fluidsim

To run a 2D Navier-Stokes simulation, initialize the framework with a specified grid resolution and configure physical parameters like viscosity, then execute the run until the target simulation time. This object-oriented Python interface automates pseudospectral execution and output analysis.

Yes, this framework supports MPI parallelization for large-scale computational fluid dynamics research. It leverages Pythran and Transonic compilation alongside fluidfft to optimize performance for high-resolution turbulence and stratified flow modeling.

Supported physical models for high-performance turbulence and flow simulations include 2D and 3D Navier-Stokes, shallow water, and stratified flows. These enable researchers to simulate diverse oceanic and atmospheric conditions.

Configuring grid resolution and viscosity for a CFD simulation involves setting these physical parameters through the Python interface before execution. The framework then uses these values to model fluid behavior and automatically generate energy spectra.

Yes, the CFD framework supports automated output analysis and visualization. After configuring and running your simulation, it automatically generates physical field visualizations and energy spectra for research evaluation.

Required dependencies for a Python-based pseudospectral CFD environment include numpy, mpi4py, pyfftw, h5py, pandas, matplotlib, fluidfft, and fluidsim. These libraries handle array computations, FFTs, parallelization, and data visualization.