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πŸš€ A parallelized numerical solver for magnetohydrodynamics (MHD) equations, developed using finite volume methods to simulate 🌌 jets in the interstellar medium. This code is optimized for large-scale astrophysical simulations with ⚑ MPI-based parallelism, making it suitable for high-performance computing environments πŸ–₯️. Perfect for exploring th

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garethcmurphy/Interstellar-Jet-MHD-Simulation

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MPI-MHD-FV

This is a numerical code developed to solve the magnetohydrodynamics equations. It was developed primarily for jets in the interstellar medium.

Status

DOI

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

What things you need to install the software and how to install them

Docker
cmake (minimum 2.8.7)
gcc
ctest
Google Test
hdf5

Installing

A step by step series of examples that tell you have to get a development env running

Say what the step will be

docker build -t mhd .

And repeat

docker run -it mhd /bin/bash 

You can use

python3 ../see.py 

to plot a file

Running the tests

Google Test/ctest platform is used

ctest -VV

Deployment

Add additional notes about how to deploy this on a live system

Built With

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

  • Hat tip to anyone who's code was used
  • Inspiration
  • etc

About

πŸš€ A parallelized numerical solver for magnetohydrodynamics (MHD) equations, developed using finite volume methods to simulate 🌌 jets in the interstellar medium. This code is optimized for large-scale astrophysical simulations with ⚑ MPI-based parallelism, making it suitable for high-performance computing environments πŸ–₯️. Perfect for exploring th

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