How does FluidSim maintain performance if it is written in Python?

It uses Pythran or Transonic for static compilation and leverages optimized FFT libraries to achieve performance comparable to traditional C++ or Fortran CFD codes.

Can I run these simulations on a high-performance cluster?

Yes, FluidSim supports MPI parallelization and can be deployed on clusters to handle high-resolution simulations that require significant computational resources.

Does it include tools for data visualization?

Yes, it provides built-in tools for plotting physical fields like vorticity and velocity, as well as energy spectra and spatial means directly from Python.

FluidSim CFD Framework

Name: FluidSim CFD Framework
Author: jimmc414

byjimmc414

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324

Data Science & ML

Conducts high-performance computational fluid dynamics simulations using Python-based pseudospectral methods and MPI parallelization.

About

FluidSim is a specialized Claude Code Skill designed for scientific research and engineering, providing a robust Python framework for simulating complex fluid flows. It enables users to solve Navier-Stokes equations, shallow water equations, and stratified flows using efficient pseudospectral methods with FFT support. By bridging the gap between Python's ease of use and the performance of Fortran/C++, this skill allows for seamless parameter configuration, simulation execution, and interactive post-processing for turbulence research, vortex dynamics, and geophysical studies.

Key Features

High-performance execution via Pythran/Transonic and MPI parallelization for HPC clusters
Advanced pseudospectral methods using Fast Fourier Transforms (FFT) for periodic domains
324 GitHub stars
Hierarchical parameter management for granular control over domain resolution and physics
Integrated output analysis for energy spectra, spatial means, and physical field plotting
Supports multiple solvers including 2D/3D Navier-Stokes and shallow water equations

Use Cases

Simulating 2D/3D turbulence to study energy cascades and vortex dynamics
Modeling geophysical flows like oceanic stratified layers or atmospheric shallow water systems
Validating CFD algorithms using Taylor-Green vortex or other analytical flow patterns

About

Key Features

High-performance execution via Pythran/Transonic and MPI parallelization for HPC clusters
Advanced pseudospectral methods using Fast Fourier Transforms (FFT) for periodic domains
324 GitHub stars
Hierarchical parameter management for granular control over domain resolution and physics
Integrated output analysis for energy spectra, spatial means, and physical field plotting
Supports multiple solvers including 2D/3D Navier-Stokes and shallow water equations

Use Cases

Simulating 2D/3D turbulence to study energy cascades and vortex dynamics
Modeling geophysical flows like oceanic stratified layers or atmospheric shallow water systems
Validating CFD algorithms using Taylor-Green vortex or other analytical flow patterns