# Tilted bottom boundary layer example

This example simulates a two-dimensional oceanic bottom boundary layer in a domain that's tilted with respect to gravity. We simulate the perturbation away from a constant along-slope (y-direction) velocity constant density stratification. This perturbation develops into a turbulent bottom boundary layer due to momentum loss at the bottom boundary modeled with a quadratic drag law.

This example illustrates

• changing the direction of gravitational acceleration in the buoyancy model;
• changing the axis of rotation for Coriolis forces.

## Install dependencies

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

using Pkg
pkg"add Oceananigans, NCDatasets, CairoMakie"

## The domain

We create a grid with finer resolution near the bottom,

using Oceananigans
using Oceananigans.Units

Lx = 200meters
Lz = 100meters
Nx = 64
Nz = 64

# Creates a grid with near-constant spacing refinement * Lz / Nz
# near the bottom:
refinement = 1.8 # controls spacing near surface (higher means finer spaced)
stretching = 10  # controls rate of stretching at bottom

# "Warped" height coordinate
h(k) = (Nz + 1 - k) / Nz

# Linear near-surface generator
ζ(k) = 1 + (h(k) - 1) / refinement

# Bottom-intensified stretching function
Σ(k) = (1 - exp(-stretching * h(k))) / (1 - exp(-stretching))

# Generating function
z_faces(k) = - Lz * (ζ(k) * Σ(k) - 1)

grid = RectilinearGrid(topology = (Periodic, Flat, Bounded),
size = (Nx, Nz),
x = (0, Lx),
z = z_faces)
64×1×64 RectilinearGrid{Float64, Periodic, Flat, Bounded} on CPU with 3×0×3 halo
├── Periodic x ∈ [0.0, 200.0)  regularly spaced with Δx=3.125
├── Flat y
└── Bounded  z ∈ [-0.0, 100.0] variably spaced with min(Δz)=0.868817, max(Δz)=6.55496

Let's make sure the grid spacing is both finer and near-uniform at the bottom,

using CairoMakie

lines(zspacings(grid, Center()), znodes(grid, Center()),
axis = (ylabel = "Depth (m)",
xlabel = "Vertical spacing (m)"))

scatter!(zspacings(grid, Center()), znodes(grid, Center()))


## Tilting the domain

We use a domain that's tilted with respect to gravity by

θ = 3 # degrees
3

so that $x$ is the along-slope direction, $z$ is the across-slope direction that is perpendicular to the bottom, and the unit vector anti-aligned with gravity is

ĝ = [sind(θ), 0, cosd(θ)]
3-element Vector{Float64}:
0.052335956242943835
0.0
0.9986295347545738

Changing the vertical direction impacts both the gravity_unit_vector for Buoyancy as well as the rotation_axis for Coriolis forces,

buoyancy = Buoyancy(model = BuoyancyTracer(), gravity_unit_vector = -ĝ)
coriolis = ConstantCartesianCoriolis(f = 1e-4, rotation_axis = ĝ)
ConstantCartesianCoriolis{Float64}: fx = 5.23e-06, fy = 0.00e+00, fz = 9.99e-05

where above we used a constant Coriolis parameter $f = 10^{-4} \, \rm{s}^{-1}$. The tilting also affects the kind of density stratified flows we can model. In particular, a constant density stratification in the tilted coordinate system

@inline constant_stratification(x, z, t, p) = p.N² * (x * p.ĝ[1] + z * p.ĝ[3])
constant_stratification (generic function with 1 method)

is not periodic in $x$. Thus we cannot explicitly model a constant stratification on an $x$-periodic grid such as the one used here. Instead, we simulate periodic perturbations away from the constant density stratification by imposing a constant stratification as a BackgroundField,

N² = 1e-5 # s⁻² # background vertical buoyancy gradient
B∞_field = BackgroundField(constant_stratification, parameters=(; ĝ, N² = N²))
BackgroundField{typeof(Main.var"##269".constant_stratification), @NamedTuple{ĝ::Vector{Float64}, N²::Float64}}
├── func: constant_stratification (generic function with 1 method)
└── parameters: (ĝ = [0.052335956242943835, 0.0, 0.9986295347545738], N² = 1.0e-5)

We choose to impose a bottom boundary condition of zero total diffusive buoyancy flux across the seafloor,

$$$∂_z B = ∂_z b + N^{2} \cos{\theta} = 0.$$$

This shows that to impose a no-flux boundary condition on the total buoyancy field $B$, we must apply a boundary condition to the perturbation buoyancy $b$, math ∂_z b = - N^{2} \cos{\theta}.

#

∂z_b_bottom = - N² * cosd(θ)
b_bcs = FieldBoundaryConditions(bottom = negative_background_diffusive_flux)
Oceananigans.FieldBoundaryConditions, with boundary conditions
├── west: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── east: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── south: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── north: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── bottom: GradientBoundaryCondition: -9.9863e-6
├── top: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
└── immersed: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)

## Bottom drag

We impose bottom drag that follows Monin–Obukhov theory. We include the background flow in the drag calculation, which is the only effect the background flow enters the problem,

V∞ = 0.1 # m s⁻¹
z₀ = 0.1 # m (roughness length)
κ = 0.4  # von Karman constant

z₁ = first(znodes(grid, Center())) # Closest grid center to the bottom
cᴰ = (κ / log(z₁ / z₀))^2 # Drag coefficient

@inline drag_u(x, t, u, v, p) = - p.cᴰ * √(u^2 + (v + p.V∞)^2) * u
@inline drag_v(x, t, u, v, p) = - p.cᴰ * √(u^2 + (v + p.V∞)^2) * (v + p.V∞)

drag_bc_u = FluxBoundaryCondition(drag_u, field_dependencies=(:u, :v), parameters=(; cᴰ, V∞))
drag_bc_v = FluxBoundaryCondition(drag_v, field_dependencies=(:u, :v), parameters=(; cᴰ, V∞))

u_bcs = FieldBoundaryConditions(bottom = drag_bc_u)
v_bcs = FieldBoundaryConditions(bottom = drag_bc_v)
Oceananigans.FieldBoundaryConditions, with boundary conditions
├── west: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── east: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── south: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── north: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
├── bottom: FluxBoundaryCondition: ContinuousBoundaryFunction drag_v at (Nothing, Nothing, Nothing)
├── top: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)
└── immersed: DefaultBoundaryCondition (FluxBoundaryCondition: Nothing)

## Create the NonhydrostaticModel

We are now ready to create the model. We create a NonhydrostaticModel with an UpwindBiasedFifthOrder advection scheme, a RungeKutta3 timestepper, and a constant viscosity and diffusivity. Here we use a smallish value of $10^{-4} \, \rm{m}^2\, \rm{s}^{-1}$.

ν = 1e-4
κ = 1e-4
closure = ScalarDiffusivity(ν=ν, κ=κ)

model = NonhydrostaticModel(; grid, buoyancy, coriolis, closure,
timestepper = :RungeKutta3,
tracers = :b,
boundary_conditions = (u=u_bcs, v=v_bcs, b=b_bcs),
background_fields = (; b=B∞_field))
NonhydrostaticModel{CPU, RectilinearGrid}(time = 0 seconds, iteration = 0)
├── grid: 64×1×64 RectilinearGrid{Float64, Periodic, Flat, Bounded} on CPU with 3×0×3 halo
├── timestepper: RungeKutta3TimeStepper
├── advection scheme: Upwind Biased reconstruction order 5
├── tracers: b
├── closure: ScalarDiffusivity{ExplicitTimeDiscretization}(ν=0.0001, κ=(b=0.0001,))
├── buoyancy: BuoyancyTracer with ĝ = (-0.052336, -0.0, -0.99863)
└── coriolis: ConstantCartesianCoriolis{Float64}

Let's introduce a bit of random noise at the bottom of the domain to speed up the onset of turbulence:

noise(x, z) = 1e-3 * randn() * exp(-(10z)^2 / grid.Lz^2)
set!(model, u=noise, w=noise)

## Create and run a simulation

We are now ready to create the simulation. We begin by setting the initial time step conservatively, based on the smallest grid size of our domain and either an advective or diffusive time scaling, depending on which is shorter.

Δt₀ = 0.5 * minimum([minimum_zspacing(grid) / V∞, minimum_zspacing(grid)^2/κ])
simulation = Simulation(model, Δt = Δt₀, stop_time = 1day)
Simulation of NonhydrostaticModel{CPU, RectilinearGrid}(time = 0 seconds, iteration = 0)
├── Next time step: 4.344 seconds
├── Elapsed wall time: 0 seconds
├── Wall time per iteration: NaN days
├── Stop time: 1 day
├── 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

We use a TimeStepWizard to adapt our time-step,

wizard = TimeStepWizard(max_change=1.1, cfl=0.7)
simulation.callbacks[:wizard] = Callback(wizard, IterationInterval(4))
Callback of TimeStepWizard(cfl=0.7, max_Δt=Inf, min_Δt=0.0) on IterationInterval(4)

and also we add another callback to print a progress message,

using Printf

start_time = time_ns() # so we can print the total elapsed wall time

progress_message(sim) =
@printf("Iteration: %04d, time: %s, Δt: %s, max|w|: %.1e m s⁻¹, wall time: %s\n",
iteration(sim), prettytime(time(sim)),
prettytime(sim.Δt), maximum(abs, sim.model.velocities.w),
prettytime((time_ns() - start_time) * 1e-9))

simulation.callbacks[:progress] = Callback(progress_message, IterationInterval(200))
Callback of progress_message on IterationInterval(200)

## Add outputs to the simulation

We add outputs to our model using the NetCDFOutputWriter,

u, v, w = model.velocities
b = model.tracers.b
B∞ = model.background_fields.tracers.b

B = b + B∞
V = v + V∞
ωy = ∂z(u) - ∂x(w)

outputs = (; u, V, w, B, ωy)

simulation.output_writers[:fields] = NetCDFOutputWriter(model, outputs;
filename = joinpath(@__DIR__, "tilted_bottom_boundary_layer.nc"),
schedule = TimeInterval(20minutes),
overwrite_existing = true)
NetCDFOutputWriter scheduled on TimeInterval(20 minutes):
├── filepath: /var/lib/buildkite-agent/builds/tartarus-10/clima/oceananigans/docs/src/literated/tilted_bottom_boundary_layer.nc
├── dimensions: zC(64), zF(65), xC(64), yF(1), xF(64), yC(1), time(0)
├── 5 outputs: (B, w, ωy, V, u)
└── array type: Array{Float64}
├── file_splitting: NoFileSplitting
└── file size: 19.2 KiB

Now we just run it!

run!(simulation)
[ Info: Initializing simulation...
Iteration: 0000, time: 0 seconds, Δt: 4.778 seconds, max|w|: 1.1e-03 m s⁻¹, wall time: 14.977 seconds
[ Info:     ... simulation initialization complete (9.956 seconds)
[ Info: Executing initial time step...
[ Info:     ... initial time step complete (5.197 seconds).
Iteration: 0200, time: 3.546 hours, Δt: 1.425 minutes, max|w|: 5.3e-03 m s⁻¹, wall time: 24.076 seconds
Iteration: 0400, time: 8.863 hours, Δt: 1.365 minutes, max|w|: 6.9e-03 m s⁻¹, wall time: 26.132 seconds
Iteration: 0600, time: 13.235 hours, Δt: 1.190 minutes, max|w|: 5.7e-03 m s⁻¹, wall time: 28.135 seconds
Iteration: 0800, time: 16.987 hours, Δt: 1.361 minutes, max|w|: 6.1e-03 m s⁻¹, wall time: 30.123 seconds
Iteration: 1000, time: 20.821 hours, Δt: 1.411 minutes, max|w|: 6.0e-03 m s⁻¹, wall time: 32.074 seconds
[ Info: Simulation is stopping after running for 26.734 seconds.
[ Info: Simulation time 1 day equals or exceeds stop time 1 day.


## Visualize the results

First we load the required package to load NetCDF output files and define the coordinates for plotting using existing objects:

using NCDatasets, CairoMakie

xb, yb, zb = nodes(B)
xω, yω, zω = nodes(ωy)
xv, yv, zv = nodes(V)
([1.5625, 4.6875, 7.8125, 10.9375, 14.0625, 17.1875, 20.3125, 23.4375, 26.5625, 29.6875, 32.8125, 35.9375, 39.0625, 42.1875, 45.3125, 48.4375, 51.5625, 54.6875, 57.8125, 60.9375, 64.0625, 67.1875, 70.3125, 73.4375, 76.5625, 79.6875, 82.8125, 85.9375, 89.0625, 92.1875, 95.3125, 98.4375, 101.5625, 104.6875, 107.8125, 110.9375, 114.0625, 117.1875, 120.3125, 123.4375, 126.5625, 129.6875, 132.8125, 135.9375, 139.0625, 142.1875, 145.3125, 148.4375, 151.5625, 154.6875, 157.8125, 160.9375, 164.0625, 167.1875, 170.3125, 173.4375, 176.5625, 179.6875, 182.8125, 185.9375, 189.0625, 192.1875, 195.3125, 198.4375], nothing, [0.4344083608847693, 1.303282217470314, 2.1722793473188617, 3.041419168936904, 3.9107241431416084, 4.780220246692868, 5.649937519111903, 6.519910693894487, 7.390179927024476, 8.26079163764491, 9.131799477986252, 10.00326545222724, 10.875261206921866, 11.747869519021842, 12.621186011421164, 13.495321130420056, 14.370402424632783, 15.24657717074116, 16.124015398230675, 17.00291337296054, 17.88349760825156, 18.766029482284154, 19.650810552161538, 20.538188668213625, 21.428565007224726, 22.322402160523495, 23.22023343257072, 24.122673528152244, 25.030430831905008, 25.944321513097137, 26.865285721823064, 27.794406180595843, 28.732929518320184, 29.682290742481506, 30.64414130083751, 31.62038124678409, 32.61319609381579, 33.62509902514256, 34.65897921569778, 35.71815712673464, 36.80644774933731, 37.92823290397964, 39.08854385039015, 40.29315562720595, 41.548694726134066, 42.86276191262984, 44.24407223660885, 45.702614534695954, 47.24983301231516, 48.89883381190526, 50.66461982505285, 52.56435739360966, 54.61767896995204, 56.84702627115933, 59.27803896724765, 61.93999449018695, 64.86630513774502, 68.09507927238374, 71.66975407682737, 75.63980801853194, 80.06156188607441, 84.99907797852119, 90.52516773625328, 96.72251877439135])

Read in the simulation's output_writer for the two-dimensional fields and then create an animation showing the $y$-component of vorticity.

ds = NCDataset(simulation.output_writers[:fields].filepath, "r")

fig = Figure(size = (800, 600))

axis_kwargs = (xlabel = "Across-slope distance (m)",
ylabel = "Slope-normal\ndistance (m)",
limits = ((0, Lx), (0, Lz)),
)

ax_ω = Axis(fig[2, 1]; title = "Along-slope vorticity", axis_kwargs...)
ax_v = Axis(fig[3, 1]; title = "Along-slope velocity (v)", axis_kwargs...)

n = Observable(1)

ωy = @lift ds["ωy"][:, 1, :, $n] B = @lift ds["B"][:, 1, :,$n]
hm_ω = heatmap!(ax_ω, xω, zω, ωy, colorrange = (-0.015, +0.015), colormap = :balance)
Colorbar(fig[2, 2], hm_ω; label = "s⁻¹")
ct_b = contour!(ax_ω, xb, zb, B, levels=-1e-3:0.5e-4:1e-3, color=:black)

V = @lift ds["V"][:, 1, :, $n] V_max = @lift maximum(abs, ds["V"][:, 1, :,$n])

hm_v = heatmap!(ax_v, xv, zv, V, colorrange = (-V∞, +V∞), colormap = :balance)
Colorbar(fig[3, 2], hm_v; label = "m s⁻¹")
ct_b = contour!(ax_v, xb, zb, B, levels=-1e-3:0.5e-4:1e-3, color=:black)

times = collect(ds["time"])
title = @lift "t = " * string(prettytime(times[\$n]))
fig[1, :] = Label(fig, title, fontsize=20, tellwidth=false)

fig

Finally, we record a movie.

frames = 1:length(times)

record(fig, "tilted_bottom_boundary_layer.mp4", frames, framerate=12) do i
n[] = i
end

Don't forget to close the NetCDF file!

close(ds)
closed Dataset