Quentin Duchayne, a Masters student at the National Institute of Applied Sciences (INSA) in Toulouse, France, spent 12 weeks at UNSW Sydney working with Dr Shane Keating on the fluid dynamics of cycling. As an avid competitive cyclist, Quentin greatly enjoyed the opportunity to learn more about the aerodynamics of cycling, as well as exploring a new culture in Australia. Here, Quentin reflects on his experience and describes some of his research project.
This internship was an opportunity to go abroad in Australia and live an experience. It was very enriching personally speaking. I needed to go to the unknown to get out of my comfort zone. I have also discovered a new culture and a new way of life. In Australia, there is a different way of looking at things compared to France. It is more optimistic, they enjoy more and complain less. They worry less saying all the time “no worries”.
In the first day of my internship, my tutor and I defined the project I had to realize during the next three months. The subject was part of the fluid mechanics area, especially aerodynamics. More precisely, it was about aerodynamics in cycling. The project was based on air flow modelling around cyclists in a basic 2D model in which cyclists are represented by elliptic shapes. The final objective of this work was to model air flow around a cyclist peloton and analyze the different shapes it could take according to different parameters as the direction and the strength of the wind.
Model output showing streamlines flowing around a single rider. Credit: Quentin Duchayne.
It is already known that, at racing speed (about 15m/s or 54km/h), the main resistance force for the cyclists is aerodynamic drag (90% of the total resistance). Most previously studies on cycling aerodynamics did research on the drag of a single cyclist but less deal with drag reduction due to drafting, in which two or more cyclists ride close behind each other to reduce aerodynamic drag.
All the previous studies on drafting agree to say that trailing rider benefits a large drag reduction (up to 30-40%). But some papers also demonstrated that leading rider benefits a drag reduction up to 5% due to the presence of trailing riders behind him. The wake of the leading cyclist interacts with the high-pressure area in front of the trailing cyclist and the low-pressure area behind the leading cyclist is “filled up” by the trailing cyclist giving drag reduction for both riders.
In a cyclist group, or peloton, studies have shown that it is the second-last rider who experiences the largest drag reduction. This is explained by the fact that the second-last rider benefits from both drag reduction due to the presence of the rider in front and the presence of a rider in his wake.
Streamlines around a single rider with a side-wind. Credit: Quentin Duchayne.
While riders well embedded in the peloton have a large drag reduction, all the cyclists in the peloton experience a drag reduction compared to a single cyclist at the same speed. The leading rider has the largest drag (84% to 96% of that of the isolated rider). For riders sufficiently embedded inside the peloton, the aerodynamic drag can decrease strongly, as little as 10% of the drag experience when cycling alone.
To study drag reduction and drafting in cycling pelotons, I studied simple 2D model of the air flow around cyclists with a Dedalus of package for solving partial differential equations in Python. I did several simulations with one cyclist first and then with several riders, both with a head-wind and a side-wind. The drag forces were computed and a large drag reduction was observed for the trailing riders. It is noticed that the cyclist situated behind the leading rider experience a large drag reduction. The leading rider benefits also a little drag reduction (about 5%). We also showed that the riders behind the leading rider benefit both from being in the wake of the cyclist in front and having riders behind him. These results are consistent with the scientific literature.
Movie showing four riders forming a peloton behind the lead rider. Credit: Quentin Duchayne.
In this project, I have put my knowledge of fluid mechanics into practice defining the model. I developed my computing skills. I also had to develop my ability to adapt to work efficiently rapidly. I had to acquire knowledge about the project reading scientific papers and about the Python package. I had to learn new technical notions (learn on the job) like how to do parallel runs and how to use the Dedalus package, the notions of aliasing and simulated annealing.