11: Adding a non-conservative equation

If you want to use Trixi.jl for your own research, you might be interested in a new physics model that is not present in Trixi.jl. In this tutorial, we will implement the nonconservative linear advection equation in a periodic domain

\[\left\{ \begin{aligned}&\partial_t u(t,x) + a(x) \partial_x u(t,x) = 0 \\ &u(0,x)=\sin(x) \\ &u(t,-\pi)=u(t,\pi) \end{aligned} \right.\]

where $a(x) = 2 + \cos(x)$. The analytic solution is

\[u(t,x)=-\sin \left(2 \tan ^{-1}\left(\sqrt{3} \tan \left(\frac{\sqrt{3} t}{2}-\tan ^{-1}\left(\frac{1}{\sqrt{3}}\tan \left(\frac{x}{2}\right)\right)\right)\right)\right)\]

In Trixi.jl, such a mathematical model is encoded as a subtype of Trixi.AbstractEquations.

Basic setup

Since there is no native support for variable coefficients, we need to transform the PDE to the following system:

\[\left\{ \begin{aligned}&\partial_t \begin{pmatrix}u(t,x)\\a(t,x) \end{pmatrix} +\begin{pmatrix} a(t,x) \partial_x u(t,x) \\ 0 \end{pmatrix} = 0 \\ &u(0,x)=\sin(x) \\ &a(0,x)=2+\cos(x) \\ &u(t,-\pi)=u(t,\pi) \end{aligned} \right.\]

# Define new physics
using Trixi
using Trixi: AbstractEquations, get_node_vars
import Trixi: varnames, default_analysis_integrals, flux, max_abs_speed_naive,
              have_nonconservative_terms

# Since there is no native support for variable coefficients, we use two
# variables: one for the basic unknown `u` and another one for the coefficient `a`
struct NonconservativeLinearAdvectionEquation <: AbstractEquations{1 #= spatial dimension =#,
                                                                   2 #= two variables (u,a) =#}
end

varnames(::typeof(cons2cons), ::NonconservativeLinearAdvectionEquation) = ("scalar", "advection_velocity")

default_analysis_integrals(::NonconservativeLinearAdvectionEquation) = ()


# The conservative part of the flux is zero
flux(u, orientation, equation::NonconservativeLinearAdvectionEquation) = zero(u)

# Calculate maximum wave speed for local Lax-Friedrichs-type dissipation
function max_abs_speed_naive(u_ll, u_rr, orientation::Integer, ::NonconservativeLinearAdvectionEquation)
    _, advection_velocity_ll = u_ll
    _, advection_velocity_rr = u_rr

    return max(abs(advection_velocity_ll), abs(advection_velocity_rr))
end


# We use nonconservative terms
have_nonconservative_terms(::NonconservativeLinearAdvectionEquation) = Trixi.True()

# This "nonconservative numerical flux" implements the nonconservative terms.
# In general, nonconservative terms can be written in the form
#   g(u) ∂ₓ h(u)
# Thus, a discrete difference approximation of this nonconservative term needs
# - `u mine`:  the value of `u` at the current position (for g(u))
# - `u_other`: the values of `u` in a neighborhood of the current position (for ∂ₓ h(u))
function flux_nonconservative(u_mine, u_other, orientation,
                              equations::NonconservativeLinearAdvectionEquation)
    _, advection_velocity = u_mine
    scalar, _             = u_other

    return SVector(advection_velocity * scalar, zero(scalar))
end

flux_nonconservative (generic function with 1 method)

The implementation of nonconservative terms uses a single "nonconservative flux" function flux_nonconservative. It will basically be applied in a loop of the form

du_m(D, u) = sum(D[m, l] * flux_nonconservative(u[m], u[l], 1, equations)) # orientation 1: x

where D is the derivative matrix and u contains the nodal solution values.

Now, we can run a simple simulation using a DGSEM discretization.

# Create a simulation setup
using Trixi
using OrdinaryDiffEq

equation = NonconservativeLinearAdvectionEquation()

# You can derive the exact solution for this setup using the method of
# characteristics
function initial_condition_sine(x, t, equation::NonconservativeLinearAdvectionEquation)
    x0 = -2 * atan(sqrt(3) * tan(sqrt(3) / 2 * t - atan(tan(x[1] / 2) / sqrt(3))))
    scalar = sin(x0)
    advection_velocity = 2 + cos(x[1])
    SVector(scalar, advection_velocity)
end

# Create a uniform mesh in 1D in the interval [-π, π] with periodic boundaries
mesh = TreeMesh(-Float64(π), Float64(π), # min/max coordinates
                initial_refinement_level=4, n_cells_max=10^4)

# Create a DGSEM solver with polynomials of degree `polydeg`
# Remember to pass a tuple of the form `(conservative_flux, nonconservative_flux)`
# as `surface_flux` and `volume_flux` when working with nonconservative terms
volume_flux  = (flux_central, flux_nonconservative)
surface_flux = (flux_lax_friedrichs, flux_nonconservative)
solver = DGSEM(polydeg=3, surface_flux=surface_flux,
               volume_integral=VolumeIntegralFluxDifferencing(volume_flux))

# Setup the spatial semidiscretization containing all ingredients
semi = SemidiscretizationHyperbolic(mesh, equation, initial_condition_sine, solver)

# Create an ODE problem with given time span
tspan = (0.0, 1.0)
ode = semidiscretize(semi, tspan)

# Set up some standard callbacks summarizing the simulation setup and computing
# errors of the numerical solution
summary_callback = SummaryCallback()
analysis_callback = AnalysisCallback(semi, interval=50)
callbacks = CallbackSet(summary_callback, analysis_callback)

# OrdinaryDiffEq's `solve` method evolves the solution in time and executes
# the passed callbacks
sol = solve(ode, Tsit5(), abstol=1.0e-6, reltol=1.0e-6,
            save_everystep=false, callback=callbacks)

# Print the timer summary
summary_callback()

# Plot the numerical solution at the final time
using Plots: plot
plot(sol)

You see a plot of the final solution.

We can check whether everything fits together by refining the grid and comparing the numerical errors. First, we look at the error using the grid resolution above.

error_1 = analysis_callback(sol).l2 |> first

0.00029609575838976756

Next, we increase the grid resolution by one refinement level and run the simulation again.

mesh = TreeMesh(-Float64(π), Float64(π), # min/max coordinates
                initial_refinement_level=5, n_cells_max=10^4)

semi = SemidiscretizationHyperbolic(mesh, equation, initial_condition_sine, solver)

tspan = (0.0, 1.0)
ode = semidiscretize(semi, tspan);

summary_callback = SummaryCallback()
analysis_callback = AnalysisCallback(semi, interval=50)
callbacks = CallbackSet(summary_callback, analysis_callback);

sol = solve(ode, Tsit5(), abstol=1.0e-6, reltol=1.0e-6,
            save_everystep=false, callback=callbacks);
summary_callback()

error_2 = analysis_callback(sol).l2 |> first

1.8602128505947232e-5

error_1 / error_2

15.917305285527064

As expected, the new error is roughly reduced by a factor of 16, corresponding to an experimental order of convergence of 4 (for polynomials of degree 3).

Summary of the code

Here is the complete code that we used (without the callbacks since these create a lot of unnecessary output in the doctests of this tutorial). In addition, we create the struct inside the new module NonconservativeLinearAdvection. That ensures that we can re-create structs defined therein without having to restart Julia.

Define new physics

module NonconservativeLinearAdvection

using Trixi
using Trixi: AbstractEquations, get_node_vars
import Trixi: varnames, default_analysis_integrals, flux, max_abs_speed_naive,
              have_nonconservative_terms

# Since there is not yet native support for variable coefficients, we use two
# variables: one for the basic unknown `u` and another one for the coefficient `a`
struct NonconservativeLinearAdvectionEquation <: AbstractEquations{1 #= spatial dimension =#,
                                                                   2 #= two variables (u,a) =#}
end

varnames(::typeof(cons2cons), ::NonconservativeLinearAdvectionEquation) = ("scalar", "advection_velocity")

default_analysis_integrals(::NonconservativeLinearAdvectionEquation) = ()


# The conservative part of the flux is zero
flux(u, orientation, equation::NonconservativeLinearAdvectionEquation) = zero(u)

# Calculate maximum wave speed for local Lax-Friedrichs-type dissipation
function max_abs_speed_naive(u_ll, u_rr, orientation::Integer, ::NonconservativeLinearAdvectionEquation)
    _, advection_velocity_ll = u_ll
    _, advection_velocity_rr = u_rr

    return max(abs(advection_velocity_ll), abs(advection_velocity_rr))
end


# We use nonconservative terms
have_nonconservative_terms(::NonconservativeLinearAdvectionEquation) = Trixi.True()

# This "nonconservative numerical flux" implements the nonconservative terms.
# In general, nonconservative terms can be written in the form
#   g(u) ∂ₓ h(u)
# Thus, a discrete difference approximation of this nonconservative term needs
# - `u mine`:  the value of `u` at the current position (for g(u))
# - `u_other`: the values of `u` in a neighborhood of the current position (for ∂ₓ h(u))
function flux_nonconservative(u_mine, u_other, orientation,
                              equations::NonconservativeLinearAdvectionEquation)
    _, advection_velocity = u_mine
    scalar, _            = u_other

    return SVector(advection_velocity * scalar, zero(scalar))
end

end # module



# Create a simulation setup
import .NonconservativeLinearAdvection
using Trixi
using OrdinaryDiffEq

equation = NonconservativeLinearAdvection.NonconservativeLinearAdvectionEquation()

# You can derive the exact solution for this setup using the method of
# characteristics
function initial_condition_sine(x, t, equation::NonconservativeLinearAdvection.NonconservativeLinearAdvectionEquation)
    x0 = -2 * atan(sqrt(3) * tan(sqrt(3) / 2 * t - atan(tan(x[1] / 2) / sqrt(3))))
    scalar = sin(x0)
    advection_velocity = 2 + cos(x[1])
    SVector(scalar, advection_velocity)
end

# Create a uniform mesh in 1D in the interval [-π, π] with periodic boundaries
mesh = TreeMesh(-Float64(π), Float64(π), # min/max coordinates
                initial_refinement_level=4, n_cells_max=10^4)

# Create a DGSEM solver with polynomials of degree `polydeg`
# Remember to pass a tuple of the form `(conservative_flux, nonconservative_flux)`
# as `surface_flux` and `volume_flux` when working with nonconservative terms
volume_flux  = (flux_central, NonconservativeLinearAdvection.flux_nonconservative)
surface_flux = (flux_lax_friedrichs, NonconservativeLinearAdvection.flux_nonconservative)
solver = DGSEM(polydeg=3, surface_flux=surface_flux,
               volume_integral=VolumeIntegralFluxDifferencing(volume_flux))

# Setup the spatial semidiscretization containing all ingredients
semi = SemidiscretizationHyperbolic(mesh, equation, initial_condition_sine, solver)

# Create an ODE problem with given time span
tspan = (0.0, 1.0)
ode = semidiscretize(semi, tspan);

# Set up some standard callbacks summarizing the simulation setup and computing
# errors of the numerical solution
summary_callback = SummaryCallback()
analysis_callback = AnalysisCallback(semi, interval=50)
callbacks = CallbackSet(summary_callback, analysis_callback);

# OrdinaryDiffEq's `solve` method evolves the solution in time and executes
# the passed callbacks
sol = solve(ode, Tsit5(), abstol=1.0e-6, reltol=1.0e-6,
            save_everystep=false);

# Plot the numerical solution at the final time
using Plots: plot
plot(sol);

Package versions

These results were obtained using the following versions.

using InteractiveUtils
versioninfo()

using Pkg
Pkg.status(["Trixi", "OrdinaryDiffEq", "Plots"],
           mode=PKGMODE_MANIFEST)

Julia Version 1.10.3
Commit 0b4590a5507 (2024-04-30 10:59 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 4 × AMD EPYC 7763 64-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)
Environment:
  JULIA_PKG_SERVER_REGISTRY_PREFERENCE = eager
Status `~/work/Trixi.jl/Trixi.jl/docs/Manifest.toml`
⌃ [1dea7af3] OrdinaryDiffEq v6.66.0
  [91a5bcdd] Plots v1.40.4
  [a7f1ee26] Trixi v0.7.10-pre `~/work/Trixi.jl/Trixi.jl`
Info Packages marked with ⌃ have new versions available and may be upgradable.

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