WaterLily.jl is a real-time fluid simulator written in pure Julia. This is an experimental project to take advantage of the active scientific community in Julia to accelerate and enhance fluid simulations. If you want to play around with a much more fully developed and documented solver right now, you should head over to LilyPad.
WaterLily.jl solves the unsteady incompressible 2D or 3D Navier-Stokes equations on a Cartesian grid. The pressure Poisson equation is solved with a geometric multigrid method. Solid boundaries are modelled using the Boundary Data Immersion Method.
The user can set the boundary conditions, the initial velocity field, the fluid viscosity (which determines the Reynolds number), and immerse solid obstacles using a signed distance function. These examples and others are found in the examples.
Flow over a circle
We define the size of the simulation domain as
m cells. The circle has radius
R=m/8 and is centered at
[m/2,m/2]. The flow boundary conditions are
[U=1,0] and Reynolds number is
ν (Greek "nu" U+03BD, not Latin lowercase "v") is the kinematic viscosity of the fluid.
using WaterLily using LinearAlgebra: norm2 function circle(n,m;Re=250) # Set physical parameters U,R,center = 1., m/8., [m/2,m/2] ν=U*R/Re @show R,ν body = AutoBody((x,t)->norm2(x .- center) - R) Simulation((n+2,m+2), [U,0.], R; ν, body) end
The second to last line defines the circle geometry using a signed distance function. The
AutoBody function uses automatic differentiation to infer the other geometric parameter automatically. Replace the circle's distance function with any other, and now you have the flow around something else... such as a donut, a block or the Julia logo. Finally, the last line defines the
Simulation by passing in the
dims=(n+2,m+2) and the other parameters we've defined.
Now we can create a simulation (first line) and run it forward in time (second line)
circ = circle(3*2^6,2^7); sim_step!(circ,t_end=10)
Note we've set
n,m to be multiples of powers of 2, which is important when using the (very fast) Multi-Grid solver. We can now access and plot whatever variables we like. For example, we could print the velocity at
println(sim.flow.u[I]) or plot the whole pressure field using
using Plots contour(sim.flow.p')
A set of flow metric functions have been implemented and the examples use these to make gifs such as the one above.
3D Taylor Green Vortex
You can also simulate a nontrivial initial velocity field by passing in a vector function.
function TGV(p=6,Re=1e5) # Define vortex size, velocity, viscosity L = 2^p; U = 1; ν = U*L/Re function uλ(i,vx) # vector function x,y,z = @. (vx-1.5)*π/L # scaled coordinates i==1 && return -U*sin(x)*cos(y)*cos(z) # u_x i==2 && return U*cos(x)*sin(y)*cos(z) # u_y return 0. # u_z end # Initialize simulation Simulation((L+2,L+2,L+2), zeros(3), L; uλ, ν, U) end
The velocity field is defined by the vector component
i and the 3D position vector
vx. We scale the coordinates so the velocity will be zero on the domain boundaries and then check which component is needed and return the correct expression.
You can simulate moving bodies in Waterlily by passing a coordinate
AutoBody in addition to the
using LinearAlgebra: norm2 using StaticArrays function hover(L=2^5;Re=250,U=1,amp=0,thk=1+√2) # Set viscosity ν=U*L/Re @show L,ν # Create dynamic block geometry function sdf(x,t) y = x .- SVector(0.,clamp(x,-L/2,L/2)) norm2(y)-thk/2 end function map(x,t) α = amp*cos(t*U/L); R = @SMatrix [cos(α) sin(α); -sin(α) cos(α)] R * (x.-SVector(3L+L*sin(t*U/L)+0.01,4L)) end body = AutoBody(sdf,map) Simulation((6L+2,6L+2),zeros(2),L;U,ν,body,ϵ=0.5) end
In this case, the
sdf defines a line segment from
-L/2 ≤ x ≤ L/2 with a thickness
thk. To make the line segment move, we define a coordinate tranformation function
map(x,t). In this example, the coordinate
x is shifted by
(3L,4L) at time
t=0, which moves the center of the segment to this point. However, the horizontal shift varies harmonically in time, sweeping the segment left and right during the simulation. The example also rotates the segment using the rotation matrix
R = [cos(α) sin(α); -sin(α) cos(α)] where the angle
α is also varied harmonically. The combined result is a thin flapping line, similar to a cross-section of a hovering insect wing.
One important thing to note here is the use of
StaticArrays to define the
map. This speeds up the simulation around a factor of 10 compared to using normal arrays since it reduces the number of allocations needed for every point at every time step. Hopefully, we'll get the allocations down to zero soon.
- Immerse obstacles defined by 3D meshes or 2D lines using GeometryBasics.
- GPU acceleration with CUDA.jl.
- Split multigrid method into its own repository, possibly merging with AlgebraicMultigrid or IterativeSolvers.