"The current record for a bicycle travelling across flat road is 133.78 km/h, set in 2012 by a Dutch team at the World Human Powered Speed Challenge, which takes place every year in the Nevada desert."
"This September, a team from IUT Annecy aims to beat that record. The team used artificial-intelligence-based software developed by Neural Concept, an EPFL startup, to boost the performance of its bike. In just a few minutes, Neural Concept''s technology can calculate the optimal shape of a bike to make it as aerodynamic as possible. It can also be used for aerodynamics calculations in a number of other applications. The company is presenting its software in Stockholm today at the International Conference on Machine Learning. From the outside, the IUT Annecy team''s recumbent bike looks more like a tiny racecar than a human-powered bicycle. It was custom-made to fit closely to the cyclist''s body. During the Challenge, he will have to ride down a 200-meter stretch of straight, flat road as fast as possible, after a run-up of 8 km. The design objective clearly isn''t cyclist comfort, but making the most out of every inch of the vehicle. Existing aerodynamic design methods require an enormous amount of computing power. Traditionally bicycle engineers think up different forms and then test them using computer simulation. But here, for the first time, engineers employed optimization software – rather than their own intuition – to define the recumbent bike fairing. The IUT Annecy team used Neural Concept''s software, specifying the bike''s maximum length and width and the space needed for the drivetrain and wheels. The program then sorted through all kinds of shapes, quickly comparing them in order to come up with the best one. For instance, the program helped the engineers determine the best location for the vehicle''s maximum width."
"To develop the technology behind the software, researchers at EPFL''s Computer Vision Laboratory trained a convolutional neural network to calculate the aerodynamic properties of various forms represented by generic polygon meshes, which are collections of points used to generate 3-D shapes. This type of artificial intelligence works by running through several layers, categorizing information from the simplest to the most complex. In the initial layers, the program identifies a shape''s contours; then it assigns the contours to an object and determines what category the object belongs to based on the expected outcome."
"Collections of points used to generate 3-D shapes."
Proof of concept; results not yet available