
The stability and convergence of the Fourier pseudo-spectral method are analyzed for the three dimensional incompressible Navier-Stokes equation, coupled with a variety of time-stepping methods, of up to fourth order temporal accuracy. An aliasing error control technique is applied in the error estimate for the nonlinear convection term, while an a-priori assumption for the numerical solution at the previous time steps will also play an important role in the analysis. In addition, a few multi-step temporal discretization is applied to achieve higher order temporal accuracy, while the numerical stability is preserved. These semi-implicit numerical schemes use a combination of explicit Adams-Bashforth extrapolation for the nonlinear convection term, as well as the pressure gradient term, and implicit Adams-Moulton interpolation for the viscous diffusion term, up to the fourth order accuracy in time. Optimal rate convergence analysis and error estimates are established in details. It is proved that, the Fourier pseudo-spectral method coupled with the carefully designed time-discretization is stable provided only that the time-step and spatial grid-size are bounded by two constants over a finite time. Some numerical results are also presented to verify the established convergence rates of the proposed schemes.
Citation: Cheng Wang. Convergence analysis of Fourier pseudo-spectral schemes for three-dimensional incompressible Navier-Stokes equations[J]. Electronic Research Archive, 2021, 29(5): 2915-2944. doi: 10.3934/era.2021019
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The stability and convergence of the Fourier pseudo-spectral method are analyzed for the three dimensional incompressible Navier-Stokes equation, coupled with a variety of time-stepping methods, of up to fourth order temporal accuracy. An aliasing error control technique is applied in the error estimate for the nonlinear convection term, while an a-priori assumption for the numerical solution at the previous time steps will also play an important role in the analysis. In addition, a few multi-step temporal discretization is applied to achieve higher order temporal accuracy, while the numerical stability is preserved. These semi-implicit numerical schemes use a combination of explicit Adams-Bashforth extrapolation for the nonlinear convection term, as well as the pressure gradient term, and implicit Adams-Moulton interpolation for the viscous diffusion term, up to the fourth order accuracy in time. Optimal rate convergence analysis and error estimates are established in details. It is proved that, the Fourier pseudo-spectral method coupled with the carefully designed time-discretization is stable provided only that the time-step and spatial grid-size are bounded by two constants over a finite time. Some numerical results are also presented to verify the established convergence rates of the proposed schemes.
In this paper we consider the three-dimensional incompressible Navier-Stokes equations (NSEs) over the domain
∂tu+u⋅∇u+∇p=νΔu, | (1.1) |
∇⋅u=0, | (1.2) |
with a periodic boundary condition (for any
Dαu(0,y,z)=Dαu(1,y,z),Dαu(x,0,z)=Dαu(x,1,z),Dαu(x,y,0)=Dαu(x,y,1). | (1.3) |
Here
The NSEs can also be formulated in terms of the pressure Poisson equation (PPE), given by
∂tu+u⋅∇u+∇p=νΔu, | (1.4) |
Δp=−∇⋅(u⋅∇)u, | (1.5) |
Note that the incompressibility constraint (1.2) has been replaced by the pressure Poisson equation (1.5). Taking the divergence of (1.4) and using the pressure Poisson equation (1.5), we arrive at
∂t(∇⋅u)=Δ(∇⋅u). | (1.6) |
Due to the periodic boundary condition and initial value:
∇⋅u≡0. | (1.7) |
A proof of the equivalence between (1.4)-(1.5) and the classical formulation (1.1)-(1.2) within the framework of strong solutions is straightforward. See the related works [33,34], etc.
Spectral and pseudo-spectral methods have been actively studied since the 1970s [26]. For nonlinear equations, the theoretical foundation was laid in the pioneering work [37] for steady-state spectral solutions. For time-dependent nonlinear PDEs, there have been many related works of one-dimensional conservation laws [42,43], semi-discrete viscous Burgers' equation and Navier-Stokes equations by E [21,22], etc. On the other hand, most existing works considered the spatial approximation with the time variable kept continuous. Among the existing works for the fully discrete pseudospectral method applied to nonlinear problems, a time step constraint of the form
In this work, we consider the fully discrete Fourier pseudo-spectral spatial approximation for the three-dimensional incompressible Navier-Stokes equations, combined with a variety of suitably chosen semi-implicit multistep methods in the temporal discretization. The theoretical analysis for the Fourier Galerkin spectral schemes has been reported in a few earlier works [29,30], while the analysis for the pseudo-spectral schemes turns out to be more challenging. To overcome the well-known difficulties associated with the aliasing errors appearing in the Fourier pseudo-spectral method, an aliasing error control technique, which was originally developed in a recent work [28], is applied for the incompressible Navier-Stokes equations. In addition, the Fourier pseudo-spectral spatial approximation is combined with stable time-discretization of up to fourth order which are specially tailored for the numerical stability. In more details, we adopt an explicit multi-step Adams-Bashforth approach for the fluid convection term and pressure gradient term, combined with an implicit Adams-Moulton method for the diffusion term, following similar numerical ideas in [13,28]. Moreover, the pressure variable is solved by a pressure Poisson equation, instead of being teated as a Lagrange multiplier. As a result, the computed velocity vector is proved to be divergence-free at a discrete level, so that it is
The rest of the article is organized as follows. In Section 2 we review of the Fourier pseudo-spectral spatial approximation, and recall the aliasing error control technique. The first order numerical scheme is proposed in Section 3, and the detailed stability and convergence analyses are provided in Section 4. The higher order numerical schemes (in temporal accuracy) are proposed and analyzed in Section 5. The numerical results of accuracy check are presented in Section 6. Finally, the concluding remarks are given in Section 7.
The Fourier series of a function
f(x,y,z)=∞∑l,m,n=−∞ˆfl,m,ne2πi(lx+my+nz),withˆfl,m,n=∫Ωf(x,y,z)e−2πi(lx+my+nz)dxdydz. |
In turn, the truncated series is the projection onto the space
PNf(x,y,z)=N∑l,m,n=−Nˆfl,m,ne2πi(lx+my+nz). |
If
‖f(x,y,z)−PNf(x,y,z)‖≤CMN−m, |
in which
The projection operator is one approach to a Fourier series approximation. However, sometimes we want an approximation which matches the function at a given set of points. For this purpose, an interpolation operator
(INf)(x,y,z)=N∑l,m,n=−N(ˆfNc)l,m,ne2πi(lx+my+nz), |
where the
The Fourier series and the formulas for its projection and interpolation allow us to easily take derivatives in the
‖∂kf(x,y,z)−∂kPNf(x,y,z)‖≤‖f(m)‖Nk−m,for0≤k≤m, |
‖∂kf(x,y,z)−∂kINf(x,y,z)‖≤‖f‖HmNk−m,for0≤k≤m,m>d2. |
Also see the related discussion of approximation theory [5] by Canuto and Quarteroni. Recall that the collocation coefficients
These properties of the Fourier projection and interpolation form the basis of the Fourier spectral and pseudospectral methods. The Fourier spectral method for (1.4)-(1.5) relies on the projection operator
The major advantage of the collocation method is that it easier to implement, and very efficient due to the fast Fourier transform. The ease of implementation comes from the fact that the collocation approach is point-wise, which avoids many difficulties when evaluating three dimensional nonlinear terms. On the other hand, the Galerkin spectral method is much easier to analyze, because it does not suffer from aliasing errors. Many works detailing the stability and convergence analysis of spectral methods exist, . In this work, we focus on the analysis of the Fourier pseudo-spectral method applied to (1.4)-(1.5). Despite the aliasing errors that appear in the collocation method, we are able to establish its stability and convergence properties for a fixed final time.
Given a collocation approximation to the function
f(xi,yj,zk)=(INf)i,j,k=N∑l,m,n=−N(ˆfNc)l,m,ne2πi(lxi+myj+nzk), | (2.1) |
we can define the discrete differentiation operators
The same process is performed for the second derivatives
ΔNf=(D2Nx+D2Ny+D2Nz)f,∇Nf=(DNxfDNyfDNzf),∇N⋅(fgh)=DNxf+DNyg+DNzh, | (2.2) |
in the point-wise sense. It is also straightforward to verify that
∇N⋅∇Nf=ΔNf. | (2.3) |
Since the pseudo-spectral differentiation is taken at a point-wise level, we need to introduce a discrete
‖f‖2=√⟨f,f⟩,with⟨f,g⟩=(12N+1)32N∑i,j,k=0fi,j,kgi,j,k. | (2.4) |
This discrete
⟨f,g⟩=N∑l,m,n=−N(ˆfNc)l,m,n¯(ˆgNc)l,m,n=N∑l,m,n=−N(ˆgNc)l,m,n¯(ˆfNc)l,m,n, |
where
Furthermore, a detailed calculation shows that the following formulas of integration by parts are also valid at the discrete level [11,27,28,48]:
⟨f,∇N⋅(g1g2g3)⟩=−⟨∇Nf,(g1g2g3)⟩,⟨f,ΔNg⟩=−⟨∇Nf,∇Ng⟩. | (2.5) |
In this section we state a lemma which will be helpful later in bounding the aliasing error for the nonlinear term. Consider a function
INφ(x,y,z)=N∑l,m,n=−NˆpNl,m,ne2πi(lx+my+nz), |
where
The following lemma will enable us to obtain an
Lemma 2.1. [28] For any
‖INφ‖Hk≤(√2)d‖φ‖Hk. | (2.6) |
For the incompressible NSEs (1.1), we treat the nonlinear convection term explicitly for the sake of numerical convenience, and the diffusion term implicitly to preserve an
un+1−unΔt+12(un⋅∇Nun+∇N⋅(un⊗un))+∇Npn=νΔNun+1, | (3.1) |
or, in more detail,
un+1−unΔt+12(un⋅∇Nun+∇N⋅(unun))+DNxpn=νΔNun+1, | (3.2) |
vn+1−vnΔt+12(un⋅∇Nvn+∇N⋅(vnun))+DNypn=νΔNvn+1, | (3.3) |
wn+1−wnΔt+12(un⋅∇Nwn+∇N⋅(wnun))+DNzpn=νΔNwn+1. | (3.4) |
Note that the nonlinear term is a spectral approximation to the skew-symmetric form
Remark 3.1. In this work, we use a collocation Fourier spectral differentiation other than the Galerkin spectral method. It is well-known that the discrete Fourier expansion (2.1) may contain aliasing errors, while the spectral accuracy is preserved as long as the exact solution is smooth enough. Also note that in the fully discrete scheme (3.1), the gradient is computed in Fourier space by using formulas (2.2), and the multiplication of
This approach is very different from the Galerkin approach, in which the nonlinear convection term has to be expanded in all wave length. There is no simple formula to compute these coefficients, even if it is treated explicitly. Meanwhile, in spite of aliasing errors appearing in the pseudo-spectral method, we are able to establish its local in time stability and convergence property in this work.
Note that the pressure gradient is also updated explicitly in scheme (3.1). Such an explicit treatment avoids its coupling with the divergence-free constraint for the velocity vector. The pressure field is solved by a Fourier spectral algorithm for the pressure Poisson equation (1.5). In more detail, the following Poisson equation is formulated in Fourier space:
ΔNpn=−12∇N⋅(un⋅∇Nun+∇N⋅(un⊗un))=−∇N⋅(Fn1Fn2Fn3), | (3.5) |
with the nonlinear force term given by (3.2)-(3.4):
Fn1=12(unDNxun+vnDNyun+wnDNzun+DNx(unun)+DNy(unvn)+DNz(unwn)),Fn2=12(unDNxvn+vnDNyvn+wnDNzvn+DNx(vnun)+DNy(vnvn)+DNz(vnwn)),Fn3=12(unDNxwn+vnDNywn+wnDNzwn+DNx(wnun)+DNy(wnvn)+DNz(wnwn)). | (3.6) |
This Poisson equation is solved using a discrete periodic boundary condition:
pn(0,j,k)=pn(2N+1,j,k),pn(i,0,k)=pn(i,2N+1,k),pn(i,j,0)=pn(i,j,2N+1). | (3.7) |
After the nonlinear convection terms are computed in physical space, we could apply FFT to obtain the Fourier coefficients of the force term in a collocation way. Subsequently, the pressure field can be determined by a combination of backward FFT, with the corresponding Fourier coefficients divided by the eigenvalues of the Poisson operator. An implementation of the discrete periodic boundary condition (3.7) would not effect the spectral accuracy of the numerical scheme.
Taking a divergence of scheme (3.1) in Fourier space gives
(∇N⋅un+1)−(∇N⋅un)Δt+∇N⋅(un⋅∇Nun)+∇N⋅∇Npn=ν∇N⋅ΔNun+1. | (3.8) |
Meanwhile, we observe that the divergence and Laplacian operators are commutative in Fourier space:
∇N⋅ΔNun+1=ΔN(∇N⋅un+1). | (3.9) |
In addition, a combination of formula (2.3) and the pressure Poisson equation (3.5) shows that
∇N⋅(un⋅∇Nun)+∇N⋅∇Npn=∇N⋅(un⋅∇Nun)+ΔNpn≡0. | (3.10) |
As a result, its substitution into (3.8) implies that
(∇N⋅un+1)−(∇N⋅un)Δt=νΔN(∇N⋅un+1). | (3.11) |
It is a discrete version of heat equation (1.6) for the divergence field, with implicit Euler in time, Fourier spectral in space.
With the periodic boundary condition and initial incompressibility constraint
∇N⋅u0≡0, | (3.12) |
we arrive at the divergence-free property for the numerical velocity vector in Fourier spectral space:
∇N⋅un≡0,∀n≥0. | (3.13) |
The following lemma is needed.
Lemma 3.1. For any vector field
⟨∇Nϕ,v⟩=0. | (3.14) |
Proof. An application of integration by parts formula (2.5) shows that
⟨∇Nϕ,v⟩=−⟨ϕ,∇N⋅v⟩=0. | (3.15) |
As a result, due to the divergence-free property (3.13) for the numerical velocity vector
⟨∇Npn,un⟩=0. | (3.16) |
In fact, a careful analysis shows that the pressure Poisson equation (3.5) is equivalent to the Helmholtz decomposition of the nonlinear convection term in Fourier space, i.e.,
un⋅∇Nun=vn+∇Npn,with∇N⋅vn≡0. | (3.17) |
For simplicity, we denote
In a more general case, we have the following estimate for the Helmholtz decomposition in finite-dimensional Fourier space.
Lemma 3.2. For any vector function
f=v+∇Nϕ,withv=PNf,∇N⋅v≡0, | (3.18) |
we have the identity
‖f‖22=‖v‖22+‖∇Nϕ‖22. | (3.19) |
Proof. Taking a discrete
‖f‖22=⟨f,f⟩=⟨v+∇Nϕ,v+∇Nϕ⟩=‖v‖22+‖∇Nϕ‖22+2⟨∇Nϕ,v⟩=‖v‖22+‖∇Nϕ‖22, | (3.20) |
in which the
Recall the application of projection method to incompressible NSEs, with a periodic boundary condition and collocation Fourier spectral in space:
Stage 1:u∗n−unΔt+12(un⋅∇Nun+∇N⋅(un⊗un))=νΔNu∗n, | (3.21) |
Stage 2:{un+1−u∗nΔt+∇NΦn+1=0,∇N⋅un+1=0. | (3.22) |
The projection method was initiated with the pioneering work of A. Chorin [18], R. Temam [44]. Also see the related works [2,23,36,39], among others.
Note that each stage is equivalent to a Poisson equation in Fourier space. In other words, two Poisson equations need to be solved at each time step, one for the intermediate velocity vector, the other for the pressure. In more detail, stage 2 is exactly a Helmholtz decomposition for
u∗n=un+1+∇N(ΔtΦn+1),with∇N⋅un+1=0. | (3.23) |
Moreover, its substitution into stage 1 gives
un+1−unΔt+12(un⋅∇Nun+∇N⋅(un⊗un))+∇N(Φn+1−νΔtΔNΦn+1)=νΔNun+1, | (3.24) |
in which we utilized the commutative property between Fourier operators
(I−νΔtΔN)Φn+1=pn. | (3.25) |
Note that the above equation always has a unique solution in Fourier space, since all the eigenvalues associated with the left hand operator are positive. It is also observed that both sides in equation (3.25) turn out to be the gradient part in the Helmholtz decomposition of the nonlinear convection term
A more detailed calculation also shows its equivalence to the gauge method; see the related works [24,25,47], etc. In the gauge formulation, a gauge variable
Stage 1:an+1−anΔt+12(un⋅∇Nun+∇N⋅(un⊗un))=νΔNan+1, | (3.26) |
Stage 2:{un+1−an+1+∇Nϕn+1=0,∇N⋅un+1=0. | (3.27) |
Similarly, both stages lead to a Poisson equation in Fourier space. With a Helmholtz decomposition for
un+1−unΔt+12(un⋅∇Nun+∇N⋅(un⊗un))+∇N(ϕn+1−ϕnΔt−νΔNϕn+1)=νΔNun+1. | (3.28) |
Therefore, a comparison between (3.1) and (3.29) implies their equivalence if we set
ϕn+1−ϕnΔt−νΔNϕn+1=pn, | (3.29) |
which is a discrete heat equation for
Note that the numerical solution
Our stability and convergence analysis will bound the error between this spatially continuous version of the numerical solution and the exact solution. To bound this function we will be looking at the
‖w‖l∞(0,T∗;H2)=max0≤k≤Nk‖wk(x)‖H2,Nk=[T∗Δt], |
i.e., we create a discrete time-dependent function by taking the
‖w‖l2(0,T∗;H3)=√ΔtNk∑k=0‖wk(x)‖2H3. |
Theorem 4.1. For any final time
‖uΔt,h−ue‖l∞(0,T∗;H2)+√ν‖uΔt,h−ue‖l2(0,T∗;H3)≤C(Δt+hm), | (4.1) |
‖pΔt,h−pe‖l∞(0,T∗;H2)≤C(Δt+hm), | (4.2) |
provided that the time step
Δt≤L1(T∗,ν),h≤L2(T∗,ν). |
Note that the convergence constant in (4.1), (4.2) depend on the exact solution, as well as
The convergence analysis follows a combination of consistency analysis for the pseudo-spectral scheme (3.1) and a stability analysis for the numerical error function. In the consistency analysis, instead of a direct comparison between the numerical solution and the exact solution, we construct an approximate solution by projecting the exact solution onto
This approach avoids an application of the inverse inequality. As a result, an unconditional numerical stability (time step vs. spatial grid size) is obtained, and no scaling law is required between
Let
UN(x,t)=PNue(x,t),PN(x,t)=PNpe(x,t). | (4.3) |
The following approximation estimate is clear from our discussion from Section 2:
‖UN−ue‖L∞(0,T∗;Hr)≤Chm‖ue‖L∞(0,T∗;Hm+r),forr≥0, | (4.4) |
‖PN−pe‖L∞(0,T∗;Hr)≤Chm‖pe‖L∞(0,T∗;Hm+r),forr≥0. | (4.5) |
As a result, an application of Sobolev embedding in 3-D gives
‖UN−ue‖L∞(Ω)≤C‖UN−ue‖H2≤Chm‖ue‖L∞(0,T∗;Hm+2), | (4.6) |
at any fixed time. In particular, since
‖UN‖L∞(0,T∗;Hr)≤‖ue‖L∞(0,T∗;Hr),forr≥0,so that‖UN‖L∞(0,T∗;W2,∞)≤C‖UN‖L∞(0,T∗;H4)≤C‖ue‖L∞(0,T∗;H4)≤C‖ue‖H1(0,T∗;H4), | (4.7) |
in which the 3-D Sobolev embedding of
In addition, it is also observed that the projected velocity vector
∇⋅uN=∇⋅(PNu)=PN(∇⋅u)≡0, | (4.8) |
due to the fact that the exact solution
Looking at the time derivative of the projection operator, we observe that
∂k∂tkUN(x,t)=∂k∂tkPNue(x,t)=PN∂kue(x,t)∂tk; | (4.9) |
in other words,
‖∂kt(UN−ue)‖≤Chm‖∂ktue‖Hm,for0≤k≤2. | (4.10) |
The bounds on the projection error and its derivatives, namely (4.4), (4.5), (4.6) and (4.10), indicate that
∂tUN+12(UN⋅∇UN+∇⋅(UN⊗UN))−∇PN−νΔUN=∂tue+ue⋅∇ue−∇pe−νΔue+τ1=τ1,with‖τ1‖L2≤Chm, | (4.11) |
in which Hölder's inequality was utilized to estimate the nonlinear term and the last step is based on the fact that (
Now consider the time discrete version of the equation. The temporal grid is discretized using equidistant points
Moreover, for the approximate solution (
Un⋅∇NUn=UnN⋅∇UnN,ΔNUn+1=ΔUn+1N,∇NPn=∇PnNsinceUnN=INUn,PnN=INPn | (4.12) |
∇N⋅(Un⊗Un)=∇⋅(UnN⊗UnN)+τn2,‖τn2‖L2h≤Chm‖UnN‖2Hm+3, | (4.13) |
ΔUn+1N=ΔUnN+τn3,‖τ3‖l2(0,T∗;L2h)≤CΔt‖UN‖H1(0,T∗;H2)≤CΔt, | (4.14) |
where
Un+1N−UnNΔt=∂tUN(⋅,tn)+τn4(⋅),‖τ4(⋅)‖l2(0,T∗)≤CΔt‖UN(⋅)‖H2(0,T∗), | (4.15) |
at a point-wise level (in space), in which the derivation is based on an integral form of Taylor's formula. Furthermore, by the observation (4.9), we arrive at
‖τ4‖l2(0,T∗;L2h(Ω))≤CΔt‖∂2tUN‖L2(0,T∗;H2(Ω))≤CΔt‖∂2tue‖L2(0,T∗;H2(Ω))≤CΔt. | (4.16) |
Consequently, a combination of (4.11) and (4.12), (4.14), (4.15), (4.16) implies the consistency of the approximate solution
Un+1−UnΔt+12(Un⋅∇NUn+∇N⋅(Un⊗Un))−∇NPn−νΔNUn+1=τn,withτ=τ1+τ2+τ3+τ4,‖τ‖l2(0,T∗;L2h)≤C(Δt+hm). | (4.17) |
In other words, the projection of the exact solution satisfies the numerical scheme (3.1) up to an
In addition, we also observe that the
‖τ‖l2(0,T∗;H1h):=√ΔtNk∑k=0‖τkN(x)‖2H1≤C(Δt+hm), | (4.18) |
where
Another key feature of the approximation solution
∇N⋅Un=∇⋅UnN≡0,∀n, | (4.19) |
in which the first step is based on the fact that
The point-wise numerical error grid function is given by
eni,j,k=Uni,j,k−uni,j,k,qni,j,k=Pni,j,k−pni,j,k. | (4.20) |
The difference between scheme (3.1) and the consistency (4.17) shows that
en+1−enΔt+12(en⋅∇NUn+un⋅∇Nen+∇N⋅(en⊗(Un+un)))+∇Nqn=νΔNen+1+τn, | (4.21) |
∇N⋅en+1≡0. | (4.22) |
Note that the divergence-free property for the numerical velocity vector is derived from a combination of (4.19) and the same property for the numerical solution as given by (3.13). Such a property assures that the numerical error for the velocity is orthogonal to that one for the pressure gradient in the discrete
To facilitate the presentation below, we denote
The constructed approximate solution has a
‖UN‖L∞(0,T∗;W2,∞)≤C∗,i.e.‖UnN‖L∞≤C∗,‖∇UnN‖L∞≤C∗,‖∇(∇UnN)‖L∞≤C∗,∀n≥0, | (4.23) |
which comes from the regularity of the constructed solution.
We assume a-priori that the numerical error function for the velocity has an
‖enN‖H32+δ≤1,with enN=INen, | (4.24) |
so that the
‖unN‖H32+δ=‖UnN−enN‖H32+δ≤‖Un‖H32+δ+‖enN‖H3/2+δ≤C∗+1:=˜C0,‖un‖∞≤‖unN‖L∞≤Cδ‖unN‖H32+δ≤Cδ˜C0:=˜C1. | (4.25) |
It is noticed that the first estimate in (4.25) comes from the triangular inequality, while the second bound is based on the 3-D Sobolev embedding from
Lemma 4.1. Under the a-priori assumption (4.24), the numerical error function for the first order scheme (3.1) satisfies
‖en+1‖2+√νΔtn+1∑k=1‖∇Nek‖22≤M1(Δt+hm). | (4.26) |
Proof. Taking a discrete
⟨en+1−en,2en+1⟩−2νΔt⟨ΔNen+1,en+1⟩+2Δt⟨∇Nqn,en+1⟩=−Δt⟨en⋅∇NUn,en+1⟩−Δt⟨un⋅∇Nen,en+1⟩−Δt⟨∇N⋅(en⊗Un),en+1⟩−Δt⟨∇N⋅(en⊗un),en+1⟩+2Δt⟨τn,en+1⟩. | (4.27) |
The time marching term and the truncation error term can be handled in a straightforward way:
⟨en+1−en,2en+1⟩=‖en+1‖22−‖en‖22+‖en+1−en‖22, | (4.28) |
2⟨τn,en+1⟩≤‖τn‖22+‖en+1‖22, | (4.29) |
in which a discrete Cauchy inequality was utilized. A discrete version of the integration by parts formula (2.5) can be applied to analyze the diffusion term:
−2⟨ΔNen+1,en+1⟩=2⟨∇Nen+1,∇Nen+1⟩=2‖∇Nen+1‖22. | (4.30) |
For the pressure gradient term, we observe that the divergence-free property of
⟨∇Nqn,en+1⟩=0, | (4.31) |
with the help of Lemma 2.
For the numerical error of the nonlinear convection, we see that the first term can be handled by the Cauchy inequality, with the
−⟨en⋅∇NUn,en+1⟩≤‖∇NUn‖∞⋅‖en‖2⋅‖en+1‖2≤C∗‖en‖2⋅‖en+1‖2≤12C∗‖en‖22+12C∗‖en+1‖22. | (4.32) |
Similar analysis can be applied to the second nonlinear error term:
−⟨un⋅∇Nen,en+1⟩≤‖un‖∞⋅‖∇Nen‖2⋅‖en+1‖2≤˜C1‖∇Nen‖2⋅‖en+1‖2≤12ν‖∇Nen‖22+˜C212ν‖en+1‖22, | (4.33) |
in which the
−⟨∇N⋅(en⊗Un),en+1⟩=⟨en⊗Un,∇Nen+1⟩≤14ν‖∇Nen+1‖22+(C∗)2ν‖en‖22, | (4.34) |
−⟨∇N⋅(en⊗un),en+1⟩=⟨en⊗un,∇Nen+1⟩≤14ν‖∇Nen+1‖22+˜C21ν‖en‖22. | (4.35) |
As a result, a substitution of (4.28), (4.29), (4.30), (4.31), (4.32), (4.33), (4.34) and (4.35) into (4.27) gives
‖en+1‖22−‖en‖22+‖en+1−en‖22+32νΔt‖∇Nen+1‖22≤34νΔt‖∇Nen‖22+(˜C21+(C∗)2ν+C∗+1)Δt(‖en+1‖22+‖en‖22)+Δt‖τn‖22. |
Summing in time gives
‖en+1‖22+n∑k=0‖ek+1−ek‖22+34νΔtn+1∑k=1‖∇Nek‖22≤˜C2Δtn+1∑k=0‖ek‖22+Δtn∑k=0‖τn‖22+Ch2m, |
with
It is observed that the
Taking a discrete
2⟨en+1−en,Δ2Nen+1⟩−2νΔt⟨ΔNen+1,Δ2Nen+1⟩+2Δt⟨∇Nqn,Δ2Nen+1⟩=−Δt(⟨en⋅∇NUn,Δ2Nen+1⟩+⟨un⋅∇Nen,Δ2Nen+1⟩)−Δt⟨∇N⋅(en⊗(Un+un)),Δ2Nen+1⟩+⟨τn,Δ2Nen+1⟩. | (4.36) |
The time marching term, truncation error term and the diffusion term can be analyzed as
2⟨en+1−en,Δ2Nen+1⟩=‖ΔNen+1‖22−‖ΔNen‖22+‖ΔN(en+1−en)‖22,2⟨τn,Δ2Nen+1⟩=−2⟨∇Nτn,∇NΔNen+1⟩ | (4.37) |
≤4ν‖∇Nτn‖22+14ν‖∇NΔNen+1‖22, | (4.38) |
−2⟨ΔNen+1,Δ2Nen+1⟩=2⟨∇NΔNen+1,∇NΔNen+1⟩=2‖∇NΔNen+1‖22. | (4.39) |
Similar to (4.31), with the help of Lemma 2, the error for the pressure gradient is orthogonal to
⟨∇Nqn,Δ2Nen+1⟩=0, | (4.40) |
due to the fact that
∇N⋅(Δ2Nen+1)=ΔN(∇N⋅en+1)≡0. | (4.41) |
Lemma 4.2. Under the a-priori assumption (4.24), we have the following estimates for the nonlinear error terms
−⟨en⋅∇NUn,Δ2Nen+1⟩≤14ν‖∇NΔNen+1‖22+48(C∗)2ν(‖∇Nen‖22+‖en‖22), | (4.42) |
−⟨un⋅∇Nen,Δ2Nen+1⟩≤14ν‖∇NΔNen+1‖22+C˜C21ν‖ΔNen‖22, | (4.43) |
−⟨∇N⋅(en⊗Un),Δ2Nen+1⟩≤14ν‖∇NΔNen+1‖22+48(C∗)2ν(‖∇Nen‖22+‖en‖22), | (4.44) |
−⟨∇N⋅(en⊗un),Δ2Nen+1⟩≤14ν‖∇NΔNen+1‖22+C˜C21ν‖ΔNen‖22. | (4.45) |
Proof. We start with an application of summation by parts to the first term:
−⟨en⋅∇NUn,Δ2Nen+1⟩=⟨∇N(en⋅∇NUn),∇NΔNen+1⟩. | (4.46) |
The remaining work is focused on the nonlinear expansion of
‖∇N(en⋅∇NUn)‖2=‖∇(IN(enN⋅∇UnN))‖L2≤2√2‖∇(enN⋅∇UnN)‖L2. | (4.47) |
The first step is based on the fact that
‖∇(enN⋅∇UnN)‖L2=‖∇UnN⋅∇enN+enN⋅∇(∇UnN)‖L2≤‖∇UnN⋅∇enN‖L2+‖enN⋅∇(∇UnN)‖L2≤‖∇UnN‖L∞⋅‖∇enN‖L2+‖enN‖L2⋅‖∇(∇UnN)‖L∞≤C∗(‖∇enN‖L2+‖enN‖L2)=C∗(‖∇Nen‖2+‖en‖2), |
with the regularity fact (4.23) applied in the last step. Its combination with (4.47) yields
‖∇N(en⋅∇NUn)‖2≤2√2C∗(‖∇Nen‖2+‖en‖2), |
which in turn also gives
‖∇N(en⋅∇NUn)‖2≤2√6C∗(‖∇Nen‖2+‖en‖2). |
Going back to (4.46), we get (4.42), the estimate for the first nonlinear convection error term:
−⟨en⋅∇NUn,Δ2Nen+1⟩≤2√6C∗(‖∇Nen‖2+‖en‖2)⋅‖∇NΔNen+1‖2≤14ν‖∇NΔNen+1‖22+48(C∗)2ν(‖∇Nen‖22+‖en‖22). |
The analysis of the second nonlinear convection error term also starts from the summation by parts:
−⟨un⋅∇Nen,Δ2Nen+1⟩=⟨∇N(un⋅∇Nen),∇NΔNen+1⟩. | (4.48) |
Similarly, Lemma 1 has to be utilized to deal with the nonlinear expansion of
‖∇N(un⋅∇Nen)‖2=‖∇(IN(unN⋅∇enN))‖L2≤2√2‖∇(unN⋅∇enN)‖L2,‖∇(unN⊗∇enN)‖L2=‖∇enN⋅∇unN+unN⋅∇(∇enN)‖L2≤‖∇enN⊗∇unN‖L2+‖unN⋅∇(∇enN)‖L2≤‖∇unN‖L3⋅‖∇enN‖L6+‖unN‖L∞⋅‖∇(∇enN)‖L2≤C˜C1‖ΔenN‖L2=C˜C1‖ΔNen‖2. |
Note that the a-priori assumption (4.24)-(4.25), combined with the following 3-D Sobolev embedding and elliptic regularity were used in the last step.
‖∇unN‖L3≤C‖unN‖H32≤C˜C1,‖∇enN‖L6≤C‖ΔenN‖L2,‖unN‖L∞≤˜C1,‖∇(∇enN)‖L2≤C‖ΔenN‖L2. |
This in turn shows that
‖∇N(un⋅∇Nen)‖2≤2√2‖∇(unN⋅∇enN)‖L2≤C˜C1‖ΔNen‖2. |
Going back to (4.48), we arrive at (4.43), the second inequality of Lemma 4.2:
−⟨un⋅∇Nen,Δ2Nen+1⟩≤C˜C1‖ΔNen‖2⋅‖∇NΔNen+1‖2≤12ν‖∇NΔNen+1‖22+C˜C21ν‖ΔNen‖22. |
The third and fourth estimates in lemma 5 can be derived in the same manner. The details are skipped for the sake of brevity.
A substitution of (4.37)-(4.40) and Lemma 5 into (4.36) indicates that
‖ΔNen+1‖22−‖ΔNen‖22+‖ΔN(en+1−en)‖22+34νΔt‖∇NΔNen+1‖22≤C˜C21Δtν‖ΔNen‖22+96(C∗)2Δtν(‖∇Nen‖22+‖en‖22)+4Δtν‖∇Nτn‖22,≤C(˜C21+(C∗)2)Δtν‖ΔNen‖22+(96(C∗)2˜C3+C)Δtν(Δt+hm)2, |
in which the
‖ΔNen+1‖22+n∑k=0‖ΔN(ek+1−ek)‖22+34νΔtn+1∑k=1‖∇NΔNek‖22≤C(96(C∗)2˜C3+C)νe˜C5T∗(Δt+hm)2≤M22(Δt+hm)2, |
with
‖en+1N‖H2≤C(‖en+1N‖L2+‖Δen+1N‖L2)≤˜C6(Δt+hm),˜C6=C(M1+M2). | (4.49) |
As a direct consequence, the point-wise convergence of the numerical scheme is established by an application of 3-D Sobolev imbedding:
‖en+1‖∞≤‖en+1N‖L∞≤C‖en+1N‖H2≤C(M1+M2)(Δt+hm). |
With the help of the
Δt≤(˜C6)−1,h≤(˜C6)−1m,with ˜C6 dependent on ν and exp(T∗). |
This completes the
What remains in the proof of Theorem 1 is the analysis for
12(en⋅∇NUn+un⋅∇Nen+∇N⋅(en⊗(Un+un)))−τn=−(en+1−enΔt−νΔNen+1)−∇Nqn. | (4.50) |
Since
ΔNqn=−12∇N⋅(en⋅∇NUn+un⋅∇Nen+∇N⋅(en⊗(Un+un)))+∇N⋅τn. | (4.51) |
In more detail, the estimate (4.23) for the constructed approximation solution, the recovered a-priori assumption (4.25) for the numerical solution, together with the Sobolev space estimates (4.49) and the established
‖ΔNqn‖2≤12‖∇N⋅(en⋅∇NUn+un⋅∇Nen+∇N⋅(en⊗(Un+un)))‖2+‖∇N⋅τn‖2≤√6‖∇(enN⋅∇UnN+unN⋅∇enN+∇(enN⊗(UnN+unN)))‖L2+‖τn‖H1≤C(‖UnN‖W2,∞⋅‖enN‖H2+‖unN‖L∞⋅‖enN‖H2+‖∇unN‖L3⋅‖∇enN‖L6+‖unN‖H2⋅‖enN‖L∞)+‖τn‖H1≤C(C∗+˜C1)‖enN‖H2+‖τn‖H1≤˜C7(Δt+hm). | (4.52) |
Therefore, the
‖qnN‖H2≤C‖ΔqnN‖2=C‖ΔNqn‖2≤C˜C7(Δt+hm). | (4.53) |
This finishes the proof of Theorem 4.1.
Remark 4.1. The optimal rate convergence analysis and error estimate have been established for the proposed numerical scheme (3.1) in Theorem 4.1. In more details, such a convergence analysis is based on the consistency analysis, presented in section 4.1, combined with the linearized stability analysis for the numerical error functions, provided in section 4.2. In other words, the numerical scheme is treated as a small perturbation of the exact solution and its projection, and the optimal rate error estimate (4.49) would lead to an
‖ukN‖H2≤‖UkN−ekN‖H2≤‖UkN‖H2+‖ekN‖H2≤C∗+˜C6(Δt+hm)≤C∗+1, | (4.54) |
with the help of triangular inequality. This
In fact, this stability estimate is valid over a finite time, since the convergence estimate (4.49) is a local-in-time analysis. Because of the fully explicit treatment of the nonlinear convection term in the numerical scheme (3.1), a global-in-time estimate for the numerical solution is not directly available. Meanwhile, if the nonlinear convection is updated in a semi-implicit pattern
12(un⋅∇Nun+1+∇N⋅(un⊗un+1)), |
a global-in-time
On the other hand, for two-dimensional (2-D) incompressible Navier-Stokes equation in vorticity-stream function formulation, a global-in-time
To derive a second order scheme, we use a similar semi-implicit approach. As before, the nonlinear term is updated explicitly. For the convection term we use a standard second order Adams-Bashforth extrapolation formula, which involves the numerical solutions at time node points
NL(uk)=12(uk⋅∇Nuk+∇N⋅(uk⊗uk)), | (5.1) |
we formulate the fully discrete scheme:
un+1−unΔt+32NL(un)−12NL(un−1)+∇N(32pn−12pn−1)=νΔN(34un+1+14un−1), | (5.2) |
in which the pressure field at time steps
Similar ideas can be applied to derive third and fourth order in time schemes for (1.1)-(1.2). The nonlinear convection term and the pressure gradient are updated by an explicit Adams-Bashforth extrapolation formula, with the time node points
un+1−unΔt+k−1∑i=0Bi(NL(un−i)+∇Npn−i)=νΔN(D0un+1+k−1∑i=0Dj(i)un−j(i)). | (5.3) |
in which
D0>k−1∑i=0|Dj(i)|. | (5.4) |
To derive an Adams-Moulton formula for the diffusion term, whose coefficients satisfy the condition (5.4), we require a stretched stencil. In particular, for the third order scheme, it can be shown that a stencil comprised of the node points
un+1−unΔt+2312NL(un)−43NL(un−1)+512NL(un−2)+∇N(2312pn−43pn−1+512pn−2)=νΔN(23un+1+512un−1−112un−3). | (5.5) |
Similarly, the pressure field at time steps
For the fourth order scheme, we use an Adams-Moulton interpolation at node points
un+1−unΔt+5524NL(un)−5924NL(un−1)+3724NL(un−2)−38NL(un−3)+∇N(5524pn−5924pn−1+3724pn−2−38pn−3)=νΔN(7571152un+1+4701152un−1−1181152un−5+431152un−7), | (5.6) |
in which the
It is a well-known numerical difficulty for incompressible fluid flow to ensure the divergence-free condition for the computed velocity vector, especially for the higher order (in time) schemes. We will prove that all the multi-step schemes proposed above satisfy this condition at the discrete level.
For simplicity, we only present the analysis for the third order scheme (5.5). The divergence-free properties of (5.2) and (5.6) can be derived in the same manner. Taking a divergence of (5.5) gives
(∇N⋅un+1)−(∇N⋅un)Δt+∇N⋅(2312NL(un)−43NL(un−1)+512NL(un−2))+ΔN(2312pn−43pn−1+512pn−2)=ν∇N⋅ΔN(23un+1+512un−1−112un−3). | (5.7) |
Meanwhile, using the Poisson equation (3.5) (applied at
ΔN(2312pn−43pn−1+512pn−2)=−∇N⋅(2312NL(un)−43NL(un−1)+512NL(un−2)), | (5.8) |
since
(∇N⋅un+1)−(∇N⋅un)Δt=νΔN∇N⋅(23un+1+512un−1−112un−3). | (5.9) |
With the periodic boundary condition and initial incompressibility constraint
Numerical stability and full order convergence (at a fixed final time) are valid for all these multi-step schemes, provided that condition (5.4) is satisfied. The result is given in the following theorem. For simplicity of presentation, we denote
Theorem 5.1. For any final time
‖uΔt,h−ue‖l∞(0,T∗;H2)+√ν‖uΔt,h−ue‖l2(0,T∗;H3)≤C(ΔtK+hm), | (5.10) |
‖pΔt,h−pe‖l∞(0,T∗;H2)≤C(ΔtK+hm), | (5.11) |
provided that the time step
Δt≤L3(T∗,ν),h≤L4(T∗,ν). |
Note that the convergence constants in (5.10), (5.11) also depend on the exact solution, as well as
We only present analysis for the third order scheme (5.5) with
Proof. We look at the approximate solution
Un+1N−UnN=∫tn+1tn∂tUN(⋅,t)dt,2312UnN⋅∇UnN−43Un−1N⋅∇Un−1N+512Un−2N⋅∇Un−2N=1Δt∫tn+1tnUN⋅∇UN(⋅,t)dt+τn2,with‖τ2‖l2(0,T∗;L2h)≤CΔt3‖UN‖2H3(0,T∗;H3)≤CΔt3,∇⋅(2312UnN⊗UnN−43Un−1N⊗Un−1N+512Un−2N⊗Un−2N)=1Δt∫tn+1tn∇⋅(UN⊗UN)dt+τn3, |
with‖τ3‖l2(0,T∗;L2h)≤CΔt3‖UN⊗UN‖H3(0,T∗;H1)≤CΔt3,∇(2312PnN−43Pn−1N+512Pn−2N)=1Δt∫tn+1tn∇PN(⋅,t)dt+τn4,with‖τ4‖l2(0,T∗;L2h)≤CΔt3‖PN‖H3(0,T∗;H1)≤CΔt3,Δ(23Un+1N+512Un−1N−112Un−3N)=1Δt∫tn+1tnΔUN(⋅,t)dt+τn5,with‖τ5‖l2(0,T∗;L2h)≤CΔt3‖UN‖H3(0,T∗;H2)≤CΔt3. |
That in turn leads to the third order consistency of the approximate solution
(5.12) |
with
Subsequently, with the numerical error function denoted by (4.20) (while
(5.13) |
Similarly, the constructed solution
(5.14) |
for any
Taking a discrete
(5.15) |
Since the diffusion coefficient at
The estimates for the nonlinear convection terms are given below. The details are skipped.
Going back to (5.15) results in
Consequently, summing in time, applying the discrete Gronwall inequality and using a simple fact that
under the a-priori
Similar convergence analysis in
hence,
In turn, the a-priori
Finally, the error estimate for the pressure can be carried out in the same way as in section 4.3. The technical details are skipped for the sake of brevity.
Remark 5.1. For the second order in time method, treating the diffusion term with a standard second order Adams-Moulton formula leads to a Crank-Nicolson scheme. In the third order case, we observe that a direct application of the Adams-Moulton formula at the nodes
which violates the stability condition (5.4). Numerical experiments also showed that this method is unstable in time. This case highlights the need to choose an appropriate time-discretization to couple with the pseudospectral method.
Remark 5.2. There have been extensive numerical simulation works of high-order multi-step, semi-implicit schemes applied to nonlinear time-dependent PDEs. Among them, it is worth mentioning the
In this section we perform a numerical accuracy check for the proposed numerical schemes (3.1), (5.2), (5.5), (5.6), in the first, second, third and fourth order temporal accuracy orders, respectively. The computational domain is chosen as
(6.1) |
The viscosity parameter is taken as
In the accuracy check for the temporal accuracy, we fix the spatial resolution as
In this paper we develop stable time-stepping methods of order up to four for the numerical solution of the three dimensional incompressible Navier-Stokes equation, with Fourier pseudo-spectral spatial approximation, and present stability and convergence analyses for these fully discrete methods.
In the first order (in time) scheme, a semi-implicit approach is applied, which updates the nonlinear convection term and the pressure gradient term explicitly, and treats the diffusion term implicitly. The pressure variable is solved by a pressure Poisson equation, instead of being teated as a Lagrange multiplier. As a result, the computed velocity vector is proved to be divergence-free at a discrete level, so that it is
Similar ideas could be applied to develop higher order in time schemes using a multi-step approach, with numerical stability preserved. For the sake of numerical efficiency, the semi-implicit pattern is kept, with a standard Adams-Bashforth extrapolation formula (with the given accuracy) applied to the nonlinear term and the pressure gradient term, while the diffusion term is treated using an implicit Adams-Moulton interpolation formula on certain time node points. On the other hand, the numerical stability requires specialized stretched stencil in the Adams-Moulton approximation. We present these multi-step schemes with accuracy up to fourth order and show that, coupled with the Fourier pseudo-spectral spatial approximation, these multi-step numerical schemes are stable and convergent. Some numerical results have also been presented, which demonstrate the perfect convergence rates of the proposed schemes.
This work is supported in part by NSF DMS-2012669.
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