# swirl-lm

**Repository Path**: mirrors_google-research/swirl-lm

## Basic Information

- **Project Name**: swirl-lm
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-08-12
- **Last Updated**: 2025-12-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![DOI](https://zenodo.org/badge/445670579.svg)](https://zenodo.org/badge/latestdoi/445670579)
# Swirl-LM: Computational Fluid Dynamics in TensorFlow
*This is not an official Google product*

Swirl-LM is a computational fluid dynamics (CFD) simulation framework that is
accelerated by the Tensor Processing Unit (TPU). It solves the three dimensional
variable-density Navier-Stokes equation using a low-Mach approximation, and the
governing equations are discretized by a finite-difference method on a
collocated structured mesh. It is implemented in TensorFlow.

## Installation

To use Swirl-LM, you will need access to
[TPUs on Google Cloud](https://cloud.google.com/tpu/docs/tpus).
For small simulations, the easiest way to access TPUs is to use Google Colab. To
see a demo, you can open one of
[the example notebooks](swirl_lm/example)
and follow the notebook's instructions.

To run large simulations, you will need to create TPU Nodes or VMs in your
Google Cloud project. See [the
instructions](swirl_lm/example/tgv)
for the stand-alone demo on how to set up TPU Nodes and [the docs about Cloud
TPUs](docs/cloud_tpu.md) to set up TPM VMs.

## Citation

If you extend or use this package in your work (except the components in the
`ext`  subpackage, in which case please reference the information within that
subpackage), please cite the
[paper](https://www.sciencedirect.com/science/article/abs/pii/S0010465522000108)
as

```
@ARTICLE{Wang2022-ln,
  title     = "A {TensorFlow} simulation framework for scientific computing of
               fluid flows on tensor processing units",
  author    = "Wang, Qing and Ihme, Matthias and Chen, Yi-Fan and Anderson,
               John",
  abstract  = "A computational fluid dynamics (CFD) simulation framework for
               fluid-flow prediction is developed on the Tensor Processing Unit
               (TPU) platform. The TPU architecture is featured with
               accelerated dense matrix multiplication, large high bandwidth
               memory, and a fast inter-chip interconnect, making it attractive
               for high-performance scientific computing. The CFD framework
               solves the variable-density Navier-Stokes equation using a
               low-Mach approximation, and the governing equations are
               discretized by a finite-difference method on a collocated
               structured mesh. It uses the graph-based TensorFlow as the
               programming paradigm. The accuracy and performance of this
               framework is studied both numerically and analytically,
               specifically focusing on effects of TPU-native single precision
               floating point arithmetic. The algorithm and implementation are
               validated with canonical 2D and 3D Taylor-Green vortex
               simulations. To demonstrate the capability for simulating
               turbulent flows, simulations are conducted for two
               configurations, namely decaying homogeneous isotropic turbulence
               and a turbulent planar jet. Both simulations show good
               statistical agreement with reference solutions. The performance
               analysis shows a linear weak scaling and a superlinear strong
               scaling up to a full TPU v3 pod with 2048 cores.",
  journal   = "Comput. Phys. Commun.",
  publisher = "North-Holland",
  volume    =  274,
  pages     = "108292",
  month     =  may,
  year      =  2022,
  issn = {0010-4655},
  doi = {https://doi.org/10.1016/j.cpc.2022.108292},
  url = {https://www.sciencedirect.com/science/article/pii/S0010465522000108},
  keywords  = "Tensor processing unit; TensorFlow; Computational fluid
               dynamics; High performance computing"
}

```