lbmpy 1.4
Code Generation for Lattice Boltzmann Methods
Requires Python: >=3.10

lbmpy
=====

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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:
```python
from pystencils import Target
from lbmpy.session import *

ch = create_channel(domain_size=(300, 100, 100), force=1e-7, method=Method.TRT,
                    equilibrium_order=2, compressible=True,
                    relaxation_rates=[1.97, 1.6], optimization={'target': Target.GPU})
```

To find out more, check out the interactive [tutorial notebooks online with binder](https://mybinder.org/v2/gh/mabau/lbmpy/master?filepath=doc%2Fnotebooks).


Installation
------------

For local installation use pip:

```bash
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.
```bash
pip install lbmpy[interactive,gpu,doc]
```


Documentation
-------------

Read the docs [here](http://pycodegen.pages.i10git.cs.fau.de/lbmpy) and
check out the Jupyter notebooks in `doc/notebooks`. 

Contributing
-------
To see how to open issues, submit bug reports, create feature requests or submit your additions to lbmpy please refer to
[contribution documentation](https://i10git.cs.fau.de/pycodegen/pystencils/-/blob/master/CONTRIBUTING.md) of pystencils since lbmpy is heavily build on pystencils.


Many thanks go to the [contributors](https://i10git.cs.fau.de/pycodegen/lbmpy/-/blob/master/AUTHORS.txt) of lbmpy.


### Please cite us

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

Overview:
  - F. Hennig et al, Advanced Automatic Code Generation for Multiple Relaxation-Time Lattice Boltzmann Methods. SIAM Journal on Scientific Computing, 2023. https://doi.org/10.1137/22M1531348 ([Preprint](https://arxiv.org/abs/2211.02435))
  - 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 ([Preprint](https://arxiv.org/abs/2001.11806))

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


### Further Reading

- F. Hennig et al, Automatic Code Generation for the Cumulant Lattice Boltzmann Method. ICMMES, 2021. [Poster Link](https://www.researchgate.net/publication/353224406_Automatic_Code_Generation_for_the_Cumulant_Lattice_Boltzmann_Method)