Zhu, Peng and Wu2022a). At the predictor step, the physical variables at the midpoint of the calculation step are evaluated with

(3.17)\begin{equation} X^{n+0.5}=X^n+\varGamma^{n}\,\Delta t, \end{equation}

in which $X$ goes through position $\boldsymbol x$, velocity $\boldsymbol v$, stress $\boldsymbol \sigma$ and concentration $C$, and $\varGamma ={\rm d}X/{\rm d}t$ is the material time derivative of $X$. At the corrector step, the final value at the end of the calculation step is obtained with

(3.18)\begin{equation} X^{n+1}=X^n+\varGamma^{n+0.5}\,\Delta t. \end{equation}

Here, $\varGamma ^{n+0.5}$ is determined with $X^{n+0.5}$ from the predictor step. Note that the time step $\Delta t$ used in the time integration is limited by the CFL condition

(3.19)\begin{equation} \Delta{t}=\chi_{CFL}\times\min \left(\sqrt{h/a_{max}},\frac{h}{c_s}\right)\!, \end{equation}

where $\chi _{CFL}$ is the CFL coefficient, taken as 0.1 in this study, and $a_{max}$ denotes the maximum acceleration among all material particles.

3.4. Numerical implementations

The proposed SPH model is developed based on our in-house code LOQUAT, an open-source SPH solver (Peng et al. Reference Peng, Wang, Wu, Yu, Wang and Chen2019). In each calculation step, four subroutines are executed serially, including (1) nearest neighbouring particle searching, (2) boundary treatment, (3) particle interaction, and (4) time integration. Boundary treatment refers to the fulfilment of specific boundary conditions, which will be illustrated in detail in § 4. Particle interaction in this work means that the SPH approximation of the material time derivative of velocity and concentration from the support domain, i.e. (3.9) and (3.13). The introduction to time integration was presented in the previous subsection. Here, we focus mainly on the illustration of the first subroutine.

An SPH particle interacts only with its nearest neighbouring particles (NNP) located within the influence domain, so the prerequisite of SPH computations is identifying these particles. This process is referred to as nearest neighbouring particle searching (NNPS) in the literature. A naive strategy is to conduct the calculation of all pairwise distances between particles $i$ and $j$. Particle $j$ is found to belong to the support domain of particle $i$ provided that the distance is less than $2h$. However, this method would be prohibitively expensive for large-scale problems, as the computational burden of such an operation is of order $O(N^2)$. Therefore, various alternatives have been proposed to improve search efficiency. Typical examples include the Verlet neighbour list, cell-linked list and tree searching algorithms (Liu & Liu Reference Liu and Liu2003; Viccione, Bovolin & Carratelli Reference Viccione, Bovolin and Carratelli2008).

Among all the approaches, this study adopts the cell-linked list scheme to advance NNPS. The first step in implementing this algorithm is to cover the whole calculation domain with a virtual square lattice mesh whose length is equal to $2h$, as shown in figure 3. Each cell is indexed by $\alpha$, $\beta$ and $\gamma$ according to its position. With this background grid, all SPH particles can be binned into the corresponding cells. From figure 3, we can find that the support domain is encircled by the neighbour cells so that NNPS can be confined to the $M$ particles from these cells. As a result, the neighbour search operation is scaled down to the order $O(MN)$. The cell-linked list scheme needs to store all particles in each cell, which is memory-consuming. Green (Reference Green2010) proposed a modification by sorting all the particles according to their associated cell index, thus only the beginning and ending particle indexes are necessary for each cell. This modified cell-linked list not only consumes less memory, but also has a high data accessing rate since all information is stockpiled in memory serially.

Figure 3.

Sketch of the virtual square lattice mesh.

4.1. Kinematic and dynamic boundary condition

A proper formulation of boundary conditions for SPH is crucially important to achieve physically meaningful and quantitatively correct results (Adami, Hu & Adams Reference Adami, Hu and Adams2012). However, the implementation of boundary conditions in SPH is far from trivial and is often regarded as a major weakness compared to the FEM, thus identified as one of the grand challenges (Vacondio et al. Reference Vacondio, Altomare, De Leffe, Hu, Le Touzé, Lind, Marongiu, Marrone, Rogers and Souto-Iglesias2021). For instance, in a rotating drum, two boundary conditions are concerned, i.e. a free surface and rigid wall boundary, as shown in figure 4. One distinct advantage of the SPH method is the intrinsic fulfilment of the kinematic and dynamic boundary conditions on the free surfaces (Colagrossi et al. Reference Colagrossi, Antuono and Le Touzé2009; Lyu & Sun Reference Lyu and Sun2022).

Figure 4.

Sketch of a granular flow with a free surface and rigid wall boundary.

On the rigid wall boundaries, the dummy particle scheme, initially proposed for fluid flow (Adami et al. Reference Adami, Hu and Adams2012), and later modified for geomaterials (Peng et al. Reference Peng, Wang, Wu, Yu, Wang and Chen2019; Zhan et al. Reference Zhan, Peng, Zhang and Wu2019; Zhu et al. Reference Zhu, Peng, Wu and Wang2022b), is employed for modelling the rigid wall boundary due to its accuracy and efficiency for regular boundary geometries (Valizadeh & Monaghan Reference Valizadeh and Monaghan2015). As shown in figure 5, at the beginning of the simulation, several layers of dummy particles (depending on the ratio of smoothing length to the initial particle spacing, i.e. $h/\Delta r$) are arranged outside the calculation domain. The fictitious particles participate in the interaction if they are located in the support domain of the material particles. However, unlike the internal particles, whose field variables are updated according to the governing equations, the necessary information of the dummy particles is extrapolated from those material particles close to the boundary (named boundary particles, hereafter). For a non-slip boundary condition, the following extrapolation scheme is applied:

(4.1)\begin{equation} \boldsymbol u_d=2\boldsymbol u_w-\frac{\displaystyle\sum_{m=1}^{n}\boldsymbol u_mW_{dm}V_m}{\displaystyle\sum_{m=1}^{n}W_{dm}V_m}, \end{equation}

where the subscripts $d$ and $m$ refer to dummy and material, respectively, and $\boldsymbol u_w$ represents the prescribed velocity of the rigid wall. In the scenario of a free-slip boundary, the procedure remains the same with (4.1) for the normal component of the velocity vector, while the tangential part is modified as follows:

(4.2)\begin{equation} \left.\begin{array}{c} u_d^{n}=2 u_w^{n}-\dfrac{\displaystyle\sum_{m=1}^{n} u_m^{n}W_{dm}V_m}{\displaystyle\sum_{m=1}^{n}W_{dm}V_m},\\ u_d^\tau=\dfrac{\displaystyle\sum_{m=1}^{n} u_m^{\tau}W_{dm}V_m}{\displaystyle\sum_{m=1}^{n}W_{dm}V_m}, \end{array}\right\} \end{equation}

where the superscripts $n$ and $\tau$ represent the normal and tangential directions. To avoid the particle penetration through the rigid wall, the dummy particles are assigned the following stress (Adami et al. Reference Adami, Hu and Adams2012; Peng et al. Reference Peng, Wang, Wu, Yu, Wang and Chen2019):

(4.3)\begin{equation} \boldsymbol \sigma_d = \dfrac{\displaystyle\sum_{m=1}^{n}\boldsymbol\sigma_mW_{dm}V_m+\boldsymbol{\mathsf{I}} \circ\sum_{m=1}^{n}m_m\boldsymbol g\otimes\boldsymbol r_{dm}W_{dm}}{\displaystyle\sum_{j=1}^{n}W_{dm}V_m}, \end{equation}

in which $\circ$ denotes the Hadamard product, and $\boldsymbol r_{dm}=\boldsymbol r_d - \boldsymbol r_m$.

Figure 5.

Dummy particle scheme for the rigid wall boundary.

4.2. Inhomogeneous Neumann boundary condition

The above section presents the procedure for achieving the kinematic and dynamic boundary conditions. We proceed to show strategies on how to realize the concentration-related boundary conditions.

Provided that there is no concentration flux of small particles across the boundary, the mathematical expression can be written as follows (Gray & Chugunov Reference Gray and Chugunov2006):

(4.4)\begin{equation} \boldsymbol n \boldsymbol{\cdot} \left(Q\,\dfrac{\boldsymbol g}{\| \boldsymbol g\| }+D\,\boldsymbol{\nabla} C\right)=0,\quad \forall \boldsymbol x \in \partial\varOmega, \end{equation}

in which $\boldsymbol n$ denotes the normal vector on $\partial \varOmega$ pointing towards $\varOmega$. Equation (4.4) reveals that the segregation and diffusion fluxes have identical magnitude but opposite direction along the normal of the boundary. After some mathematical manipulations, an inhomogeneous Neumann boundary condition about $C$ can be reached as follows:

(4.5)\begin{equation} D\,\dfrac{\partial C}{\partial \boldsymbol n}=f(C)=-VF\,\dfrac{\boldsymbol n \boldsymbol{\cdot} \boldsymbol g}{\| \boldsymbol g\| },\quad \forall \boldsymbol x \in \partial\varOmega. \end{equation}

Here, $V$ and $F$ denote the segregation velocity and segregation functions (cf. (2.6)).

4.2.1. Continuum surface reaction scheme for solid boundaries

It is challenging to enforce the inhomogeneous Neumann boundary condition within the SPH framework directly. Following the spirit of the continuum surface reaction (CSR) method (Ryan, Tartakovsky & Amon Reference Ryan, Tartakovsky and Amon2010), the inhomogeneous Neumann boundary condition can be replaced by the homogeneous Neumann boundary condition and a volumetric source term added into the segregation–diffusion equation (Wang et al. Reference Wang, Hu, Zhang and Pan2019), reading

(4.6)$$\begin{gather} \dfrac{\partial C}{\partial \boldsymbol n}=0,\quad \forall \boldsymbol x \in \partial\varOmega, \end{gather}$$

(4.7)$$\begin{gather}\dfrac{{\rm d}C}{{\rm d}t}=-V\mathcal{F}\,\boldsymbol{\nabla}_{g}C-F\,\boldsymbol{\nabla}_{g}V+\boldsymbol{\nabla}(D\,\boldsymbol{\nabla} C )+q. \end{gather}$$

The implementation of the homogeneous Neumann boundary condition in SPH can be enforced by assuming that the virtual and concerned material particles have identical concentrations, i.e. $C_j=C_i$ (Wang et al. Reference Wang, Hu, Zhang and Pan2019), which is achieved intrinsically with the SPH form of the Laplace operator adopted in (3.13). The last term on the right-hand side of (4.7), i.e. $q$, is the volumetric source term, which is related to $f(C)$ through the expression

(4.8)\begin{equation} q=- f( C)\int_{\varOmega_{s}}[\boldsymbol n(\boldsymbol x)+\boldsymbol n(\boldsymbol x^\prime)]\boldsymbol{\cdot}\boldsymbol{\nabla}_{\boldsymbol{x}}W(\boldsymbol x-\boldsymbol x^\prime,h)\,{\rm d}\kern0.06em \boldsymbol x^\prime, \end{equation}

where the integral domain $\varOmega _{s}$ (as seen in figure 5) denotes the missing support truncated by the boundary. The SPH approximation to (4.8) is

(4.9)\begin{equation} q_{i}=-f( C_i)\sum_{j\in\varOmega_{s}}(\boldsymbol n_i+\boldsymbol n_j)\,\boldsymbol{\nabla}_iW_{ij}V_j. \end{equation}

Here, the normal vectors at points $i$ and $j$, i.e. $\boldsymbol n_i$ and $\boldsymbol n_j$, are evaluated with

(4.10)\begin{equation} \boldsymbol n_i=\dfrac{\displaystyle\sum_{j\in\varOmega\cup\varOmega_{s}}(c_i-c_j)\,\boldsymbol{\nabla}_iW_{ij}V_j}{\left| \displaystyle\sum_{j\in\varOmega\cup\varOmega_{s}}(c_i-c_j)\,\boldsymbol{\nabla}_iW_{ij}V_j\right| }, \end{equation}

where $c$ represents a colour function whose value is 0 for the material particles, and 1 for the fictitious particles. However, the CSR scheme introduced above is found only with first-order accuracy, thus errors could arise near the boundary (Pan et al. Reference Pan, Kim, Perego, Tartakovsky and Parks2017). To remedy this, a degenerate function $\psi _d$ is introduced, and the segregation–diffusion equation is modified as (Wang et al. Reference Wang, Hu, Zhang and Pan2019)

(4.11)\begin{equation} \dfrac{{\rm d}C}{{\rm d}t}=-V\mathcal{F}\,\boldsymbol{\nabla}_{g}C-F\,\boldsymbol{\nabla}_{g}V+\boldsymbol{\nabla}(\psi_dD\,\boldsymbol{\nabla} C )+q, \end{equation}

where $\psi _d$ can be specified with

(4.12)\begin{equation} \psi_d(\boldsymbol x)=\begin{cases} 1, & \boldsymbol x\in\varOmega,\\ 0, & \boldsymbol x\in\partial\varOmega,\\ -1, & \boldsymbol x\in\varOmega_{s}. \end{cases} \end{equation}

The corresponding SPH form of the Laplace operator in (4.11) can be given as

(4.13)\begin{equation} {\boldsymbol{\nabla}(\psi_dD\,\boldsymbol{\nabla} C )}=\sum_{j=1}^{n}\dfrac{m_j}{\rho_j}\,(\tilde\psi_{di}D_i+\tilde\psi_{dj}D_j)C_{ij}\,\dfrac{\boldsymbol{x}_{ij}}{\| \boldsymbol{x}_{ij}\| ^2+\eta^2}\boldsymbol{\cdot}\boldsymbol{\nabla}_i{ W_{ij}}, \end{equation}

where $\tilde \psi _{d}$ represents the smoothed counterpart of $\psi _{d}$, defined as

(4.14)\begin{equation} \tilde\psi_{di}=\dfrac{\displaystyle\sum_{j\in\varOmega\cup\varOmega_{s}}\psi_{dj}W_{ij}V_j}{\displaystyle\sum_{j\in\varOmega\cup\varOmega_{s}}W_{ij}V_j}. \end{equation}

4.2.2. Hybrid CSR scheme

The conventional CSR (CCSR) scheme introduced above evaluates physical fields based on the missing support domain, e.g. (4.9) and (4.10). This can be achieved trivially on the rigid wall boundary, i.e. $\partial \varOmega _{W}$, where the missing support domain is supplemented by the fictitious particles. However, the implementation on the dynamic free surface will be troublesome, particularly when the geometry is irregular. To fulfil (4.6) on the free surface, we put forward a modified CSR (MCSR) scheme that can avoid introducing additional fictitious particles because the SPH approximation to the volumetric source term ($q$) and degenerate function ($\tilde \psi _{d}$) is carried out using the material particles.

This new variant has the same boundary condition and governing equation as (4.6) and (4.11), but with the following kernel approximation to the volumetric source term:

(4.15)\begin{equation} q=2\,f( C)\,\boldsymbol n(\boldsymbol x)\boldsymbol{\cdot}\int_{\varOmega}\boldsymbol{\nabla}_{\boldsymbol{x}}W(\boldsymbol x-\boldsymbol x^\prime,h)\,{\rm d}\kern0.06em \boldsymbol x^\prime. \end{equation}

Correspondingly, the SPH approximation to $q$ is

(4.16)\begin{equation} q_{i}=2\,f( C_i)\sum_{j\in\varOmega}\boldsymbol n_i\,\boldsymbol{\nabla}_iW_{ij}V_j. \end{equation}

Here, $\boldsymbol n_i$ is evaluated with

(4.17) \begin{equation} \boldsymbol n_i=\begin{cases} \dfrac{\displaystyle\sum_{j\in\varOmega}\boldsymbol{\nabla}_iW_{ij}V_j}{\displaystyle\left| \sum_{j\in\varOmega}\boldsymbol{\nabla}_iW_{ij}V_j\right|}, & i\in\varOmega_{F},\\ \dfrac{\displaystyle\sum_{j\in\varOmega_{F}}\boldsymbol{\nabla}_iW_{ij}V_j}{\displaystyle\left| \sum_{j\in\varOmega_{F}}\boldsymbol{\nabla}_iW_{ij}V_j\right| }, & i\in\varOmega_{f}. \end{cases} \end{equation}

In this equation, $\varOmega _{F}$ denotes the subset consisting of free surface particles, and $\varOmega _{f}$ indicates the particles in the vicinity of the free surface, as shown in figure 4. The procedure to identify these particles will now be presented. The smoothed degenerate function has the expression

(4.18)\begin{equation} \tilde\psi_{di}=2\sum_{j\in\varOmega}W_{ij}V_j-1. \end{equation}

Note that the volumetric source term should vanish (i.e. $q_{i}=0$) for the material particles with full support domain (i.e. $\varOmega _{I}$ in figure 4). This is satisfied intrinsically within the CCSR scheme as the approximation to $q$ is based on the fictitious particles. However, the MCSR scheme employs the material particles, so non-zero value always appears due to the distorted particle distribution, especially for a granular flow with large deformation. One effective strategy to mitigate this issue is to restrict the MCSR scheme to only those material particles in the vicinity of the free surface (i.e. $\varOmega _{F}$ and $\varOmega _{f}$ in figure 4). To achieve this, the following boundary particle searching algorithm is proposed.

Step 1, identifying the surface particles (i.e. $\varOmega _{F}$, $\varOmega _{W}$ in figure 4). In this step, the surface detection scheme proposed by Marrone et al. (Reference Marrone, Colagrossi, Le Touzé and Graziani2010) is adopted with some optimization. In the optimized version, a rough-refining two-stage strategy is used to advance the searching. First, those non-empty virtual cells are divided into three categories. Type I is the outermost layer, featured with some empty neighbour cells. Type II is the second outermost layer, which are intermediate neighbour cells of Type I. The remaining cells, i.e. the interior ones, consist of Type III. Since the lattice mesh has length $2h$, we can conclude that all the particles located in Type I cells could have truncated support. In comparison, particles in Type III cells have complete support. In this manner, Type II cells can be regarded as transitional regions, as some particles in Type II cells will have full support in $\varOmega$, while the rest do not. Accordingly, we can reduce the further searching in the next stage to only those particles from Type I cells. Subsequently, the surface particles are detected from the potential candidates by searching the so-called ‘umbrella-shaped’ region, as sketched in figure 6. A concerned particle will be considered as a surface particle if the ‘umbrella-shaped’ region is particle-free, i.e.

(4.19)\begin{equation} \left.\begin{array}{c@{}} \nexists \,j\in\{ \boldsymbol x_j\,|\, |\boldsymbol x_{ij}|\geq\sqrt{2}h \text{ and } |\boldsymbol x_{jT}|< h \},\\ \nexists \,j\in\left\{ \boldsymbol x_j\,|\,|\boldsymbol x_{ij}|<\sqrt{2}h \text{ and } \boldsymbol x_{ij} \boldsymbol{\cdot}\boldsymbol x_{iT} >\dfrac{\sqrt{2}}{2}\,|\boldsymbol x_{ij} |\,h \right\}. \end{array}\right\} \end{equation}

Figure 6.

Sketch of the boundary particle detection scheme.

Step 2, determining the surface vicinity particles (i.e. $\varOmega _f$, $\varOmega _w$ in figure 4). One can observe from figure 6 that the surface vicinity particles come from Type I or II cells, thus the searching operation is also of a small amount compared to the total SPH particle number. In this work, an SPH particle is considered a free-surface vicinity particle (i.e. $\boldsymbol x_i\in \varOmega _{f}$) when the following condition is satisfied:

(4.20)\begin{equation} \min\{ | \boldsymbol x_{ij}| \,|\,\boldsymbol x_j\in\varOmega_{F}\} < 2h-\Delta r. \end{equation}

The identification of $\varOmega _{w}$ is based on its definition, i.e.

(4.21)\begin{equation} \varOmega_{w}:=\{ \boldsymbol x_i\,|\,\exists \boldsymbol x_j \in\varOmega_{s}, \text{ but }\boldsymbol x_i \notin\varOmega_{W}\}. \end{equation}

In practice, CCSR is used for particles in domains $\varOmega _{W}$ and $\varOmega _{w}$, while MCSR is used for those in domains $\varOmega _{F}$ and $\varOmega _{f}$. This implementation scheme is named the hybrid continuum surface reaction (HCSR) method in this study. Note that the identification of $\varOmega _{W}$ and $\varOmega _{w}$ is unnecessary within the framework of CCSR, but indispensable for obtaining accurate segregation flux at the boundary $\partial \varOmega$, which will be introduced in the next subsection for the whole boundary particles.

4.3. Accurate approximation to the segregation flux gradient

The SPH approximation of the segregation flux gradient adopted in (3.13) has $C^1$ consistency at the interior particles, but this level of accuracy is infeasible at the boundary particles due to the particle deficiency. The mirroring technique with antisymmetric extension of $C$ and $V$ can restore the $C^1$ consistency close to the boundaries (Macia et al. Reference Macia, Antuono, González and Colagrossi2011). However, this method needs to introduce virtual particles around the boundary $\partial \varOmega$, which is difficult for implementation when the boundary has an irregular shape, as discussed earlier. In this study, we employ the approach proposed by Liu & Liu (Reference Liu and Liu2011) to obtain the accurate approximation with $C^1$ consistency for $\boldsymbol {\nabla }_{g}C$ and $\boldsymbol {\nabla }_{g}V$ in (2.10). Details can be found in Appendix B. This method needs only the information from material particles, thus avoiding introduction of fictitious particles. Note that this technique is restricted to boundary particles ($\varOmega -\varOmega _{I}$). For the interior particles ($\varOmega _{I}$), the standard SPH approximation, i.e. (3.13), is used for obtaining the segregation flux gradient.

5.1. Constant segregation velocity and diffusivity

In this subsection, both segregation velocity and diffusivity are assumed constant, following the practice in Van der Vaart et al. (Reference Van der Vaart, Gajjar, Épely-Chauvin, Andreini, Gray and Ancey2015). The obtained concentration distributions at various time instants are compared with experimental observations from Van der Vaart et al. (Reference Van der Vaart, Gajjar, Épely-Chauvin, Andreini, Gray and Ancey2015), and the numerical prediction using the pdepe routine of MATLAB subject to the same initial and boundary conditions. In MATLAB calculation, this problem is simplified as a one-dimensional problem with (2.5) as the governing equation, and the domain is discretized with 201 nodes. Here, we regard the MATLAB calculation result as a first-principles benchmark. Consistent with Van der Vaart et al. (Reference Van der Vaart, Gajjar, Épely-Chauvin, Andreini, Gray and Ancey2015), the geometry and time are normalized by the height $H$ and the period $T$, respectively. A series of tests shows that the best agreement between the segregation time (the critical time to reach steady state) and the initial small-particle concentration is obtained with the following constant non-dimensional segregation velocity and diffusivity (Van der Vaart et al. Reference Van der Vaart, Gajjar, Épely-Chauvin, Andreini, Gray and Ancey2015):

(5.2)\begin{equation} \left.\begin{aligned} {[V]}^{const}&=V^{const}T/B=3.0\times10^{-2},\\ {[D]}^{const}&=D^{const}T/B^2=1.01\times10^{-3}. \end{aligned}\right\} \end{equation}

Therefore, these values are applied in this study.

The physical model in SPH simulation is partitioned with $\Delta r=0.01H$, which accounts for 4000 material particles and 858 fictitious particles. We first examine the performance of the HCSR method on the boundary particle searching and the normal direction identification, which is presented in figures 8 and 9. It is found that the boundary particles are identified accurately and classified at all three typical time instants. Also, the majority of the normal vectors are perpendicular to the boundaries, as expected. The satisfactory behaviour of the HCSR method indicates its capability in the boundary treatment, and gives us confidence in further segregation analysis.

Figure 8.

Boundary particle identification in the HCSR method at (a) $t/T = 0.25$, (b) $t/T = 0$ or 0.5, and (c) $t/T = 0.75$.

Figure 9.

Normal direction on the boundary particles at (a) $t/T = 0.25$, (b) $t/T = 0.5$ or 1.0, and (c) $t/T = 0.75$.

The experimental results show that the small beads migrate faster than the large ones, as seen by the asymmetric vertical distribution of the concentration $C$ for the first 20 periods in figure 10(a). The symmetric segregation flux fails to reproduce this feature, which is, however, well captured by the asymmetric scheme (figures 10b,c). As the segregation continues, a relatively homogeneous mixture is found between $t/T=20$ and $t/T=40$. Afterwards, a reversed distribution pattern of $C$ starts to evolve, i.e. small beads are enriched at the bottom, compared to large ones at the top of the granular assembly. Finally, an equilibrium state is reached under the common effect of gravity-driven segregation and diffusion-induced remixing after $t/T=60$. Figures 10(d,e) present close agreement between the SPH prediction and MATLAB calculation at three specific time instants for both segregation flux schemes. All curves in figure 10(d) pass through and are symmetric about the centre point $(z/H, C)=(0.5,0.5)$. In comparison, relatively irregular patterns are observed in figure 10(e) with asymmetric segregation flux. The good consistency between the SPH prediction and the experimental observation as well as the MATLAB calculation result demonstrates the capability of the proposed SPH model, particularly the effectiveness of the HCSR method on boundary condition treatment.

Figure 10.

Temporal evolution of the concentration of small glass beads. (a) Experimental observation from Van der Vaart et al. (Reference Van der Vaart, Gajjar, Épely-Chauvin, Andreini, Gray and Ancey2015). (b,c) The SPH predictions with symmetric and asymmetric segregation flux, respectively. (d,e) Vertical distributions of concentration $C$ at three specific time instants from SPH prediction (lines) and MATLAB solution (symbols) with symmetric and asymmetric segregation flux.

The convergence performance is another key concern for a newly developed solver. Figure 11 presents the concentration distribution at $t/T=80$ in the case of asymmetric segregation flux with four resolution levels ($\Delta r/H = 0.005$, 0.01, 0.02 and 0.04). Good agreement is observed between MATLAB calculation and the numerical predictions for all resolution levels. Even the coarsest partition scheme, i.e. $\Delta r=0.04 H$, with only 25 material particles in the height direction, gives a reasonable prediction. Furthermore, as the partition becomes finer, the SPH prediction converges rapidly to the MATLAB calculation result. The convergence property of the proposed SPH method can be observed quantitatively from the values of the mean absolute error (MAE) and root mean square error (RMSE) with the following expressions:

(5.3)$$\begin{gather} \text{MAE}=\dfrac{\displaystyle\sum_{i=1}^{n}|C_i^{SPH}-C_i^{MATLAB}|}{n}, \end{gather}$$

(5.4)$$\begin{gather}\text{RMSE}=\sqrt{\dfrac{\displaystyle\sum_{i=1}^{n}( C_i^{SPH}-C_i^{MATLAB})^2 }{n}}. \end{gather}$$

Both MAE and RMSE of the concentration error against the MATLAB results are plotted in figure 12, with satisfactory order of convergence 1.25.

Figure 11.

Consistency test for the proposed SPH method with the MATLAB pdepe calculation result as benchmark. Curves represent the concentration distribution at $t/T=80$ with the asymmetric segregation flux scheme.

Figure 12.

The error of concentration at $t/T=80$.

5.2. Varying segregation velocity and diffusivity

In this subsection, the functional forms of segregation velocity $[ V]$ and diffusivity $[ D]$ will be employed to demonstrate the capability of the developed SPH model. To do this, $[{D}]$ and $[ {V}]$ in this subsection are assumed to have the following expressions:

(5.5a,b)\begin{equation} [D] =\dfrac{\dot\gamma}{\dot\gamma_{m}}\left( \dfrac{d_{avg}}{\bar{d}}\right) ^2[D]^{const} ,\quad [V] =\dfrac{\dot\gamma}{\dot\gamma_{m}}\,\dfrac{d_{avg}}{\bar{d}}\,[ V]^{const}, \end{equation}

where $\dot \gamma _{m}=2\omega \gamma _0/{\rm \pi}$ denotes the shear rate averaged over one complete cycle; $\bar {d}= (d^{S}+d^{L})/2$ is introduced for non-dimensionalization, and its effect on the simulation result will be discussed; $[D]^{const}=1.01\times 10^{-3}$ and $[ V] ^{const}=3.0\times 10^{-2}$ are kept the same as in the previous subsection. Substituting (2.7a,b) into (5.5a,b), the specific expressions and values of $\chi _{D}$ and $\chi _{V}$ can be derived as

(5.6a,b)\begin{equation} \chi_{D} =\dfrac{B^2[D] ^{const}}{T\dot\gamma_{m}{\bar d}^2}=0.0307,\quad \chi_{V} =\dfrac{B[V]^{const} }{T\dot\gamma_{m}{\bar d}}=0.11. \end{equation}

The value of $\chi _{D}$ obtained here is comparable with previous reports (for instance, $\chi _{D}\approx 0.053$ in Bridgwater (Reference Bridgwater1980), $\chi _{D}\approx 0.022$ in Hsiau & Shieh (Reference Hsiau and Shieh1999), and $\chi _{D}\approx 0.042$ in Fry et al. (Reference Fry, Umbanhowar, Ottino and Lueptow2019)). Substituting $\chi _{V} =0.11$ into (5.5a,b) leads to

(5.7)\begin{equation} V=0.11\dot\gamma d_{avg}=0.11\sim0.22\dot\gamma d_{S}, \end{equation}

which is in line with the experimental observation (Schlick et al. Reference Schlick, Fan, Isner, Umbanhowar, Ottino and Lueptow2015a)

(5.8)\begin{equation} V=0.26\dot\gamma d^{S} \log\left( \dfrac{d^{L}}{d^{S}}\right). \end{equation}

The initial conditions are aligned with (5.1), and the whole domain is discretized with $\Delta r=0.01H$.

The temporal evolution of particle-size segregation in the shear cell is presented in figure 13, which shows the concentration distributions for both the whole duration and five typical time instants. Also, three cases with different $\bar {d}$ values are reproduced numerically, namely, $\bar {d}=d^{S}$, $( d^{S}+d^{L})/2$ and $d^{L}$. From figure 13, it is observed clearly that with a greater $\bar {d}$, the particle segregation develops at a much slower pace. For instance, the cases with $\bar {d}$ from the smallest to the greatest reach the equilibrium state after approximately 40, 60 and 80 periods, respectively. This feature could also be verified from the concentration distributions in figures 13(d–f). Despite the fastest segregation evolution, it is found that the numerical simulation with $\bar d=4$ mm has the most pronounced discrepancy to the complete inverse grading at the equilibrium state, as shown by the slope of the curves at the mid-height of the shear cell in figure 13(h). Moreover, when comparing figures 13(a–c) to the temporal development of small-particle concentration from the experiment, i.e. figure 10(a), we find that numerical prediction with intermediate value $\bar d=6$ mm has the best match among all three cases. Furthermore, this value also generates the result with an approaching agreement to the numerical simulation with constant $[ V]$ and $[ D]$.

Figure 13.

SPH predictions of the shear cell experiment with functional forms of segregation velocity $V$ and diffusivity $D$: (a–c) $\bar d=4$, 6, 8 mm; (d–h) concentration distribution along the height at $t/T=20$, 40, 60, 80, 120.

The above findings indicate the significance of the parameters $[ D]$ and $[ V]$ on the particle segregation evolution. On one hand, the segregation velocity $[ V]$ with a smaller $\bar d$ would be greater, and the particle segregation would evolve at a faster pace as a result. On the other hand, the concentration distribution at the steady state is determined by the ratio between the non-dimensional segregation velocity and diffusivity, i.e. the Péclet number, such that

(5.9)\begin{equation} Pe=\dfrac{[V] }{[D] }=\dfrac{\bar d[V] ^{const}}{d_{avg}[D] ^{const}}. \end{equation}

This equation implies a stronger segregation relative to the diffusion at the steady state with a greater $\bar d$, which explains the concentration distribution in figure 13(h).

Figure 14 shows the time series of the depth-averaged concentration of small particles from theoretical calculation, experimental data and numerical simulations. In this case, the upper and lower halves of the column are filled with large and small particles, respectively, thus leading to the initial depth-averaged concentration equal to 0.5. Theoretically, this value should be constant throughout the numerical simulation as the granular material is assumed incompressible here, as shown by the scatters in figure 14. The experimental data are quite close to the theoretical values except for a maximum relative error of approximately 10$\,\%$ showing up at $t=20 T$, which could be due to the measurement error from the refractive index matched fluid technique (Trewhela et al. Reference Trewhela, Ancey and Gray2021a). In comparison, good agreement is observed between the theoretical value and the results from SPH simulations for all four cases. This satisfactory match examines solidly the effectiveness of the proposed HCSR scheme in realizing the inhomogeneous Neumann boundary condition.

Figure 14.

Temporal evolution of the depth-averaged concentration of small particles.

6.1. Kinematic analysis

The steady velocity field for the granular flow in a rotating drum is illustrated in figure 17. The flat surfaces for both EV and EN symbolize the typical rolling mode. This is consistent with the Froude number ($Fr=\omega ^2R/g$) criterion, i.e. the rolling mode occurs when $10^{-4}< Fr<10^{-2}$ ($Fr=9.79\times 10^{-4}$ in this case). Under this mode, the whole granular bed can be divided into two regions by a rigid body limit, such as the orange dashed curves in figure 17. In the literature, the area above the rigid body limit is termed the active zone, and the remaining area is termed the passive zone. In the passive zone, the granular material moves as a rigid body together with the drum. On the contrary, the grains in the active zone flow significantly faster than those from the underlying zone, and experience dramatic shearing.

Figure 17.

Velocity field for the granular flow in a rotating drum at $t=20$ s in cases (a) EV and (b) EN.

In this case, both segregation velocity ($V$) and diffusivity ($D$) are varied and proportional to the shear rate ($\dot \gamma$). Therefore, it is of considerable importance to analyse the global distribution of the shear rate, which is shown in figure 18. Here, rigid body limits similar to those in figure 17 are identified. We can find that in the passive zone, both EV and EN cases present almost zero shear rates, revealing the rigid motion of granular material as mentioned above. With a further step, the active zone is divided into two layers, respectively named as the upper active zone and the lower active zone in this study, separated by the maximum shearing surface, as shown by the white dotted line in figure 18. The shear rate in the upper active zone increases along the direction of gravity, while it decreases in the lower active zone. This division is useful for the segregation analysis, which will be given in the next subsection. For a better understanding, the shear rate distribution against the normalized mid-chord depth $\bar {d}_{mc}$ (cf. figure 17 for location) is drawn in figure 19. In both cases, the four curves are close to each other, indicating the steady-state flow condition.

Figure 18.

Shear rate for the granular flow in a rotating drum at $t=20$ s in cases (a) EV and (b) EN. Here, RBL and MSS are short for rigid body limit and maximum shearing surface, respectively.

Figure 19.

Shear rate along the mid-chord for the cases (a) EV and (b) EN, at four time instances.

Comprehensive analysis of the granular flow has been conducted in our previous work (Zhu et al. Reference Zhu, Peng and Wu2022a), where both the inside and surface velocity fields of the grain assembly are presented. In this study, we focus on the discrepancy between the single-phase model adopted here and dual-constituent granular assembly in the real world. To achieve this, the velocity magnitude distributions along the mid-chord of the granular bed for both EV and EN cases are given in figures 20 and 21, aiming to demonstrate the capability of the proposed SPH model to reproduce properly the kinematic information of the granular flow in the rotating drum. A noticeable discrepancy between the velocity profiles from this study and Yang et al. (Reference Yang, Zheng, Bai and Yu2021) is observed in figures 20 and 21. In general, the SPH scheme gives a greater prediction to the velocity near the free surface than the Eulerian FEM. In comparison, high consistency among the results from all three methods is observed in the passive zone. When taking the DEM data as benchmark, an error analysis shows that SPH and the Eulerian FEM give similar accuracy. For instance, at $t=40$ s, the MAE is equal to 0.42 and 0.43 cm s$^{-1}$ for the Eulerian FEM and SPH results, respectively, in the case of EV, but 0.29 and 0.17 cm s$^{-1}$ in the case of EN. The deviation in the numerical reproduction probably originates from the different numerical schemes adopted by the two studies. In the work of Yang et al. (Reference Yang, Zheng, Bai and Yu2021), the granular flow is modelled with the Eulerian FEM, the drum is taken as a Lagrangian structure immersed in the Eulerian domain, and the concentration information is updated with the finite difference method. In comparison, only one numerical method, i.e. SPH, is used in the current work for all aspects. Also, Yang et al. (Reference Yang, Zheng, Bai and Yu2021) adopts the immersed boundary method and the volume of fraction technique to realize the frictional contact boundary condition, which could lead to mass loss in simulation. In this study, the drum wall is simply assumed to be non-slip, and the Lagrangian nature of the SPH method ensures the mass conservation throughout the whole simulation. With regard to the computational cost, SPH usually can achieve a calculation efficiency with two orders using a consumer-level GPU card ascribed to its parallelism when compared to the similar numerical code run on a CPU card (Zhan et al. Reference Zhan, Peng, Zhang and Wu2019). The authors’ previous work shows that the calculation efficiency is improved approximately 100 times when comparing the time consumption of the SPH model from this study and that of the Eulerian FEM. More details can be found in Zhu et al. (Reference Zhu, Peng and Wu2022a) for readers with an interest in computation efficiency.

Figure 20.

Velocity profile along the mid-chord of the granular bed in the case of EV at (a) $t=20$ s, (b) $t=30$ s and (c) $t=40$ s. The scattered points are from the DEM simulation, while the dashed line represents the Eulerian FEM prediction from Yang et al. (Reference Yang, Zheng, Bai and Yu2021).

Figure 21.

Velocity profile along the mid-chord of the granular bed in the case of EN at (a) $t=20$ s, (b) $t=30$ s and (c) $t=40$ s. The scattered points are from the DEM simulation, while the dashed line represents the Eulerian FEM prediction from Yang et al. (Reference Yang, Zheng, Bai and Yu2021).

The previous study (Zhu et al. Reference Zhu, Peng and Wu2022a) reveals that the bed profile will remain stable after a half circulation in the rolling regime, indicating the formation of a steady-state velocity field. This explains the consistent velocity distribution along the mid-chord for all three time instants from both the SPH and Eulerian FEM predictions in figures 20 and 21, as the rotation rate in this case is fixed at 5 rpm. In comparison, an increment is observed in terms of the surface velocity obtained from the DEM simulation. For instance, in the case of EV, the surface velocity ascends from around 6 cm s$^{-1}$ at $t=20$ s to 7 cm s$^{-1}$ at $t=40$ s. The reason for this change could be twofold. One reason is that the flow layer will dilate due to the strong shearing. On the other hand, some grains on the free surface will collide and bounce into the air due to the inadequate resistance from the underlying granular bed, showing some gas properties. Therefore, an advanced constitutive model dictating the shear dilatancy and gas-like behaviour of the granular material is needed for a more precise prediction, but this is out of the scope of this study. In addition, the numerical result from the DEM simulation shows an almost identical velocity distribution between the small and large grains, which means that the segregation-induced percolation is negligible compared to the mean velocity. Therefore, the single-phase model adopted in this study can capture the kinematic information accurately. In general, both qualitative and quantitative satisfactions are achieved by the SPH model.

6.2. Segregation evolution

The correct implementation of the HCSR scheme is the prerequisite for predicting precisely the segregation evolution. Likewise, in this subsection, we first demonstrate the capability of the proposed HCSR scheme on boundary particle searching and normal direction identification in case of the rotating drum, which is shown in figures 22 and 23. Here, the geometry of the granular bed is more complex than that in the shear cell problem as the kinematic information is updated according to the momentum equation. Notwithstanding, the HCSR scheme still performs well with both EN and EV cases.

Figure 22.

Boundary particle searching and classification in a rotating drum for cases (a) EN and (b) EV.

Figure 23.

Normal direction of the boundary particles in a rotating drum for cases (a) EN and (b) EV.

Figures 24 and 25 present snapshots of the grain-size distribution for EV and EN cases from both DEM and SPH simulations, and are in good agreement with one another. In this case, particle segregation happens mainly in the active zone, where grains are subjected to severe shearing. When flowing through the active zone, the small grains migrate downwards and enrich the lower active zone, while the large grains float to the free surface and concentrate there. After entering into the passive zone, the granular materials rotate clockwise like a rigid body to the other side of the rigid body limit, and restart the segregation process in the active zone again. Such circulation repeats until reaching the dynamic equilibrium between the segregation and diffusion effects. The predictions for the concentration along the mid-chord from DEM and SPH are given in figure 26, from which we can find qualitative agreement between the results from these two methods. The discrepancy could originate from the following three aspects. First, as pointed out in Yang et al. (Reference Yang, Zheng, Bai and Yu2021), the concentration profile obtained from the DEM simulation will be influenced by the sample size (i.e. the specific area to calculate the average concentration) to a great degree. Second, the constitutive model that dictates the mechanical behaviour of granular materials plays a key role in kinematics, which will influence the segregation evolution through the shear rate. In this study, the simple $\mu (I)$ elastoplasticity is employed to model the size-bidisperse granular assembly. This model could reproduce the major features, but some key aspects are missing, such as the shear dilatancy and the gas-like behaviour of the granular materials on the free surface. Therefore, a more advanced constitutive model is necessary for a better simulation result. Last, but not least, the parameters $V$ and $D$ are two key players in particle-size segregation, but have complex scaling laws with multiple factors (Trewhela et al. Reference Trewhela, Ancey and Gray2021a). In this study, (2.7a,b) is indeed simplified, and the coefficients $\chi _{D}$ and $\chi _{V}$ are calibrated from the oscillatory shear cell test.

Figure 24.

Snapshots of grain distribution from the DEM (upper images, reprinted from Yang et al. Reference Yang, Zheng, Bai and Yu2021) and SPH (lower images) for the EV case. In the DEM, red and green particles denote large and small grains, respectively, while $C$ represents the concentration of small grains in SPH.

Figure 25.

Snapshots of grain distribution from the DEM (upper images, reprinted from Yang et al. Reference Yang, Zheng, Bai and Yu2021) and SPH (lower images) for the EN case. In the DEM, red and green particles denote large and small grains, respectively, while $C$ represents the concentration of small grains in SPH.

Figure 26.

Concentration along the mid-chord in the cases (a) EV and (b) EN. The scattered points represent the result from the DEM, and the solid lines denote the SPH prediction.

Figure 27 presents the temporal evolution of the system-wide concentration for small grains. In the cases EV and EN, the initial concentration is 0.5 and 0.11, respectively. These two values should stay constant due to the incompressibility assumption adopted in this study, as the scatters demonstrate in figure 27. Good consistency is found between the results from the SPH simulation and theory in both cases. In comparison, a slight divergence is observed in the EV case. The accuracy of the proposed HCSR scheme depends on the source term $q$. According to (4.16), the key issue is to obtain the accurate normal vectors calculated with (4.10). In this process, some error will take place, and the discrepancy will be magnified by the value of the inhomogeneous Neumann boundary condition $f( C)$. As seen in figure 23, the majority of the calculated normal vectors are accurately perpendicular to the boundary, while on the free surface, the disorder can be found locally. This is believed to be the origin of the difference between the simulation and theory results. On the other hand, the concentration $C$ on the free surface is much smaller in the case of EN than EV (see figure 26), leading to the inhomogeneous Neumann boundary condition $f(C)$, of less significance for the former than the latter. This explains the better agreement in the EN case, as shown in figure 27. Therefore, higher accuracy is achievable with an improved algorithm for calculating the normal directions of those boundary particles, which deserves further study in the future. Nevertheless, satisfactory performance on concentration conservation is observed in the current proposed method.

Figure 27.

Temporal evolution of global concentration for small grains.

In the following, we analyse the segregation mechanism behind the concentration evolution. From (2.10), we can find that a dynamic equilibrium will be reached finally under the effect of segregation and diffusion. In particular, two factors promote the particle segregation: the vertical gradients of concentration $\boldsymbol {\nabla }_{g}C$, and the segregation velocity $\boldsymbol {\nabla }_{g}V$. The latter takes no effect when the segregation velocity is assumed constant, such as in the benchmark problem in § 5.1. In comparison, $\boldsymbol {\nabla }_{g}V$ rather than $\boldsymbol {\nabla }_{g}C$ triggers the segregation in the rotating drum as the concentration is globally homogeneous at the initial stage. Figures 28 and 29 illustrate the spatial distribution of the concentration rate along the mid-chord and its three components caused by $-F\,\boldsymbol {\nabla }_{g}V$, $-V\mathcal {F}\,\boldsymbol {\nabla }_{g}C$ and $\boldsymbol {\nabla }(D\,\boldsymbol {\nabla } C )$, denoted as $\dot C_1$, $\dot C_2$ and $\dot C_3$.

Figure 28.

Concentration rate distribution along the mid-chord for the EN case at (a) $t=5$ s, (b) $t=10$ s, (c) $t=20$ s and (d) $t=40$ s, with $\dot C_{1}$, $\dot C_{2}$ and $\dot C_{3}$ representing the contributions from $-F\,\boldsymbol {\nabla }_{g}V$, $-V\mathcal {F}\,\boldsymbol {\nabla }_{g}C$ and $\boldsymbol {\nabla }(D\,\boldsymbol {\nabla } C )$.

Figure 29.

Concentration rate distribution along the mid-chord for the EV case at (a) $t=5$ s, (b) $t=10$ s, (c) $t=20$ s and (d) $t=40$ s, with $\dot C_{1}$, $\dot C_{2}$ and $\dot C_{3}$ representing the contributions from $-F\,\boldsymbol {\nabla }_{g}V$, $-V\mathcal {F}\,\boldsymbol {\nabla }_{g}C$ and $\boldsymbol {\nabla }(D\,\boldsymbol {\nabla } C )$.

(1) Effect of vertical gradient of segregation velocity, $-F\boldsymbol {\nabla }_{g}V$. It is observed that $\dot C_1$ plays the dominant role in the concentration evolution, particularly at the beginning such as at $t=5$ s. In figure 28, $\dot C_1$ ascends monotonically from a negative point on the free surface to the peak value as $\bar {d}_{mc}$ increases, and then plummets to zero at approximately $\bar {d}_{mc}=0.24$, after which $\dot C_1=0$ holds until the drum wall. Also, both the starting point and the peak value rise as the segregation proceeds. The reason for the change is twofold. On the one hand, the velocity field after $t=5$ s reaches the steady state, and the radial shear rate $\dot \gamma$ appears quadratic (figure 19), leading to the constant distribution of $\boldsymbol {\nabla }_{g}V$ along the mid-chord. On the other hand, as the segregation develops, the concentration $C$ decreases in the upper active zone and increases in lower part, leading to the smaller segregation function $F$ in the upper active zone and greater $F$ in the lower active zone. A similar change is found in the EV case, but ${\dot C}_1$ decreases in both zones. This difference originates from the fact that the initial concentration is 0.5 in the EV case, while it is 0.11 in the EN case. As a result, the segregation function $F$ in the EV case drops in the whole active zone as the segregation develops.

(2) Effect of vertical gradient of concentration, $-V\mathcal {F}\boldsymbol {\nabla }_{g}C$. The effect of ${\dot C}_2$ is initially insignificant compared to ${\dot C}_1$, but its contribution becomes more pronounced as segregation develops, due to the increasing vertical concentration gradient $\boldsymbol {\nabla }_{g}C$ in the active zone. As time proceeds, ${\dot C}_2$ approaches a quadratic distribution as $\dot \gamma$ does, indicating the dominant influence of shear rate in ${\dot C}_2$. Here, $\mathcal {F}$ is another key factor determining the magnitude of ${\dot C}_2$. Due to the application of the symmetric form of the segregation function here, $\mathcal {F}=0$ holds when $C=0.5$. As a result, ${\dot C}_2$ shrinks to zero near $\bar d_{mc}=0.1$ in the EV case, as shown in figure 29. Furthermore, the sign of ${\dot C}_2$ is determined by $\mathcal {F}$ since both $V$ and $\boldsymbol {\nabla }_{g}C$ are positive in most parts of the active zone.

(3) Effect of diffusion, $\boldsymbol {\nabla }(D\,\boldsymbol {\nabla } C )$. First, an apparent drop in ${\dot C}_3$ is observed on the free surface as time proceeds. This is ascribed to the fact that the source term $q$ in (4.7) will become insignificant when $C$ gets close to zero or unity. A minor role is played by ${\dot C}_3$ in the remaining active zone during the whole process. This is because the effect of ${\dot C}_3$ in figures 28 and 29 could be understood approximately as the description of the curvature of the concentration distribution along the mid-chord. From figure 26, it is found that in the active domain, the concentration distribution is quite close to being linear. Therefore, the diffusion effect, in general, is insubstantial. Note that in this simulation, only 40 seconds are reproduced and the steady state has not been reached yet. Otherwise, the magnitude of the segregation and diffusion should be equal but opposite.