CaNS-World

CaNS-World / CaNS-EIGEN

Public

A code for fast, massively-parallel direct numerical simulations (DNS) of canonical flows

cfd fluid-dynamics fortran gpu-acceleration high-performance-computing
15
3
100% credibility
Found May 02, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Fortran
AI Summary

CaNS-EIGEN is an advanced simulation tool for modeling incompressible fluid flows in 3D spaces using efficient math solvers on regular or stretched grids.

How It Works

1
🔍 Discover CaNS-EIGEN

You hear about this tool from science papers or experts studying how liquids and gases move.

2
📥 Get the project

Download the ready-to-use files to your computer so you can start simulating flows.

3
⚙️ Plan your flow

Pick a simple flow like water in a pipe or box, and set the space size and time length.

4
🚀 Run the simulation

Launch it on strong computers to calculate every twist and swirl of the fluid in 3D.

5
👀 See the magic

Open the output with easy picture tools to watch colorful maps of speeds and pressures.

🎉 Unlock fluid secrets

Enjoy precise views of real-world flows to fuel your discoveries and experiments.

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AI-Generated Review

What is CaNS-EIGEN?

CaNS-EIGEN is a Fortran codebase for running fast, massively-parallel direct numerical simulations of canonical incompressible flows, like channels, ducts, or lid-driven cavities. It tackles the Poisson equation on 3D Cartesian grids—even non-uniform ones—using a direct solver that mixes FFTs and GEMM operations for speed on HPC setups. Developers get plug-and-play input files to switch flows, with hybrid MPI/OpenMP parallelization and GPU acceleration via OpenACC and cuFFT.

Why is it gaining traction?

It stands out with direct solvers that crush iterative methods for canonical benchmarks, delivering code-fast performance on GPUs and CPUs without custom tweaks. Pencil decompositions via cuDecomp or diezDecomp handle huge grids scalably, and new I/O backends like ADIOS2/HDF5 add compression for post-processing. The GitHub readme and Python utils make visualization painless, hooking sim folks tired of slow, rigid CFD codes.

Who should use this?

Turbulence researchers or CFD engineers simulating canonical flows at high Re on supercomputers, especially those needing non-uniform grids or GPU boosts. It's for teams extending base solvers to complex cases, like adding fictitious domains, without rewriting Poisson/Helmholtz logic from scratch.

Verdict

Grab it if you're in DNS workflows—solid features and recent papers back its speed claims—but with 15 stars and 1.0% credibility score, it's early-stage; test on your cluster first as docs are good but examples are basic. Worth watching for production use.

(198 words)

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