https://github.com/gpavanb1/splitfvm

1D Finite-Volume with AMR and steady-state solver using Newton and Split-Newton
https://github.com/gpavanb1/splitfvm

1d amr cfd finite-volume newton-raphson optimization python

Last synced: about 1 month ago
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1D Finite-Volume with AMR and steady-state solver using Newton and Split-Newton

• Host: GitHub
• URL: https://github.com/gpavanb1/splitfvm
• Owner: gpavanb1
• License: mit
• Created: 2023-01-02T06:08:53.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-01-03T06:16:37.000Z (almost 2 years ago)
• Last Synced: 2023-12-16T15:55:12.575Z (about 1 year ago)
• Topics: 1d, amr, cfd, finite-volume, newton-raphson, optimization, python
• Language: Python
• Homepage:
• Size: 124 KB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# Note

This repository has been archived. Please refer to [SplitFXM](https://github.com/gpavanb1/SplitFXM)

# SplitFVM

[![Downloads](https://pepy.tech/badge/splitfvm)](https://pepy.tech/project/splitfvm)

![img](https://github.com/gpavanb1/SplitFVM/blob/main/assets/logo.jpg)

1D [Finite-Volume](https://en.wikipedia.org/wiki/Finite_volume_method) with [adaptive mesh refinement](https://en.wikipedia.org/wiki/Adaptive_mesh_refinement) and steady-state solver using Newton and [Split-Newton](https://github.com/gpavanb1/SplitNewton) approach

## What does 'split' mean?

The system is divided into two and for ease of communication, let's refer to first set of variables as "outer" and the second as "inner".

* Holding the outer variables fixed, Newton iteration is performed till convergence using the sub-Jacobian

* One Newton step is performed for the outer variables with inner held fixed (using its sub-Jacobian)

* This process is repeated till convergence criterion is met for the full system (same as in Newton)

## How to install and execute?

Just run 

```

pip install splitfvm

```

There is an [examples](https://github.com/gpavanb1/SplitFVM/examples) folder that contains a test model - [Advection-Diffusion](https://en.wikipedia.org/wiki/Convection%E2%80%93diffusion_equation)

You can define your own equations by simply creating a derived class from `Model` and adding to the `_equations` using existing or custom equations!

A basic driver program is as follows

```

# Define the problem

m = AdvectionDiffusion(c=0.2, nu=0.001)

# Define the domain and variables

# ng stands for ghost cell count

d = Domain.from_size(20, 2, ["u", "v", "w"]) # nx, ng, variables

# Set IC and BC

ics = {"u": "gaussian", "v": "rarefaction"}

bcs = {

    "u": {

        "left": "periodic",

        "right": "periodic"

    },

    "v": {

        "left": {"dirichlet": 3},

        "right": {"dirichlet": 4}

    },

    "w": {

        "left": {"dirichlet": 2},

        "right": "periodic"

    }

}

s = Simulation(d, m, ics, bcs)

# Advance in time or to steady state

s.evolve(dt=0.1)

bounds = [[-1., -2., 0.], [5., 4., 3.]]

iter = s.steady_state(split=True, split_loc=1, bounds=bounds)

# Visualize

draw(d, "label")

```

## Whom to contact?

Please direct your queries to [gpavanb1](http://github.com/gpavanb1)

for any questions.

## Acknowledgements

Do visit its [Finite-Difference](https://github.com/gpavanb1/SplitFDM) cousin

Special thanks to [Cantera](https://github.com/Cantera/cantera) and [WENO-Scalar](https://github.com/comp-physics/WENO-scalar) for serving as an inspiration for code architecture