A high-order interpolation for the finite volume method: The Coupled Least Squares reconstruction

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A high-order interpolation for the finite volume method:

The Coupled Least Squares reconstruction

Florian Haider, Bernard Courbet, Jean-Pierre Croisille

To cite this version:

Florian Haider, Bernard Courbet, Jean-Pierre Croisille. A high-order interpolation for the finite

volume method: The Coupled Least Squares reconstruction. Computers and Fluids, Elsevier, 2018,

176, pp.20-39. �10.1016/j.compfluid.2018.09.009�. �hal-01879576�

A High-Order interpolation for the Finite Volume Method: the Coupled Least Squares Reconstruction

Florian Haider*, Bernard Courbet* and Jean-Pierre Croisille**

* Onera, 92320 Châtillon, France,

bernard.courbet@onera.fr,florian.haider@onera.fr.

** IECL, UMR CNRS 7502, Mathématiques, Univ. de Lorraine, France, jean-pierre.croisille@univ-lorraine.fr

Abstract

We consider the Finite Volume Method on irregular grids for conservation laws with a particular poly- nomial reconstruction. This reconstruction is based on a least squares approach to compute consistent approximations of the first, second and third order derivatives. The resulting reconstruction is cubic.

It is called the Coupled Least Squares reconstruction. It is obtained by a three stages iteration. At each iteration, only data located in a compact stencil, and not beyond, are accessed. A linear stability analysis is given in the case of regular and irregular one-dimensional grids. Numerical results for various problems, including shock tubes, vortex and accoustic wave propagation, support the interest of this approach. The reconstruction algorithm is presented in detail in the one dimensional case. An outline of the multidimensional case in also given.

This work was announced in [14].

Keywords: Finite volume method - High order method - k-exact method - Cubic reconstruction - Numerical flux - Linear stability - Irregular grid - Coupled Least Squares.

1. Introduction

1.1. Finite volume schemes with high order reconstruction

In [13, 14], a high order reconstruction approach for finite volume approximations was introduced for the purpose of gas dynamics simulations on general polyhedral grids. This approach, which is called the Coupled Least Squares reconstruction, belongs to the k− exact methods [4]. The purpose of this paper is to give a detailed account of a particular algorithm of the reconstruction. The presentation is mainly restricted to the one dimensional context to emphasize the logic of the numerical procedure.

Consider the conservation law

∂

t

u + ∂

x

f (u) = 0. (1.1)

We start from the discrete in space, continuous in time, integral version of (1.1):

dv

_α

(t)

dt = − 1

|T

α

|

h f

_α+1/2

(t) − f

_α−1/2

(t) i

. (1.2)

The grid is made of cells T

α

and is depicted on Fig 1.1. In (1.2) t 7→ v

α

(t) approximates t 7→ u ¯

α

(t) defined by

¯

u

α

(t) = 1

|T

α

| Z

Tα

u(x, t)dx, (1.3)

*Manuscript

Click here to download Manuscript: paper-Haider-Courbet-Croisille-R4.pdf Click here to view linked References

T

_α−1

T

α

T

α+1

x

_α−1

x

_α

x

_α+1

h

_α−1,α

h

α,α+1

Figure 1.1: The cell T

α

and the two neighboor cells T

_α±1

. The barycenters of the cells T

_α±1

and T

α

are the points x

_α±1

and x

α

, repectively. In addition, h

_α−1,α

= x

α

− x

_α−1

and h

α,α+1

= x

α+1

− x

α

.

where u(x, t) is the solution of (1.1). Throughout the paper, the numerical flux t 7→ f

_α+1/2

(t) is of the form:

f

_α+1/2

(t) = F

w

α

[V (t)](x

⁻_α+1/2

), w

α+1

[V (t)](x

⁺_α+1/2

)

. (1.4)

where:

• The function (u

_L

, u

_R

) 7→ F (u

_L

, u

_R

) denotes some numerical flux function, typically the HLLC flux in the case of the Euler equations [29].

• The reconstruction x 7→ w

α

[V (t)](x) is an interpolant based on the vector V (t) = [v

1

(t), v

2

(t), . . . , v

N

(t)]

^T

. The reconstruction expounded in this paper has the form of a Taylor expansion of u(x) around the barycenter x

α

of the cell T

α

, i.e. (we take out the time dependency for clarity):

w

α

[¯ u](x) ' u(x

α

) + u

⁰

(x

α

)(x − x

α

) + 1

2 u

⁰⁰

(x

α

)(x − x

α

)

²

+ 1

6 u

⁰⁰⁰

(x

α

)(x − x

α

)

³

. (1.5) where ¯ u = [¯ u

α

] is the vector of averages of u(x) over the cells T

α

. The relation (1.5) is a 3− exact reconstruction. The main task is to define a consistent approximation to u

⁰

(x

α

), u

⁰⁰

(x

α

) and u

⁰⁰⁰

(x

α

), where u(x) is supposed to be known by ¯ u only.

The main observation is the following: to preserve fourth order accuracy in (1.5), it is sufficient to calculate approximations of u

⁰

(x

_α

), u

⁰⁰

(x

_α

) and u

⁰⁰⁰

(x

_α

) with order 3, 2 and 1, respectively. It is the purpose of this paper to show how this can be obtained in several steps of an iterative procedure along which the accuracy is progessively enhanced. Moreover, at each step, there is an access only to data located in a small neighborhood of any given cell, and therefore it is called a compact k-exact reconstruction.

This paper is the companion paper of [14] where the multidimensional aspect on general polyhedral grids of our algorithm is addressed. This study has been initiated in [12], in the context of the package CEDRE, a parallel multisolver CFD code for aerothermochemistry

¹

.

Our point of view is thus the one of a generic computational procedure for any finite volume code and for general compressible flows. For an overview on the k− exact approach for high order computations in CFD, compared to other approaches, we refer to the review paper [30].

1.2. High order finite volume schemes

We consider the finite volume method (1.2) for a conservation law (1.1) on a general irregular grid made of cells T

α

, which support approximations v

α

(t) of the averages u ¯

α

(t). As explained in Section 1.1, we want to define the reconstructed polynomial w

α

[V (t)](x) which is used in the numerical flux f

_α+1/2

(t) in (1.4). This polynomial must have the following properties (we drop the t dependency).

1CEDRE is being developed at Onera, France, http://cedre.onera.fr

First w

α

[V ](x) depends linearly on the data v

β

in the cells T

β

belonging to a small neighborhood of T

α

. In particular, it does not use gradient data as for example in the Discontinuous Galerkin approximation, but only finite volume data. Furthermore, no flow based local parameters are used in a nonlinear way to define w

α

[V ](x). Consequently, our reconstruction is decoupled from all nonlinear treatments such as slope limiting or monotonicity preserving procedures. Thus the reconstruction step is decoupled from the limitation procedure. The reconstruction step can be applied in principle to any finite volume code including complex fluid modelling such as turbulent, multiphasic, reactive, spherical flows, etc. With this approach, the nonlinear treatments are performed in a second step, after the reconstruction step.

Having in view general CFD codes, the design of the reconstruction must of course be multidimensional.

Moreover, it must allow an easy parallel implementation. This is obtained by mean of the procedure in several stages presented in Section 2 and Section 3.

1.3. Related works

Reconstruction beyond linear in the finite volume method on irregular grids has been a longstanding issue of great importance, both practical and theoretical in CFD. It takes place in the challenge to go beyond second order with the finite volume method. The problem was introduced in [17, 1] where various polynomial interpolations on triangles were studied. Quadratic reconstruction is undertaken in [4, 7]. The series of studies [22, 21] explores the finite volume method with quadratic reconstruction for compressible Fluid Dynamics. In many works, the reconstruction is analyzed in conjunction with the limiter and with the positivity preserving property [3, 2], which are considered as constraints on the reconstruction. This is for example the case in the well known ENO/WENO procedure. In this approach, local high order reconstruction functions are based on stencils which vary according to local sensors related the solution, e.g. the total variation. We refer to [16, 18, 27, 28]. Another option [9] is where the reconstruction in finite volumes is considered in the general framework of the Discontinuous Galerkin approach. We also refer to the studies [19, 31], with interest into application oriented simulations, where reconstructions beyond linear are presented. Finally we mention the recent study [23] where a variant of compact k-exact reconstruction is considered. In this study the authors use an upwinded scheme only up to third order and switch to a fourth order centered scheme in vorticity dominated regions. This is in contrast to our scheme which is fully upwinded up to fourth order independently of the flow regime.

1.4. Outline

The outline of the paper is as follows. In Section 2 we present the principle of our reconstruction algorithm. In Section 2.1, the general d− dimensional framework is set up for the reconstructed cubic polynomial w

α

[¯ u](x). From Section 2.2 on, we switch to the one dimensional case to present how the reconstruction uses at each step only data in the direct neighborhood of T

α

. In Section 3 and Section 4 the detail of the algorithm to calculate the cubic reconstruction is summarized (algorithm 1). This is based on fully centered approximate derivatives ˜ σ

_α

, θ ˜

_α

and ψ ˜

_α

in the cubic reconstruction (1.5) giving rise to a linearly stable semidiscrete system (1.2), even on irregular grids. Our analysis is based on the numerical computation of discrete spectra. Finally we show in Section 5 numerical results in one and multidimension illustrating our approach.

2. Coupled Least Squares Reconstruction 2.1. The d− dimensional case

The Coupled Least Squares method is a particular method to implement a k− exact reconstruction.

This method has been already been introduced in [14]. Here we specifically focus on the piecewise cubic

reconstruction (i.e k = 3). Let Ω ⊂ R

^d

, d ≥ 1 be a domain with periodic boundary conditions. We

consider a grid, a priori irregular, consisting of N general polyhedral cells T

α

as on Fig. 2.1.

x

α

T

^α

T

_β

h

αβ

x

β

Figure 2.1: Two cells of a general polyhedral grid in the two dimensional case, (d = 2

Ω =

N

[

α=1

T

α

. (2.1)

The tensor notation used in this section is summarized in Appendix 8. The reconstruction algorithm proceeds in several steps. At each step, the only data that are used are located in a small neighborhood W

α

of the cell T

_α

. The set W

α

is a parameter of the method. A typical choice of W

α

is the first neighborhood

²

defined by

V

⁽¹⁾α

= {β ∈ [1 : N ], | T

β

is adjacent to T

α

}. (2.2) Let x ∈ Ω 7→ u(x) be a given regular function. The value of u and of the first, second and third order derivatives at x

α

are denoted by

u

α

= u(x

α

), D

⁽¹⁾

u

α

= D

_x⁽¹⁾

u(x

α

), D

⁽²⁾

u

α

= D

⁽²⁾_x

u(x

α

), D

⁽³⁾

u

α

= D

⁽³⁾_x

u(x

α

). (2.3) The derivative D

^(l)

u

α

is a symmetric tensor of order l. We also note the mean value

¯ u

_α

= 1

|T

α

| Z

T_α

u(x)dx. (2.4)

Consider the particular case where u(x) = p(x) is a cubic polynomial. The values p ¯

β

− p ¯

α

and the three derivatives D

^(l)

p

α

, l = 1, 2, 3, satisfy for all β ∈ W

α

the following Taylor expansions (see Appendix 8 for the notation),



 

 



 

 



¯

p

β

− p ¯

α

= D

⁽¹⁾

p

α

• (z

⁽¹⁾_αβ

− x

⁽¹⁾α

) +

¹₂

D

⁽²⁾

p

α

• (z

⁽²⁾_αβ

− x

⁽²⁾α

) +

¹₆

D

⁽³⁾

p

α

• (z

⁽³⁾_αβ

− x

⁽³⁾α

), D

⁽¹⁾

p

β

= D

⁽¹⁾

p

α

+ D

⁽²⁾

p

α

• h

⁽¹⁾_αβ

+

¹₂

D

⁽³⁾

p

α

• h

⁽²⁾_αβ

,

D

⁽²⁾

p

β

= D

⁽²⁾

p

α

+ D

⁽³⁾

p

α

• h

⁽¹⁾_αβ

.

(2.5)

2The first neighborhood of the cellT_αis also called thedirector theVon Neumannneighborhood.

For a general function u(x) these Talor expansions become



 

 



 

 



¯

u

_β

− u ¯

_α

= D

⁽¹⁾

u

_α

• (z

⁽¹⁾_αβ

− x

⁽¹⁾α

) +

¹₂

D

⁽²⁾

u

_α

• (z

⁽²⁾_αβ

− x

⁽²⁾α

) +

¹₆

D

⁽³⁾

u

_α

• (z

⁽³⁾_αβ

− x

⁽³⁾α

) + O(h

⁴

), D

⁽¹⁾

u

_β

= D

⁽¹⁾

u

_α

+ D

⁽²⁾

u

_α

• h

⁽¹⁾_αβ

+

¹₂

D

⁽³⁾

u

_α

• h

⁽²⁾_αβ

+ O(h

³

),

D

⁽²⁾

u

_β

= D

⁽²⁾

u

_α

+ D

⁽³⁾

u

_α

• h

⁽¹⁾_αβ

+ O(h

²

).

(2.6) Therefore, due to the O(h

^p

) terms in (2.6), the quantities u ¯

_β

− u ¯

_α

, D

⁽¹⁾

u

_α

, D

⁽²⁾

u

_α

and D

⁽³⁾

u

_α

are ap- proximate solutions of the system (2.5). Consider now the problem to approximate the three derivatives D

⁽¹⁾

u

_α

, D

⁽²⁾

u

_α

and D

⁽³⁾

u

_α

by consistent values. We look for symmetric tensors called σ ˜

_α

, θ ˜

_α

and ψ ˜

_α

satisfying



 

 

 

 

˜

σ

α

= D

⁽¹⁾

u

α

+ O(h

³

), θ ˜

α

= D

⁽²⁾

u

α

+ O(h

²

), ψ ˜

_α

= D

⁽³⁾

u

_α

+ O(h).

(2.7)

An essential observation is that for all symmetric tensors σ ˜

α

, θ ˜

α

and ψ ˜

_α

of order 1, 2 and 3 respectively, satisfying (2.7), the same system than (2.6) holds. For all β ∈ W

α

we have:



 

 



 

 



¯

u

_β

− u ¯

_α

= ˜ σ

_α

• (z

⁽¹⁾_αβ

− x

⁽¹⁾α

) +

¹₂

θ ˜

_α

• (z

⁽²⁾_αβ

− x

⁽²⁾α

) +

¹₆

ψ ˜

_α

• (z

⁽³⁾_αβ

− x

⁽³⁾α

) + O(h

⁴

),

˜

σ

_β

= ˜ σ

_α

+ ˜ θ

_α

• h

⁽¹⁾_αβ

+

¹₂

ψ ˜

_α

• h

⁽²⁾_αβ

+ O(h

³

), θ ˜

β

= ˜ θ

α

+ ˜ ψ

_α

• h

⁽¹⁾_αβ

+ O(h

²

).

(2.8)

The Coupled Least Squares method consists in approximately solving (2.8), when taking out the O(h

^p

) terms. Otherwise stated, we solve for ¯ u = [¯ u

1

, u ¯

2

, . . . u ¯

N

] 7→ σ ˜

α

[¯ u], θ ˜

α

[¯ u], ψ ˜

_α

[¯ u] solution of the system



 

 



 

 



¯

u

_β

− u ¯

_α

= ˜ σ

_α

[¯ u] • (z

⁽¹⁾_αβ

− x

⁽¹⁾α

) +

¹₂

θ ˜

_α

[¯ u] • (z

⁽²⁾_αβ

− x

⁽²⁾α

) +

¹₆

ψ ˜

_α

[¯ u] • (z

⁽³⁾_αβ

− x

⁽³⁾α

), (a),

˜

σ

_β

[¯ u] = ˜ σ

_α

[¯ u] + ˜ θ

_α

[¯ u] • h

⁽¹⁾_αβ

+

¹₂

ψ ˜

_α

[¯ u] • h

⁽²⁾_αβ

, (b), θ ˜

_β

[¯ u] = ˜ θ

_α

[¯ u] + ˜ ψ

_α

[¯ u] • h

⁽¹⁾_αβ

, (c).

(2.9)

Solving (2.9) raises two questions. First the linear system (2.9) does not have a solution in general.

This system is rectangular with m

α

1 + d + d(d − 1)/2

equations and d + d(d + 1)/2 + d(d + 1)(d + 2)/6 unknowns. For example in two dimensions and for a grid made of triangles, there are 18 equations and 9 unknowns. Therefore (2.9) is overdetermined. This leads to seek a solution in the least squares sense. Second, equations (2.9

a

), (2.9

b

) and (2.9

c

) couple unknowns in differents cells. To overcome these problems, the following strategy is used. The system (2.9) is rewritten as:



 

 



 

 



h

αβ

. σ ˜

α

[¯ u] = ˜ u

β

− u ¯

α

−

¹₂

θ ˜

α

[¯ u] • (z

⁽²⁾_αβ

− x

⁽²⁾α

) −

¹₆

ψ ˜

_α

[¯ u] • (z

⁽³⁾_αβ

− x

⁽³⁾α

), (a) h

αβ

. θ ˜

α

[¯ u] = ˜ σ

β

[¯ u] − σ ˜

α

[¯ u] −

¹₂

ψ ˜

_α

[¯ u] • h

⁽²⁾_αβ

, (b)

h

_αβ

. ψ ˜

_α

[¯ u] = ˜ θ

_β

[¯ u] − θ ˜

_α

[¯ u] (c).

(2.10)

Let us now consider in each equation of (2.10) the unknowns in the right-hand-side as parameters. A

least squares resolution is applied to each equation in (2.10). Consider first (2.10)

a

. The right-hand

side is denoted by

f δu

⁽⁰⁾_αβ

= ˜ u

β

− u ¯

α

− 1 2

θ ˜

α

[¯ u] • (z

⁽²⁾_αβ

− x

⁽²⁾_α

) − 1 6

ψ ˜

_α

[¯ u] • (z

⁽³⁾_αβ

− x

⁽³⁾_α

), (2.11) Therefore (2.10)

a

is rewritten as

h

_αβ

. σ ˜

_α

[¯ u] = f δu

⁽⁰⁾_αβ

, ∀β ∈ W

α

. (2.12) This is a linear system of m

_α

equations with d unknowns which are [˜ σ

_α,1

, . . . σ ˜

_α,d

]. The least squares solution gives that σ ˜

_α

[¯ u] is solution of

min

σ_α∈R^d

X

β∈Wα

|f δu

⁰_αβ

− σ

α

• h

⁽¹⁾_α

|

²

. (2.13) On the other hand, the standard least squares slope in cell T

α

is solution of

min

σα∈R^d

X

β∈Wα

|¯ u

β

− u ¯

α

− σ

α

• h

⁽¹⁾_α

|

²

. (2.14)

Denote C

_α

and H

^T_α

the d × m

_α

matrices (m

_α

= # W

α

)

C

_α

= [c

_αβ1

, c

_αβ2

, . . . c

_αβmα

], H

^T_α

= [h

_αβ1

, h

_αβ2

, . . . h

_αβmα

]. (2.15) Assuming rank(H

α

) = d, the unique solution of (2.14) is given by the least squares operator A

α

defined by

A

α

: [¯ u] 7→ σ ¯

^LS_α

[¯ u] = X

β∈Wα

c

αβ

(¯ u

β

− u ¯

α

), (2.16) where

c

_αβ

= (H

^T_α

H

_α

)

⁻¹

h

⁽¹⁾_αβ

(= O(1/h)). (2.17) As a result, σ ˜

_α

[¯ u] solution of (2.13) is expressed as:

˜

σ

α

[¯ u] = ¯ σ

^LS_α

[¯ u] − a ¯

⁽³⁾_α

. ˜ θ

α

[¯ u] − b ¯

⁽⁴⁾_α

. ψ ˜

_α

[¯ u]. (2.18) where the two tensors ¯ a

⁽³⁾α

and b ¯

⁽⁴⁾α

depend only on the grid and are given by

¯ a

⁽³⁾_α

=

z

⁽²⁾_αβ

− x

⁽²⁾_α

⊗ c

_αβ

, b ¯

⁽⁴⁾_α

=

z

⁽³⁾_αβ

− x

⁽³⁾_α

⊗ c

_αβ

. (2.19)

The relation (2.18) is the first relation of the Coupled Least Squares method. Note that at this stage, θ ˜

α

[¯ u] and ψ ˜

_α

[¯ u] are still parameters and are not determined. Consider now the least squares resolution of (2.10)

b

. As before we consider ψ ˜

_α

[¯ u] in the right-hand side of (2.10)

b

as a fixed parameter. We denote

f δu

⁽¹⁾_αβ

= ˜ σ

β

[¯ u] − σ ˜

α

[¯ u] − 1 2

ψ ˜

_α

[u] • h

⁽²⁾_αβ

, β ∈ W

α

. (2.20)

In (2.10)

b

, the symmetric tensor θ ˜

_α

[¯ u] is the unknown and δu f

⁽¹⁾_αβ

is at the moment considered to be known.

There are d(d + 1)/2 unknowns and dm

_α

equations. The linear system is as before overdetermined. It can be expressed as

H

_α⁽²⁾

θ ˜

^α

[¯ u] = δu f

⁽¹⁾_αβ

β

, (2.21)

where the size of the matrix H

α⁽²⁾

is m

α

d × d(d + 1)/2. Again (2.21) is solved by least squares. This leads to

θ ˜

^α

[¯ u] = H

_α⁽²⁾

+

δu e

⁽¹⁾_α

, (2.22)

where δu e

⁽¹⁾_α

= [ δu e

⁽¹⁾_α

1

, . . . , δu e

⁽¹⁾_α

mα

]

^T

and where H

α⁽²⁾

⁺

denotes the standard Moore-Penrose inverse of the matrix H

α⁽²⁾

. It is defined by [10, 8]:

H

_α⁽²⁾

+

= H

_α⁽²⁾

^T

H

_α⁽²⁾

−1

H

_α⁽²⁾

T

. (2.23)

Therefore the second relation in the space of symmetric tensors of order 2 (i.e. in R

^d(d+1)/2

) is

˜ θ

^α

[¯ u] =

H

_α⁽²⁾

⁺

h

˜

σ

_β

[¯ u] − σ ˜

_α

[¯ u] − 1 2

ψ ˜

_α

[¯ u] • h

⁽²⁾_αβ

β

i . (2.24)

Proceeding similarly for the third relation (2.10)

_c

provides a relation of the form ψ ˜

^α

[¯ u] =

H

_α⁽³⁾

+

h

θ ˜

_β

[¯ u] − ˜ θ

_α

[¯ u]

β

i

. (2.25)

The relation (2.25) is an identity in the space of symmetric tensor of order 3 and H

α⁽³⁾

again is a suitable least squares matrix. The final linear system with unknowns σ ˜

α

[¯ u], ˜ θ

α

[¯ u] and ψ ˜

_α

[¯ u] is of the form



 

 



 

 



σ ˜

α

[¯ u] + ¯ a

⁽³⁾α

. ˜ θ

α

[¯ u] + ¯ b

⁽⁴⁾α

. ψ ˜

_α

[¯ u] = ¯ σ

^LS_α

[¯ u], (a) θ ˜

_α

[¯ u] + a

⁽³⁾α

. ψ ˜

_α

[¯ u] = ˜ θ

^LS_α

[¯ u], (b) ψ ˜

_α

[¯ u] = ˜ ψ

^LS_α

[¯ u] (c).

(2.26)

In (2.26) we have denoted







θ ˜

^LS_α

[¯ u] = H

α⁽²⁾

+

h

( ˜ σ

β

[¯ u] − σ ˜

α

[¯ u])

β

i , ψ ˜

^LS_α

[¯ u] =

H

α⁽³⁾

⁺

h

(˜ θ

β

[¯ u] − θ ˜

α

[¯ u])

β

i .

(2.27)

and where a

⁽³⁾α

is a third tensor depending only on the grid.

Definition 2.1. The system (2.26) is called the Coupled Least Squares system for the cubic reconstruc- tion.

Two methods to solve (2.26) can be considered. First, the Jacobi (or Gauss Seidel) iteration can be used. This approach is presented in [14]. Due to the triangular form of (2.26), a second method consists in using a forward/backward substitution method. The rest of the paper is devoted to the presentation of this second method in the particular case of the one dimensional setting. The interest of the one dimensional case is that it permits to have a better view on the algebra of the algorithm. In particular, it permits to make fully explicit the two steps of the method: first establishing the system (2.26) and second solving it. Note in addition that, even in one dimension, the reconstruction procedure on a nonequispaced grid is by no way straightforward. It clearly deserves an independent study in itself.

Having solved (2.26) in one way or another, the piecewise cubic reconstruction is given by w

α

[¯ u](x) = ¯ u

α

+ ˜ σ

α

[¯ u]•

(x−x

α

)−x

⁽¹⁾_α

+ 1

2 θ ˜

α

[¯ u]•

(x−x

α

)

^⊗2

−x

⁽²⁾_α

+ 1

6

ψ ˜

_α

[¯ u]•

(x−x

α

)

^⊗3

−x

⁽³⁾_α

. (2.28)

Remark 2.2. A full mathematical study in the d− dimensional case, d ≥ 2 is not completed yet. An important (and difficult) issue is to determine the geometric condition on the grid under which the matrices

H

α⁽²⁾

+

and H

α⁽³⁾

+

do exist. In the absence of such a condition so far, we rely on numerical experiments.

2.2. The one-dimensional case

From now on, we consider the one dimensional case. First we give the detail of the agebra in Section 2.3 in the one dimensional case (d = 1). This permits to emphasize the logic of the reconstruction procedure. In this case, the calculations presented in Section 2.1 are much simpler since all tensors reduce to scalars. The neighborhood W

α

is W

α

= V

^(1)α

with

³

V

⁽¹⁾α

= {T

_α−1

, T

α

, T

α+1

}. (2.29)

The vector h

αβ

is defined by (see Fig. 1.1)

h

_αβ

= x

_β

− x

_α

, β = α − 1, α, α + 1. (2.30) In particular, h

αα

= 0. The hypothesis (8.12) on the irregular grid becomes

ch ≤ h

_α

≤ Ch, ch ≤ h

_αβ

≤ Ch, (2.31)

where c and C are positive constants. Let u(x) be a given regular function. The values u

α

, σ

α

, θ

α

and ψ

α

are (see (2.3)):

u

_α

= u(x

_α

), σ

_α

= u

⁰

(x

_α

), θ

_α

= u

⁰⁰

(x

_α

), ψ

_α

= u

⁰⁰⁰

(x

_α

). (2.32) The Taylor expansion of u(x) at x

α

is:

u(x) = u

α

+ σ

α

(x − x

α

) + 1

2 θ

α

(x − x

α

)

²

+ 1

6 ψ

α

(x − x

α

)

³

+ O(h

⁴

). (2.33) The average u ¯

α

of u(x) over T

α

satisfies:

¯

u

_α

= u

_α

+ |T

α

|

²

24 θ

_α

+ O(h

⁴

), (2.34)

Inserting u ¯

α

in (2.33) in replacement of u

α

leads to expanding u(x) in the form:

u(x) = ¯ u

α

+ σ

α

(x − x

α

) + θ

α

1

2 (x − x

α

)

²

− 1 24 |T

α

|

²

! + 1

6 ψ

α

(x − x

α

)

³

+ O(h

⁴

). (2.35) This is why we look for an approximant w

α

[¯ u](x) of u(x) of the form:

w

_α

[¯ u](x) = ¯ u

_α

+ ˜ σ

_α

(x − x

_α

) + ˜ θ

_α

1

2 (x − x

_α

)

²

− 1 24 |T

_α

|

²

+ 1

6

ψ ˜

_α

(x − x

_α

)

³

. (2.36) where σ ˜

α

, θ ˜

α

and ψ ˜

α

are suitable approximations to σ

α

, θ

α

and ψ

α

, respectively. Note that (2.36) is the one dimensional form of the multidimensional reconstruction (2.28). Defining the reconstruction w

_α

[¯ u](x) is therefore equivalent to define ˜ σ

_α

, θ ˜

_α

and ψ ˜

_α

and this is the topic of the rest of the paper.

In order to define a fourth order VF scheme, the reconstruction (2.36) is finally inserted in the flux function as indicated in (1.4).

3We could as well consider a priori the five points dependence stencilWα={Tα−2,Tα−1,T_α,T_α+1,T_α+2}

2.3. Coupled Least Squares at the continuous level

In this section, we show how to calculate ˜ σ

α

, θ ˜

α

and ψ ˜

α

appearing in (2.36). As already mentionned, data are located in the compact stencil W

α

= V

^(1)α

only. Let us start by approximating the derivative σ

_α

by a linear relation of the form

σ

_α

' X

β∈Wα

c

_αβ

(¯ u

_β

− u ¯

_α

). (2.37)

with coefficients c

αβ

to be defined. The consistency relation is X

β∈Wα

c

_αβ

h

_αβ

= 1. (2.38)

The relation (2.38) express the property that the slope σ

0

of a linear function u(x) = u

0

+ σ

0

x is left invariant by the slope operator (2.37). Our privilegiate choice for c

_αβ

in (2.38) is the least squares slope defined by

¯

σ

_α^LS

= X

β∈Wα

c

^LS_αβ

(¯ u

_β

− u ¯

_α

), (2.39)

where c

^LS_αβ

is (see (2.17)),

c

^LS_αβ

= h

αβ

/ X

β∈Wα

h

²_αβ

. (2.40)

The least squares slope is consistent since it satisfies (2.38). Henceforth we write c

_αβ

instead of c

^LS_αβ

. Note that

c

_αβ

= O(1/h). (2.41)

In the sequel, we also need the least squares values σ

_α^LS

, θ

^LS_α

and ψ

_α^LS

defined by the relations:



 

 

 



 

 

 



σ

_α^LS

= X

β∈Wα

c

_αβ

(u

_β

− u

_α

), (a) θ

_α^LS

= X

β∈Wα

c

αβ

(σ

β

− σ

α

), (b) ψ

_α^LS

= X

β∈Wα

c

αβ

(θ

β

− θ

α

). (c)

(2.42)

Lemma 2.3. The approximate derivatives σ

_α^LS

, θ

^LS_α

, ψ

^LS_α

in (2.42) satisfy the consistency relations



 

 

 

 

σ

_α

+ a

_α

θ

_α

+ b

_α

ψ

_α

= σ

^LS_α

+ O(h

³

), (a) θ

α

+ a

α

ψ

α

= θ

^LS_α

+ O(h

²

), (b)

ψ

α

= ψ

_α^LS

+ O(h), (c)

(2.43)

with



 

 

 

 

σ

_α

= u

⁰

(x

_α

), θ

α

= u

⁰⁰

(x

α

), ψ

α

= u

⁰⁰⁰

(x

α

).

(2.44)

Using the l-momentum H

α^(l)

in T

α

defined by:

H

_α^(l)

= X

β∈Wα

h

^l_αβ

, (2.45)

the coefficients a

α

, b

α

in (2.43) are:

a

_α

= 1 2

H

α⁽³⁾

H

α⁽²⁾

, b

_α

= 1 6

H

α⁽⁴⁾

H

α⁽²⁾

. (2.46)

Furthermore, replacing in (2.43)

a

the pointwise least squares value σ

^LS_α

by σ ¯

^LS_α

, gives

σ

_α

+ ¯ a

_α

θ

_α

+ ¯ b

_α

ψ

_α

= ¯ σ

_α^LS

+ O(h

³

), (2.47) where the modified coefficients ¯ a

α

and ¯ b

α

are

¯

a

_α

= a

_α

+ 1 24

X

β∈Wα

c

_αβ

|T

β

|

²

− |T

α

|

²

and ¯ b

_α

= b

_α

+ 1 24

X

β∈Wα

c

_αβ

|T

β

|

²

h

_αβ

. (2.48)

Proof. For all cells T

_β

∈ W

α

, the Taylor expansion of u(x

_β

) − u(x

_α

) is u

_β

− u

_α

= σ

_α

h

_αβ

+ 1

2 θ

_α

h

²_αβ

+ 1

6 ψ

_α

h

³_αβ

+ O(h

⁴

). (2.49) Therefore the slope σ

^LS_α

in (2.42)

_a

satisfies

σ

_α^LS

= X

β∈Wα

c

αβ

σ

α

h

αβ

+ 1

2 θ

α

h

²_αβ

+ 1 6 ψ

α

h

³_αβ

+ O(h

³

). (2.50)

Using (2.40) and (2.38), the identity (2.50) becomes (2.43)

a

where a

_α

and b

_α

are defined in (2.46). The relations (2.43)

_bc

are obtained in the same way. We now prove (2.47). We deduce from (2.34), (2.42) and (2.49) that (note that |T

_α

|

²

= O(h

²

)),

σ

_α^LS

= X

β∈Wα

c

αβ

(¯ u

β

− u ¯

α

) − 1 24

X

β∈Wα

c

αβ

(|T

β

|

²

θ

β

− |T

α

|θ

²_α

) + O(h

⁴

)

= ¯ σ

^LS_α

− 1 24

X

β∈Wα

c

αβ

|T

β

|

²

(θ

α

+ h

αβ

ψ

α

) − |T

α

|

²

θ

α

+ O(h

⁴

)

= ¯ σ

^LS_α

− θ

α

24 X

β∈Wα

c

αβ

|T

β

|

²

− |T

α

|

²

!

− ψ

α

24 X

β∈Wα

c

_αβ

|T

_β

|

²

h

_αβ

!

+ O(h

⁴

).

Replacing in the preceding relation σ

^LS_α

by (see (2.43)

_a

)

σ

^LS_α

= σ

α

+ a

α

θ

α

+ b

α

ψ

α

+ O(h

³

), (2.51) we deduce that

σ

α

+ θ

α

a

α

+ 1 24

X

β∈Wα

c

αβ

|T

β

|

²

− |T

α

|

²

!

+ ψ

α

b

α

+ 1 24

X

β∈Wα

c

αβ

|T

β

|

²

h

αβ

!

= ¯ σ

^LS_α

+ O(h

³

).

This proves (2.47), with the coefficients ¯ a

α

, ¯ b

α

given in (2.48).

3. A Cubic Reconstruction Procedure

In this section the Coupled Least Squares resolution procedure is detailed. We show how to use Lemma 2.3 to define our cubic reconstruction. Let us start from the relations (2.47) and (2.43)

bc

above relating σ

_α

, θ

_α

, ψ

_α

, σ ¯

^LS_α

, θ

^LS_α

and ψ

^LS_α

. These relations are, (compare (2.26)):



 

 

 

 

σ

α

+ ¯ a

α

θ

α

+ ¯ b

α

ψ

α

= ¯ σ

^LS_α

+ O(h

³

), (a) θ

α

+ a

α

ψ

α

= θ

^LS_α

+ O(h

²

), (b)

ψ

α

= ψ

_α^LS

+ O(h). (c)

(3.1)

Recall that σ

α

, θ

α

and ψ

α

denote the exact derivatives u

⁰

(x

α

), u

⁰⁰

(x

α

) and u

⁰⁰⁰

(x

α

), respectively. How- ever, consider for a moment the system (3.1) as a 3 ×3 linear system with σ

α

, θ

α

and ψ

α

as "unknowns".

A forward-backward “resolution” provides Taylor expansions of σ

α

, θ

α

and ψ

α

in terms of the value σ ¯

_β^LS

. This is expressed in the following

Proposition 3.1. The exact derivatives u

⁰

(x

α

) = σ

α

, u

⁰⁰

(x

α

) = θ

α

, u

⁰⁰⁰

(x

α

) = ψ

α

satisfy the relations



 

 

 



 

 

 



ψ

α

= X

β∈Wα

c

^∗_αβ

X

γ∈V_β

˜

c

βγ

¯ σ

_γ^LS

− σ ¯

_β^LS

− X

δ∈Wα

˜

c

αδ

(¯ σ

_δ^LS

− σ ¯

^LS_α

)

!

+ O(h), (a) θ

α

= −˜ a

α

ψ

α

+ X

β∈V_α

˜

c

αβ

σ ¯

_β^LS

− σ ¯

_α^LS

+ O(h

²

), (b) σ

α

= −¯ a

α

θ

α

− ¯ b

α

ψ

α

+ ¯ σ

_α^LS

+ O(h

³

), (c)

(3.2)

where

• The coefficients ¯ a

α

and ¯ b

α

are given in (2.48),

• The coefficients ˜ a

α

and ˜ c

αβ

are defined by:

˜

a

_α

= a

α

+ P

β∈Wα

c

αβ

(¯ b

β

− ¯ b

α

+ ¯ a

β

h

αβ

) 1 + P

β∈Wα

c

αβ

(¯ a

β

− ¯ a

α

) , (3.3) and

˜

c

αβ

= c

αβ

1 + P

β∈Wα

c

αβ

(¯ a

β

− a ¯

α

) . (3.4)

• The coefficient c

^∗_αβ

is given by

c

^∗_αβ

= c

αβ

1 + P

δ∈Wα

c

_αδ

(˜ a

_δ

− ˜ a

_α

) . (3.5)

Proof. First note that Equation (3.2)

_(c)

is identical to Equation (3.1)

_(a)

and therefore there is nothing to prove. Second, consider the proof of (3.2)

_(b)

. The relation (3.1)

_(b)

is

θ

α

+ a

α

ψ

α

= θ

_α^LS

+ O(h

²

) (3.6)

Replacing in the right-hand side θ

^LS_α

by its value in (2.42)

(b)

gives θ

α

+ a

α

ψ

α

= X

β∈Wα

c

αβ

(σ

β

− σ

α

) + O(h

²

). (3.7)

Consider now the equation (2.47) in all cells T

β

σ

β

+ ¯ a

β

θ

β

+ ¯ b

β

ψ

β

= ¯ σ

_β^LS

+ O(h

³

). (3.8) For all cells T

β

close to T

α

, a Taylor expansion gives

θ

β

= θ

α

+ h

αβ

ψ

α

+ O(h

²

), (a)

ψ

β

= ψ

α

+ O(h). (b) (3.9)

Replacing in (3.8) the values of θ

β

and ψ

β

given in (3.9) yields (note that ¯ a

α

= O(h) and ¯ b

α

= O(h

²

)) σ

β

+ ¯ a

β

(θ

α

+ h

αβ

ψ

α

) + ¯ b

β

ψ

α

= ¯ σ

^LS_β

+ O(h

³

), (3.10) or equivalently

σ

β

= ¯ σ

^LS_β

− a ¯

β

θ

α

− (¯ a

β

h

αβ

+ ¯ b

β

)ψ

α

+ O(h

³

). (3.11) Inserting finally (3.11) in (3.8) gives

θ

α

+ a

α

ψ

α

= X

β∈Wα

c

αβ

σ ¯

_α^LS

− ¯ a

β

θ

α

− (¯ a

β

h

αβ

+ ¯ b

β

)ψ

α

− σ

α

!

+ O(h

²

)

= X

β∈Wα

c

αβ

σ ¯

_β^LS

− ¯ a

β

θ

α

− (¯ a

β

h

αβ

+ ¯ b

β

)ψ

α

− σ ¯

^LS_α

+ ¯ a

α

θ

α

+ ¯ b

α

ψ

α

!

+ O(h

²

).

Collecting the terms in θ

α

and ψ

α

in the left-hand-side gives

1 + X

c

αβ

(¯ a

β

− a ¯

α

)

θ

α

+

a

α

+ X

β

c

αβ

(¯ b

β

− ¯ b

α

+ ¯ a

β

h

αβ

)

ψ

α

= X

β∈Wα

c

αβ

¯

σ

^LS_β

− σ ¯

^LS_α

+ O(h

²

).

Dividing each side by the coefficient

1 + X

β∈Wα

c

_αβ

(¯ a

_β

− ¯ a

_α

), (3.12)

this is rewritten as

θ

α

+ ˜ a

α

ψ

α

= X

β∈Wα

˜ c

αβ

¯

σ

_β^LS

− σ ¯

_α^LS

+ O(h

²

), (3.13)

where ˜ a

α

, ˜ c

αβ

are given in (3.4). Therefore (3.2)

_(b)

is proved and we prove (3.2)

_(c)

in a similar way.

Using (3.13 ) at x

β

and the relation ψ

β

= ψ

α

+ O(h) gives the following expression for θ

β

θ

_β

= −˜ a

_β

ψ

_α

+ X

γ∈Wβ

˜ c

_βγ

¯

σ

^LS_γ

− σ ¯

^LS_β

+ O(h

²

). (3.14)

The identity (2.42)

_(c)

is expressed using (3.1), (3.14) and (3.9)

_(b)

as ψ

α

= X

β∈Wα

c

αβ

(θ

β

− θ

α

)

= X

β∈Wα

c

αβ

− ˜ a

β

ψ

β

+ X

γ∈Wβ

˜

c

βγ

σ ¯

^LS_γ

− σ ¯

_β^LS

− θ

α

+ O(h)

= X

β∈Wα

c

αβ

˜

a

α

ψ

α

− ˜ a

β

ψ

β

+ X

γ∈Wβ

˜

c

βγ

σ ¯

_γ^LS

− σ ¯

^LS_β

− X

δ∈Wα

˜

c

αδ

(¯ σ

_δ^LS

− σ ¯

^LS_α

)

+ O(h)

= ψ

α

− X

β∈Wα

c

αβ

(˜ a

β

− ˜ a

α

)

!

+ X

β∈Wα

c

_αβ

X

γ∈Wβ

˜

c

_βγ

σ ¯

_γ^LS

− σ

^LS_β

− X

δ∈Wα

˜

c

_αδ

(¯ σ

^LS_δ

− σ ¯

^LS_α

)

+ O(h).

Collecting in the left hand side all the terms in ψ

α

gives:

1 + X

δ∈Wα

c

αδ

(˜ a

δ

− ˜ a

α

)

ψ

α

= X

β∈Wα

c

αβ

X

γ∈Wβ

˜

c

βγ

σ ¯

^LS_γ

− σ ¯

_β^LS

− X

δ∈Wα

˜

c

_αδ

(¯ σ

_δ^LS

− σ ¯

^LS_α

)

!

+ O(h).

Dividing by the coefficient

1 + P

δ

c

αδ

(˜ a

δ

− a ˜

α

)

yields (3.2)

(a)

where c

^∗_αβ

is given in (3.5) and the

proof is complete.

Finally we define the quantities ψ ˜

α

, θ ˜

α

and σ ˜

α

by droping in the relations (3.2) the O(h

^p

) terms. This suggests the following

Corollary 3.2. Let us define the values ψ ˜

α

, θ ˜

α

and σ ˜

α

by



 

 

 

 

 

 

ψ ˜

α

= P

β∈Wα

c

^∗_αβ

P

γ∈Wβ

˜

c

βγ

σ ¯

_γ^LS

− σ ¯

_β^LS

− P

δ∈Vα

˜

c

αδ

(¯ σ

^LS_δ

− σ ¯

^LS_α

)

! , (a) θ ˜

α

= −˜ a

α

ψ ˜

α

+ P

β∈Wα

˜

c

αβ

(¯ σ

_β^LS

− σ ¯

^LS_α

), (b)

˜

σ

_α

= −¯ a

_α

θ ˜

_α

− ¯ b

_α

ψ ˜

_α

+ ¯ σ

^LS_α

. (c)

(3.15)

These three values are approximations to ψ

α

, θ

α

and σ

α

respectively with the following accuracy:



 

 

 

 

ψ ˜

_α

= ψ

_α

+ O(h), (a) θ ˜

α

= θ

α

+ O(h

²

), (b)

˜

σ

α

= σ

α

+ O(h

³

). (c)

(3.16)

Proof. The consistency relations (3.16) result directly from comparing (3.15) and (3.2) and from the

fact that a

α

= O(h), a ˜

α

= O(h), ¯ a

α

= O(h) and ¯ b

α

= O(h

²

).

The outcome of the preceding analysis is the following algorithm to calculate ψ ˜

α

, θ ˜

α

and ˜ σ

α

. Suppose given a function u(x) with averages in the cells T

α

given by ¯ u = [¯ u

1

, u ¯

2

, . . . , u ¯

N

]. The relations (3.15) translate in the following computational procedure:

Algorithm 1. 1. Using (2.39), compute σ ¯

^LS_α

in terms of the averages u ¯

_α

for all α.

2. Compute P

β∈Wα

˜

c

_αβ

(¯ σ

_β^LS

− σ ¯

_α^LS

) with c ˜

_αβ

given in (3.4), for all α.

3. Using (3.15)

_(a)

, compute the approximate third order derivative ψ ˜

_α

for all α.

4. Using (3.15)

_(b)

, compute the second order derivative θ ˜

α

for all α.

5. Using (3.15)

(c)

, compute the first order derivative σ ˜

α

for all α.

Our cubic reconstruction has finally the following form:

Proposition 3.3. Let ¯ u = [¯ u

1

, u ¯

2

, . . . , u ¯

N

] be the vector of the averages of a given function u(x) over the cells T

α

of the grid. Then the cubic reconstruction polynomial defined by (2.36) is a fourth order accurate reconstruction of u(x) near T

α

.

Proof. This is a simple consequence of (2.35).

Remark 3.4. The difficulty mentioned in Remark 2.2 does not exist here, since the system (2.43) (droping the O(h

^p

) terms) consists of 3 equations with 3 unknowns σ ˜

_α

, θ ˜

_α

and ψ ˜

_α

. Note however that the logic of the method is the same in the one-dimensional and the multidimensional cases.

Remark 3.5. The approximations σ ˜

α

, θ ˜

α

and ψ ˜

α

depend by construction on data located in cells T

β

with β ∈ {α − 3, α − 2, α − 1, α, α + 1, α + 2, α + 3}. This dependence stencil of the reconstruction is not the accessed stencil at each step of Algorithm 1, which is the compact stencil W

α

= {T

_α−1

, T

α

, T

α+1

}.

Remark 3.6. The preceding analysis does not apply at boundary cells since the neighborood set V

α

for such cells is not complete. The question of defining a fourth order reconstruction at boundary cells is difficult. It may require one-sided approximations. We do not elaborate further on this point in this paper. In practice, second-order reconstruction is used in boundary cells.

4. A Fourth Order Finite Volume method 4.1. Semi-discrete in space approximation

Let us come back to the conservation law (1.1)

∂

t

u + ∂

x

f (u) = 0. (4.1)

The semi-discrete scheme in (1.2) is dv

_α

(t)

dt = − 1

|T

α

| h

f

α+1/2

(t) − f

_α−1/2

(t) i

, 1 ≤ α ≤ N, (4.2)

where

• The vector V (t) = [v

1

(t), v

2

(t), · · · , v

N

(t)]

^T

∈ R

^N

has components v

α

(t) defined by:

v

_α

(t) ' ¯ u

_α

(t) = 1

|T

α

| Z

T_α

u(x, t)dx, 1 ≤ α ≤ N, (4.3)

and where u(x, t) is the solution of (4.1).

• The reconstruction operator V = [v

1

, v

2

, . . . , v

N

] 7→ w[V ]

α

(x) is given by (2.36).

• The numerical flux function is denoted by (u

L

, u

R

) 7→ F (u

L

, u

R

).

Consider the particular case of the linear convection equation

∂

t

u + c∂

x

u = 0, c > 0, x ∈ [0, 1], (4.4) with periodic boundary conditions. In this case, the function t 7→ u ¯

_α

(t) satisfies the equation:

d¯ u

_α

(t) dt = − c

|T

α

|

u(x

_α,α+1

, t) − u(x

_α−1,α

, t)

, (4.5)

In this case, the semi-discrete scheme (4.2) is:

dv

_α

dt = − c

|T

α

| w

_α

[V (t)](x

_α,α+1

) − w

_α−1

[V (t)](x

_α−1,α

)

!

. (4.6)

where the polynomial w

α

[V ](x) in (2.36) is calculated by the Algorithm 1 in Section 3. In matrix form, the scheme (4.6) is expressed as the linear dynamical system:

d

dt V (t) = JV (t), J ∈ M

N

( R ), (4.7) with V (t) = [v

1

(t), v

2

(t) · · · , v

N

(t)]

^T

∈ R

^N

. The coefficients J

αβ

of J are such that:

N

X

β=1

J

αβ

V

β

= − c

|T

α

| w

α

[V ](x

α,α+1

) − w

α−1

[V ](x

α−1,α

)

!

. (4.8)

Note that a proof of the fourth order accuracy in space of the scheme (4.6) is by no way easy in the case of a general irregular grid. In the case of a regular grid, we have the following

Proposition 4.1. On a regular grid with stepsize h, the scheme (4.6) is fourth order accurate with respect to the equation

d¯ u

α

(t) dt = − c

h

u(x

α,α+1

, t) − u(x

α−1,α

, t)

. (4.9)

Proof. We refer to the Appendix for a proof. Further comments in the particular case of an irregular

grid are also given.

4.2. Statistical analysis for linear stability

An important question concerning the reconstruction (2.36) is whether it provides stability of the linear system (4.7). This is related to the location of the spectrum of the matrix J in the complex plane.

Due to the irregularity of the grid, this spectrum is not analytically known in general, and therefore we must rely on some numerical evaluation. We refer to [15] for the same stability problem in three dimensions and in the case of a piecewise linear reconstruction,

Consider a periodic grid with cells of length |T

α

| = κ

_α

h. The coefficient κ

_α

stands for a measure of the irregularity of the grid. The κ

_α

satisfy P

α

κ

_α

= 1. The spectral abscissa of J is defined by:

Λ = max

1≤k≤N

Re(λ

_k

). (4.10)

Figure 4.1: Left panel: spectral abscissa Λ (4.10) for the scheme (4.6) for a sample of 5000 random irregular grids of size 64. The scale along y is 10

⁻¹³

. Right panel: distribution of the spectral absissa Λ for a sample of 5000 random grids of size 64: a Gaussian distribution around the value 0 is clearly observed. This indicates a stable scheme.

The dynamical system (4.7) is stable when Λ ≤ 0. We adopt here an elementary statistical point of view. The spectral absissa Λ is evaluated using a sample of irregular grids. Each grid of the sample has N cells. The irregularity factor κ

α

, 1 ≤ α ≤ N, is randomly selected so that

κ

α

∼ 1 + Cr

α

> 0, (4.11)

where C ∈ [0, 1) is a constant and r = [r

α

] ∈ [−1, 1]

^N

is a random array of size N. The magnitude of the constant C determines the irregularity of the grid. Testing a large sample of such grids provides a statistical evaluation of the value of the spectral abscissa Λ. Doing so, no instability was observed, to computer accuracy. On Fig. 4.1 (left panel), we display the spectral abscissa as a function of the index k of 5000 randomly selected grids of size N = 64. This was also performed (not shown) using a sample of 1000 grids of size N = 32. In both cases, the irregularity constant in (4.11) was choosen as C = 0.99, which corresponds to highly irregular grids. As can be observed, Λ ' 0 to computer accuracy. On Fig. 4.1 (right panel) the distribution of Λ is represented, which appears to be Gaussian. Furthermore a T-test at the confidence level α = 0.05 with null hypothesis on the mean value Λ = 0 ¯ (vs Λ ¯ 6= 0) provides a non-rejection and a confidence interval of [Λ

min

, Λ

max

] = [−0.0085(−14), 0.14(−14)] (for Λ). ¯ This clearly indicates that the eigenvalues of the matrix J are located in the left complex half-plane and therefore we can infer that that the linear dynamical system (4.6) is stable.

Note that due to the irregularity of the grid, a theoretical localization of Λ seems a difficult problem of spectral analysis.

4.3. Dissipation and dispersion analysis

In the particular case of a regular grid with step-size h, the approximation (4.6) can be analytically derived. The coefficients H

α⁽²⁾

, H

α⁽³⁾

and H

α⁽⁴⁾

in (2.45) are

H

_α⁽²⁾

= 2h

²

, H

_α⁽³⁾

= 0, H

_α⁽⁴⁾

= 2h

⁴

. The coefficients a

α

, a ˜

α

, a ¯

α

, b

α

, ¯ b

α

in (2.46) are

a

α

= ˜ a

α

= ¯ a

α

= 0, b

α

= h

²

6 , ¯ b

α

= 5

24 h

²

.

The coefficients c

α,α+1

, c

_α,α−1

, c

^∗_α,α+1

, c

^∗_α,α−1

in (2.42) and (3.5) are c

α,α+1

= 1

2h , c

_α,α−1

= − 1

2h , c

^∗_α,α+1

= 1

2h , c

^∗_α,α−1

= − 1 2h . The least squares slope is

¯

σ

_α^LS

= u ¯

α+1

− u ¯

_α−1

2h . (4.12)

The values of ψ ˜

_α

, θ ˜

_α

, ψ ˜

_α

in (3.15) are



 

 

 



 

 

 



ψ ˜

_α

=

_(2h)¹₂

σ ¯

^LS_α+2

+ ¯ σ

_α−2^LS

− 2¯ σ

_α^LS

! , θ ˜

α

=

_2h¹

σ ¯

^LS_α+1

− σ ¯

_α−1^LS

! ,

˜

σ

α

=

⁵³₄₈

σ ¯

^LS_α

−

₉₆⁵

σ ¯

_α+2^LS

−

₉₆⁵

¯ σ

_α−2^LS

.

(4.13)

In (4.6), w

α

[V ](x

α,α+1

) is given by:

w

α

[V ](x

α,α+1

) = w

α

[V ](x

α

+ h/2) (4.14)

= ¯ u

α

+ 1

2 h˜ σ

α

+ 1

12 h

²

θ ˜

α

+ 1 48 h

³

ψ ˜

α

. The scheme (4.6) is therefore

dv

α

dt = − c h

"

v

α

(t) + h

2 σ ˜

α

(t) + h

²

12

θ ˜

α

(t) + h

³

48

ψ ˜

α

(t)

(4.15)

−

v

α−1

(t) + h

2 σ ˜

α−1

(t) + h

²

12

θ ˜

α−1

(t) + h

³

48

ψ ˜

α−1

(t)

# .

We now give a dissipation and dispersion analysis of (4.15). Such an analysis is an important indication to interpret the numerical behaviour of the scheme. For each given wavenumber k, −N/1+1 ≤ k ≤ N/2, we consider the periodic initial function u

^k₀

(x):

u

^k₀

(x) = exp(2ikπx). (4.16)

The discrete initial data at x

_α

= αh is

u

^k_0,α

= exp(iαφ

k

) where φ

k

= 2πkh ∈ (−π, π]. (4.17) The solution of the system (4.7) is:

u

^k_α

(t) = exp(2ikπ(x

α

− c ˆ

k

t)), (4.18) where the numerical velocity ˆ c

_k

, which depends on k, is decomposed into real and imaginary parts as

ˆ

c

_k

= ˆ c

_k,R

+ iˆ c

_k,I

. (4.19)

Definition 4.2. The dissipation and the dispersion functions are defined by







φ

_k

∈ [0, π[7→ D

₁

(φ

_k

) =

^ˆck,I_c

, (a) φ

_k

∈ [0, π[7→ D

₂

(φ

_k

) =

^ˆck,R_c

(b).

(4.20)