Elsevier

Applied Numerical Mathematics

Volume 153, July 2020, Pages 248-275
Applied Numerical Mathematics

A meshless technique based on generalized moving least squares combined with the second-order semi-implicit backward differential formula for numerically solving time-dependent phase field models on the spheres

https://doi.org/10.1016/j.apnum.2020.02.012Get rights and content

Abstract

In the current research paper, the generalized moving least squares technique is considered to approximate the spatial variables of two time-dependent phase field partial differential equations on the spheres in Cartesian coordinate. This is known as a direct approximation (it is the standard technique for generalized finite difference scheme [69], [77]), and it can be applied for scattered points on each local sub-domain. The main advantage of this approach is to approximate the Laplace-Beltrami operator on the spheres using different types of distribution points simply, in which the studied mathematical models are involved. In fact, this scheme permits us to solve a given partial differential equation on the sphere directly without changing the original problem to a problem on a narrow band domain with pseudo–Neumann boundary conditions. A second-order semi-implicit backward differential formula (by adding a stabilized term to the chemical potential that is the second-order Douglas-Dupont-type regularization) is applied to approximate the temporal variable. We show that the time discretization considered here guarantees the mass conservation and energy stability. Besides, the convergence analysis of the proposed time discretization is given. The resulting fully discrete scheme of each partial differential equation is a linear system of algebraic equations per time step that is solved via an iterative method, namely biconjugate gradient stabilized algorithm. Some numerical experiments are presented to simulate the phase field Cahn-Hilliard, nonlocal Cahn-Hilliard (for diblock copolymers as microphase separation patterns) and crystal equations on the two-dimensional spheres.

Introduction

Many phenomena in the applied and natural sciences can be formulated as partial differential equations (PDEs) on surfaces. We can point out texture synthesis in computer graphics [87], flow and solidification of a thin fluid film [72], brain imaging [67], phase separation patterns for diblock copolymers on spherical surfaces [82] and surfactant distribution on a moving interface [93]. One of the most important mathematical models defined on surfaces is the phase field equation. As mentioned in [54], a phase field model is a mathematical formulation to solve interfacial problems [54]. Additionally, in this model, the phase field or order parameter is introduced to identify one phase from the other [54]. This parameter also takes distinct values (for example 0 and 1 in a binary system) in each of the phase with a smooth transition between both values [54].

Some applications of the phase field models can be observed in solidification dynamics [13], viscous fingering [12], fracture dynamics [38], vesicle dynamics [49] and the other applications [10], [35], [47], [84]. Usually, these models are constructed to reproduce a given interfacial dynamics. For example, in solidification problems, the front dynamics are given by a diffusion equation for either concentration or temperature in the bulk and some boundary conditions at the interface (a local equilibrium condition and a conservation law) which constitutes the sharp interface model [51]. Some well-known phase field models are Allen-Cahn (AC) and Allen-Cahn vesicle equations [56], [97], conservative Allen-Cahn (CAC) equation [50], Cahn-Hilliard (CH) equation [14], [15], [26] and the phase field crystal (PFC) equation [33], [34], [62]. As mentioned in [63], the dynamic of these equations derived as a gradient flow of the free energy, admits an energy dissipation law, which justifies its thermodynamic consistency and leads to a mathematically well–posed model [63]. As it is said in [48], the directed self–assembly of block copolymers in thin films is an emerging technology for nanoscale patterning. A diblock copolymer consists of two blocks, each of a different type of monomer, which are joined chemically to each other. When the temperature is lowered below a critical point, the two sequences become incompatible and the copolymer melt undergoes phase separation. This results in the occurrence of periodic structures such as lamellae, spheres, cylinders, and gyroids [48]. This phase ordering or separation may occur on static or dynamic surfaces [82] such as lipid bilayer membranes [8], crystal growth on curved surfaces [9], and phase separation within thin film [78]. The above explanations can be considered as a reason for studying the phase field models on the sphere.

Finding the analytical solution of PDEs on the surfaces specially the phase field equation is somewhat complicated. Thus, simple and accurate numerical techniques play an important role for solving such problems. In recent years, different numerical methods have been developed for the solution of PDEs on surfaces. Generally, these techniques are divided into intrinsic and embedded, narrow-band methods [40]. The first group of these methods uses coordinates intrinsic to the surface, and for discretization the differential operators a surface based mesh will be applied [16], [17], [30], [31], [32], while the second group extends the partial differential equation (PDE) in R3 to a narrow band around the surface, and then modifies the differential operators so that the solution is restricted to the surface [2], [11], [66]. As mentioned in [40], intrinsic methods have the benefit, in which the obtained discretization scheme is consistent with the dimension of the main problem, but in narrow-band methods, the surface differential operators are posed in extrinsic coordinates so that all coordinate singularities can be avoided, and the standard methods such as finite difference (FD) schemes (see also [25], [27], [39], which show finding the numerical solution of some applicable models via FD methods) and finite element methods (FEMs) can be applied on 3D Cartesian grids and 3D unstructured meshes, respectively [30], [40], [86]. As it is said in [40], these methods also lead to degenerate surface differential operators because they allow diffusion occurs only in directions tangential to the surface, and it makes difficulty for using implicit discretization techniques in time-dependent problems [40]. Due to these problems in solving PDEs on surfaces, Fuselier and Wright [40] provided a new numerical scheme based on radial basis functions (RBFs) to solve equations defined on surfaces. Also, they derived the error analysis of the RBFs approximation for target functions in Sobolev spaces on the two-dimensional smooth embedded submanifold of R3. Besides, authors of [70] have provided a new error analysis for completing the error analysis given in [40], and they have found a new numerical scheme based on RBFs method combined with a first–order time splitting technique for solving AC equation on surfaces.

Authors of [6] studied the arrangement of N particles on the sphere by solving the PFC equation on the spheres via different numerical methods such as an implicit approach, which describes [6] the surface using a phase field description, a parametric finite element approach and a spectral method based on nonequispaced fast Fourier transforms on the spheres [6]. To solve PFC and CH equations on the sphere numerically, a finite element method (FEM) with the block-preconditioner, which is considered and analyzed for the obtained linear system from the proposed discretization is applied [73], [74]. In [75], authors have solved numerically a type of the PFC model that involves a density and a polarization field on the sphere, and they also have found three types of crystals in their simulations. Authors of [83] presented a method based on the application of a diffuse interface framework for solving two-phase problems involving a material quantity on an interface. They [83] also used FD methods with a block-structured adaptive grid, and they applied the nonlinear multigrid approach to solve nonlinear systems. In [71], a new mathematical model is introduced for amoeboid motion, which has application in cell migration, and it is solved via isogeometric analysis combined with spline-based FEM. In [61], the authors applied a narrow band neighborhood of a curved surface using FD scheme for the solution of PFC equation. Finite element approximation is implemented and analyzed for CH equation on surfaces [29]. Another recent research work for finding the numerical solution of PFC equation on surfaces can be found in [58], in which an efficient linear second-order unconditionally stable direct discretization method is employed. An unconditionally energy-stable second-order time-accurate scheme is applied to solve CH equation on surfaces [57]. The CH equation on surfaces is solved using FD scheme based on the closest point method [42]. Also, this equation is simulated via FEM in space and semi-implicit Euler scheme in time [76]. In [24], a local RBFs method is employed to solve numerically CH, Swift–Hohenberg and PFC equations in two- and three-dimensional spaces. The interested reader can also refer to [40] and references therein for more details.

The main concern in solving the phase field mathematical models such as PFC and CH is to show the energy stability during a long time simulation [20], [36], [46], [55], [63], [64], [91], [94], [95]. Therefore, choosing a proper time discretization plays an important role for obtaining the numerical solution of these equations. Different types of the second-order semi-implicit backward time formula (SBDF2) have been considered for discretization the time variable of the phase field models in the previous works [20], [63], [64], [94]. In such methods, the stabilized term, i.e., a second-order Douglas-Dupont regularization is also added to the proposed time discretization, which ensures the energy stability under certain condition. As can be seen in next section, the PFC and CH models are sixth and fourth-orders nonlinear PDEs, respectively. To avoid solving the nonlinear algebraic equations at each time step, the linear and nonlinear terms can be discretized implicity and explicitly, respectively. Of course, in some research works mentioned here, both linear and nonlinear terms are discretized implicitly, which it can be solved via the nonlinear multigrid method or Newton's iteration procedure.

To the best of our knowledge, we use a SBDF2 method by adding a stabilized term for approximating the time variable of the CH, nonlocal CH and PFC equations defined on the spheres, and a meshless technique based on generalized moving least squares (GMLS) is applied to approximate the spatial variables. In this technique, there is no need to use a background mesh or triangulation for approximation [37], [69], [89], [90], which can be applied on the studied equations simply. For the first time, the moving least squares (MLS) approximation on the unit sphere was introduced by Wendland in [90]. The implementation of this approach may not be simple for solving PDEs on spheres and the other manifolds because the PDE operators should be approximated by non-closed-form and complicated shape functions [69]. Therefore, Mirzaei in his research work [69] introduced a direct approximation based on GMLS [68] on the spheres with an error analysis. Another formulation of GMLS approximation based on projected gradient of the shape functions is given in [23] for solving reaction-diffusion equations on surfaces. Recently, authors of [45] have introduced new formulations for GMLS approximation defined on different manifolds, and they have employed this technique for finding the numerical solutions of hydrodynamic flows. For more details about the GMLS approximation on spheres and the other manifolds, the interested reader can refer to [23], [45], [69], [90].

To see more studies about MLS approximation and its improvements, here we mention some important research works as follows. The improved moving least squares (IMLS) approximation is considered for introducing the boundary element–free method (BEFM), which could be used to solve 2D Helmholtz problems efficiently [18]. A combination of the field integral equation (FIE) with the complex variable moving least squares (CVMLS) approximation is done in [19] that is called a complex variable boundary element–free method (CVBEFM), and it is applied for solving the exterior Neumann problem of the Helmholtz equation [19]. The complex variable element–free Galerkin (CVEFG) method is introduced and applied for solving 3D elliptic problems and 3D wave equations in [59]. This technique uses the CVMLS approximation to form shape functions. Also, in [96], the variational multiscale interpolating element–free Galerkin (VMIEFG) method is developed and applied to obtain the numerical solution of the nonlinear Darcy-Forchheimer model, in which the interpolating MLS approximation is used to construct the shape functions. Recently, authors of [1] have applied the interpolating stabilized element–free Galerkin technique for solving the Oldroyd model as a generalized incompressible Navier-Stokes equation. Besides, the uniqueness and existence of the developed numerical method in [1] are investigated. Also, the interested reader can refer to [18], [19], [59], [96] for more details.

The CH equation on the sphere S2 of radius r0>0 is formulated as follows [42], [57], [73], [76]{u(x,t)t=MΔS2μ(x,t),μ(x,t)=F(u(x,t))ϵ2ΔS2u(x,t),xS2,t>0, with suitable initial conditions for continuous functions u(x,t) and μ(x,t), and F(u(x,t)):=f(u(x,t)). ΔS2 is the well-known Laplace-Beltrami operator. Also, u(x,t) denotes the concentration of one component of a binary mixture, and it is the order parameter. μ(x,t)=δE(u(x,t))δu is the chemical potential, F(u(x,t))=0.25(u(x,t)21)2 is a free energy density, M>0 is the mobility, and ϵ is a positive constant related to interfacial thickness [42], [57], [73], [76]. By considering u(x,t):=u, Eq. (1.1) is obtained from a constrained gradient flow in the H1 Hilbert space of the Helmholtz free energy functional as follows [42], [57], [73], [76]E(u)=S2(F(u)+ϵ22|S2u|2)dx. This model is used to describe a wide number of phase separation processes from co-polymer systems to lipid membrane [42]. Also, it is applied for spinodal decomposition of two component alloys [76], and it is considered for mathematical description of a quick quench scenario followed by that of a much slower coarsening [76]. As it is said in [60], one of the vital concepts of the CH equation is the interface between two phases such as α and β. It has a finite thickness where the composition u changes gradually. When the binary system approaches the equilibrium state composed of α phase with u=uαeq and β phase with u=uβeq>uαeq, the regions where u(x,t)=uαeq and u(x,t)=uβeq correspond to the α and β phases, respectively whereas the region where u(x,t) varies gradually from uαeq to uβeq denotes the interface between α and β phases. Some important applications of CH equation can be observed in image processing [28], planet formation [85] and cancer growth [65]. Other applications of CH equation can be found in [60] and references therein.

The crystal structure is a crystal (crystalline solid) based on the microscopic arrangement of atoms inside it. When the atoms find a periodic organization, a crystal is a solid. There are three different types of a crystal, which contain large crystal such as snowflakes, diamonds, and table salt, polycrystals such as most metals, rocks, ceramics, and ice and amorphous solids such as glass, wax and many plastics (see [98], where most materials of this paragraph are taken from [98]).

The PFC equation was first introduced by Elder and his co-workers [33], [34], [81] that is a model for simulation of imperfections in material properties such as vacancies, grain boundaries and dislocations. This model also has some important properties, which can be seen in [7], [33], [34], [81].

The PFC equation on the sphere S2 of radius r0>0 is formulated as follows [5], [6], [61], [73]{u(x,t)t=ΔS2μ(x,t),μ(x,t)=F(u(x,t))+2ΔS2u(x,t)+ΔS2v(x,t),v(x,t)=ΔS2u(x,t),xS2,t>0, with the proper initial conditions for continuous functions u(x,t), μ(x,t) and v(x,t), and F(u(x,t)):=f(u(x,t)), in which F is known as the free energy density. For this equation, the function f is defined as f(u(x,t)):=u(x,t)3+(1ϵ)u(x,t). ΔS2 is the Laplace-Beltrami operator that is defined on S2. Also, u(x,t) represents density field, μ(x,t)=δE(u(x,t))δu is the chemical potential, where E(u(x,t)):=E(u) represents free-energy, which it is defined as follows [73]E(u)=S2(|S2u|2+12(ΔS2u)2+12(1ϵ)u2+14u4)dx, where ϵ is a positive constant [5], [6], [61], [73].

As mentioned in [6], the original formulation of the PFC equation (1.3) returns to one of the Hilbert problems that is the distribution of N points on a sphere. This problem is formulated as an optimization problem, which is difficult to be solved numerically when N increases [6]. Instead of solving this problem, authors of [6] derived a mathematical formulation based on PDE defined on surface, i.e., PFC equation to study the ordering of interacting particles on a sphere. For further information, the interested reader can refer to [6] and references therein.

This equation has many applications, which can be addressed as follows. A simulation of heterogeneous nucleation and growth in an undercooled melt [43], crystal growth in a supercooled liquid [44], the PFC simulations in nanostructure formation for example, in semiconductor nanowires, and it also can be applied for the quantitative simulations: critical wavelength. Moreover, to simulate the solidification microstructures formation, they play important role in various applications such as commercial casting [35], and it is known as eutectic and dendritic solidification. The potential application (epitaxial growth) of the PFC model can be found in the technologically important process of thin film growth [35].

The reminder of this paper is as follows. The mass conservation and energy stable properties of both models are proved in Section 2. In Section 3, a SBDF2 scheme with adding a stabilized term, i.e., AΔtΔS2(un+1un) (a second-order Douglas-Dupont regularization) is given to the time discretization of CH and PFC equations, where A is a positive constant. In Section 4, we show that the time discretization presented here satisfies the mass conservation property, and also energy stability under the assumption on A. The error analysis of the proposed time discretization is given in Section 5. In Section 6, the GMLS approximation on the spheres is discussed briefly. In next section, i.e., in Section 7, the fully discrete schemes of the equations are derived. Some numerical results and simulations are presented in Section 8 for CH, nonlocal CH and PFC equations defined on the two-dimensional spheres. Finally some concluding remarks are collected at Section 9.

Section snippets

The mass conservation and energy stability properties

In this section, we have shown the mass conservation and the energy stability properties for Eqs. (1.3) on the spheres S2 of radius r0>0 due to the time variable. In [57], these properties are proved for Eq. (1.1).

We need the following lemma, namely “Green-Beltrami identity”, which will be used to illustrate the mass conservation and energy stability properties for the mathematical models studied here.

Lemma 2.1

[4] For any fC2(S2) and gC1(S2), the following relation is concluded.S2gΔS2fdx=S2S2g.S2f

The time discretization

In this section, a SBDF2 scheme is considered for discretization Eqs. (1.1) and (1.3) in time. By dividing the time interval [0,T] into M sub-intervals such that T=MΔt, where Δt is the time step and by defining tn:=nΔt, the SBDF2 time-stepping approximation (by adding a second-order Douglas-Dupont regularization) for Eq. (1.1) can be written as follows{3un+14un+un12Δt=ΔS2μn+1,μn+1=2f(un)f(un1)ϵ2ΔS2un+1AΔtΔS2(un+1un), where A is a positive constant and n=0,1,...,M1. Also, un+1 and μn+1

Stability analysis

Here, we only show the mass conservation and energy stability of the proposed time discretization for Eq. (3.4). The same procedure can be done for Eq. (3.1), which we omitted it for brevity.

Theorem 4.1

The scheme (3.4) is mass conservative.

Proof

For n=0, according to Eqs. (3.4) and (3.6) and using the definition of inner-product in L2(S2), we can write(3u13u0ΔtΔS2μ02Δt,1)L2(S2)=(ΔS2μ1,1)L2(S2). Therefore(3u13u02Δt,1)L2(S2)=(ΔS2μ1,1)L2(S2)+12(ΔS2μ0,1)L2(S2). Using Green-Beltrami identity for terms in the

Error estimate

Our goal of this section is to present the error estimate for the time discretization proposed here. We only give the error estimate of the time discretization for PFC equation. This also can be done in a similar manner for CH equation. We first note that using a careful Taylor expansion gives||u(t1)u1||CΔt2, where C>0 is independent of Δt.

Assumption 3

We assume that the initial value of u has sufficient regularity. With this, we can suppose that the exact solution of Eq. (1.3) satisfies the following

The GMLS approximation on the spheres

The GMLS approximation on the d-dimensional spheres has been introduced by Mirzaei [69]. Here, we briefly review this technique for the two-dimensional spheres, i.e., d=2 to approximate the Laplace-Beltrami operator. More details on this subject can be found in [69], where most materials of the current section are taken from that.

Suppose that uCm+1(S2) is a function defined on the two-dimensional sphere for some m0. As it is said in [69], the smoothness of a function u on the spheres (or any

The fully discrete scheme

The GMLS technique presented in Section 6 will be applied to approximate Eqs. (3.4) and (3.6) (i.e., obtaining the fully discrete scheme for PFC equation) with respect to the spatial variables. It also should be noted that the fully discrete scheme for CH equation can be obtained in the same manner, which we omit it for brevity.

Note that we use Eqs. (6.7) and (6.5) without considering sign “⁎”. We now suppose that X={x1,x2,...,xN} is a set of N scattered points on S2 of radius r0>0. The

Numerical results

In this section, some numerical simulations are provided for demonstrating the ability of the developed numerical scheme in solving the CH, nonlocal CH and PFC equations on the two-dimensional spheres. The two-dimensional sphere of radius r0>0 is defined as followsS2={(x,y,z)R3:x2+y2+z2=r02}. The spherical harmonics of at most degree m=2 are used in GMLS approximation [4], [69]. Besides, the weight function is chosen to be a compactly supported Wendland's function, and it is defined as follows

Conclusion

The generalized moving least squares approximation in combination with a second-order semi-implicit backward differential formula has been successfully applied to solve numerically the high-order nonlinear time-dependent phase field crystal (PFC) and Cahn-Hilliard (CH) equations on the two-dimensional spheres. In order to ensure the energy stability of the time discretization presented here, we followed the literature, and we added a stabilized term that is the second-order Douglas–Dupont

Acknowledgement

The authors are very grateful to three reviewers for carefully reading this paper and for their comments and suggestions, which have improved the quality of the paper.

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