This paper focuses on the unconditionally optimal error estimates of a linearized second-order scheme for a nonlocal nonlinear parabolic problem. The first step of the scheme is based on Crank–Nicholson method while the second step is the second-order BDF method.
A rigorous error analysis is done, and optimal L2 error estimates are derived using the error splitting technique. Some numerical simulations are presented to confirm the study’s theoretical analysis.
Optimal L2 error estimates and energy norm.
The goal of this research article is to present and establish the unconditionally optimal error estimates of a linearized second-order BDF finite element scheme for the reaction-diffusion problem. An optimal error estimate for the proposed methods is derived by using the temporal-spatial error splitting techniques, which split the error between the exact solution and the numerical solution into two parts, that is, the temporal error and the spatial error. Since the spatial error is not dependent on the time step, the boundedness of the numerical solution in L∞-norm follows an inverse inequality immediately without any restriction on the grid mesh.
1. Introduction
In this paper, we consider the following parabolic problem with nonlocal nonlinearity:
where , d ≥ 1 is again a domain with a smooth boundary ∂Ω, a and f are functions to be defined in the next section and l denote a continuous linear form on L2(Ω) given by
where g is a function on L2(Ω).
The study of nonlocal parabolic problems has received considerable attention in recent years ([1–3] and the references therein). This kind of problems arises in various situations, for instance, u could describe the density of a population (for instance, bacteria) subject to spreading. The diffusion coefficient a is then supposed to depend on the entire population in the domain rather than on the local density, that is, moves are guided by considering the global state of the medium. The problem is nonlocal in the sense that the diffusion coefficient is determined by a global quantity. Besides its mathematical motivation because of the presence of the nonlocal term a(l(u)), such problems come from physical situations related to migration of a population of bacteria in a container in which the velocity of migration v = a∇u depends on the global population in a subdomain Ω′ ⊂ Ω given by a(l(u)).
Simsen and Ferreira [4] have discussed not only the existence and uniqueness of solutions for this problem but also continuity with respect to initial values, the exponential stability of weak solutions and important results on the existence of a global attractor. The numerical methods for the nonlocal problems have been investigated by many authors as like in Refs [5, 6] and the references therein. However, they are restricted to nonlocal reaction terms or nonlocal boundary conditions. Chaudhary et al. [7] studied the convergence analysis of the Crank–Nicolson finite element method for the nonlocal problem involving the Dirichlet energy. Mbehou et al. [8] studied (1.1) using the Crank–Nicolson Galerkin finite element method. The main focus on this paper was to present the exponential decay and vanishing of the solutions in finite time. They also derived the optimal convergence order in L2-norm using Pr with r ≥ 1 finite elements. Yin and Xu [9] applied the finite-volume method to obtain approximate solutions for a nonlocal problem on reactive flows in porous media and derived the optimal convergence order in the L2 norm. Almeida et al. [10] presented convergence analysis for a fully discretized approximation to a nonlocal problem involving a parabolic equation with moving boundaries, with the finite element method applied for the space variables and the Crank–Nicolson method for the time. Recently, Yang et al. [11] derived the unconditional optimal error estimate of Galerkin FEMs for the time-dependent Klein–Gordon–Schrodinger equations using the error splitting technique. Also in Ref. [12], Yang and Jiang applied the linearized second-order backward differentiation formulae (BDF) Galerkin Finite element methods (FEMs) for the Landau-Lifshitz equations to derive the unconditional optimal error estimates.
Our goal in this research article is to give and establish the unconditionally optimal error estimates of a linearized second-order BDF finite element scheme for the reaction-diffusion problem (1.1). Using Pr (r ≥ 1) finite element to approximate the solution of (1.1), the optimal error estimates O(Δt2 + hr+1) in L2 norm are derived using the error splitting technique.
This paper is organized as follows. In Section 2, we recall few known results and present few regularities, which are used in the proof of the optimal error estimates. To prove the optimal error estimates by the error splitting technique, the temporal errors and the spatial errors are shown in Sections 3 and 4, respectively. Finally numerical results are presented in Section 5 to demonstrate our theoretical analysis.
2. Preliminaries and main results
Let (d ≥ 1) be a bounded domain with a smooth boundary ∂Ω = Γ. The standard notations (see for instance Refs [13, 14]) will be used throughout this work. The Lebesgue space is denoted Lp(Ω), 1 ≤ p ≤ ∞, with norms but the L2(Ω)-norm will be denoted by ‖ ⋅‖. For any nonnegative integer m and real number p ≥ 1, the classical Sobolev spaces:
equipped with the semi-norm
and the norm
with the usual extension when p = ∞. When p = 2, Wm,p(Ω) is the Hilbert space Hm(Ω) with the scalar product:
The norm of Hm(Ω) will be denoted by ‖ ⋅‖m. It should be mentioned that Dα stands for the derivative in the sense of distribution, while α = (α1, …, αd) denotes a multi-index of length |α| = α1 + ⋯ + αd. We also employ the standard notation of Bochner spaces, such as Lq(0, T, X) with norm
where X is an Hilbert space and ‖ ⋅‖X the norm of X. For all these notions on Sobolev spaces and Bochner spaces, we refer to Refs [13, 15].
Throughout this paper, the following known inequalities will be frequently used [13].
Let us now suppose that α is a nonnegative constant and p > 1. Simsen and Ferreira [4] proved the existence and uniqueness of global solution under the following hypotheses.
u0 ∈ L2(Ω).
is Lipschitz–continuous function, that is, there exists γ > 0 such that |f(s) − f(t)| ≤ γ|s − t|, for all and f(0) = 0.
is bounded with 0 < m ≤ a(s) ≤ M, for all with , where λ1 is the first eigenvalue of .
is Lipschitz–continuous with .
is a continuous linear form, i.e. there exists g ∈ L2(Ω) such that l(u) = lg(u) = ∫Ω g(x)u(x)dx, for all u ∈ L2(Ω).
for all r ≥ 1,
(cf. Ref. [16]). For all p ∈ (1, ∞) and τ ≥ 0, there exists a generic constant C = C(p, d) such that for all with d ≥ 1 we have
(cf. Ref. [17]). Let ak, bk, ck and γk, for integers k ≥ 0, be the positive numbers such that
Remark. If the first sum on the right hand side of (2.12) extends only up to n − 1, then estimate (2.13) holds for all k > 0 with σk = 1.
(Hk-estimate of elliptic equations [18]). Suppose that v is a solution of the boundary value problem
Let be a uniform triangular or tetrahedral partition of Ω into triangles or tetrahedrons. Thus, let denote the mesh size, where hK = diam(K) = max{‖x − y‖, x, y ∈ K}, and Vh be the finite dimensional subspace of , which consists of continuous piecewise polynomials of degree r ≥ 1 on .
Let {tn| tn = nΔt; 0 ≤ n ≤ N} be a uniform partition of [0, T] with time step Δt = T/N. We write un = u(x, tn), Un ≈ u(x, tn) and for any sequence of functions define
The following telescope formula is for n ≥ 2
Under the above notations, we propose the following linearized second-order BDF Galerkin finite element scheme associated to (1.1), which is to find such that
Step 1: For , find such that for all vh ∈ Vh
where is given by
Step 2: For 2 ≤ n ≤ N, find such that for all vh ∈ Vh
Πh is an interpolation operator from to Vh.
Assume that the hypotheses (H1)– (H5) hold. Then the fully discrete system defined in (2.16)–(2.18) has a unique solution which satisfies
Proof. 1 For the existence, taking , and in (2.16)–(2.18), respectively, the existence and uniqueness of , and are from the Lax–Milgram theorem and the hypothesis (H3).
Let in (2.16), we have
Drop the third term of the left hand side, use the lower bound of a(⋅) and (H2),
and
Now, let in (2.17), the same arguments used above give us
Taking in (2.18), using the lower bound of a(⋅), (H2) and dropping the third term of the left hand side lead to
From the telescope (2.15), we obtain
That is
The relation (2.19) is obtained by summing up the above relation (2.21) and using the discrete Gronwall lemma 2.3.
The main result of this work is presented in the following theorem.
Suppose that system (1.1) has a unique solution u satisfying (H6). Then the fully discrete system defined in (2.16)–(2.18) has a unique solution , and
The proof of this theorem will be done in the following sections.
3. Error estimates for the semi-discrete problem
Let us introduce the corresponding time discrete system associated with (1.1)
Step 1: for U0 = u0, find U1 by
where is the solution to
Step 2: for 2 ≤ n ≤ N, find Un by
The weak formulations of (3.1)–(3.3) are defined as follows: find such that for all
and for 2 ≤ n ≤ N
with such that
The existence and uniqueness of the solution to problems (3.4)–(3.6) can be easily proved by using Lax–Milgram theorem.
Let u be the exact solution of (1.1). Then, u satisfies the following equations:
where satisfies
, R1 and Rn are, respectively, the truncation errors given by
By Taylor formula and relation (2.9) with τ = 1, it is easy to see that
Let us denote
We have the following assumption.
Testing the above equation by yield
Using the left bound of a(⋅) to the left hand side and Young’s inequality to the right hand side, we obtain
The proof ended by dropping the third term of the left hand side and applying (3.10) to the right hand side.
Based upon (3.11), we have
Testing the above equation by e1 and using the fact that , we have
We have
Taking these estimates into (3.13), we obtain the desire result.
The main result in this section is as follows.
Proof. The proof of this theorem will be done using the mathematical induction.
In view of (3.11) and (3.12), the inequality (3.14) holds for n = 0, 1. Since U0 = u0, the inquality (3.15) holds for n = 0. Now, let us assume that (3.14) and (3.15) hold for n ≤ m with m ≤ N − 1. Then we need to prove the inequality for n = m + 1. By the definition of and the induction assumption, .
Multiply (3.17) by 4Δten and integrate it over Ω. The use of the telescope formula to the resulting equation leads to
Use the lower bound of a(⋅) and drop certain positive terms on the left hand side of the above equation leads to
We have
Therefore,
Summing up the above inequality and using the discrete Gronwall inequality, we get
From ‖en‖ ≤ CΔt2, we have
Applying Lemma 2.4 for the linear elliptic problems (3.1)–(3.3) with the induction assumptions gives the H2 estimate
Using (2.3), we have
which concludes the proof.
4. Error estimates for the fully discrete problem
In this section, we will prove the optimal spatial error estimates. Let Πh be an interpolation operator and be a Ritz projection operator defined by
Then we have the following lemma.
(cf. Ref. [19]). If , then
Let us denote
From lemma 4.1, we have
Proof. From equations (2.17) and (3.6), satisfies the following equation:
Setting in the above equations leads to
From (4.5) and (4.6), we have
From the inverse inequality, and
The main result in this section is as follows.
Proof. The proof of this result will be done by mathematical induction. Since , (4.11) holds for n = 0. To compute the error estimate (4.10) for n = 1, subtract (3.4) from (2.16) and take ,
From (4.5) and (4.6), we have
which proves (4.10) for n = 1.
Now, we assume that (4.10) and (4.11) hold for n = m − 1, 2 ≤ m ≤ N, then we need to show it also holds for n = m. By the definition of and the induction assumption, .
If one takes and uses the telescope formula, one obtains
That is
The quantities Ki, i = 1, …, 5 can be bounded by the similar way Ti, i = 1, …, 5:
Taking these bounds into (4.14), we obtain
Sum up the above inequality and use the discrete Gronwall Lemma 2.3 leads to
From the inverse inequality, and
5. Numerical results
In this section, we present several numerical simulations to illustrate our theoretical analysis. Since the resulting matrix of the linear system (2.16)–(2.18) is sparse, symmetric and positive definite, an incomplete Cholesky factorization is performed and the result is used as preconditioner in the preconditioned conjugate method iterative solver (see for instance Refs [20, 21]).
To analyze the convergence rate, we consider the following problem.
with Ω = (0,1)2, the coefficient α = 1, p = 3.5
g is chosen correspondingly to the exact solution
We simulated the above problem on uniform meshes with a linear finite element approximation (r = 1) and T = 0.1.
For the convergence with respect to the mesh size h, we choose Δt = h2 and we solve problem (2.16)–(2.18) with different values of h (h = 1/5; 1/10; 1/15; 1/20; 1/25); from our theoretical analysis, the L2-norm errors are in order O(h2 + Δt2) = O(h2 + h4) ∼ O(h2). H1-norm errors are in order O(h + Δt2) = O(h + h4) ∼ O(h). In Figure 1, we plot the log of errors against log(h). One can see that for L2-norm, the slope is almost 2, and for H1 − norm, the slope is almost 1, which are in good agreement with our theoretical analysis.
For the convergence with respect to the time step Δt, h is fixed (h = 0.01), and we solve problem (2.16)–(2.18) with different time steps Δt = 0.1; 0.05; 0.025; 0.0125 (Δt = 0.1 × 21−l, l = 1, …, 4), and the L2-norm errors are in order O(h2 + Δt2) ∼ O(Δt2). Figure 2 shows the plots of log L2-error norm against log(Δt). Again, one can see that the slope is almost 2. These results are consistent with our theoretical analysis.
6. Conclusion
We have presented and analyzed a linearized second-order BDF Galerkin finite element method for the nonlocal parabolic problems. We have proved the L2 and energy error estimates using sufficient conditions on the exact solution. We also presented some numerical experiments on Matlab’s environment, and our numerical results confirm the theoretical analysis. The results in this paper lay the foundation for developing finite element based numerical methods for more general and complicated nonlocal problems both stationary and evolutionary.


