Two dimensional turbulence example

In this example, we initialize a random velocity field and observe its turbulent decay in a two-dimensional domain. This example demonstrates:

How to run a model with no tracers and no buoyancy model.
How to use computed Fields to generate output.

Install dependencies

First let's make sure we have all required packages installed.

using Pkg
pkg"add Oceananigans, CairoMakie"

We instantiate the model with an isotropic diffusivity. We use a grid with 128² points, a fifth-order advection scheme, third-order Runge-Kutta time-stepping, and a small isotropic viscosity. Note that we assign Flat to the z direction.

using Oceananigans

grid = RectilinearGrid(size=(128, 128), extent=(2π, 2π), topology=(Periodic, Periodic, Flat))

model = NonhydrostaticModel(; grid,
                            timestepper = :RungeKutta3,
                            advection = UpwindBiasedFifthOrder(),
                            closure = ScalarDiffusivity(ν=1e-5))

NonhydrostaticModel{CPU, RectilinearGrid}(time = 0 seconds, iteration = 0)
├── grid: 128×128×1 RectilinearGrid{Float64, Periodic, Periodic, Flat} on CPU with 3×3×0 halo
├── timestepper: RungeKutta3TimeStepper
├── advection scheme: Upwind Biased reconstruction order 5
├── tracers: ()
├── closure: ScalarDiffusivity{ExplicitTimeDiscretization}(ν=1.0e-5)
├── buoyancy: Nothing
└── coriolis: Nothing

Random initial conditions

Our initial condition randomizes model.velocities.u and model.velocities.v. We ensure that both have zero mean for aesthetic reasons.

using Statistics

u, v, w = model.velocities

uᵢ = rand(size(u)...)
vᵢ = rand(size(v)...)

uᵢ .-= mean(uᵢ)
vᵢ .-= mean(vᵢ)

set!(model, u=uᵢ, v=vᵢ)

Setting up a simulation

We set-up a simulation that stops at 50 time units, with an initial time-step of 0.1, and with adaptive time-stepping and progress printing.

simulation = Simulation(model, Δt=0.2, stop_time=50)

Simulation of NonhydrostaticModel{CPU, RectilinearGrid}(time = 0 seconds, iteration = 0)
├── Next time step: 200 ms
├── Elapsed wall time: 0 seconds
├── Wall time per iteration: NaN days
├── Stop time: 50 seconds
├── Stop iteration : Inf
├── Wall time limit: Inf
├── Callbacks: OrderedDict with 4 entries:
│   ├── stop_time_exceeded => Callback of stop_time_exceeded on IterationInterval(1)
│   ├── stop_iteration_exceeded => Callback of stop_iteration_exceeded on IterationInterval(1)
│   ├── wall_time_limit_exceeded => Callback of wall_time_limit_exceeded on IterationInterval(1)
│   └── nan_checker => Callback of NaNChecker for u on IterationInterval(100)
├── Output writers: OrderedDict with no entries
└── Diagnostics: OrderedDict with no entries

The TimeStepWizard helps ensure stable time-stepping with a Courant-Freidrichs-Lewy (CFL) number of 0.7.

wizard = TimeStepWizard(cfl=0.7, max_change=1.1, max_Δt=0.5)
simulation.callbacks[:wizard] = Callback(wizard, IterationInterval(10))

Callback of TimeStepWizard(cfl=0.7, max_Δt=0.5, min_Δt=0.0) on IterationInterval(10)

Logging simulation progress

We set up a callback that logs the simulation iteration and time every 100 iterations.

using Printf

function progress_message(sim)
    max_abs_u = maximum(abs, sim.model.velocities.u)
    walltime = prettytime(sim.run_wall_time)

    return @info @sprintf("Iteration: %04d, time: %1.3f, Δt: %.2e, max(|u|) = %.1e, wall time: %s\n",
                          iteration(sim), time(sim), sim.Δt, max_abs_u, walltime)
end

add_callback!(simulation, progress_message, IterationInterval(100))

Output

We set up an output writer for the simulation that saves vorticity and speed every 20 iterations.

Computing vorticity and speed

To make our equations prettier, we unpack u, v, and w from the NamedTuple model.velocities:

u, v, w = model.velocities

NamedTuple with 3 Fields on 128×128×1 RectilinearGrid{Float64, Periodic, Periodic, Flat} on CPU with 3×3×0 halo:
├── u: 128×128×1 Field{Face, Center, Center} on RectilinearGrid on CPU
├── v: 128×128×1 Field{Center, Face, Center} on RectilinearGrid on CPU
└── w: 128×128×1 Field{Center, Center, Face} on RectilinearGrid on CPU

Next we create two Fields that calculate (i) vorticity that measures the rate at which the fluid rotates and is defined as

\[ω = ∂_x v - ∂_y u \, ,\]

ω = ∂x(v) - ∂y(u)

BinaryOperation at (Face, Face, Center)
├── grid: 128×128×1 RectilinearGrid{Float64, Periodic, Periodic, Flat} on CPU with 3×3×0 halo
└── tree: 
    - at (Face, Face, Center)
    ├── ∂xᶠᶠᶜ at (Face, Face, Center) via identity
    │   └── 128×128×1 Field{Center, Face, Center} on RectilinearGrid on CPU
    └── ∂yᶠᶠᶜ at (Face, Face, Center) via identity
        └── 128×128×1 Field{Face, Center, Center} on RectilinearGrid on CPU

We also calculate (ii) the speed of the flow,

\[s = \sqrt{u^2 + v^2} \, .\]

s = sqrt(u^2 + v^2)

UnaryOperation at (Face, Center, Center)
├── grid: 128×128×1 RectilinearGrid{Float64, Periodic, Periodic, Flat} on CPU with 3×3×0 halo
└── tree: 
    sqrt at (Face, Center, Center) via identity
    └── + at (Face, Center, Center)
        ├── ^ at (Face, Center, Center)
        │   ├── 128×128×1 Field{Face, Center, Center} on RectilinearGrid on CPU
        │   └── 2
        └── ^ at (Center, Face, Center)
            ├── 128×128×1 Field{Center, Face, Center} on RectilinearGrid on CPU
            └── 2

We pass these operations to an output writer below to calculate and output them during the simulation.

filename = "two_dimensional_turbulence"

simulation.output_writers[:fields] = JLD2OutputWriter(model, (; ω, s),
                                                      schedule = TimeInterval(0.6),
                                                      filename = filename * ".jld2",
                                                      overwrite_existing = true)

JLD2OutputWriter scheduled on TimeInterval(600 ms):
├── filepath: ./two_dimensional_turbulence.jld2
├── 2 outputs: (ω, s)
├── array type: Array{Float64}
├── including: [:grid, :coriolis, :buoyancy, :closure]
├── file_splitting: NoFileSplitting
└── file size: 27.1 KiB

Running the simulation

Pretty much just

run!(simulation)

[ Info: Initializing simulation...
[ Info: Iteration: 0000, time: 0.000, Δt: 1.00e-01, max(|u|) = 7.6e-01, wall time: 0 seconds
[ Info:     ... simulation initialization complete (7.582 seconds)
[ Info: Executing initial time step...
[ Info:     ... initial time step complete (10.020 seconds).
[ Info: Iteration: 0100, time: 6.444, Δt: 6.40e-02, max(|u|) = 3.1e-01, wall time: 21.449 seconds
[ Info: Iteration: 0200, time: 13.611, Δt: 8.08e-02, max(|u|) = 3.0e-01, wall time: 25.629 seconds
[ Info: Iteration: 0300, time: 20.983, Δt: 7.44e-02, max(|u|) = 3.2e-01, wall time: 29.413 seconds
[ Info: Iteration: 0400, time: 27.876, Δt: 7.16e-02, max(|u|) = 2.8e-01, wall time: 34.050 seconds
[ Info: Iteration: 0500, time: 35.201, Δt: 7.94e-02, max(|u|) = 2.9e-01, wall time: 38.244 seconds
[ Info: Iteration: 0600, time: 42.230, Δt: 7.53e-02, max(|u|) = 2.7e-01, wall time: 42.318 seconds
[ Info: Simulation is stopping after running for 47.051 seconds.
[ Info: Simulation time 50 seconds equals or exceeds stop time 50 seconds.

Visualizing the results

We load the output.

ω_timeseries = FieldTimeSeries(filename * ".jld2", "ω")
s_timeseries = FieldTimeSeries(filename * ".jld2", "s")

times = ω_timeseries.times

85-element Vector{Float64}:
  0.0
  0.6
  1.2
  1.7999999999999998
  2.4
  3.0
  3.6
  4.2
  4.8
  5.3999999999999995
  5.999999999999999
  6.599999999999999
  7.199999999999998
  7.799999999999999
  7.873991326266233
  8.4
  9.0
  9.6
 10.2
 10.799999999999999
 11.399999999999999
 11.999999999999998
 12.599999999999998
 13.199999999999998
 13.799999999999997
 14.399999999999997
 14.999999999999996
 15.599999999999996
 16.199999999999996
 16.799999999999997
 17.4
 18.0
 18.6
 19.200000000000003
 19.800000000000004
 20.400000000000006
 21.000000000000007
 21.60000000000001
 22.20000000000001
 22.80000000000001
 23.400000000000013
 24.000000000000014
 24.60000000000002
 25.2
 25.8
 26.400000000000002
 27.000000000000004
 27.600000000000005
 28.200000000000006
 28.800000000000008
 29.400000000000013
 30.0
 30.6
 31.200000000000003
 31.800000000000004
 32.400000000000006
 33.00000000000001
 33.60000000000001
 34.20000000000002
 34.800000000000004
 35.4
 36.0
 36.6
 37.2
 37.800000000000004
 38.400000000000006
 39.000000000000014
 39.6
 40.2
 40.800000000000004
 41.400000000000006
 42.00000000000001
 42.60000000000001
 43.20000000000001
 43.80000000000001
 44.40000000000002
 45.0
 45.60000000000001
 46.2
 46.800000000000004
 47.40000000000001
 48.0
 48.6
 49.2
 49.800000000000004

Construct the $x, y, z$ grid for plotting purposes,

xω, yω, zω = nodes(ω_timeseries)
xs, ys, zs = nodes(s_timeseries)

and animate the vorticity and fluid speed.

using CairoMakie
set_theme!(Theme(fontsize = 24))

fig = Figure(size = (800, 500))

axis_kwargs = (xlabel = "x",
               ylabel = "y",
               limits = ((0, 2π), (0, 2π)),
               aspect = AxisAspect(1))

ax_ω = Axis(fig[2, 1]; title = "Vorticity", axis_kwargs...)
ax_s = Axis(fig[2, 2]; title = "Speed", axis_kwargs...)

We use Makie's Observable to animate the data. To dive into how Observables work we refer to Makie.jl's Documentation.

n = Observable(1)

Observable(1)

Now let's plot the vorticity and speed.

ω = @lift interior(ω_timeseries[$n], :, :, 1)
s = @lift interior(s_timeseries[$n], :, :, 1)

heatmap!(ax_ω, xω, yω, ω; colormap = :balance, colorrange = (-2, 2))
heatmap!(ax_s, xs, ys, s; colormap = :speed, colorrange = (0, 0.2))

title = @lift "t = " * string(round(times[$n], digits=2))
Label(fig[1, 1:2], title, fontsize=24, tellwidth=false)

fig

Finally, we record a movie.

frames = 1:length(times)

@info "Making a neat animation of vorticity and speed..."

record(fig, filename * ".mp4", frames, framerate=24) do i
    n[] = i
end

[ Info: Making a neat animation of vorticity and speed...

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