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Uttkarsh99 Oct 1, 2020
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[![DOI](https://jose.theoj.org/papers/10.21105/jose.00021/status.svg)](https://doi.org/10.21105/jose.00021)

**CFD Python**, a.k.a. the **12 steps to Navier-Stokes**, is a practical module for learning the foundations of Computational Fluid Dynamics (CFD) by coding solutions to the basic partial differential equations that describe the physics of fluid flow.
The module was part of a course taught by [Prof. Lorena Barba](http://lorenabarba.com) between 2009 and 2013 in the Mechanical Engineering department at Boston University (Prof. Barba since moved to the George Washington University).
**CFD Python**, a.k.a. the **12 steps to Navier-Stokes**, is a practical module for learning the foundations of Computational Fluid Dynamics (CFD) by coding solutions to the fundamental partial differential equations that describe the physics of the fluid flow.
The module was part of a course taught by Prof. Lorena Barba (https://lorenabarba.com) between 2009 and 2013 in the Mechanical Engineering department at Boston University (Prof. Barba since moved to the George Washington University).

The module assumes only basic programming knowledge (in any language) and some background in partial differential equations and fluid mechanics. The "steps" were inspired by ideas of Dr. Rio Yokota, who was a post-doc in Prof. Barba's lab until 2011, and the lessons were refined by Prof. Barba and her students over several semesters teaching the CFD course.
We wrote this set of Jupyter notebooks in 2013 to teach an intensive two-day course in Mendoza, Argentina.
The module assumes only basic programming knowledge (in any language) and some background in partial differential equations and fluid mechanics. The "steps" were inspired by ideas of Dr. Rio Yokota, who was a post-doc in Prof. Barba's lab until 2011, and the lessons were refined by Prof. Barba and her students over several semesters teaching the CFD course. We wrote this set of Jupyter notebooks in 2013 to conduct an intensive two-day course in Mendoza, Argentina.

Guiding students through these steps (without skipping any!), they learn many valuable lessons. The incremental nature of the exercises means they get a sense of achievement at the end of each assignment, and they feel they are learning with low effort. As they progress, they naturally practice code re-use and they incrementally learn programming and plotting techniques. As they analyze their results, they learn about numerical diffusion, accuracy and convergence.
In about four weeks of a regularly scheduled course, they become moderately proficient programmers and are motivated to start discussing more theoretical matters.
Guiding students through these steps (without skipping any!), they learn many valuable lessons. The incremental nature of the exercises means they get a sense of achievement at the end of each assignment, and they feel they are learning with low effort.

As they progress, they naturally practice code re-use, and they incrementally learn programming and plotting techniques. As they analyze their results, they learn about numerical diffusion, accuracy and convergence. In about four weeks of a regularly scheduled course, they become moderately proficient programmers. They are motivated to start discussing more theoretical matters.

## How to use this module

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