https://github.com/llnl/pyranda

A Python driven, Fortran powered Finite Difference solver for arbitrary hyperbolic PDE systems. This is the mini-app for the Miranda code.
https://github.com/llnl/pyranda

finite-elements fortran proxy-application python solver

Last synced: 3 days ago
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A Python driven, Fortran powered Finite Difference solver for arbitrary hyperbolic PDE systems. This is the mini-app for the Miranda code.

• Host: GitHub
• URL: https://github.com/llnl/pyranda
• Owner: LLNL
• License: other
• Created: 2018-04-25T15:17:58.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2024-12-06T16:28:50.000Z (about 2 months ago)
• Last Synced: 2025-01-22T14:06:54.548Z (11 days ago)
• Topics: finite-elements, fortran, proxy-application, python, solver
• Language: Fortran
• Size: 2.37 MB
• Stars: 64
• Watchers: 11
• Forks: 26
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# pyranda

[![Build Status](https://github.com/LLNL/pyranda/actions/workflows/regression-tests.yaml/badge.svg)](https://github.com/LLNL/pyranda/actions)

A Python driven, Fortran powered Finite Difference solver for arbitrary hyperbolic PDE systems.  This is the mini-app for the Miranda code.

The PDE solver defaults to a 10th order compact finite difference method for spatial derivatives, and a 5-stage, 4th order Runge-Kutta scheme for temporal integration.  Other numerical methods will be added in the future.

  

Pyranda parses (through a simple interpreter) the full definition of a system of PDEs, namely:

  - a domain and discretization (in 1D, 2D or 3D)

  - governing equations written on RHS of time derivatives.

  - initial values for all variables

  - boundary conditions

## Prerequisites

At a minimum, your system will need the following installed to run pyranda. (see install notes for detailed instructions) 

- A fortran compiler with MPI support

- python 2.7, including these packages

  - numpy

  - mpi4py

## Tutorials

A few tutorials are included on the [project wiki page](https://github.com/LLNL/pyranda/wiki) that cover the example below, as well as few others.  A great place to start if you want to discover what types of problems you can solve.

## Example Usage - Solve the 1D advection equation in less than 10 lines of code

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LLNL/pyranda/blob/master/examples/tutorials/notebooks/advection.ipynb)

The one-dimensional advection equation is written as:

![Advection](http://mathurl.com/y7qnvzeg.png)

where phi is a scalar and where c is the advection velocity, assumed to be unity.  We solve this equation 

in 1D, in the x-direction from (0,1) using 100 points and evolve the solution .1 units in time.

### 1 - Import pyranda

`from pyranda import pyrandaSim`

### 2 - Initialize a simulation object on a domain/mesh

`pysim = pyrandaSim('advection',"xdom = (0.0 , 1.0 , 100 )")`

### 3 - Define the equations of motion

`pysim.EOM(" ddt(:phi:)  =  - ddx(:phi:) ")`

### 4 - Initialize variables

`pysim.setIC(":phi: = 1.0 + 0.1 * exp( -(abs(meshx-.5)/.1 )**2 )")`

### 5 - Integrate in time

`dt = .001`  

`time = 0.0`  

`while time < .1:`    

   `time = pysim.rk4(time,dt)`  

### 6 - Plot the solution

`pysim.plot.plot('phi')`



## Cite

Please us the folowing bibtex, when you refer to this project.

```

  @misc{pyrandaCode,

    title  = {Pyranda: A Python driven, Fortran powered Finite Difference solver for arbitrary hyperbolic PDE systems and mini-app for the LLNL Miranda code},

    author = {Olson, Britton},

    url    = https://github.com/LLNL/pyranda},

    year   = {2023}

  }

```