Comments (3)
Thanks for the feedback! It seems like we might be able to use this code if we ever get around to doing it. In which case I'll talk to you and see how we can proceed.
That said I'm happy to transfer the repo to anyone who's interested in using/developing it as I'm not sure I can continue developing it (at least not by myself).
I definitely would like to take up this challenge at some point, but there's no way I have time for this now, unfortunately. But this seems like very nice work. I've been reading the PR you mentioned and it seems like a nice piece of code :)
from atmosfoolery.jl.
Hey @tomchor!
Yeah apologies about the lack of documentation, this repo is in a dormant state for now. The only documentation might be this Oceananigans.jl PR (including tests): CliMA/Oceananigans.jl#1079
@thabbott implemented the current equation set so he's more qualified than I am to say (and I could be wrong) but Atmosfoolery.jl solves the compressible non-hydrostatic Navier-Stokes equations with either energy or entropy as a prognostic variable (and allowing for multiple moist species).
I beleive his implementation is based on Satoh (2002) [see equations (1)-(3) and add diffusion] and Satoh (2003). Both papers are attached below.
from atmosfoolery.jl.
@tomchor Depending on what you guys are interested in simulating, Atmosfoolery.jl might be an option. Since you're already familiar with Oceananigans.jl, Atmosfoolery.jl should feel familiar as I tried to make sure we can reuse all the abstractions, output writers, advection schemes, etc.
I don't think Atmosfoolery.jl is currently super useful for simulation atmospheric turbulence (or any simulations with severe CFL constraints due to acoustic waves) because it doesn't have an acoustic time stepper yet so you might be limited to taking tiny time steps.
We have conducted some tests (see CliMA/Oceananigans.jl#1079) so I'm somewhat confident that Atmosfoolery.jl produces the right answers but in general it's not very well-tested (certainly not at the same level as Oceananigans.jl).
That said I'm happy to transfer the repo to anyone who's interested in using/developing it as I'm not sure I can continue developing it (at least not by myself).
from atmosfoolery.jl.
Related Issues (20)
- Account for bulk viscosity HOT 1
- Abstraction for velocity fields without extra memory allocations HOT 3
- Adding pressure as a prognostic thermodynamic variable HOT 1
- Override getproperty to access thermodynamic variable
- Stress tensor divergence terms are incomplete?
- Does it make sense to support non-zero bulk viscosities?
- Questions about setting up DYCOMS verification experiment HOT 4
- The name `JULES.jl` may be too generic HOT 8
- Non-atmospheric test case: compressible Kelvin–Helmholtz HOT 2
- Milestones until we can submit a JOSS paper? HOT 2
- Update JULES.jl to work with the latest version of Oceananigans.jl (v0.40.0)
- Need a `RungeKutta3` time stepper abstraction
- Some high-level documentation of the numerical methods
- Use KernelAbstractions.jl HOT 1
- Add higher-order advections schemes, e.g. WENO-5 HOT 1
- Do we really need halos of size 2?
- Momentum forcings have the wrong names
- Abstractions for reference states
- 😱 GPU memory leak for 2D compressible models HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from atmosfoolery.jl.