118 \copyrightinfo2021
ERROR ESTIMATES AND BLOW-UP ANALYSIS OF
A FINITE-ELEMENT APPROXIMATION FOR THE
parabolic-elliptic Keller-Segel system
Abstract.
The Keller-Segel equations are widely used for describing chemotaxis in biology. Recently, a new fully discrete scheme for this model was proposed in [46], mass conservation, positivity and energy decay were proved for the proposed scheme, which are important properties of the original system. In this paper, we establish the error estimates of this scheme. Then, based on the error estimates, we derive the finite-time blowup of nonradial numerical solutions under some conditions on the mass and the moment of the initial data.
Key words and phrases:
parabolic-elliptic systems, finite element method, error estimates, finite-time blowup.1991 Mathematics Subject Classification:
65M12, 35K61, 35K55, 92C171. Introduction.
Keller and Segel first proposed a nonlinear model in the 1970s to describe the effect of cell aggregation in [27, 28]. A simplified Keller-Segel model in 2-D is given by
(1.1) | ||||
(1.2) |
where is a bounded domain with smooth boundary . The unknown and represent the concentration of the organism and chemoattractant respectively. The parameters are positive constants with being the sensitivity of chemotaxis. The model is supplemented with initial conditions
and no flux boundary conditions
where denotes the unit outward normal vector to the boundary , represents differentiation along on .
A different version of the Keller-Segel model consists in replacing (1.2) by
(1.3) |
The equation (1.1) describes the motion of the organism . The term is the flux, and the effect of diffusion and that of chemotaxis are competing for to vary. The equation (1.2) describes the change in concentration of the chemoattractant , it is influenced by the diffusion and the decay of the chemoattractant as well as the growth of the organism. In general, the chemoattractant particles are much smaller than the organism particles, thus it diffuses faster, which means that the diffusion of the chemoattractant will reach the equilibrium state in a relatively short time. The model (1.1)-(1.2) is called parabolic–elliptic system. On the other hand, (1.1) with (1.3) is a parabolic–parabolic system.
The solution of the Keller-Segel model (1.1)-(1.2) has several well-known properties, particularly, it may blow up in finite time. Various aspects and results for the classical Keller-Segel model since 1970, along with some open questions, are summarized in [25]. Positivity, mass conservation and energy dissipation of Keller-Segel equations can be found in [35],[36],[47],[29] and [6], which plays an important role to study the Keller-Segel system. Blanchet, Dolbeault and Perthame presented in [3] a detail proof of the existence of weak solutions when the initial mass is below the critical mass, above which any solution to the parabolic-elliptic systems blows up in finite time in the whole Euclidean space. In [37], Nagai demonstrated the finite-time blowup of nonradial solutions under some assumptions on the mass and the moment of the initial data. As for the parabolic-parabolic systems, Blanchet proved in [2] the optimal critical mass of the solutions in with . Wei proved that for every nonnegative initial data in , the 2-D Keller-Segel equation is globally well-posed if and only if the total mass in [49].
Although the large time behavior of the solution of the Keller-Segel model (1.1)-(1.2) has been well studied, there is still much to explore on the numerical side. Since the Keller-Segel equations possess three important properties: positivity, mass conservation and energy dissipation, it is preferable that numerical schemes can preserve these properties at the discrete level. In [26], the existence of weak solutions and upper bounds for the blow-up time for time-discrete (including the implicit Euler, BDF and Runge-Kutta methods) approximations of the parabolic-elliptic Keller-Segel models in the two-dimensional whole space are established. Liu, Li and Zhou proposed a numerical method in [34] which preserves both positivity and asymptotic limit, the proposed numerical method does not generate negative density if initialized properly under a less strict stability condition. Saito and Suzuki presented a finite difference scheme in [42] which satisfies the conservation of a discrete norm.
Some finite element methods are proposed in previous works. Saito presented a finite element scheme for parabolic-elliptic systems in [43] that satisfies both positivity and mass conservation properties. Under some assumptions on the regularity of solutions, the error estimates were established. Saito further constructed the finite element methods to the parabolic-parabolic systems in [44] and derived error analysis by using analytical semigroup theory. Gurusamy and Balachandran proposed a finite element method for parabolic-parabolic systems and established the existence of approximate solutions by using Schauder’s fixed point theorem in [23]. Further the error estimates for the approximate solutions in -norm were derived.
The discontinuous Galerkin methods can be also used to solve the Keller-Segel equations. Epshteyn and Kurganov developed a family of new interior penalty discontinuous Galerkin methods and proved error estimates for the proposed high-order discontinuous Galerkin methods in [15]. Epshteyn and Izmirlioglu further constructed a discontinuous Galerkin method for Keller-Segel model in [16] and obtained fully discrete error estimates for the proposed scheme. In 2017, Li, Shu and Yang applied the local discontinuous Galerkin (LDG) method to 2D Keller–Segel chemotaxis model in [30], they improved the results upon [15] and gave optimal rate of convergence under special finite element spaces before the blow-up occurs. In 2019, Guo, Li and Yang constructed a consistent numerical energy and prove the energy dissipation with the LDG discretization in [22].
Another important numerical methods for Keller-Segel models are finite volume methods since the positivity property can be naturally preserved. Filbet proposed in [18] a finite volume scheme for the parabolic-elliptic system, and by assuming the CFL condition and the initial datum , he proved existence and uniqueness of the numerical solution by using the Browder fixed point theorem, and showed that the numerical approximation converges to the exact solution under some assumptions. In 2016, Zhou and Saito proposed a finite volume scheme in [52], and established error estimates in norm with a suitable for the two dimensional case under some regularity assumptions of solutions and admissible mesh. By focusing on the radially symmetirc solution, they derived some a prior estimates to study the blow-up phenomenon of numerical solution.
There have been growing interests in positivity-preserving analysis for gradient flows with logarithmic energy potential. Some theoretical analysis of the positivity-preserving property and the energy stability have been explored for these numerical schemes for certain systems, such as Cahn-Hilliard systems in [7, 10, 11, 51, 12], the Poisson-Nernst-Planck-Cahn-Hilliard systems in [41], the Poisson-Nernst-Planck systems in [32], the thin film model without slope selection in [31] and a structure-preserving, operator splitting scheme for reaction-diffusion systems in [33]. The techniques of the higher order consistency analysis combined with rough error estimate and refined one have been presented in [32, 13, 14] which will be utilized in the following to obtain the convergence analysis.
Recently, a new approach for constructing positivity preserving schemes was proposed in [46]. The key for this approach is to write as in (1.1), and then use a convex splitting idea to construct mass conservative, bound preserving, and uniquely solvable schemes for (1.1)-(1.2) and for (1.1)-(1.3). The main purposes of this paper are to establish the convergence of the fully discrete scheme proposed in [46], and to show the finite-time blowup of numerical solutions under some conditions on the mass and moments of the initial data. More precisely, let be an approximation of , where is the time step and . Let be the initial mass, and be the moment of . Our first goal is to establish the error estimates for the fully discrete scheme proposed in [46] (cf. Theorem 6). Another important feature of the Keller-Segel system (1.1)-(1.2) is that the solution may blow up in finite time under certain conditions on the initial data. Our second goal is to show that the numerical solution will also blow up in finite time under similar conditions on the initial data (cf. Theorem 15). Many previous works (see [42, 43, 46]) show that the numerical solution seems to blow up under large initial data by several numerical experiments. However, there is still much to explore on the theoretical proof of blowup phenomenon besides the radial numerical solution in [52] mentioned before.
The rest of the paper is organized as follows. In Section 2, we recall some properties of the classical Keller-Segel equations, including its finite-time blowup behavior. In Section 3, we introduce the fully discrete scheme constructed in [46] and carry out a rigorous error analysis. In Section 4, we show that the numerical solution will blow up in finite time under suitable conditions on the initial data.
2. The Keller-Segel equations
In this section, we recall some properties for the Keller-Segel system (1.1)-(1.2) with no flux boundary conditions. In addition, we assume the initial value , and satisfies
It was shown in [35] that there exist some such that (1.1)-(1.2) is well posed in the time interval . Moreover, it holds that
Theorem 1.
The following result is shown in [37].
Lemma 2.
[37] Let and , where is the distance between and . Then there exist positive constants depending only on and such that for ,
(2.1) | ||||
where with
where .
The finite-time blowup behavior is then proved using the above result.
Theorem 3.
Moreover, the following pointwise estimates for is established in [19]. An application of the Neumann semigroup leads to
In this paper, we assume that
3. The fully discrete scheme and error estimates
In this section, we describe the fully discrete scheme in [46] for (1.1)-(1.2), construct the error equations and establish the error estimates.
We now give a precise description of our finite element space . Given a triangulation for , we let consists of all the vertices excluding those where Dirichlet boundary conditions are prescribed. We define to be the finite element space spanned by the piecewise linear continuous functions based on . Let be a triangle of the triangulation , and be its vertices, we define the quadrature formula
We recall that [48]
We then define the discrete inner product in by
the corresponding norm is defined by . We have the following estimates in [48] for the quadrature error.
Lemma 4.
Let denote the quadrature error, then we have
Applying Lemma 4, the norm has the following property
(3.1) |
Let be the Lagrange interpolation operator, which has the approximation property [4] that for all ,
(3.2) |
The fully discrete scheme proposed in [46] for (1.1)-(1.2) is to find such that for all ,
(3.3) | |||
(3.4) |
with the initial value . Here, the represents the usual inner product, the is the discrete inner product defined above, and is the forward Euler difference quotient approximating to defined by
In this setting, the authors in [46] proved the following.
Lemma 5.
We denote by the exact solution pair to the original equations (1.1)-(1.3), and all the upper bounds for the exact solution are denoted as . We set , and denote
The following theorem is the main result of this section.
Theorem 6.
Assume and the exact solution pair is smooth enough for a fixed final time . Then, provided and are sufficiently small and under the mild mesh-sizes requirement , we have the following error estimates
where , is independent of and .
The proof for this theorem will be carried out with a sequence of procedures that we describe below.
Remark 7.
The mesh-sizes requirement in Theorem 6 is proposed to obtain a higher order consistency analysis via a perturbation argument, which is needed to get the separation property and the bound for the numerical solution.
3.1. Higher order consistent approximation to (3.3)-(3.4)
In this subsection, we apply the perturbation argument method in [32] to the finite element scheme to construct such that
is consistent with the given numerical scheme (3.3)-(3.4) at the order . The following lemma is used to construct and the proof is given in Appendix.
By applying a perturbation argument, a higher order consistency is satisfied for , which is needed to obtain the separation property and a bound for the numerical solution.
Lemma 8.
Suppose that and is smooth enough, then there exist bounded smooth functions , such that satisfies
(3.5) |
for all , , where and denotes the duality product satisfying
(3.6) |
where depends on the regularity of the solution .
Remark 9.
Under the conditions that the exact solution for some , and is sufficiently small, we obtain that
(3.7) |
Since the correction functions only depend on the exact solution , they are bounded in norm. Then, we can obtain the following bound for :
(3.8) |
3.2. A rough error estimate
In this subsection, we derive the strict separation property and a uniform bound for the numerical solution.
We recall the following inverse estimate in [4, p.111, Lemma 4.5.3].
Lemma 10.
Given a quasi-uniform triangulation on domain , and be a finite-dimensional subspace of , where and . Then there exists a positive constant such that for all , we have
where is independent of .
Lemma 11.
Assume that are non-negative sequences such that
Then
Define an alternative error function:
Subtracting the numerical scheme (3.3) from the consistency estimate (3.5) implies that
(3.9) |
where
Since only depends on the exact solution, we can assume
(3.10) |
Lemma 12.
Proof.
We shall first make the following assumption at the previous time step:
(3.11) |
Then, we will demonstrate that such an assumption will be recovered at the next time step in Section 3.3.
Using the inverse inequality, we obtain a bound for the numerical error function:
(3.12) |
A combination of the above with (3.8), we get a bound for at the previous time step:
Because of (3.12), taking sufficiently small, we have
Then the strict separation property is valid for :
(3.13) |
Taking in (3.9) leads to
(3.14) |
Now we deal with the left hand side of (3.14). To proceed the first term on the left hand side of (3.14), notice that since stated in Remark 20, using the Hölder inequality, we have
(3.15) | ||||
where lies between and , and (3.8) has been utilized in the second inequality. As for the second term on the left hand side of (3.14), using the strict separation property of the numerical solution (3.13), we have
(3.16) |
Next, we deal with the right hand side of (3.14). We apply the Hölder inequality and the Young inequality:
(3.17) | ||||
An application of the Cauchy-Schwarz inequality and (3.10) leads to
Using the inequality (3.6), we have
(3.18) |
Substitution of (3.15)-(3.18) into (3.14) leads to
Then we have the following estimate by applying
(3.19) |
Again, taking in the error equation (3.9) leads to
(3.20) |
Now we estimate the first term on the right hand side of (3.20). Using the Young inequality, we have
(3.21) | ||||
where (3.11) and have been used in the last inequality. For the second term on the right hand side of (3.20), we have
(3.22) | ||||
where (3.19) and the inverse inequality have been used in the last inequality. Substitution of (3.21)-(3.22) and (3.6) into (3.20) leads to
Then, we can obtain a rough estimate for :
An application of the inverse inequality implies that
We take sufficiently small such that
A combination of above with (3.7) leads to the strict separation property:
In addition, we can obtain the following bound for the numerical solution at time step :
which completes the proof. ∎
3.3. Recovery of the assumption (3.11)
In this subsection, the assumption will be recovered at the next time step.
Taking in (3.9), we arrive at
The second term on the left hand side of above inequality can be rewritten as
where lies between and . Utilizing the strict separation property and the bound for the numerical solution, we obtain the following estimate
where is a positive constant satisfying . Combining above inequalities leads to
(3.23) | ||||
Using Cauchy-Schwarz inequality and the strict separation property of the numerical solution, we have
An application of (3.10) shows that
The last term on the right hand side of (3.23) can be estimated as follows
Substitution of above into (3.23) leads to
multiplying on both sides and summing this inequality from 0 to leads to
Choosing sufficiently small such that , using the Gronwall inequality (Lemma 11), we derive that
then we obtain , the assumption (3.11) is recovered at the next time step.
3.4. Proof of Theorem 6
In this subsection, we shall make use of the strict separation property and the uniform bound for the numerical solution derived in the above to prove Theorem 6.
A weak formulation of (1.1)-(1.2) is
(3.24) | |||
(3.25) |
Substituting into (3.24) at , we have
(3.26) | ||||
where represents the truncation error. Similarly, substituting into (3.25) at leads to
(3.27) | ||||
We rewrite the numerical scheme (3.3) as
(3.28) | ||||
We split the error functions as
Then using the property of the interpolation (3.2), we have
(3.29) | |||
Subtracting the numerical scheme formulation (3.28) and (3.4) from the weak form (3.26) and (3.27), we obtain the following error equations:
(3.30) | ||||
(3.31) | ||||
for all , where are defined before and is defined as follows
Taking in (3.30) leads to
(3.32) |
Now we estimate the terms on the right-hand side of (3.33). For the first term , applying the Cauchy-Schwarz inequality and the Young inequality yields
(3.33) | ||||
In order to estimate above, taking in (3.31) and applying Lemma 4 leads to
where the property of the interpolation has been used in the first inequality. Thus we obtain the following estimate for :
(3.34) |
Applying Lemma 4 indicates that
A combination of the above estimates for with (3.29) leads to
(3.35) |
Substitution of above into (3.33) leads to
(3.36) |
Next we estimate the second term . For the first term, we can derive [48]
(3.37) | ||||
An application of the property of the interpolation and the Young inequality leads to
(3.38) | ||||
Using the Cauchy-Schwarz inequality and the property of the interpolation, we have
(3.39) | ||||
Similarly, we have
(3.40) | ||||
Now we apply the Cauchy-Schwarz inequality and the Young inequality:
(3.41) | ||||
Notice that
therefore, using Lemma 4 leads to the following estimate for :
(3.42) |
We recall the quadrature formula defined before and Lemma 4, and arrive at
(3.43) |
An application of the Cauchy-Schwarz inequality and the property of the interpolation leads to
(3.44) | ||||
Substituting estimates (3.37)-(3.44) into and applying the property , we obtain
(3.45) | ||||
It remains to bound each term of . Now we use the Cauchy-Schwarz inequality and the Young inequality:
(3.46) | ||||
where we have used the following inequality:
An application of the strict separation property and the bound of the numerical solution leads to
(3.47) | ||||
Combining the estimates (3.46)-(3.47), we obtain
(3.48) |
Finally, combining (3.36),(3.45) and (3.48) in (3.32), we find
(3.49) | ||||
Multiplying by on both sides of (3.49) and summing up from to , we get
Assuming , since and is small enough, and applying the discrete Gronwall inequality (Lemma 11) to the above leads to
A combination of the above estimates for and with (3.29) leads to the desired error estimates for . Finally, we obtain the error estimates for from (3.34) and (3.35).
The proof of Theorem 6 is complete.
4. Finite-time blowup
In this section, we discuss whether the solution of the fully discrete scheme (3.3)-(3.4) will blow up in finite time.
We first prove a discrete analog of Lemma 2. Taking in (3.3), where is defined as in Lemma 2, then from (3.2), we have the following error estimate
(4.1) |
Lemma 13.
Assume that is smooth enough for a fix time , let . Under the mild mesh-sizes requirement , if , then and the following inequality holds
Proof.
We rewrite (3.3) as
(4.2) | ||||
where Lemma 2 has been used in the last inequality, and are defined as follows
Thanks to (4.1), we obtain
where the error estimates and have been used in the last inequality. Substitution of above into (4.2) leads to
(4.3) |
Next, we estimate respectively. Utilizing the property of the interpolation operator in (4.1), we have
(4.4) |
We derive from Theorem 6 that
(4.5) | ||||
where the property of interpolation and have been used. Noticing the definition of , we have
(4.6) |
An application of the strict separation property, the bound for the numerical solution and indicates that
(4.7) | ||||
Utilizing the property of the interpolation operator in (4.1), we have
(4.8) | ||||
An application of the Cauchy-Schwarz inequality and the error estimates leads to
(4.9) | ||||
where has been used in the last inequality. An application of Lemma 4 to leads to
(4.10) |
Using the property of the interpolation operator as in the proof of the Theorem 6, we have
(4.11) |
Finally, a substitution of (4.4)-(4.11) into (4.3) implies that
The proof is complete. ∎
Remark 14.
The positive constant in Lemma 13 depends on the regularity of the exact solutions.
We can then derive the following discrete analog of Theorem 3.
Theorem 15.
Proof.
Obviously, we can derive the following inequality from Lemma 13
Denote , we have the following inequality
(4.12) |
Under the condition that , we can choose sufficiently small such that . Since is sufficiently small, we have
We claim that the following inequality holds for all
(4.13) |
We prove the above inequality by induction. Using the inequality (4.12) for , we have
Now assume that (4.13) holds for , we have
Notice that is decreasing about , we have
Next summing (4.13) over shows that
Hence, if the solution exists for all , then becomes negative provided that . This is a contradiction to the positivity of . Thus, the proof is complete. ∎
Remark 16.
Note that in the classical Keller-Segel system, the solution may blow up in finite time. Based on the error estimates, we prove that the numerical solution can also blow up under large initial value. There are several numerical examples in [46] to validate the blowup behavior of the numerical solution to the fully discrete scheme (3.3)-(3.4). The analysis of Theorem 15 depends on the regularity of the solution, it is very interesting whether we can still have similar results under weak regularity, we will continue to conduct on this issue in the future.
5. Conclusion
In this paper, we established error estimates for a fully discrete scheme proposed in [46] for the classical parabolic-elliptic Keller-Segel system, and showed that the numerical solution will blow up in finite time under some assumptions, similar to the situation for the exact solution of the classical parabolic-elliptic Keller-Segel system.
Acknowledgments
W. Chen is supported by the National Natural Science Foundation of China (NSFC) 12071090 and the work of J. Shen is partially supported by NSF Grant DMS-2012585 and AFOSR Grant FA9550-20-1-0309.
Appendix A Appendix
Lemma 17.
Denote , where is defined as , then the following estimate holds for all ,
(A.1) |
Proof.
Let and , we have the following equations:
Then we have
Taking and denoting by in the above equation leads to
From [48] and Lemma 4, we have the following estimate
Combing the above estimates with the elliptic regularity estimate leads to
The error estimate can be obtained by using the duality argument
Combing above estimates with the definitions of and shows that
which completes the proof. ∎
In order to obtain Lemma 8, we proceed in several steps. Firstly, we deal with and construct as follows.
Lemma 18.
Assume that and is smooth enough, then there exists a bounded smooth function , such that satisfies
(A.2) |
for all , , where is defined as in Lemma 17 and
Proof.
From (A.2), we have the following equality:
(A.3) | ||||
where the operators and are defined as in Lemma 17. Moreover, the left hand of (A.3) can be rewritten as
where equation (3.24) has been used.
Step 1: Construction for . For any , define as
we can show that is well defined. Using the Cauchy-Schwarz inequality and the property of the interpolation, we obtain
then the following estimate holds for :
(A.4) |
and the positive constant
An application of Lemma 17 leads to
Combining (A.4) with few direct calculations shows that the following estimate holds for :
where
then is well-defined. Combining with (A.3) leads to the following linear partial differential equation for :
(A.5) |
for all . From [17, Chapter7.1, Theorem 3], there exists a weak solution of (A.5). In addition, from [17, Chapter7.1, Theorem 7], suppose that is smooth enough such that is smooth enough in , and the -order compatibility conditions hold for , then the problem (A.5) has a smooth enough solution in .
Step 2: Construction for . Let be
Similarly, the following estimate holds for as discussed in (A.4):
(A.6) |
where the positive constant
Combining (A.6) with few calculations yields the following estimate for :
where the positive constant
then the consistency for is obtained, which leads to Lemma 18. ∎
Remark 19.
Taking in (A.2) leads to for , i.e., preserves mass conservation property. Choosing suitable initial condition such that , we obtain .
After repeated application of the perturbation argument as illustrated in Lemma 18, Lemma 8 can be proved.
Proof of Lemma 8.
The duality product is well-defined since the fact that is uniformly bounded as and . We can construct by solving the following linear partial differential equation:
(A.7) |
for all . As discussed in Step 1 above, the problem (A.7) has a smooth enough solution in .
By repeated application of the methods in Step 2 above, we can construct by such that the consistency for is arrived:
(A.8) |
for all , , where
where is a positive constant depending on the derivatives of , such that is well-defined.
Remark 20.
Similarly, taking in (3.5) leads to for . Choosing the initial condition such that (, , ), we obtain .
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