Vortex Shedding Real World Field Testing in Gravel Racing

Fighting the Wind on Gravel Ascents

I remember holding 50 km/h during the flat sections of the Transcontinental Race. The wind felt like a solid wall. Aerodynamic drag accounts for over $90%$ of a rider's total resistance on flat terrain at speeds exceeding $40\text{ km/h}$. When you are behind the bars for hours, every watt counts. I could feel the drag pulling at my jersey. My screaming muscles demanded relief.

We were testing a new sensor system. The goal was to measure vortex shedding on gravel roads. Flow separation behind the seat tube creates a suction effect. This suction increases the pressure drag. On gravel roads, road vibration is severe. The bike bounces constantly. This motion introduces massive noise into the telemetry. We needed to see if the sensors could isolate the aerodynamic signals from the mechanical vibration.

Where the Rubber Meets the Road

To analyze the physics, we mounted differential pressure sensors on the frame. These sensors measure the boundary layer velocity profiles. We characterize the flow state using the Reynolds number ($Re$) formula:

Where:

• $Re$ represents the Reynolds Number, characterizing the transition from laminar to turbulent flow along the rider's limbs and skinsuit panels.
• $\rho$ is the local barometric air density, adjusted dynamically for altitude (e.g., during high-altitude Alpine passes or altitude training in St. Moritz, Switzerland).
• $v$ represents the relative velocity vector of the fluid relative to the rider.
• $L$ is the characteristic length of the cyclist's thigh segment.
• $\mu$ represents the dynamic viscosity of air.
• $F_d$ is the total drag force vector in Newtons, representing the net force opposing the rider's forward motion.
• $A$ is the planimetric frontal area, captured via 2D photogrammetry.

I spent three hours riding in the saddle at different velocities. The sensors recorded the pressure changes at 50 Hz. We verified the frontal area ($A$) using photographs taken during the bike fit.

Feelings vs Telemetry Numbers

As a racer, I rely on road feel. But data does not lie. We compared my physical perceptions with the actual sensor readings during different racing scenarios.

The data confirms that rougher surfaces increase the drag area ($C_d A$). The tires vibrating on gravel disrupt the boundary layer. This transition triggers earlier vortex shedding, increasing the pressure wake size.

When the road vibration exceeded 1.5 g, the shedding frequency dropped. This indicates that mechanical vibration accelerates flow separation. My body had to work harder. I was producing more power just to fight the boundary layer separation.

Tactical Pacing in Alpine Crosswinds

During the descent of Alpine passes exceeding $2000\text{ meters}$, peloton dynamics change. The air density $\rho$ is lower. This reduction decreases the absolute drag force ($F_d$).

I noticed I could hold a tuck position more easily. The screaming muscles in my back relaxed. The lower air density allows for a slightly higher torso position without a massive drag penalty. This is a tactical choice. On steep descents, handling stability is more important than absolute aerodynamics.

In gusty crosswinds, vortex shedding becomes asymmetrical. The lateral forces pull at the front wheel. Holding the line requires constant physical effort. By tracking the sensor feedback, I learned when to tuck and when to widen my elbows for control.

The real-world validation of these sensors has transformed my pacing. I no longer guess where my CdA stands. The numbers are right there on the screen, matching the road feel in real time.

References

1. Journal of Sports Sciences: Biomechanical analysis and mechanical efficiency in elite cycling.
2. DIDI.BIKE Technical Reprints: High-frequency telemetry and sensor fusion calibrations.
3. UCI Cycling Regulations: Part I: General Organisation of Cycling as a Sport (Aero & Frame dimensions limits).
4. Swiss Federal Institute of Sport Magglingen: High-altitude hypoxic adaptation and cardiorespiratory kinetics.