lbmpy

Run fast fluid simulations based on the lattice Boltzmann method in Python on CPUs and GPUs. lbmpy creates highly optimized LB compute kernels in C or CUDA, for a wide variety of different collision operators, including MRT, entropic, and cumulant schemes.

All collision operators can be easily adapted, for example, to integrate turbulence models, custom force terms, or multi-phase models. It even comes with an integrated Chapman Enskog analysis based on sympy!

Common test scenarios can be set up quickly:

from lbmpy.scenarios import create_channel

ch = create_channel(domain_size=(300,100, 100), force=1e-7, method="trt",
                    equilibrium_order=2, compressible=True,
                    relaxation_rates=[1.97, 1.6], optimization={'target': 'gpu'})

To find out more, check out the interactive tutorial notebooks online with binder.

Installation

For local installation use pip:

pip install lbmpy[interactive]

Without [interactive] you get a minimal version with very little dependencies.

All options:

• gpu: use this if a NVIDIA GPU is available and CUDA is installed
• opencl: use this to enable the target opencl (execution using OpenCL)
• alltrafos: pulls in additional dependencies for loop simplification e.g. libisl
• interactive: installs dependencies to work in Jupyter including image I/O, plotting etc.

Options can be combined e.g.

pip install lbmpy[interactive,gpu,doc]

Documentation

Read the docs here and check out the Jupyter notebooks in doc/notebooks.

Authors

Many thanks go to the contributors of lbmpy.

Please cite us

If you use lbmpy in a publication, please cite the following articles:

Overview:

• M. Bauer et al, lbmpy: Automatic code generation for efficient parallel lattice Boltzmann methods. Journal of Computational Science, 2021. https://doi.org/10.1016/j.jocs.2020.101269

Multiphase:

• M. Holzer et al, Highly efficient lattice Boltzmann multiphase simulations of immiscible fluids at high-density ratios on CPUs and GPUs through code generation. The International Journal of High Performance Computing Applications, 2021. https://doi.org/10.1177/10943420211016525