High-order compact schemes and incremental unknowns for 1D convection diffusion problems

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A.C. Muresan (Tiberiu Popoviciu Institute of Numerical Analysis, Romanian Academy)
Adrian Cristian Mureşan

Alina Curseu
Tiberiu Popoviciu Institute of Numerical Analysis, Romanian Academy

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A.C. Mureṣan and A. Curṣeu, High-order compact schemes and incremental unknowns for 1D convection diffusion problems, in Proceeding of the ‘Journees Jeunes Numericiens-Colloque en l’honeur de R. Temam’, editor: editions Scientifiques Poitou-Charentes 2001, 77-89.

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Proceeding of the ‘Journees Jeunes Numericiens-Colloque en l’honeur de R. Temam’

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High-Order Compact Schemes and Incremental Unknowns for 1D Convection Diffusion Problems *

A.C. Mureşan and Alina Curşeu
Abstract

Induced hierarchical preconditioner are investigated for linear 1D convectiondiffusion problems. In the context of high-order compact schemes we give a priori estimates and a uniform estimate of the condition number.

1 Introduction

The utilization of incremental unknowns (IU in short) with multilevel finite differences was proposed by R.Temam in [13] for the integration of elliptic partial differential equations, instead of the usual nodal unknowns. The idea, which stems from dynamical systems approach, consists in writing the approximate solution uiu_{i} in the form ui=yi+ziu_{i}=y_{i}+z_{i}, where zz is a small increment. Passing from the nodal unknowns uiu_{i} to the IUs (yi,zi)\left(y_{i},z_{i}\right) amounts to a linear change of variables, that is to say, in the language of linear algebra to the construction of a preconditioner. Many numerical simulations have shown the efficiency of such induced preconditioners.

The incremental unknowns play the role of the small structures. Unfortunately they are not always "small", in certain practical cases. Indeed this situation arises typically when the number of points is not large enough and the discretized function has strong gradients (this is the case of convection diffusion problems). A small step size (or a large number of grid points) can be a limitation for practical applications since the dimension of the system will be increased artificially. The use of high-order schemes can be a solution to the above problem. Exist two main classes of high-order schemes: explicit schemes and compact schemes. Explicit schemes directly compute the numerical derivative by employing large computational stencils for accuracy. High-order schemes achieved in this manner always require non-compact stencils that utilize grid points located beyond those directly adjacent to the node about which the differences are taken. Compact scheme, proposed by Kreiss and Oliger[6] and which was later improved upon by Lele[7], use smaller stencils but requires a scalar tridiagonal or pentadiagonal matrix inversion. Another idea (proposed,

00footnotetext: *2000 MSC Subject Classification: 65L10, 65L12, 65F35.

for convection diffusion problems, by MacKinnon, Carey, Johnson and Langerman [8],[9],[10],[11]) to obtain high-order compact schemes is to operate on the differential equation as an auxiliary relation to obtain expressions for higherorder derivatives in the truncation error. We will use this idea (also presented in [12]) to approximate the solution of the following (1D) convection-diffusion equation:

{−u′′​(x)+c​(x)​u′​(x)=f​(x),x∈(0,1)u​(0)=u​(1)=0\left\{\begin{array}[]{c}-u^{\prime\prime}(x)+c(x)u^{\prime}(x)=f(x),x\in(0,1)\\ u(0)=u(1)=0\end{array}\right.

where f∈L2​(0,1)f\in L^{2}(0,1) and c​(x)∈L∞​(0,1)c(x)\in L^{\infty}(0,1).
This equation often appears in the description of transport phenomena. The magnitude of c​(x)\mathrm{c}(\mathrm{x}) determines the ratio of the convection to diffusion. Of course the 1D problems are not difficult from a computational point of view; we consider them because the algebra is more transparent than for higher dimensions.

Numerical solution of a problem such as (1) using Incremental Unknowns has been considered in [3] and [4] but the IUs that have been used in these articles were connected to the second derivative only.

In [1], the authors propose a construction of IUs that are more adapted to the problem in the sense that they take into account and the convection term in the construction of the IUs.

We consider approximations to (1) on a uniform mesh xi=ih(h=1/2Nx_{i}=ih(h=1/2N, NN a positive integer, i=0,1,…,2N)i=0,1,\ldots,2N) having the form

βi​(ui−ui−1)+αi​(ui−ui+1)=h​fi;i=1,…,2​N.\beta_{i}\left(u_{i}-u_{i-1}\right)+\alpha_{i}\left(u_{i}-u_{i+1}\right)=hf_{i};i=1,\ldots,2N. (2)

Here ui≃u​(xi),fi=f​(xi)u_{i}\simeq u\left(x_{i}\right),f_{i}=f\left(x_{i}\right) and αi\alpha_{i} and βi\beta_{i} are real numbers whose definition depends on the discretization scheme; we have indeed, for example the two following possibilities: the firs one consists in approaching the first derivative by a (centered type) three points scheme and the second one consists in using an uncentered type scheme for the approximation of u′u^{\prime} at the grid points. For the moment we do not explicite the discretization scheme.

As usual when an IU method is implemented, two different kinds of unknowns must be distinguished: those associated with the coarse grid components and which are on GcG_{c} (coarse grid), and whose indices are even and those associated with complementary points (odd indices) which are on Gf\Gc(GfG_{f}\backslash G_{c}\left(G_{f}\right. is the fine grid),

.o×o×o…o×o..o\times o\times o\ldots o\times o.

Fig.1: Ω=(0,1),x\Omega=(0,1),x : points in Gc,o:G_{c},o: points in Gf\GcG_{f}\backslash G_{c},
If we write the system (2) at the complementary points, we obtain

(α2​i+1+β2​i+1)​u2​i+1−β2​i+1​u2​i−α2​i+1​u2​i+2=h​f2​i+1\left(\alpha_{2i+1}+\beta_{2i+1}\right)u_{2i+1}-\beta_{2i+1}u_{2i}-\alpha_{2i+1}u_{2i+2}=hf_{2i+1}

Hence, assuming that α2​i+1+β2​i+1≠0\alpha_{2i+1}+\beta_{2i+1}\neq 0, we have

u2​i+1=1α2​i+1+β2​i+1​(β2​i+1​u2​i+α2​i+1​u2​i+2)+1α2​i+1+β2​i+1​h​f2​i+1u_{2i+1}=\frac{1}{\alpha_{2i+1}+\beta_{2i+1}}\left(\beta_{2i+1}u_{2i}+\alpha_{2i+1}u_{2i+2}\right)+\frac{1}{\alpha_{2i+1}+\beta_{2i+1}}hf_{2i+1}

We note that u2​i+1u_{2i+1} is expressed as the sum of a convex combination of u2​iu_{2i} and u2​i+2u_{2i+2} which is nothing but a bilinear interpolation scheme, and a correction term whose order is connected to the order of the interpolation scheme. If we set

z2​i+1=u2​i+1−1α2​i+1+β2​i+1​(β2​i+1​u2​i+α2​i+1​u2​i+2)z_{2i+1}=u_{2i+1}-\frac{1}{\alpha_{2i+1}+\beta_{2i+1}}\left(\beta_{2i+1}u_{2i}+\alpha_{2i+1}u_{2i+2}\right) (3)

then the system (1), at the complementary points, is reduced to

z2​i+1=1α2​i+1+β2​i+1​h​f2​i+1z_{2i+1}=\frac{1}{\alpha_{2i+1}+\beta_{2i+1}}hf_{2i+1}

The numbers z2​i+1z_{2i+1} are the incremental unknowns attached to the (discrete) problem (2). They depend closely on the scheme used for the discretization.

We can obviously repeat recursively the process described above and if the coarsest grid is reduced to one point only, the preconditioned matrix becomes diagonal.

From the point of view of the matriceal framework, this construction can be summarized by the determination of two matrices SS and Tt{}^{t}T under and upper triangular respectively such that Tt​A​S{}^{t}TAS is bloc diagonal, A being the discretization matrix.

We first consider two grid levels. The discretization matrix AA is written with the hierarchical ordering (considering first the coarse grid unknowns and then the complementary ones) in the form

A~=(Λ1B1B2Λ2)\widetilde{A}=\left(\begin{array}[]{ll}\Lambda_{1}&B_{1}\\ B_{2}&\Lambda_{2}\end{array}\right)

where Λi,i=1,2\Lambda_{i},i=1,2 are invertible diagonal matrices.

Construction of SS

We want to construct a matrix SS of the form:

S=(I0G1I)S=\left(\begin{array}[]{rr}I&0\\ G_{1}&I\end{array}\right)

and such that A~​S\widetilde{A}S is upper triangular. We have

A~​S=(Λ1B1B2Λ2)​(I0G1I)=(Λ1+B1​G1B1B2+Λ2​G1Λ2)\widetilde{A}S=\left(\begin{array}[]{ll}\Lambda_{1}&B_{1}\\ B_{2}&\Lambda_{2}\end{array}\right)\left(\begin{array}[]{rr}I&0\\ G_{1}&I\end{array}\right)=\left(\begin{array}[]{ll}\Lambda_{1}+B_{1}G_{1}&B_{1}\\ B_{2}+\Lambda_{2}G_{1}&\Lambda_{2}\end{array}\right)

Therefore the under-matrix G1G_{1} satisfies G1=−Λ2−1​B2G_{1}=-\Lambda_{2}^{-1}B_{2}, hence

S=(I0−Λ2−1​B2I)S=\left(\begin{array}[]{rr}I&0\\ -\Lambda_{2}^{-1}B_{2}&I\end{array}\right)

Construction of Tt{}^{t}T

We now want to construct a matrix Tt{}^{t}T of the form:

Tt=(IG20I){}^{t}T=\left(\begin{array}[]{rr}I&G_{2}\\ 0&I\end{array}\right)

and such that Tt​A~​S{}^{t}T\widetilde{A}S is bloc diagonal. We have

A^=Tt​A~​S=(Λ1+B1​G1B1+G2​Λ20Λ2)\hat{A}={}^{t}T\tilde{A}S=\left(\begin{array}[]{rr}\Lambda_{1}+B_{1}G_{1}&B_{1}+G_{2}\Lambda_{2}\\ 0&\Lambda_{2}\end{array}\right)

and then G2G_{2} must satisfy B1+G2​Λ2=0B_{1}+G_{2}\Lambda_{2}=0.
Thus

Tt=(I−B1​Λ2−10I){}^{t}T=\left(\begin{array}[]{rr}I&-B_{1}\Lambda_{2}^{-1}\\ 0&I\end{array}\right)

and then A^\hat{A} can be written in the form

A^=Tt​A~​S=(Λ1+B1​G100Λ2)\hat{A}={}^{t}T\widetilde{A}S=\left(\begin{array}[]{rr}\Lambda_{1}+B_{1}G_{1}&0\\ 0&\Lambda_{2}\end{array}\right)

We note that since the linear system is non-symmetric, these IUs lead to a non-symmetric hierarchical preconditioner.

The first diagonal bloc of A^\hat{A} is still tridiagonal and we can repeat recursively the reduction procedure described above by using dd levels of IUs.

2 A Priori Estimates

We can obtain (see [1])a priori energy estimates that show that the (induced) IUs are indeed small structures as expected.

We multiply (2) by δi​ui,δi\delta_{i}u_{i},\delta_{i} to be fixed later, and summing these expressions on all indices ii, we obtain

∑i=12​N−1βi​δi​(ui−ui−1)​ui−∑i=12​N−1αi​δi​(ui+1−ui)​ui=∑i=12​N−1h​fi​δi​ui\sum_{i=1}^{2N-1}\beta_{i}\delta_{i}\left(u_{i}-u_{i-1}\right)u_{i}-\sum_{i=1}^{2N-1}\alpha_{i}\delta_{i}\left(u_{i+1}-u_{i}\right)u_{i}=\sum_{i=1}^{2N-1}hf_{i}\delta_{i}u_{i}

Hence,

∑i=02​N−1(βi+1​δi+1​ui+1−αi​δi​ui)​(ui+1−ui)=∑i=12​N−1h​fi​δi​ui.\sum_{i=0}^{2N-1}\left(\beta_{i+1}\delta_{i+1}u_{i+1}-\alpha_{i}\delta_{i}u_{i}\right)\left(u_{i+1}-u_{i}\right)=\sum_{i=1}^{2N-1}hf_{i}\delta_{i}u_{i}. (4)

We now choose δi\delta_{i} such that

δ0=1,δi+1=αiβi+1​δi\delta_{0}=1,\delta_{i+1}=\frac{\alpha_{i}}{\beta_{i+1}}\delta_{i}

and we make the following hypothesis [1]:
Hypothesis(H)
i. There exists two strictly positive real numbers α\alpha and β\beta such that

α≤δi≤β​∀i.\alpha\leq\delta_{i}\leq\beta\forall i.

ii. αi\alpha_{i} and βi\beta_{i} are strictly positive numbers, ∀i=0,…,2​N\forall i=0,\ldots,2N.
iii. There exists a strictly positive real number γ\gamma such that

αi≥γh​∀i\alpha_{i}\geq\frac{\gamma}{h}\forall i

We shall see later that (H) is not too restrictive for practical cases.
From (4) we infer

∑i=02​N−1αi​δi​(ui+1−ui)2=∑i=12​N−1h​fi​δi​ui\sum_{i=0}^{2N-1}\alpha_{i}\delta_{i}\left(u_{i+1}-u_{i}\right)^{2}=\sum_{i=1}^{2N-1}hf_{i}\delta_{i}u_{i} (5)

We recall the discrete Poincaré inequality (see e.g. [2]):
Lemma 1 Let ui,i=0,…,2​Nu_{i},i=0,\ldots,2N be a sequence of real numbers such that u0=u2​N=0u_{0}=u_{2N}=0. Then we have

∑i=12​N−1h​ui2≤∑i=02​N−11h​(ui+1−ui)2\sum_{i=1}^{2N-1}hu_{i}^{2}\leq\sum_{i=0}^{2N-1}\frac{1}{h}\left(u_{i+1}-u_{i}\right)^{2}

Using (H), Cauchy-Schwartz inequality and Poincaré inequality, we bound the right-hand side of (5) by

∑i=12​N−1h​fi​δi​ui≤β​∑i=12​N−1h​fi​ui≤β​∑i=02​N−1h​ui2​∑i=12​N−1h​fi2≤β​∑i=02​N−11h​(ui+1−ui)2​∑i=12​N−1h​fi2\sum_{i=1}^{2N-1}hf_{i}\delta_{i}u_{i}\leq\beta\sum_{i=1}^{2N-1}hf_{i}u_{i}\leq\beta\sqrt{\sum_{i=0}^{2N-1}hu_{i}^{2}}\sqrt{\sum_{i=1}^{2N-1}hf_{i}^{2}}\leq\beta\sqrt{\sum_{i=0}^{2N-1}\frac{1}{h}\left(u_{i+1}-u_{i}\right)^{2}}\sqrt{\sum_{i=1}^{2N-1}hf_{i}^{2}} (6)

Also, using (H) we can bound the left-hand side of (5) by

∑i=02​N−1αi​δi​(ui+1−ui)2≥α​∑i=02​N−1αi​(ui+1−ui)2≥α​γ​∑i=02​N−11h​(ui+1−ui)2.\sum_{i=0}^{2N-1}\alpha_{i}\delta_{i}\left(u_{i+1}-u_{i}\right)^{2}\geq\alpha\sum_{i=0}^{2N-1}\alpha_{i}\left(u_{i+1}-u_{i}\right)^{2}\geq\alpha\gamma\sum_{i=0}^{2N-1}\frac{1}{h}\left(u_{i+1}-u_{i}\right)^{2}. (7)

From (6) and (7) we have:

∑i=02​N−11h​(ui+1−ui)2≤β2α2​γ2​∑i=12​N−1h​fi2\sum_{i=0}^{2N-1}\frac{1}{h}\left(u_{i+1}-u_{i}\right)^{2}\leq\frac{\beta^{2}}{\alpha^{2}\gamma^{2}}\sum_{i=1}^{2N-1}hf_{i}^{2} (8)

We now introduce the incremental unknowns defined by (3). Since

∑i=02​N−11h​(ui+1−ui)2=∑i=0N−11h​(u2​i+1−u2​i)2+∑i=0N−11h​(u2​i+2−u2​i+1)2\sum_{i=0}^{2N-1}\frac{1}{h}\left(u_{i+1}-u_{i}\right)^{2}=\sum_{i=0}^{N-1}\frac{1}{h}\left(u_{2i+1}-u_{2i}\right)^{2}+\sum_{i=0}^{N-1}\frac{1}{h}\left(u_{2i+2}-u_{2i+1}\right)^{2}

we find, setting y2​i=u2​iy_{2i}=u_{2i}

∑i=0N−11h​(z2​i+1+1α2​i+1+β2​i+1​(β2​i+1​y2​i+α2​i+1​y2​i+2)−y2​i)2\sum_{i=0}^{N-1}\frac{1}{h}\left(z_{2i+1}+\frac{1}{\alpha_{2i+1}+\beta_{2i+1}}\left(\beta_{2i+1}y_{2i}+\alpha_{2i+1}y_{2i+2}\right)-y_{2i}\right)^{2}
+∑i=0N−11h​(y2​i+2−z2​i+1−1α2​i+1+β2​i+1​(β2​i+1​y2​i+α2​i+1​y2​i+2))2≤β2α2​γ2​∑i=12​N−1h​fi2+\sum_{i=0}^{N-1}\frac{1}{h}\left(y_{2i+2}-z_{2i+1}-\frac{1}{\alpha_{2i+1}+\beta_{2i+1}}\left(\beta_{2i+1}y_{2i}+\alpha_{2i+1}y_{2i+2}\right)\right)^{2}\leq\frac{\beta^{2}}{\alpha^{2}\gamma^{2}}\sum_{i=1}^{2N-1}hf_{i}^{2}

which yields

∑i=0N−1(z2​i+1+α2​i+1α2​i+1+β2​i+1​(y2​i+2−y2​i))2++∑i=0N−1(z2​i+1−β2​i+1α2​i+1+β2​i+1​(y2​i+2−y2​i))2≤β2α2​γ2​∑i=12​N−1h2​fi2\begin{gathered}\sum_{i=0}^{N-1}\left(z_{2i+1}+\frac{\alpha_{2i+1}}{\alpha_{2i+1}+\beta_{2i+1}}\left(y_{2i+2}-y_{2i}\right)\right)^{2}+\\ +\sum_{i=0}^{N-1}\left(z_{2i+1}-\frac{\beta_{2i+1}}{\alpha_{2i+1}+\beta_{2i+1}}\left(y_{2i+2}-y_{2i}\right)\right)^{2}\leq\frac{\beta^{2}}{\alpha^{2}\gamma^{2}}\sum_{i=1}^{2N-1}h^{2}f_{i}^{2}\end{gathered}

Replacing fi=f​(xi)f_{i}=f\left(x_{i}\right) by fi=12​h​∫xi−1xi+1f​(x)​𝑑xf_{i}=\frac{1}{2h}\int_{x_{i-1}}^{x_{i+1}}f(x)dx and using Hölder inequality we

∑i=0N−1(z2​i+1+α2​i+1α2​i+1+β2​i+1​(y2​i+2−y2​i))2++∑i=0N−1(z2​i+1−β2​i+1α2​i+1+β2​i+1​(y2​i+2−y2​i))2≤β24​α2​γ2​∑i=12​N−1[∫xi−1xi+1f​(x)​𝑑x]2≤≤β24​α2​γ2​∑i=12​N−1(∫xi−1xi+1f2​(x)​𝑑x)​(∫xi−1xi+1𝑑x)=β22​α2​γ2​h​∑i=12​N−1(∫xi−1xi+1f2​(x)​𝑑x)≤β2α2​γ2​h​‖f‖2\begin{gathered}\sum_{i=0}^{N-1}\left(z_{2i+1}+\frac{\alpha_{2i+1}}{\alpha_{2i+1}+\beta_{2i+1}}\left(y_{2i+2}-y_{2i}\right)\right)^{2}+\\ +\sum_{i=0}^{N-1}\left(z_{2i+1}-\frac{\beta_{2i+1}}{\alpha_{2i+1}+\beta_{2i+1}}\left(y_{2i+2}-y_{2i}\right)\right)^{2}\leq\frac{\beta^{2}}{4\alpha^{2}\gamma^{2}}\sum_{i=1}^{2N-1}\left[\int_{x_{i-1}}^{x_{i+1}}f(x)dx\right]^{2}\leq\\ \leq\frac{\beta^{2}}{4\alpha^{2}\gamma^{2}}\sum_{i=1}^{2N-1}\left(\int_{x_{i-1}}^{x_{i+1}}f^{2}(x)dx\right)\left(\int_{x_{i-1}}^{x_{i+1}}dx\right)=\frac{\beta^{2}}{2\alpha^{2}\gamma^{2}}h\sum_{i=1}^{2N-1}\left(\int_{x_{i-1}}^{x_{i+1}}f^{2}(x)dx\right)\leq\frac{\beta^{2}}{\alpha^{2}\gamma^{2}}h\|f\|^{2}\end{gathered}

where ‖f‖=|f|L2​(0,1)\|f\|=|f|_{L^{2}(0,1)}.
We now develop the left-hand side of this inequality and we obtain:

A=2​∑i=0N−1z2​i+12+∑i=0N−1α2​i+12+β2​i+12(α2​i+1+β2​i+1)2​(y2​i+2−y2​i)2+2​∑i=0N−1α2​i+1−β2​i+1α2​i+1+β2​i+1​z2​i+1​(y2​i+2−y2​i).A=2\sum_{i=0}^{N-1}z_{2i+1}^{2}+\sum_{i=0}^{N-1}\frac{\alpha_{2i+1}^{2}+\beta_{2i+1}^{2}}{\left(\alpha_{2i+1}+\beta_{2i+1}\right)^{2}}\left(y_{2i+2}-y_{2i}\right)^{2}+2\sum_{i=0}^{N-1}\frac{\alpha_{2i+1}-\beta_{2i+1}}{\alpha_{2i+1}+\beta_{2i+1}}z_{2i+1}\left(y_{2i+2}-y_{2i}\right).

Using Young inequality, we then find

2​∑i=0N−1α2​i+1−β2​i+1α2​i+1+β2​i+1​z2​i+1​(y2​i+2−y2​i)≥−ε​∑i=0N−1z2​i+12−1ε​∑i=0N−1(α2​i+1−β2​i+1)2(α2​i+1+β2​i+1)2​(y2​i+2−y2​i)2.2\sum_{i=0}^{N-1}\frac{\alpha_{2i+1}-\beta_{2i+1}}{\alpha_{2i+1}+\beta_{2i+1}}z_{2i+1}\left(y_{2i+2}-y_{2i}\right)\geq-\varepsilon\sum_{i=0}^{N-1}z_{2i+1}^{2}-\frac{1}{\varepsilon}\sum_{i=0}^{N-1}\frac{\left(\alpha_{2i+1}-\beta_{2i+1}\right)^{2}}{\left(\alpha_{2i+1}+\beta_{2i+1}\right)^{2}}\left(y_{2i+2}-y_{2i}\right)^{2}.

Here ε\varepsilon is a strictly positive real number which will be fixed later. Hence

A≥(2−ε)​∑i=0N−1z2​i+12+∑i=0N−1(α2​i+12+β2​i+12(α2​i+1+β2​i+1)2−1ε​(α2​i+1−β2​i+1)2(α2​i+1+β2​i+1)2)​(y2​i+2−y2​i)2A\geq(2-\varepsilon)\sum_{i=0}^{N-1}z_{2i+1}^{2}+\sum_{i=0}^{N-1}\left(\frac{\alpha_{2i+1}^{2}+\beta_{2i+1}^{2}}{\left(\alpha_{2i+1}+\beta_{2i+1}\right)^{2}}-\frac{1}{\varepsilon}\frac{\left(\alpha_{2i+1}-\beta_{2i+1}\right)^{2}}{\left(\alpha_{2i+1}+\beta_{2i+1}\right)^{2}}\right)\left(y_{2i+2}-y_{2i}\right)^{2}

Since αi>0\alpha_{i}>0 and βi>0,∀i\beta_{i}>0,\forall i, we have

1(α2​i+1+β2​i+1)2​(α2​i+12+β2​i+12−1ε​(α2​i+1−β2​i+1)2)≥(1−1ε)​α2​i+12+β2​i+12(α2​i+1+β2​i+1)2.\frac{1}{\left(\alpha_{2i+1}+\beta_{2i+1}\right)^{2}}\left(\alpha_{2i+1}^{2}+\beta_{2i+1}^{2}-\frac{1}{\varepsilon}\left(\alpha_{2i+1}-\beta_{2i+1}\right)^{2}\right)\geq\left(1-\frac{1}{\varepsilon}\right)\frac{\alpha_{2i+1}^{2}+\beta_{2i+1}^{2}}{\left(\alpha_{2i+1}+\beta_{2i+1}\right)^{2}}.

We set ξi=αiβi\xi_{i}=\frac{\alpha_{i}}{\beta_{i}} and we introduce the function ϕ​(ξ)=1+ξ2(1+ξ)2\phi(\xi)=\frac{1+\xi^{2}}{(1+\xi)^{2}} for ξ>0\xi>0. It is easy to check that ϕ​(ξ)≥12,∀ξ>0\phi(\xi)\geq\frac{1}{2},\forall\xi>0. Hence

α2​i+12+β2​i+12(α2​i+1+β2​i+1)2=ϕ​(ξ2​i+1)≥12,∀i=0,…,N\frac{\alpha_{2i+1}^{2}+\beta_{2i+1}^{2}}{\left(\alpha_{2i+1}+\beta_{2i+1}\right)^{2}}=\phi\left(\xi_{2i+1}\right)\geq\frac{1}{2},\forall i=0,\ldots,N

and we then have

1(α2​i+1+β2​i+1)2​(α2​i+12+β2​i+12−1ε​(α2​i+1−β2​i+1)2)≥12​(1−1ε).\frac{1}{\left(\alpha_{2i+1}+\beta_{2i+1}\right)^{2}}\left(\alpha_{2i+1}^{2}+\beta_{2i+1}^{2}-\frac{1}{\varepsilon}\left(\alpha_{2i+1}-\beta_{2i+1}\right)^{2}\right)\geq\frac{1}{2}\left(1-\frac{1}{\varepsilon}\right).

Thus, taking ε=32\varepsilon=\frac{3}{2}, we obtain, after some simplifications

A≥16​∑i=0N−1z2​i+12+16​∑i=0N−1(y2​i+2−y2​i)2A\geq\frac{1}{6}\sum_{i=0}^{N-1}z_{2i+1}^{2}+\frac{1}{6}\sum_{i=0}^{N-1}\left(y_{2i+2}-y_{2i}\right)^{2}

We have then the following result:
Proposition 2 [1]Under the hypothesis (H)(H), the incremental unknowns defined by (3) satisfy the following a priori estimates:

∑i=0N−1z2​i+12≤6​β2α2​β2​h∑i=0N−1(y2​i+2−y2​i)2≤6​β2α2​β2​h.\begin{gathered}\sum_{i=0}^{N-1}z_{2i+1}^{2}\leq\frac{6\beta^{2}}{\alpha^{2}\beta^{2}}h\\ \sum_{i=0}^{N-1}\left(y_{2i+2}-y_{2i}\right)^{2}\leq\frac{6\beta^{2}}{\alpha^{2}\beta^{2}}h.\end{gathered}

Proposition is valid for general definition of the IUs given in (3), under Hypothesis (H). In particular, the scheme used for the discretization of the convective term is not specified.
i. Centered Convection-Diffusion IUs

We have

αi=1h​(1−ci​h2),βi=1h​(1+ci​h2).\alpha_{i}=\frac{1}{h}\left(1-\frac{c_{i}h}{2}\right),\beta_{i}=\frac{1}{h}\left(1+\frac{c_{i}h}{2}\right).

Hence, if there exists a strictly positive real number γ\gamma such that

1−maxi⁡|ci|​h2≥γ1-\max_{i}\frac{\left|c_{i}\right|h}{2}\geq\gamma

then the asumptions i., ii. and iii. of (H) are satisfied. This condition can be satisfied by taking hh small enough.
ii. Uncentered Convection-Diffusion IUs

  • •

    If ci≥0c_{i}\geq 0 then

αi=αi+=1h,βi=βi+=1h+ci.\alpha_{i}=\alpha_{i}^{+}=\frac{1}{h},\quad\beta_{i}=\beta_{i}^{+}=\frac{1}{h}+c_{i}.

Hence, αi\alpha_{i} and βi\beta_{i} are stricly positive real numbers.

  • •

    If ci≤0c_{i}\leq 0 then

αi=αi−=1h−ci,βi=βi−=1h\alpha_{i}=\alpha_{i}^{-}=\frac{1}{h}-c_{i},\quad\beta_{i}=\beta_{i}^{-}=\frac{1}{h}

Here again, αi\alpha_{i} and βi\beta_{i} are strictly positive real numbers.
In conclusion, and without any asumption, the asumptions i., ii. and iii. of (H) are satisfied.

3 High-order compact schemes (HOC)

The real advantage of (HOC) lies not in the increased accuracy (although this is sometimes important), but rather in the fact that problems requiring fine grid can be solved on coarse grids. This implies that less memory and thus less time is required to solve the same problem to the same accuracy. Since "time is money", (HOC) schemes directly reduce the expense of approximating a differential equation numerically.

High-order compact (HOC) schemes of the type studied here increase the accuracy of the standard central difference approximation from O​(h2)O\left(h^{2}\right) to O​(h4)O\left(h^{4}\right) by including compact approximation to the leading truncation error terms. The idea is to operate on the differential equation as an auxiliary relation to obtain expressions for higher-order derivatives in the truncation error.

We define δxn​ui,n=1,2\delta_{x}^{n}u_{i},n=1,2 to be the standard central difference operator for the nn - th derivative of uu at point ii on a uniform grid of mesh size hh,

δx​ui=ui+1−ui−12​h,δx2​ui=ui+1−2​ui+ui−1h2\delta_{x}u_{i}=\frac{u_{i+1}-u_{i-1}}{2h},\delta_{x}^{2}u_{i}=\frac{u_{i+1}-2u_{i}+u_{i-1}}{h^{2}}

Central differencing (1) yields

−δx2​ui+ci​δx​ui−τi=fi-\delta_{x}^{2}u_{i}+c_{i}\delta_{x}u_{i}-\tau_{i}=f_{i} (9)

where τi\tau_{i} is the local truncation error at node ii,

τi=h212​[2​c​d3​ud​x3−d4​ud​x4]i+O​(h4)\tau_{i}=\frac{h^{2}}{12}\left[2c\frac{d^{3}u}{dx^{3}}-\frac{d^{4}u}{dx^{4}}\right]_{i}+O\left(h^{4}\right) (10)

We seek to approximate the leading term in (10) and include it in the difference formulation to yield an O​(h4)O\left(h^{4}\right) method. Assuming the solution is sufficiently regular, we may accomplish this by differentiating (1) to yield

d3​ud​x3|i=[c​d2​ud​x2+d​cd​x​d​ud​x−d​fd​x]i\left.\frac{d^{3}u}{dx^{3}}\right|_{i}=\left[c\frac{d^{2}u}{dx^{2}}+\frac{dc}{dx}\frac{du}{dx}-\frac{df}{dx}\right]_{i}

which can be approximated compactly as

d3​ud​x3|i=ci​δx2​ui+δx​ci​δx​ui−δx​fi+O​(h2)\left.\frac{d^{3}u}{dx^{3}}\right|_{i}=c_{i}\delta_{x}^{2}u_{i}+\delta_{x}c_{i}\delta_{x}u_{i}-\delta_{x}f_{i}+O\left(h^{2}\right) (11)

and similarly

d4​ud​x4|i=[c​d3​ud​x3+2​d​cd​x​d2​ud​x2+d2​cd​x2​d​ud​x−d2​fd​x2]i\displaystyle\left.\frac{d^{4}u}{dx^{4}}\right|_{i}=\left[c\frac{d^{3}u}{dx^{3}}+2\frac{dc}{dx}\frac{d^{2}u}{dx^{2}}+\frac{d^{2}c}{dx^{2}}\frac{du}{dx}-\frac{d^{2}f}{dx^{2}}\right]_{i} (12)
=ci​d3​ud​x3|i+2​δx​ci​δx2​ui+δx2​ci​δx​ci−δx2​fi+O​(h2)\displaystyle=\left.c_{i}\frac{d^{3}u}{dx^{3}}\right|_{i}+2\delta_{x}c_{i}\delta_{x}^{2}u_{i}+\delta_{x}^{2}c_{i}\delta_{x}c_{i}-\delta_{x}^{2}f_{i}+O\left(h^{2}\right)

Relations (11) and (12) can be combined with (10) to yield the new truncation error expression:

τi=h212​[(ci2−2​δx​ci)​δx2​ui+(ci​δx​ci−δx2​ci)​δx​ui−ci​δx​fi+δx2​fi]+O​(h4)\tau_{i}=\frac{h^{2}}{12}\left[\left(c_{i}^{2}-2\delta_{x}c_{i}\right)\delta_{x}^{2}u_{i}+\left(c_{i}\delta_{x}c_{i}-\delta_{x}^{2}c_{i}\right)\delta_{x}u_{i}-c_{i}\delta_{x}f_{i}+\delta_{x}^{2}f_{i}\right]+O\left(h^{4}\right)

which can be use to increase the accuracy of the approximation scheme. The resulting high-order compact scheme is

−Ai​δx2​ui+Ci​δx​ui=Fi+O​(h4)-A_{i}\delta_{x}^{2}u_{i}+C_{i}\delta_{x}u_{i}=F_{i}+O\left(h^{4}\right) (13)

where

Ai=1+h212​(ci2−2​δx​ci)\displaystyle A_{i}=1+\frac{h^{2}}{12}\left(c_{i}^{2}-2\delta_{x}c_{i}\right) (14)
Ci=ci+h212​(δx2​ci−ci​δx​ci)\displaystyle C_{i}=c_{i}+\frac{h^{2}}{12}\left(\delta_{x}^{2}c_{i}-c_{i}\delta_{x}c_{i}\right) (15)
Fi=fi+h212​(δx2​fi−ci​δx​fi)\displaystyle F_{i}=f_{i}+\frac{h^{2}}{12}\left(\delta_{x}^{2}f_{i}-c_{i}\delta_{x}f_{i}\right) (16)

We can write (13) in the form (2) where:

αi=Aih−Ci2,βi=Aih+Ci2,fi:=Fi​ for ​i=1,…,2​N\alpha_{i}=\frac{A_{i}}{h}-\frac{C_{i}}{2},\beta_{i}=\frac{A_{i}}{h}+\frac{C_{i}}{2},f_{i}:=F_{i}\text{ for }i=1,\ldots,2N

and we can define IU in this case. If we consider 1D convection diffusion problem with constant coefficients then:

Ai=1+h2​c212,Ci=c​ and ​Fi=fi​ for ​i=1,…,2​NA_{i}=1+\frac{h^{2}c^{2}}{12},C_{i}=c\text{ and }F_{i}=f_{i}\text{ for }i=1,\ldots,2N

In this case we can see that, without any asumptions, the hypothesis (H) are satisfied.

4 Condition Number Analysis

The condition number is defined by

κ=max⁡|λk|min⁡|λk|\kappa=\frac{\max\left|\lambda_{k}\right|}{\min\left|\lambda_{k}\right|} (17)

where {λk}\left\{\lambda_{k}\right\} is the set of eigenvalues of the real matrix we wish to solve.
The tridiagonal matrices generated by compact 1D numerical schemes lend themselves to theoretical eigenvalue analysis, and our hope is that we can draw some conclusions from such analysis that might apply to higher dimensions.

We will consider 1D convection diffusion problem with constant coefficients. For a tridiagonal Toeplitz matrix which has the form

TN​(c,d,e)=(decd⋅⋅⋅⋅⋅⋅⋅⋅⋅ecd)T_{N}(c,d,e)=\left(\begin{array}[]{cccccc}d&e&&&&\\ c&d&\cdot&&&\\ &\cdot&\cdot&\cdot&&\\ &&\cdot&\cdot&\cdot&\\ &&&\cdot&\cdot&e\\ &&&&c&d\end{array}\right)

the eigenvalues are known explicitly (see [5]),

d+2​(c​e)1/2​cos⁡(k​π/(N+1)),k=1,…,Nd+2(ce)^{1/2}\cos(k\pi/(N+1)),k=1,\ldots,N

Using this result, we can obtain condition number for each of the 3 schemes (see [12]):

κH​O​C=c2​h2+12+(c2​h2+6​c​h+12)​(c2​h2−6​c​h+12)c2​h2+12−(c2​h2+6​c​h+12)​(c2​h2−6​c​h+12),\displaystyle\kappa_{HOC}=\frac{c^{2}h^{2}+12+\sqrt{\left(c^{2}h^{2}+6ch+12\right)\left(c^{2}h^{2}-6ch+12\right)}}{c^{2}h^{2}+12-\sqrt{\left(c^{2}h^{2}+6ch+12\right)\left(c^{2}h^{2}-6ch+12\right)}}, (18)
κC​D​S={2+4−c2​h22+4−c2​h2,|c​h|≤2|c|2,|c​h|>2,\displaystyle\kappa_{CDS}=\left\{\begin{array}[]{c}\frac{2+\sqrt{4-c^{2}h^{2}}}{2+\sqrt{4-c^{2}h^{2}}},|ch|\leq 2\\ \frac{|c|}{2},|ch|>2\end{array},\right. (19)
κC​D​S={2+4−c2​h22+4−c2​h2,|c​h|≤2|c|2,|c​h|>2,\displaystyle\kappa_{CDS}=\left\{\begin{array}[]{c}\frac{2+\sqrt{4-c^{2}h^{2}}}{2+\sqrt{4-c^{2}h^{2}}},|ch|\leq 2\\ \frac{|c|}{2},|ch|>2\end{array},\right. (19)
κU​D​S=|c​h|+2+|c​h|+2|c​h|+2−|c​h|+2\displaystyle\kappa_{UDS}=\frac{|ch|+2+\sqrt{|ch|+2}}{|ch|+2-\sqrt{|ch|+2}} (20)

For HOC scheme, as c​h→0ch\rightarrow 0, the condition number behaves as c−2​h−2c^{-2}h^{-2} and for large c​hch, the condition number behaves as c2​h2c^{2}h^{2}. This means that for large NN and |c​h||ch| very large or very small, the condition number is extremely large and may pose some difficulty for an iterative solver. This is one instance where CDS and UDS appear to compare favorably with HOC, except of course for the fact that for c​h>2ch>2, CDS is oscillatory and for large c​hch, the UDS models an overly diffusive problem.

Introducing IU, after using all the levels (what means LL if h=12Lh=\frac{1}{2^{L}} ) we will obtain a diagonal matrix which have the elements,

α1=c2​h2+126​h2,αn=(c2​h2+6​c​h+1212​h2)2n−1+(c2​h2−6​c​h+1212​h2)2n−1∏i=0n−2[(c2​h2+6​c​h+1212​h2)2i+(c2​h2−6​c​h+1212​h2)2i],n=2,…,L,\alpha_{1}=\frac{c^{2}h^{2}+12}{6h^{2}},\alpha_{n}=\frac{\left(\frac{c^{2}h^{2}+6ch+12}{12h^{2}}\right)^{2^{n-1}}+\left(\frac{c^{2}h^{2}-6ch+12}{12h^{2}}\right)^{2^{n-1}}}{\prod_{i=0}^{n-2}\left[\left(\frac{c^{2}h^{2}+6ch+12}{12h^{2}}\right)^{2^{i}}+\left(\frac{c^{2}h^{2}-6ch+12}{12h^{2}}\right)^{2^{i}}\right]},n=2,\ldots,L,

and α1>α2>…>αL\alpha_{1}>\alpha_{2}>\ldots>\alpha_{L}. In conclusion, the condition number will be:

κI​U​H​O​C=2​(12+c2​h2)​∏i=0L−2[(c2​h2+6​c​h+12)2i+(c2​h2−6​c​h+12)2i](c2​h2+6​c​h+12)2L−1+(c2​h2−6​c​h+12)2L−1\kappa_{IUHOC}=2\frac{\left(12+c^{2}h^{2}\right)\prod_{i=0}^{L-2}\left[\left(c^{2}h^{2}+6ch+12\right)^{2^{i}}+\left(c^{2}h^{2}-6ch+12\right)^{2^{i}}\right]}{\left(c^{2}h^{2}+6ch+12\right)^{2^{L-1}}+\left(c^{2}h^{2}-6ch+12\right)^{2^{L-1}}} (21)

In the next table we have a comparison between condition number of HOC scheme and condition number of HOC scheme preconditioned by IU method.

ch=2\mathrm{ch}=2 ch=5\mathrm{ch}=5 ch =10=10 ch =20=20
h c k c k c k c k
18\frac{1}{8} 16 4.1426 40 3.3550 80 8.0960 160 16.215
1.5960 1.4133 2.5549 4.5692
1.3322 1.2330 1.8357 2.8620
116\frac{1}{16} 32 4.6936 80 3.6955 160 10.639 320 31.394
1.7267 1.4915 3.1834 8.3567
1.3718 1.2524 2.1057 4.6964
1.3333 1.2333 1.8664 3.3772
132\frac{1}{32} 64 4.8523 160 3.7903 320 11.526 640 40.698
1.7646 1.5135 3.4032 10.680
1.3844 1.2584 2.2051 5.8448
1.3341 1.2335 1.8887 3.8699
1.3333 1.2333 1.8667 3.4329

A more acurate bound for kI​U​H​O​Ck_{IUHOC} can be given as follows:
Proposition 3 Let kI​U​H​O​Ck_{IUHOC} be the condition number given by (21), where h=1/2L​(L∈N)h=1/2^{L}(L\in N). Then

kI​U​H​O​C⩽2L−1=12​h,k_{IUHOC}\leqslant 2^{L-1}=\frac{1}{2h},

for each L=2,3,…L=2,3,\ldots.
Proof. Let

κI​U​H​O​C=2​(12+c2​h2)​∏i=0L−2[(c2​h2+6​c​h+12)2i+(c2​h2−6​c​h+12)2i](c2​h2+6​c​h+12)2L−1+(c2​h2−6​c​h+12)2L−1\kappa_{IUHOC}=2\frac{\left(12+c^{2}h^{2}\right)\prod_{i=0}^{L-2}\left[\left(c^{2}h^{2}+6ch+12\right)^{2^{i}}+\left(c^{2}h^{2}-6ch+12\right)^{2^{i}}\right]}{\left(c^{2}h^{2}+6ch+12\right)^{2^{L-1}}+\left(c^{2}h^{2}-6ch+12\right)^{2^{L-1}}}

be the condition number of HOC scheme preconditioned by IU method. Let us denote

A=c2​h2+6​c​h+12,B=c2​h2−6​c​h+12.A=c^{2}h^{2}+6ch+12,\quad B=c^{2}h^{2}-6ch+12.

Then A+B=2​(12+c2​h2)A+B=2\left(12+c^{2}h^{2}\right) and with this notations we shall have
kI​U​H​O​C=(A+B)​∏i=0L−2(A2i+B2i)A2L−1+B2L−1=(A+B)2​(A2+B2)​(A4+B4)​…​(A2L−2+B2L−2)A2L−1+B2L−1k_{IUHOC}=\frac{(A+B)\prod_{i=0}^{L-2}\left(A^{2^{i}}+B^{2^{i}}\right)}{A^{2^{L-1}}+B^{2^{L-1}}}=\frac{(A+B)^{2}\left(A^{2}+B^{2}\right)\left(A^{4}+B^{4}\right)\ldots\left(A^{2^{L-2}}+B^{2^{L-2}}\right)}{A^{2^{L-1}}+B^{2^{L-1}}}.
Now, using the inequality (x+y)2⩽2​(x2+y2)(x+y)^{2}\leqslant 2\left(x^{2}+y^{2}\right), for real numbers sequentially, we get

kI​U​H​O​C\displaystyle k_{IUHOC} ⩽2​(A2+B2)2​(A4+B4)​…​(A2L−2+B2L−2)A2L−1+B2L−1⩽22​(A4+B4)2​…​(A2L−2+B2L−2)A2L−1+B2L−1⩽…\displaystyle\leqslant\frac{2\left(A^{2}+B^{2}\right)^{2}\left(A^{4}+B^{4}\right)\ldots\left(A^{2^{L-2}}+B^{2^{L-2}}\right)}{A^{2^{L-1}}+B^{2^{L-1}}}\leqslant\frac{2^{2}\left(A^{4}+B^{4}\right)^{2}\ldots\left(A^{2^{L-2}}+B^{2^{L-2}}\right)}{A^{2^{L-1}}+B^{2^{L-1}}}\leqslant\ldots
…⩽2L−2​(A2L−2+B2L−2)2A2L−1+B2L−1⩽2L−1​(A2L−1+B2L−1)A2L−1+B2L−1=2L−1\displaystyle\ldots\leqslant\frac{2^{L-2}\left(A^{2^{L-2}}+B^{2^{L-2}}\right)^{2}}{A^{2^{L-1}}+B^{2^{L-1}}}\leqslant\frac{2^{L-1}\left(A^{2^{L-1}}+B^{2^{L-1}}\right)}{A^{2^{L-1}}+B^{2^{L-1}}}=2^{L-1}

Remark 4 We notice that the upper bound of the condition number does not depent of the equation’s coefficient cc. From this point of view we can say that we have a uniform condition number estimate.

References

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[13] Temam, R., Inertial Manifolds and Multigrid Methods, SIAM J. Math. Anal. 21 (1990) 1, 154-178.
"T.Popoviciu" Institute of Numerical Analysis P.O. Box 68

3400 Cluj-Napoca 1
e-mail: amuresan@ictp-acad.math.ubbcluj.ro
acurseu@ictp-acad.math.ubbcluj.ro

2001

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