Computational Fluid Dynamics (CFD) in Cycling

Author: Rahul Jayakumar

In the adrenaline-fueled world of cycle racing, where every millisecond counts, athletes and engineers are constantly seeking that elusive edge to propel them to victory. With the cycling industry worth more than USD 9,000 million in 2022 and growing at nearly 8% annually between 2022 – 28, high performance cycle design is becoming increasingly dependent on Computer Aided Engineering (CAE) tools to seek out the last bit of performance from the equipment.  Computational Fluid Dynamics (CFD) is one such tool which stands out for its pivotal role in optimizing performance. In the quest for speed and aerodynamic efficiency, CFD has become an indispensable ally, revolutionizing the way cyclists and teams approach the sport. 

At its core, cycle racing is a battle against the elements, with air resistance posing a formidable obstacle to overcome. Every movement of the cyclist and every contour of the bike interacts with the surrounding air, creating complex flow patterns that can either enhance speed or impede progress. This is where CFD steps in, offering a sophisticated method for analyzing airflow and designing equipment that moves through the air with minimal resistance. CFD can be used to analyze different aspects of individual and team performance. Optimizing rider positions, bicycle design, drafting and group riding, effect of crosswinds, and equipment optimization (Druenen and Blocken, 2021).  

Aerodynamic drag is one of the dominant forces acting on a cyclist at speeds generally observed in professional cycling. Experimental analysis has been done in the past for aerodynamic analysis related to cycling (Nonweiler,1986 , Ritchie, 2011 etc), and for optimizing accessories such as helmets (Novak et. al., 2019). Today, CFD is being increasingly used for these analysis. By simulating airflow around the cyclist and bike, areas of high drag and turbulence can be identified, enabling them to refine the design and positioning for maximum efficiency (Fig. 1). From the sleek curvature of the frame to the position of the rider’s body, every aspect is meticulously studied and fine-tuned to minimize resistance and maximize speed. Optimization of rider position is another key parameter which can be modelled using CFD.  

In recent years, CFD has also been used by sports bodies such as the UCI (Union Cycliste Internationale) to modify regulations for cycling competitions. In 2023, UCI modified the rule for the minimum distance allowed between the rider and following vehicles during road individual time trials. This was based on the study conducted by Blocken and Toparlar (2015) to analyse the aerodynamic forces generated by the following car on the the cyclist(Fig. 3). 

Drafting is used by cyclists to reduce the aerodynamic drag, where two cyclists ride close behind each other, in tandem (Barry et. al., 2016). The low pressure area behind the lead cyclist benefits the trailing cyclist. Effects of drafting and group riding have also recently been studied using CFD (Blocken et. al 2018). This helps riders position themselves in the right place to reduce aerodynamic drag. 

Moreover, CFD is instrumental in the development of cutting-edge cycling equipment, from helmets to wheels. By subjecting prototypes to virtual wind tunnel testing, manufacturers can refine their designs iteratively, ensuring that every component contributes to overall aerodynamic efficiency. The result is a new generation of gear that not only meets the demands of elite athletes but also trickles down to benefit recreational cyclists seeking enhanced performance. 

By unlocking new frontiers in aerodynamic performance, CFD empowers cyclists to push the limits of speed and endurance, rewriting the record books and inspiring future generations of riders. As technology continues to evolve and boundaries are pushed ever further, one thing remains certain: in the pursuit of victory, every advantage counts, and with CFD in their corner, cyclists have a potent weapon in their arsenal. 

References:  

1. Barry, N., Burton, D., Sheridan, J., Thompson, M., Brown, N.A.T., 2016a. Flow field interactions between two tandem cyclists. Exp. Fluid 57, 1–14.  
2. Bert Blocken, Yasin Toparlar, “A following car influences cyclist drag: CFD simulations and wind tunnel measurements”, Journal of Wind Engineering and Industrial Aerodynamics, Volume 145, 2015, Pages 178-186, ISSN 0167-6105, https://doi.org/10.1016/j.jweia.2015.06.015 . 
3. Bert Blocken, Thijs van Druenen, Yasin Toparlar, Fabio Malizia, Paul Mannion, Thomas Andrianne, Thierry Marchal, Geert-Jan Maas, Jan Diepens, “Aerodynamic drag in cycling pelotons: New insights by CFD simulation and wind tunnel testing”, Journal of Wind Engineering and Industrial Aerodynamics, Volume 179, 2018, Pages 319-337, ISSN 0167-6105, https://doi.org/10.1016/j.jweia.2018.06.011. 
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7. Ritchie, A., 2011. Quest for Speed. Cycle Publishing/Van der Plas Publications. 
8. Thijs van Druenen, Bert Blocken, “CFD simulations of cyclist aerodynamics: Impact of computational parameters”, Journal of Wind Engineering and Industrial Aerodynamics, Volume 249, 2024, 105714, ISSN 0167-6105, https://doi.org/10.1016/j.jweia.2024.105714.