A library of classical summation-by-parts (SBP) operators used in finite difference methods to get provably stable semidiscretisations, paying special attention to boundary conditions.

The following derivative operators are implemented as "lazy operators", i.e. no matrix is formed explicitly.

`periodic_derivative_operator(derivative_order, accuracy_order, xmin, xmax, N)`

These are classical central finite difference operators using `N`

nodes on the
interval `[xmin, xmax]`

.

`periodic_derivative_operator(Holoborodko2008(), derivative_order, accuracy_order, xmin, xmax, N)`

These are central finite difference operators using `N`

nodes on the
interval `[xmin, xmax]`

and the coefficients of Pavel Holoborodko.

`fourier_derivative_operator(xmin, xmax, N)`

Fourier derivative operators are implemented using the fast Fourier transform of FFTW.jl.

`derivative_operator(source_of_coefficients, derivative_order, accuracy_order, xmin, xmax, N)`

Finite difference SBP operators for first and second derivatives can be obained by using `MattssonNordström2004()`

as `source_of_coefficients`

.
Other sources of coefficients are implemented as well. To obtain a full list for all operators, use `subtypes(SourceOfCoefficients)`

.

`legendre_derivative_operator(xmin, xmax, N)`

Use Lobatto Legendre polynomial collocation schemes on `N`

, i.e.
polynomials of degree `N-1`

, implemented via PolynomialBases.jl.

Additionally, some artificial dissipation/viscosity operators are implemented. The most basic usage is `Di = dissipation_operator(D)`

,
where `D`

can be a (periodic, Fourier, Legendre, SBP FD) derivative
operator. Use `?dissipation_operator`

for more details.

Sometimes, it can be convenient to obtain an explicit (sparse, banded) matrix form of the operators. Therefore, some conversion functions are supplied, e.g.

```
julia> using SummationByPartsOperators
julia> D = derivative_operator(MattssonNordström2004(), 1, 2, 0., 1., 5)
SBP 1st derivative operator of order 2 {T=Float64, Parallel=Val{:serial}}
on a grid in [0.0, 1.0] using 5 nodes
and coefficients given in
Mattsson, Nordström (2004)
Summation by parts operators for finite difference approximations of second
derivaties.
Journal of Computational Physics 199, pp.503-540.
julia> Matrix(D)
5×5 Array{Float64,2}:
-4.0 4.0 0.0 0.0 0.0
-2.0 0.0 2.0 0.0 0.0
0.0 -2.0 0.0 2.0 0.0
0.0 0.0 -2.0 0.0 2.0
0.0 0.0 0.0 -4.0 4.0
julia> using SparseArrays
julia> sparse(D)
5×5 SparseMatrixCSC{Float64,Int64} with 10 stored entries:
[1, 1] = -4.0
[2, 1] = -2.0
[1, 2] = 4.0
[3, 2] = -2.0
[2, 3] = 2.0
[4, 3] = -2.0
[3, 4] = 2.0
[5, 4] = -4.0
[4, 5] = 2.0
[5, 5] = 4.0
julia> using BandedMatrices
julia> BandedMatrix(D)
5×5 BandedMatrix{Float64,Array{Float64,2},Base.OneTo{Int64}}:
-4.0 4.0 ⋅ ⋅ ⋅
-2.0 0.0 2.0 ⋅ ⋅
⋅ -2.0 0.0 2.0 ⋅
⋅ ⋅ -2.0 0.0 2.0
⋅ ⋅ ⋅ -4.0 4.0
```

Examples can e found in the directory `notebooks`

. In particular, examples of complete discretisations of
the linear advection equation,
the heat equation,
and the wave equation are supplied.
Further examples are supplied as tests.

12/16/2017

4 months ago

300 commits