I have shown the Github robot a passing interest in CFD-related repos and it now suggests repos I might find interesting. Waterlilly.jl was the latest. It grabbed my attention since I initially misread “WaterLily.jl is a simple and fast fluid simulator…” as “WaterLily.jl is a (very) fast (i.e. realtime) fluid simulator…”. I dabbled in Fast Fluid Dynamics (FFD) a while ago – hope to pick it up again soon.

Waterlily.jl is not an FFD simulator (things take a while to solve). Anyway these different simulation implementations are well beyond my abilities, but I wanted to have a little play with Waterlily.jl. Unfortunately (or fortunately, depending on your perspective) it is written in Julia, a language I had never heard of.

Julia setup

FYI: I got things going eventually with some screwing around (mostly to do with adding dependencies) and the steps below are what I can remember doing! You will need to fill in the gaps.

I am using VS Code on a Win10 machine. You need to install Julia from julialang.org/downloads/, and the VS Code Julia extension and clone the Waterlilly.jl repo.

Like Python, you can set up a local/ project-specific Julia environment, which I did…

From PowerShell/terminal in cloned repo’s root directory:

• type julia to enter julia REPL
• ] will enter the pkg REPL (akin to Python’s pip, I think )
• activate . (including full-stop) activates the local environment. The waterlly.jl repo containers a Project.toml file which describes the environment (eg its dependencies)
• Type instantiate to add waterlily.jl’s dependencies (takes a while)
• To run code in examples/ I just click the run button with example file open in VS Code. THe computer thinks for a bit then starts clicking out iteration info in console as it solves.

Tentative toe-dip into Julia

GIF below shows one of the 2D samples in the repo, moving a paddle through a fluid. It is generated by running TwoD_block.jl in examples/

My version below just modifies the motion to a circular mixing motion. The paddle is also 50% longer, simulation time increased and time-step reduced.

In hindsight starting with Julia via CFD is probably not a great place to start, but more interesting than ‘hello world’….and I am not really ‘doing’ CFD – this is all done by people who know what they are doing.

I found this set of instructions good as a reference & introduction to Julia. Plus some useful tit-bits I worked out after some head-scratching along the way:

1. Arrays are indexed starting with 1, not zero (link), eg x[2] in line 13 above.
2. Array broadcasting (as a syntatic thing), eg .- in line 13
3. If no implicit return in function, the result of the last line is returned (not sure if this is so in other non-typed languages like Python and Javacript), eg the result of √sum(abs2,y)-thk/2 in returned in line 16
4. unicode symbols for varables and functions, eg ϵ and √ in line 6. You access these (in VSCode) using latex-eque syntax, so \delta for δ. Certainly make things more readable.
5. Function names ending with ! modify the parameters assed to them. This is by convention, so more a stylistic thing I think. eg sim_gif! called in line 32 could be called sim_gif but in doing so we would be (wrongly) suggesting that the parameters passed to is un unaltered in the function – the simulation instance returned by block() is altered in sim_gif function.
6. Line ending ;is not required, but can be used, eg line 20

Anyway. All kind of interesting in an alternative-option-to-Python sort of way?