Monitoring Aerodynamic Performance Real Time for Cycling

Neil D. Lawrence

A cyclist’s aerodynamic position has a very strong affect on their performance. In professional cycling, extensive wind tunnel testing is used to hone a cyclist’s performance. Such testing is, however, highly expensive. In this project you will use machine learning techniques alongside the physics of cycling to estimate the aerodynamic performance of a cyclist real time whilst on a bicycle. By combining an anemometer, a power meter and an understanding of the rider’s kinetic and gravitational potential energy the power loss due to aerodynamic drag can be estimated. Software for the project will be written according to the principles of open data science.

Note that field experiments will require access to a road bicycle (power loss due to rolling resistance on a mountain bicycle is too large) and some form of GPS device (for preliminary experiments a smart phone is likely sufficient).

This project will suit students with strong analytical (mathematical) skills.