Skip to main content

dolfin_navier_scipy

DOI PyPI version Documentation Status

This python module dns provides an interface between the FEM toolbox FEniCS and SciPy in view of simulation and control of incompressible flows. Basically, FEniCS is used to discretize the incompressible Navier-Stokes equations in space. Then dns makes the discretized operators available in SciPy for use in model reduction, simulation, or control and optimization.

dns also contains a solver for the steady state and time dependent problems.

Quick Start

To get started, create the needed subdirectories and run one of the tests/time_dep_nse_.py files, e.g.

pip install sadptprj_riclyap_adi
cd tests
mkdir data
mkdir results
# export PYTHONPATH="$PYTHONPATH:path/to/repo/"  # add the repo to the path
# pip install dolfinx_navier_scipy                # or install the module using pip
python3 time_dep_nse_expnonl.py

Then, to examine the results, launch

paraview results/vel_TH__timestep.pvd

Test Cases and Examples

A selection:

  • tests/mini_setup.py: a minimal setup for a steady-state simulation
  • tests/steadystate_schaefer-turek_2D-1.py: the 2D steady-state cylinder wake benchmark by Schäfer/Turek
  • tests/steadystate_rotcyl.py: the 2D cylinder wake with a freely rotating cylinder as benchmarked in Richter et al.
  • tests/time_dep_nse_.py: time integration with Picard and Newton linearization
  • tests/time_dep_nse_expnonl.py: time integration with explicit treatment of the nonlinearity
  • tests/time_dep_nse_bcrob.py: time integration of the cylinder wake with boundary controls
  • tests/time_dep_nse_krylov.py: time integration with iterative solves of the state equations via krypy
  • tests/time_dep_nse_double_rotcyl_bcrob.py: rotating double cylinder via Robin boundary conditions

Dependencies

The latter is my home-brew module that includes the submodule lin_alg_utils with routines for solving the saddle point problem as it arises in the (v,p) formulation of the NSE.

Documentation

The (old) documentation of the code goes here.

Installation as Module

pip install dolfinx_navier_scipy

Release files for dolfinx-navier-scipy 0.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for dolfinx-navier-scipy 0.0.1
File Size Uploaded
dolfinx_navier_scipy-0.0.1.tar.gz 83.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for dolfinx-navier-scipy 0.0.1
File Interpreter ABI Platform
dolfinx_navier_scipy-0.0.1-py3-none-any.whl Python 3 none any Details

Total release size: 141.8 kB

Release files / dolfinx_navier_scipy-0.0.1.tar.gz

Download URL dolfinx_navier_scipy-0.0.1.tar.gz
Size 83.3 kB
Tags Source
SHA-256 checksum
How to use checksums
5609f55984896a44ff627df4ea0369ec39d257d0610addc87788af8aa323f08d
BLAKE2b-256 checksum
How to use checksums
fe9570ee20f2fb213fac3805c6fd04641281e6ebef3d78fd8de5834e86277063
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.12.5

Release files / dolfinx_navier_scipy-0.0.1-py3-none-any.whl

Download URL dolfinx_navier_scipy-0.0.1-py3-none-any.whl
Size 58.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
4ff5f162d6144aa13a064e7f7355eebc496a2f9fddcc26815c93cfe48f2d6bca
BLAKE2b-256 checksum
How to use checksums
e9e925e79ef43162447c404f2943319e78f2c316dc0f25ec053986b3f070c352
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.12.5

Release history Release notifications | RSS feed

This release

0.0.1 This release

2 release files

0.0.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page