🚴‍♂️ How We Calculate Aerodynamic Drag at RaceYourTrack

(👉 You can find Part 1 on the basics of the Chung method here – How We Calculate Aerodynamic Drag at RaceYourTrack?)

The so-called Chung method, developed in the early 2000s by Robert Chung, makes it possible to determine the aerodynamic drag coefficient (CdA) from real-world power data – without a wind tunnel or lab testing. A detailed description can be found in his article Estimating CdA from Power Data (PDF), licensed under Creative Commons Attribution (CC BY 3.0).

Chung’s approach is both elegant and practical: you ride multiple laps on a steady circuit, measure power and speed, and search for the parameters that bring the energy balance into equilibrium.

This made aerodynamic testing accessible to anyone with a power meter – precise, reproducible, and usable in everyday road riding.

💨 The Idea Behind the Chung Method

Chung’s method offers several key advantages:

  1. Wind compensation – On out-and-back laps, head- and tailwind largely cancel out.
  2. Robust to calibration errors – Absolute elevation drift or small power offsets have little impact because the method focuses on relative energy changes.
  3. Simple to apply – No wind tunnel or special equipment is required, just real ride data from a power meter, speed, and elevation.

📡 On-Bike Aero Sensors – Notio, Aerosensor & Co.

In parallel to the Chung method, recent years have seen the development of dedicated aero sensors mounted directly on the bike, aiming to estimate aerodynamic drag in real time.

Examples include:

  • Notio Aerometer – a compact aero sensor mounted at the front of the bike. It measures air pressure, speed, and other environmental variables, combining them with power and speed signals to compute a live CdA.
  • Aerosensor – an aerodynamics system with a streamlined probe head that measures airflow and pressure, then combines this with bike speed and power data to reveal very small changes in aerodynamic drag. Data can be streamed to compatible bike computers.

Typically, these systems:

  • measure air speed and environmental conditions (pressure, temperature, sometimes altitude),
  • combine this with data from the power meter and speed sensor,
  • calculate a live CdA and additional metrics displayed on the head unit or in companion apps.

The goal is to show riders during the ride how position, equipment, or wind affect aerodynamic drag – effectively turning the road into a personal wind tunnel.

🚧 Why Real-World Use Is So Challenging

As fascinating as these devices are, using them reliably in everyday conditions is demanding:

  • The sensor sits very close to the bike and rider, where the airflow is already heavily disturbed by frame, bars, legs, and arms.
  • Tiny changes in body position (shoulders, head, hands, even fingers) alter the effective CdA – a few millimetres can be measurable.
  • To reliably separate aerodynamic and rolling resistance, you need reasonably stable knowledge of rolling resistance (Crr), total system weight, gradient, and wind.
  • Gusty winds, braking, traffic, or small pacing errors can significantly compromise a test run.

In short: aero sensors are powerful tools, but they require very clean test protocols and experience to produce truly trustworthy results.

RaceYourTrack therefore takes a different route: we lean on the strength of the Chung method in post-ride analysis and combine it with a full physics-based simulation model.

🌍 Our Implementation for Real-World Courses

In practice, people rarely ride on perfectly flat, closed circuits. That’s why RaceYourTrack uses an extended implementation of the Chung method that works directly with real elevation profiles.

We compare the simulated elevation with the actually recorded elevation and optimise the parameters that minimise the difference between the two. This allows us to estimate aerodynamic drag (CdA) reliably even on real-world GPX courses.

As soon as power data is available in a simulation – via your own power meter – our system automatically computes the relevant values in the background. CdA and rolling resistance are determined without manual input and directly integrated into the underlying physics model.

🧩 Key Characteristics of Our Approach

  • Wind is explicitly included via weather settings in the simulation
  • Directly applicable to real GPX data – including elevation changes, climbs, and descents
  • Numerically stable, because we optimise on the height profile instead of noisy instantaneous power
  • Physically consistent through a full energy balance (potential, kinetic, rolling, and aerodynamic)
  • Automated parameter optimisation built directly into our simulation
  • No extra hardware required – a power meter and a standard GPS track are enough

🏁 Takeaways

  • The Chung method provides a robust, wind-tolerant way to determine CdA from real ride data.
  • Aero sensors like Notio or Aerosensor can deliver live CdA measurements on the road, but they are technically demanding and require very clean test conditions.
  • RaceYourTrack uses an extended Chung method in post-ride analysis and couples it with a full physics simulation.
  • Once power data is available, CdA and rolling resistance are calculated automatically – with no additional hardware or manual tweaking.

This turns a clever scientific idea into a practical tool for modern performance analysis – exactly where it matters most: on your favourite loop.

📚 Source

Our approach is based on the work of Robert Chung: Estimating CdA from Power Data (PDF), licensed under Creative Commons Attribution (CC BY 3.0).

Photocredit: Pexels / Paolo Bici

➡️ Learn more about our simulation physics at RaceYourTrack.com

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