# Announcing AeroPython!

**I haven't lectured in two years**. I've of course been teaching, but have stopped using the method known as "the lecture"—delivering a set amount of material (aka, "covering") from the front of the classroom to a group of mostly quiet, note-taking students. Like greater profs before me, I am a converted lecturer.^{1}

It was Spring 2012 when I went full-steam ahead with the flipped classroom idea for my Computational Fluid Dynamics course. I've written before about how this came about, but the impetus resulted from already having done the lecture capture, live, in a previous version of the CFD course. I uploaded the videos from that live lecture capture to YouTube (after minor editing and cutting into segments) where, since then, they have collected nearly 220,000 public views (checked 20 April'14). My challenge that semester was coming up with class activities—but that should be the topic of another post.

## Two years later

Teaching a classical **Aerodynamics** class for the first time at the George Washington University this Spring, I did not have a pre-recorded set of videos to use in the expected "move content to video" mode of the flipped classroom. But I did not want to lecture. What to do?

## Introducing AeroPython!

I decided to develop a set of lessons using IPython Notebooks, to use as the scaffold for interactive class meetings. IPython Notebooks allow me to create media-rich content for the students, embedding executable Python code within it.

One of the reasons that many lecture-based courses fail is that they "cover" way too much material. By some estimates, after exams are past, students retain only about 20% of the content from a course. What's the point of covering so much content?

So I asked myself: what is the one thing that I want students to take from a classical Aerodynamics course? The *one thing*? My answer was the use of potential flow for aerodynamic analysis, via the panel method.

With the help of my bright PhD student, Olivier Mesnard, we set about to create a series of lessons that would begin from the simplest concept in potential flow, and take students in a step-by-step fashion to build their own 2D panel-method code for lifting bodies.

We now announce the public release of the 11 lessons that make

AeroPython! Find them on the public GitHub repository.

Assuming no previous programming knowledge, we started with a "lesson zero" giving the basics of using Python for numerical computing. Each lesson is meant to be possible to complete in one 2.5-hr class meeting (but often students continue working after, if they did not finish in class). The lessons are:

Lesson 0: Python Crash Course

Lesson 1: Source & Sink

Lesson 2: Source & Sink in a Freestream

Lesson 3: Doublet

Lesson 4: Vortex

Lesson 5: Infinite row of vortices (student task)

Lesson 6: Lift on a cylinder

Lesson 7: Method of images

Lesson 8: Source Sheet

Lesson 9: Flow over a cylinder with source panels

Lesson 10: Source panel method

Lesson 11: Source-vortex panel method

## What did we do in class?

The class meets in a computer lab, and each student sits in front of a workstation (a few brought their own laptops, too). To organize class communications, Q&A, and distribute complementary material, we used Piazza. I also surprised the students with a short quiz every couple of classes; I used Socrative for the quizzes.

For the first couple of lessons, students downloaded the IPython Notebook and were guided to work on a separate interactive Python session to execute the code from each lesson. I insisted that they type the code, not copy and paste (sometimes they did, of course, copy-paste; but they quickly learned that this shortcut only got them in trouble).

After that, they got a sales pitch from me about GitHub and they all created an account and gingerly began to sync with the AeroPython repo. By Lesson 5—which is really an assignment—students were maintaining their own GitHub repositories. They had to submit their work by simply posting on Piazza a link to their notebook on GitHub!

Each class, I asked questions and discussed with the students as a group, went around the room asking questions individually while looking at their work, and let the conversations be carried by the misunderstandings, difficulties or curiosity of the students. I never concerned myself with "covering material."

As students have all completed the AeroPython lessons by now, with a month left of the semester, they have started individual class projects. Most will use their version of the panel method to either study a problem they are interested in, or to extend it with a new feature. Example projects include: adding boundary-layer correction, adding an airfoil wake, building a two-airfoil solution, comparing a doublet- with a vortex-panel method.

A few students will work on a project using a well-known open-source potential-flow solver called XFLR. I'm not concerned about them using a "package" because they already know the fundamentals, they know the limits of potential-flow solutions and they use the tool critically.

## What did I learn?

Like in my flipped CFD class two years ago, I witnessed the students in some rather unusual behavior—coming to class *early* and being hard at work by the time I arrived for our class meetings. This got me thinking about *motivation*.

But more surprising even was to see how students engaged with the material on potential flow. This is a rather dreary subject, usually offered to the students as a catalog of fundamental solutions, followed by problems by rote calculations.

The students using AeroPython in class used the tool of *computing* to investigate the subject matter, to make it palpable through the visualizations, and engage with it. I remember a dialogue with one student using Lesson 4 on Vortex Lift. He called me over declaring *"The stagnation points disappeared!" *—I looked at his code and asked for different values of the vortex strength: *"What happens if you decrease it? If you decrease it a bit more?"* Eventually, it became clear: *"Ah! They move away from the cylinder!"* All books show this, all aerodynamics teachers draw it on the board. But I'm confident my student will remember it better from having it discovered it through experimentation. This is the power of learning with computing.

It will take me a while to reflect and process the connections between motivation, engagement and learning with computing. Stay tuned!

### Reference

- Confessions of a Converted Lecturer, Eric Mazur

### Update

The AeroPython lessons are now published!

- Lorena A. Barba and Olivier Mesnard (2019). Aero Python: classical aerodynamics of potential flow using Python.
*Journal of Open Source Education*, 2(15), 45, https://doi.org/10.21105/jose.00045