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GitHub - mtoqeerpk/AeroPython: Classical Aerodynamics of potential flow using Python and Jupyter Notebooks

The AeroPython series of lessons is the core of a university course (Aerodynamics-Hydrodynamics, MAE-6226) by Prof. Lorena A. Barba at the George Washington University. The first version ran in Spring 2014 and these Jupyter Notebooks were prepared for that class, with assistance from Barba-group PhD student Olivier Mesnard. In Spring 2015, we revised and extended the collection, adding student assignments to strengthen the learning experience. The course is also supported by an open learning space in the GW SEAS Open edX platform.

The materials are distributed publicly and openly under a Creative Commons Attribution license, CC-BY 4.0

Archive:

— Barba, Lorena A.; Mesnard, Olivier (2014): AeroPython. figshare. Code. https://doi.org/10.6084/m9.figshare.1004727.v3

status

List of notebooks:

0. Getting Started

Module 1. Building blocks of potential flow

  1. Source & Sink
  2. Source & Sink in a Freestream
  3. Doublet
  4. Assignment: Source distribution on an airfoil

Module 2. Potential vortices and lift

  1. Vortex
  2. Infinite row of vortices
  3. Vortex Lift on a cylinder
  4. Assignment: Joukowski transformation

Module 3. Source-panel method for non-lifting bodies

  1. Method of Images
  2. Source Sheet
  3. Flow over a cylinder with source panels
  4. Source panel method

Module 4. Vortex-source panel method for lifting bodies

  1. Vortex-source panel method
  2. Exercise: Derivation of the vortex-source panel method
  3. Assignment: 2D multi-component airfoil

Dependencies

To use these lessons, you need Python 3, and the standard stack of scientific Python: NumPy, Matplotlib, SciPy. And of course, you need Jupyter—an interactive computational environment that runs on a web browser.

This mini-course is built as a set of Jupyter notebooks containing the written materials and worked-out solutions on Python code. To work with the material, we recommend that you start each lesson with a fresh new notebook, and follow along, typing each line of code (don't copy-and-paste!), and exploring by changing parameters and seeing what happens.

Installing via Anaconda

We highly recommend that you install the Anaconda Python Distribution. It will make your life so much easier. You can download and install Anaconda on Windows, OSX, and Linux.

After installing, to ensure that your packages are up to date, run the following commands in a terminal:

conda update conda
conda update jupyter numpy scipy matplotlib

If you prefer Miniconda (a mini version of Anaconda that saves you disk space), install all the necessary libraries to follow this course by running the following commands in a terminal:

conda update conda
conda install jupyter
conda install numpy scipy matplotlib

Without Anaconda

If you already have Python installed on your machine, you can install Jupyter using pip:

Please also make sure that you have the necessary libraries installed by running

pip install numpy scipy matplotlib

Running the notebook server

Once Jupyter is installed, open up a terminal and then run

This will start up a Jupyter session in your browser!

How to contribute to AeroPython

We accept contributions via pull request. You can also open an issue if you find a bug, or have a suggestion.

Copyright and License

(c) 2017 Lorena A. Barba, Olivier Mesnard. All content is under Creative Commons Attribution CC-BY 4.0, and all code is under BSD-3 clause (previously under MIT, and changed on November 12, 2018).

We are happy if you re-use the content in any way!

License License: CC BY 4.0