A decoupled structure preserving scheme for the Poisson-Nernst-Planck Navier-Stokes equations and its error analysis
Abstract.
We consider in this paper numerical approximations for the Poisson-Nernst-Planck-Navier-Stokes (PNP-NS) system. We propose a decoupled semi-discrete and fully discrete scheme that enjoys the nice properties of positivity preserving, mass conserving, and unconditionally energy stability. Then, we establish the well-posedness and regularity for the initial and (periodic) boundary value problem of the PNP-NS system under suitable assumptions on the initial data, and carry out a rigorous convergence analysis for the fully discretized scheme. We also present some numerical results to validate the positivity preserving property and the accuracy for our decoupled numerical scheme.
Keywords. Error analysis; PNP-NS system; Unique Solvability; Structure-preserving; Positivity-preserving.
‡Key Laboratory of Intelligent Computing and Application (Tongji University), Ministry of Education, and Department of Mathematics, Tongji University, Shanghai 200092, P. R. China (qingcheng@tongji.edu.cn).
⋆ Eastern Institute of Technology, Ningbo, Zhejiang 315200, P. R. China (jshen@eitech.edu.cn).
1. Introduction
In this paper, we consider a time-dependent system that describes the electrodiffusion of ions in an isothermal, incompressible, and viscous Newtonian fluid. Such a system is called the Poisson-Nernst-Planck-Navier-Stokes (PNP-NS) system gong2021partial ; liu2021positivity ; schmuck2011modeling , which is widely applied in fields such as microfluids which has numerous applications in lab-on-a-chip system; biology including vesicle motion, membrane fluctuations, electroporation; and electrochemistry such as porous electrode charging, desalination dynamics, dendritic growth bazant2010induced . An introduction to some basic physical and mathematical descriptions can be found in rubinstein1990electro .
We consider a solution of a monovalent symmetric strong salt. The Poisson-Nernst-Planck equations and incompressible Navier-Stokes Equations describe the system as
where and denote the velocity field of the fluid and the pressure function, respectively. The variables and represent the concentration functions of positive and negative ions in the fluid, respectively, and is the electric potential. Here represents the free charge density for a monovalent symmetric salt (here ionic valence ), and is elementary charge. is Boltzemann’s constant, is temperature and is the diffusion coefficient of ions. Moreover, , are the dielectric permittivity and viscosity of the fluid. Normalizing the electric potential by introducing : The PNP-NS system is therefore given by
(1.1) | |||
(1.2) | |||
(1.3) | |||
(1.4) | |||
(1.5) |
with , and . It is worth to note that , where is the Debye screening length bazant2010induced defined by and is a reference bulk concentration of ions. The system (1.1)-(1.5) is subjected to a set of initial and boundary conditions, which will be specified later.
There has been considerable interest in the mathematical analysis of the PNP-NS system. For example, Schmuck schmuck2009analysis established the global existence of weak solutions in three dimensions under the blocking boundary condition for and the zero Neumann boundary condition for ; Gong-Wang-Zhang gong2021partial established the existence and partial regularity of suitable weak solutions in three dimensions under the zero Neumann boundary condition for , , and ; Constantin-Ignatova constantin2019nernst proved the global existence and stability result in two dimensions, with the blocking and selective boundary conditions for and the Dirichlet boundary condition for . We emphasize that the solutions of the PNP-NS system are positive (), mass-conserving, and energy-dissipative.
In recent years, a large effort has been devoted to constructing positivity-preserving schemes for various problems in different areas MR4186541 ; MR4107225 ; MR3880256 ; MR4253790 ; liu2018positivity ; van2019positivity ; shen2016maximum ; zhornitskaya1999positivity ; chen2019positivity . There are also quite a few numerical investigations on the PNP-NS system (1.1)-(1.5). It was shown in flavell2014conservative that it is important for numerical schemes to maintain mass conservation. Prohl-Schmuck proposed in prohl2010convergent a coupled fully implicit first-order scheme with a finite-element method in space for the PNP-NS system and studied its convergence. Additionally, a first-order time-stepping method was proposed in liu2017efficient with spectral method discretization in space. Several structure-preserving numerical methods have been proposed for the PNP equations, for example, cheng2022new2 ; cheng2022new ; flavell2014conservative ; hu2020fully ; huang2021bound ; liu2021positivity ; prohl2009convergent ; shen2021unconditionally ; liu2021efficient ; ding2019positivity . There are also some recent studies reformulate the PNP system into Maxwell-Ampere Nernst-Planck(MANP) system qiao2023structure . However, there appears to be no scheme available in the literature for the PNP-NS system (1.1)-(1.5) that enjoys the properties of unique solvability, mass- and positivity-preserving, and energy stability.
In this paper, we propose a decoupled, mass- and positivity-preserving, and unconditionally energy-stable scheme for the PNP-NS system and carry out a rigorous error analysis. The main contributions of this paper include:
-
•
We propose a totally decoupled, mass- and positivity-preserving, and unconditionally energy-stable scheme for the PNP-NS system by combining the following techniques:
-
–
Rewriting the PNP system as a Wasserstein gradient flow and using the technique introduced in shen2021unconditionally to preserve positivity and energy stability for the PNP system;
-
–
Using a projection-type method guermond2006overview ; guermond1998approximation ; guermond1998stability to decouple the velocity and pressure;
-
–
Introducing an extra term as in shen2015decoupled , which allows us to treat the convective term in the PNP equations explicitly while maintaining stability.
-
–
- •
-
•
To carry out an error analysis, it is necessary to have bounds for and , which are not available through energy stability. We use an approach similar to liu2021positivity to derive these bounds by introducing a high-order asymptotic expansion for both the PNP equations and the Navier-Stokes equations.
This paper is organized as follows: In Section 2, we construct a semi-discrete (in time) scheme, followed by a fully discrete scheme with a generic spatial discretization, and prove that it preserves mass and positivity, and is unconditionally energy stable. In Section 3, we establish the well-posedness and regularity of the PNP-NS system under periodic boundary conditions. An error analysis of the fully discretized scheme is carried out in Section 4. Some numerical results are provided in Section 5.
2. A Decoupled Numerical Scheme and Its Properties
Let be a bounded domain in . We consider the time discretization of the PNP-NS system (1.1)-(1.5) subjected to boundary condition either
-
•
: the non-slip boundary condition for , the homogeneous Neumann boundary condition for , i.e., all the fluxes vanish on the boundary of :
(2.1) -
•
: the periodic boundary conditions for all variables,
along with the initial condition:
(2.2) |
For either (2.1) or the periodic boundary conditions, one observes that the mass of ions is conserved, i.e.,
Another essential property of the PNP-NS system (1.1)-(1.5) is the following energy dissipation law:
(2.3) |
where and are chemical potentials of the PNP-NS system, and is the total energy given by
2.1. Time Discretization
We first consider the time discretization. For simplicity, we set various constants for the rest analysis. In order to construct an efficient time discretization scheme, we first rewrite the right-hand side of equation (1.4) as
and introduce a modified pressure . Then, the PNP-NS system (1.1)-(1.5) can be reformulated as
(2.4) | |||
(2.5) | |||
(2.6) | |||
(2.7) | |||
(2.8) |
Depending on boundary condition choice , we define the function space :
-
•
,
-
•
,
-
•
Following some of the ideas in shen2021unconditionally ; liu2021positivity ; shen2015decoupled , we construct a first-order time discretization scheme as follows: under boundary condition being either or , for any given with , and in , we compute in three steps:
-
•
Step 1: Solve from
(2.9) (2.10) (2.11) where
-
•
Step 2: Solve from
(2.12) -
•
Step 3: Solve from
(2.13) (2.14)
The first step involves solving a coupled nonlinear system for which can be formulated as a minimization problem for a convex functional, see shen2021unconditionally and also Theorem 2.2. The second step solves a Poisson-type equation for . And the third step is equivalent to solving
(2.15) |
along with either or the periodic boundary condition, and
(2.16) |
Thus, the scheme (2.9)-(2.14) can be efficiently implemented.
Remark 1.
In (2.9) (2.10), we discretized the mobility term as , , moving the terms to the left, therefore the first step can be rewritten as:
where
This is similar to the decoupling technique introduced by shen2015decoupled , where specific additional terms are introduced such that the decoupled discrete numerical scheme is unconditionally energy stable, see Theorem 2.2 below.
2.2. Fully Discretized Scheme
In this subsection, we shall consider a generic spatial discretization for (2.9)-(2.14). Let be a set of mesh points or collocation points in . Note that should not include the points on the part of the boundary where a Dirichlet (or essential) boundary condition is prescribed, while it should include the points on the part of the boundary where a Neumann or mixed (or non-essential) boundary condition is prescribed.
We consider a Galerkin-type discretization with finite elements, spectral methods, or finite differences with summation-by-parts in a subspace , and define a discrete inner product, i.e., numerical integration, on in :
(2.17) |
where we require that the weights . We also denote the induced norm by . For finite element methods, the sum should be understood as , where is a given triangulation. We assume that there is a unique function satisfying for . Under boundary condition , let , , and be suitable discretization subspaces of , , and , respectively.
To fix the idea and without loss of generality, throughout the rest of the paper, reader can think we are discussing under spectral method discretization framework, and are subspaces of , where
Under spectral method framework, the quadrature error is very small when is large enough, and avoidable in numerical implementation by choosing quadrature points numbers bigger than basis numbers . For simplicity, throughout the rest of the paper, we ignore the quadrature error, and do not distinguish the continuous inner product and discrete inner product .
Given , with in , , and in , we proceed as follows:
-
•
Step 1: Solve from
(2.18) (2.19) (2.20) where
(2.21) -
•
Step 2: Solve from
(2.22) -
•
Step 3: Solve from
(2.23) (2.24)
We shall show below that the nonlinear system (2.18)-(2.20) in Step 1 can be interpreted as a minimization of a convex functional. In Step 2, we only need to solve a Poisson-type equation for , and Step 3 is a discrete Darcy system which can be reduced to a discrete Poisson equation for . Hence, the above scheme can be efficiently solved.
2.3. Properties of the Numerical Scheme
We show below that our decoupled numerical scheme (2.18)-(2.24) enjoys four properties: mass conservation, unique solvability, positivity-preserving, and unconditional energy stability.
Before proceeding to the proof, for any discrete positive function for all , we introduce the operator defined by
(2.25) |
The operator is invertible on the space , so we can define the inverse operator and the induced norm
If for all , then we have
Lemma 2.1.
Suppose and , then we have the estimate:
where depends only on .
Proof.
Denote . From (2.25) and using the Poincaré-Wirtinger inequality, we have
and applying the Nikolskii’s inequality, we have
where depends only on . ∎
Theorem 2.2.
Proof.
- (1)
-
(2)
Unique Solvability and Positivity Preserving: The numerical solution of (2.18)-(2.20) is obtained through the minimization of the discrete energy functional:
over the admissible space
where
is the average of (and ), and
Below we show uniqueness, solvability, and positivity for scheme (2.18)-(2.23) by suitable modifications of liu2021positivity and shen2021unconditionally .
Firstly, we observe that every term in the functional is strictly convex or linear with respect to the variables over the admissible space . To show the existence of a unique minimizer of over , we proceed as follows. For a sufficiently small , whose value is to be determined later, we define
Since is a compact subset of , there exists a minimizer of over . Next, we need to show that lies in the interior of , provided is chosen to be sufficiently small.
Suppose the contrary that for an arbitrarily small , the minimizer of occurs at the boundary of , i.e., for all . For simplicity, we only consider the case that there exists a point such that (the other case can be handled similarly). Notice that there exists another point and such that . Now we can choose the test function as , where and are Lagrange polynomials satisfying the following property: for all
where and are the Kronecker delta functions. Since is the minimizer and for small, we have
Direct computations imply
(2.26) Plugging into (2), we obtain
(2.27) It is readily seen that
and
Furthermore, using Lemma 2.1, we obtain
and
Substituting the inequalities derived above into (2), we obtain
(2.28) This is impossible for any fixed and , since we can choose to be sufficiently small. This implies that the absolute minimum of over can only occur at an interior point of , provided is chosen to be sufficiently small. Since is smooth, we conclude that there exists a solution such that
Thus, is a positive solution of the modified discrete PNP-NSE system (2.18)-(2.20). The uniqueness of positive solutions to (2.18)-(2.20) follows from the strict convexity of over . The existence and uniqueness of can be easily observed from (2.22)-(2.24).
-
(3)
Unconditional Energy Stability: We first derive the energy inequality for (2.18)-(2.20). Taking the test function in (2.18) and in (2.19), we have
(2.29) From the convexity of the function for , we know
(2.30) (2.31) Applying and the fact that
we have
(2.32) Combining (2.29), (2.30), (2.31) with (2.32) we obtain
(2.33) Now we derive the energy inequality for (2.22)-(2.24). Taking the test function in (2.22), in (2.23), we have
(2.34) and
(2.35) where we have used (2.24) that yields
and
To estimate the term in (2.34), we take the test function in (2.23), and obtain
(2.36) Combining (2.34), (2.35) with (2.36), we have
(2.37) Combining (2.33) with (2.37), we have
(2.38) Now if we denote
the terms of the right hand side of (2.38) can be rewritten as
(2.39) where we have used the following identity in the last step
Similarly, we have
(2.40) Now plug (2.39) and (2.40) into (2.38), we have
∎
3. Well-posedness and Regularity
In this section, we shall establish the well-posedness and regularity of the PNP-NS system. For simplicity, we shall focus on periodic boundary conditions , for which the regularity of the solution can be determined by the regularity of the initial conditions. More precisely, we set and assume that
(3.1) |
Theorem 3.1.
Let , and assume the initial conditions , with , are positive and satisfy , and the velocity is divergence-free. Then there exists a unique global strong solution of (1.1)–(1.5) with the initial condition (2.2) and the periodic boundary condition (3.1). Moreover, there exists a constant depending on and the initial energy , , , and such that
Furthermore,
and the velocity field satisfies
for any , where depends on initial energy, , , and .
Proof.
A similar result for blocking boundary conditions has been obtained by Constantin and Ignatova constantin2019nernst . Their argument remains applicable for periodic boundary conditions, which will be sketched here for completeness. For the full proof, refer to constantin2019nernst .
Step 1: Firstly, we have
(3.2) |
which is a direct application of Lemma 1 in constantin2019nernst , following the same proof for periodic boundary conditions.
Step 2: We aim to show and in . To see this, let be a nonnegative, -convex function such that for , and for , and
Multiplying (1.1) by and integrating over , using the periodic boundary conditions and integration by parts, we obtain that , and hence
which, combined with the Cauchy-Schwarz inequality , yields
(3.3) |
From the properties of , we have
By the Gronwall inequality and , we conclude that , and hence , which yields that in . Similarly, in .
Step 3: We aim to estimate the local uniform bound for . Because of the energy dissipation law (2.3), we have
Using (3.2) in Step 1, we know that the auxiliary function
satisfies the estimate
From the mass conservation property and (3.2), we have
Combining the previous two equations, for any and , we have
Thus, from the Sobolev embedding for any , applying (3.2) again, we have the local uniform estimate for
(3.4) |
where depends on . Similar estimates hold for .
Step 4: Now we can estimate the global bound for . To do this, taking in (3.3), we obtain
Similar estimates hold for :
From the regularity of the Poisson equation, we know that
From here we obtain
(3.5) |
where . From (3.4), we have
(3.6) |
Combining this with (3.5), we obtain
(3.7) |
Now we cover the interval with fixed time step intervals . From (3.6), for any , there exists some such that , which, combining with (3.7) and , yields
for a slightly different . Notice that the right-hand side only depends on initial energy , initial ion mass , , and ; it is independent of time . We can extend the estimate to the entire time interval by an induction argument, and from the regularity of the Poisson equation obtain the global bound
(3.8) |
where depends on , , initial energy, initial ion mass, . Returning to (3.5), we know that
(3.9) |
for some depending on and .
Step 5: Now we are ready to estimate . Multiplying (1.1) by and integrating, we have
(3.10) |
We have a global bound for , , from (3.8). And from energy law (2.3), we know that and are bounded by initial energy . Hence, we have the uniform bound for ,
Applying these bounds to (3.10), we have
Applying the local uniform bound for from (3.9) with , we cover the interval with fixed time step intervals . With a similar argument as in Step 4, we have
and for any
Hence, we have the local uniform bound of
(3.11) |
Now, multiplying (1.1) by and integrating over , we have
Therefore, for any we have
Taking the limit as , we obtain
Combining local uniform estimate (3.11) and cover interval with fixed time step intervals , with a similar induction argument as in Step 4, we have
Since the forcing term in (1.4) is in , from the energy inequality (1.1) and on the standard estimates on non-stationary Navier-Stokes equation, we have
where depends on the initial energy and other constants. This completes the proof. ∎
Corollary 3.1.1.
(Maximum principle) Assuming for some , then we have on .
Proof.
This proof follows from the positivity proof for in Theorem 3.1. ∎
Next we derive the higher order regularity for the global strong solutions obtained in Theorem 3.1 when the initial data is assumed to have higher regularity.
Theorem 3.2.
Suppose, in addition, that the initial data satisfies for . Then the solution obtained in Theorem 3.1 satisfies
Proof.
The proof proceeds by induction on . The case was proved in Theorem 3.1. Assume the theorem holds for some non-negative integer , and suppose the initial data satisfies
We can verify that
(3.12) |
Now set , , , and . Differentiating the system (1.1)–(1.5) with respect to , we find that satisfies the following system:
(3.13) | ||||
(3.14) | ||||
(3.15) | ||||
(3.16) | ||||
(3.17) |
Step 1: Multiply equation (3.13) by and integrate over . Observing that there are no boundary term contributions due to the periodic boundary condition, we obtain
Applying the induction hypothesis, we have
(3.18) |
Here depends on and the initial data and we used the estimate: for any function
(3.19) |
Therefore, from the initial condition (3.12), we have
(3.20) |
Similarly, we obtain
(3.21) |
Multiplying (3.16) by and integrating over , we have
Applying the Ladyzhenskaya inequality and the induction hypothesis, we estimate
where depends on and the initial data. We also have
Combining these estimates, we obtain
(3.22) |
Step 2: Multiply (3.13) by and integrate over to obtain
Using estimates similar to (3.18) and the results (3.20), (3.21), and (3.22), we verify that
Combining these inequalities with the initial condition (3.12), we obtain
(3.23) |
Analogously, we have
(3.24) |
Multiplying (3.16) by and integrating over , we obtain
Applying the Ladyzhenskaya inequality and induction estimates, we have
Therefore, with the initial condition (3.12), we have
(3.25) |
Using estimates (3.23), (3.24), and (3.25) in equations (3.13), (3.14), and (3.16), we verify that
This completes the proof. ∎
4. Error Analysis
In this section, we will carry out a detailed error analysis for the positivity-preserving scheme (2.18)-(2.24) under the periodic boundary condition (3.1), for which the scheme (2.18)-(2.24) can be made more specific as follows:
We denote the Fourier collocation points as . Then the discrete inner product for two functions is defined by
where is the quadrature weight in 2D.
We also introduce the corresponding induced discrete norm by for any function . We define the discrete Fourier space
and set .
Let be the exact solution of the system (1.1)-(1.5) with initial condition (2.2). Denote as the -orthogonal projections of at time onto , i.e.,
and set
In order to establish the error analysis for the pressure correction scheme of the Navier-Stokes equations (2.22)-(2.24), we need to introduce an intermediate function , defined by
We define the error functions by
The main result of this section is
Theorem 4.1.
To prove this theorem, it is vital to establish a uniform strictly positive lower bound for the numerical solution , analogous to the strictly positive lower bound property of continuous solutions described in Corollary 3.1.1. Recall that we established upper and lower bounds for in Theorem 2.2; however, the lower bound implied in (2.28) depends on the norms of previous step solutions, and is insufficient to establish a uniform strictly positive lower bound for for arbitrary . To overcome this difficulty, we use an approach similar to liu2021positivity . In Section 4.1, by assuming sufficient regularity of the PNP-NS system solution, we establish the procedure of building supplementary fields with high-order local truncation errors through Lemma 4.2. With Lemma 4.3, we perform a rough error analysis that gives the minimum order required of the error terms to establish the lower bound for the numerical solution . In Section 4.3, with Theorem 4.4, we conduct a refined error analysis, recover the assumption in Lemma 4.3, and prove the error estimates for the supplementary fields built in Lemma 4.2. Thus, Theorem 4.1 is a direct combination of Theorem 3.2 and Theorem 4.4; the proof will be presented in Section 4.3.
4.1. A rough error analysis
Assume that the solution of PNP-NS system is smooth enough. Then applying Tylor expansion to the system, one obtains
we have the following local truncation error (see more computation details in Appendix (A.1) - (A.3)):
where depends only on regularity of .
High-Order Consistent analysis. As stated above, we only have a first-order truncation error in time for and , which is insufficient to establish a priori strictly positive lower bound for the numerical solution . Using the technique similar to liu2021positivity , we will construct the supplementary fields , providing sufficient regularity for the solution , a higher order consistency local truncation error will be established.
Lemma 4.2.
Let be the solution of the PNP-NS system (1.1)-(1.5) satisfying the following properties:
-
(1)
The ionic concentrations are strictly positive
-
(2)
The solution satisfies
we can construct correction functions depending only on , such that the supplementary fields , defined by
(4.1) |
has higher order consistency truncation error as defined in (4.5)-(4.9)
Moreover, with chosen small enough, we have
-
(1)
The supplementary functions are strictly positive:
(4.2) -
(2)
The supplementary functions satisfy
(4.3)
The detail of constructing in Lemma 4.2 will be given in the Appendix.
Now we start to make an error analysis for the scheme (2.18)-(2.24) by analyzing its truncation error for supplementary fields Denote the error functions by
(4.4) |
Denote by the -orthogonal projection of at time onto . We have the expression for the consistency truncation error for the modified functions:
(4.5) | |||
(4.6) | |||
(4.7) | |||
(4.8) | |||
(4.9) |
where
Subtracting (2.18)-(2.23) from (4.5)-(4.9), we have
(4.10) | |||
(4.11) | |||
(4.12) | |||
(4.13) | |||
(4.14) | |||
(4.15) |
To simplify the presentation, we rewrite the third term in (4.10) as
Rewrite the second term of (4.10) into
Similarly, for the third and second term of (4.11), we have
and
For the Navier-Stokes equation, in (4.13), we have
and
Collecting all previous equations, the error equations (4.16)-(4.21) could be rewritten as
(4.16) | ||||
(4.17) | ||||
(4.18) | ||||
(4.19) | ||||
(4.20) | ||||
(4.21) |
To finish the error analysis, we will need Lemma 4.3 below.
Lemma 4.3.
Under the same assumption and procedure as in Lemma 4.2, we build supplementary fields , for the numerical error defined in (4.4), assume that for the error estimate holds for the -th step, i.e.
(4.22) | ||||
(4.23) | ||||
(4.24) |
under the linear refinement requirement , we have the following -estimate for the -th step, i.e.
where is independent of , and
Proof.
First, from Lemma 4.2, we can construct that satisfies (4.2) (4.3). To obtain the bound of , given the estimate (4.22), a direct application of inverse inequalities implies
where we used . Similarly, we have
Provided are sufficiently small, we have
(4.25) |
where is a small constant.
Combining (4.25) with the regularity of as in (4.3), we obtain bounds for :
(4.26) | |||
(4.27) | |||
(4.28) | |||
(4.29) |
Taking in (4.16), using the equality , we obtain the left hand side of (4.16):
(4.30) |
and the right hand side of (4.16):
(4.31) |
Similarly taking in (4.17), we obtain
(4.32) |
and
(4.33) |
From the monotonicity of for , we obtain that
(4.34) | |||
(4.35) |
From (4.18), we have
(4.36) |
Combining (4.30), (4.32), (4.34), (4.35) with (4.36), we have
(4.37) |
Summing up (4.31) and (4.33) and using (4.37), we have
(4.38) |
Using the bound of in (4.26) and (4.27), we have
(4.39) |
Applying Hölder and Young’s inequalities, for the second term in (4.38), we have
(4.40) |
and for the third term in (4.38),
(4.41) |
where . Note that by (4.3), and are bounded.
Using Hölder and Young’s inequalities, we derive
(4.42) |
Using the bound of from (4.26), and the bound of from (4.3), we obtain
(4.43) |
Similarly, we obtain
(4.44) |
From Lemma 4.2, we have
(4.45) |
where the positive constant in (4.45) is independent of and .
Plugging (4.39)-(4.45) into (4.38), we have
(4.46) |
Combing (LABEL:lem:result) with assumption (4.22), (4.23), (4.24), we derive
(4.47) |
where depends only on , independent of .
Now taking the test function in (4.16), we have
(4.48) |
where we have used the inverse inequality
Combining (4.26), (4.47) with (4.48), we have
(4.49) |
where we have used in (4.49).
Finally, using the triangle inequality and the inverse inequality, we have
Similarly, we can derive the bound for :
This completes the proof of the lemma. ∎
4.2. A refined error analysis
Firstly, for error terms as defined in (4.4), we provide following equations
(4.50) | |||
(4.51) |
Equation (4.50) could be derived as
And equation (4.51) could be established similarly.
Now we proceed to a refined error analysis. The main result is
Theorem 4.4.
Under the same assumption and procedure as in Lemma 4.2, we can build supplementary fields , provided and sufficiently small and under the linear refinement requirement , for the numerical error between numerical solution from scheme (2.18)-(2.23) and supplementary fields , as defined in (4.4), we have
for all positive integer , such that , where are positive constants that are independent of the choice of .
Proof.
The proof of Theorem 4.4 is divided into two steps:
- •
- •
Step 1: A refined error analysis with a prior assumption.
First, from the choice of initial data:
we have
Assume (4.22)-(4.24) hold for the -th time step with . Then by Lemma 4.3, we have
where is sufficiently small. Since are also bounded, we obtain
(4.52) | |||
(4.53) |
Now we proceed to the proof, which is divided into two steps.
Taking the test function in (4.16), we obtain
(4.54) |
Using and (4.50) we have
(4.55) |
Using the bounds of and given in (4.26), (4.52), we have
(4.56) |
For the last two terms in (4.54), and the right hand side terms in (4.55), applying Hölder and Young’s inequalities and the properties in (4.52), (4.26), we have
(4.57) |
(4.58) |
(4.59) |
and
(4.60) |
where we have used the elliptic estimate from (4.18) to get
From Lemma 4.2, we have
(4.61) |
Plugging (4.55), (4.56), (4.57), (4.58), (4.59), (4.60), (4.61) into (4.54), we obtain
(4.62) |
Similarly, taking in (4.17), we obtain
(4.63) |
Taking in (4.19) yields
(4.64) |
where we have used (2.24) to obtain
Taking the test function in (4.20), we obtain
(4.65) |
Summing (4.64) with (4.65), we have
(4.66) |
For the second term in (4.66), we have
(4.67) |
Taking the test function in (4.20), we obtain
(4.68) |
For the first and second term on the right hand side of (4.66), we have
(4.69) |
and
(4.70) |
Consider the first two terms on right-hand side of (LABEL:err:eq:1) and apply (4.50) (4.51), we have
(4.71) |
and
(4.72) |
Using the estimate (4.2), for the final term in (LABEL:err:eq:1), we obtain
(4.73) |
Combining all these estimates (LABEL:err:eq:1)-(LABEL:err:eq:1_3), we have
(4.74) |
From Lemma 4.2, we have
(4.75) |
Plugging (4.67), (4.68), (4.69), (4.74), (4.75) into (4.66), we obtain
(4.76) |
Now taking the test function in (4.20), and combining (4.65), we have
(4.77) |
Plugging (4.77) into (4.76), we obtain
(4.78) |
A summation of (4.62) (4.63) (4.78) leads to
(4.79) |
Note that from the induction assumption in Step 1, the above inequality holds for all , where . An application of discrete Gronwall’s inequality implies
where are positive constants, independent of . Then we obtain higher order error estimate for and are able to recover our induction assumption (4.22)-(4.24) with and chosen small enough. This completes the proof of Theorem 4.4. ∎
4.3. Proof of Theorem 4.1
Now we are ready to prove our main result Theorem 4.1 , which is a direct combination of Theorem 3.2 and Theorem 4.1.
Proof.
Given for some , from Corollary 3.1.1, we have solution in .
Remark 2.
As shown in Theorem 4.1, the numerical scheme (2.18) - (2.24) is a first-order temporal accurate scheme. There are some recent studies liu2023second which extends the PNP scheme to a 2nd order one using Crank-Nicolson type of scheme which preserves positivity, energy stability and unique solvability. However, it is challenging to extend the current method to a second-order temporal accurate scheme that still preserve those nice properties, and at the same time keeping the PNP system and NS system solving process decoupled. The major challenges are:
- •
-
•
To design a unconditionally energy stable Crank-Nicolson type numerical scheme for Navier-Stokes scheme is non-travil shen1996error , and it would take further difficulties to decouple the two systems and preserve the energy law at the same time.
5. Numerical Examples
In this section, we present numerical experiments to validate the stability, positivity, and accuracy of our numerical schemes. We consider periodic boundary conditions and implement the Fourier spectral method in .
5.1. Accuracy Test
To verify the accuracy and convergence rate of our numerical scheme, we introduce an artificial exact solution by adding external forces to the PNP-NS system, formulated as
where we set and the source terms , , and are determined from the exact solutions
defined in the domain . We use Fourier modes with different time steps . Using scheme (2.18)–(2.24), we compute the errors between the numerical solutions and the exact solutions. The results are shown in Table 1, where first-order convergence rates are observed for the different variables.
error in | Order | error in | Order | error in | Order | error in | Order | |
---|---|---|---|---|---|---|---|---|
– | – | – | – | |||||
0.98 | 0.98 | 1.00 | 0.99 | |||||
0.99 | 0.99 | 1.00 | 0.99 | |||||
1.00 | 0.99 | 1.00 | 1.00 | |||||
1.00 | 1.00 | 1.00 | 1.00 | |||||
1.00 | 1.00 | 1.00 | 1.00 |
5.2. Property Test
We also perform numerical simulations to test the mass-conserving and positivity-preserving properties of our scheme. The positivity-preserving scheme is applied to solve the following PNP-NS system:
(5.1) | ||||
We set the parameters in (5.2) to be and , with the initial data given by
The initial condition indicates that the positive and negative ions accumulate in two regions centered at and , respectively.
With time step , in Figure 1, we plot the profiles of and the velocity field at times , , , , , and . We observe that the positive and negative ions move toward each other and drag the fluid along with them. Later, the outflowing fluid between them prevents the ions from approaching each other further and carries the ions toward the corners. At the end of the computation, the fluid becomes almost electro-neutral.






We also examine the energy dissipation of the system in Figure 2(left), where the system energy is shown to be dissipative as we have proved. We plot the mass change for positive and negative ions in Figure 2(middle), showing that the mass of ions is preserved within machine precision. We also plot the minimum and maximum of in Figure 2(right), demonstrating that the ionic concentrations remain positive throughout the simulation.



6. Concluding Remarks
In this paper, we mainly consider numerical approximations for the PNP-NS system. Firstly, we give the results of unique solvability and regularity for the solution of PNP-NS system with suitable assumptions on initial conditions. To efficiently solve this coupled system, we propose a decoupled, mass-conserving, positivity-preserving and energy stable scheme which can also be unique solvable. Furthermore, we also carry out a rigorous error analysis for the fully discretized scheme, and derive optimal convergence results. The error analysis mainly depends on the bounds for the numerical solutions and , which are obtained by using a high-order asymptotic expansion for the PNP-NS system combing with a mathematical induction technique. We also present some numerical examples to validate the accuracy and stability of our decoupled scheme.
Appendix A Appendix
A.1. High order correction
Lemma A.1.
Let be the solution of the PNP-NS system (1.1)-(1.5) which satisfies the following properties:
-
(1)
The ionic concentrations are strictly positive
-
(2)
The solution satisfies
then we can construct correction functions depending only on such that the supplementary fields (defined by (4.1)) has higher order consistency truncation error(as defined in (4.5)-(4.9)):
Moreover, with chosen small enough, we have
-
(1)
The supplementary functions are strictly positive
-
(2)
The supplementary functions satisfy
Proof.
Let be the -orthogonal projection of continuous solution onto , as defined in (4). From Taylor expansion, the local truncation error may be written into two parts, time discretization error and spatial discretization error, we have
(A.1) | |||
(A.2) | |||
(A.3) |
where are the temporal part of truncation error and are the spatial part of the truncation error. From Taylor expansion, we can compute
and
Applying the regularity assumption (2), we have
(A.4) |
With those , we construct and solve the leading order temporal correction function from the following equation:
(A.5) | ||||
(A.6) | ||||
(A.7) | ||||
(A.8) | ||||
(A.9) |
subject to the periodic boundary condition and zero initial condition. The PDE system (A.5)-(A.9) is very similar to the PNP-NS system (1.1)-(1.5), and the existence of solution could be established similarly. Moreover, given the regularity of and in (A.4), the solution satisfies
(A.10) |
The discretization of the above system implies that
(A.11) | ||||
(A.12) | ||||
(A.13) | ||||
(A.14) | ||||
(A.15) |
where and are the temporal part and spatial part of the truncation error, from Taylor expansion, we have
and
From the regularity result in (A.4) and (A.10), we have
Combining (A.1)-(A.3) and (A.11)-(A.13) leads to the second order temporal local truncation error for :
(A.16) | |||
(A.17) | |||
(A.18) |
where
and
Since are bounded, we may choose so small that . And are the temporal projection of functions onto . From (2) (A.10) we have
Similarly, the next order temporal correction function is given by the following system:
(A.19) | |||
(A.20) | |||
(A.21) | |||
(A.22) | |||
(A.23) |
subject to the periodic boundary condition and zero initial condition. Then we have
The discretization of the above system implies that
(A.24) | ||||
(A.25) | ||||
(A.26) | ||||
(A.27) | ||||
(A.28) |
Finally, a combination of (A.16)-(A.18) and (A.24)-(A.26) yields the third order temporal truncation error for :
where
Since are bounded, we may find so small that . Moreover, given the regularity of , we have
∎
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