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Ingmar Jungnickel on AiRO and Scalable Aerodynamic Testing

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Manage episode 520215357 series 3694264
Content provided by Michael Liberzon and Andrew Buckrell. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Michael Liberzon and Andrew Buckrell or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Sports aerodynamicist Ingmar Jungnickel joins the show to discuss AiRO, a new CFD-based aero analysis tool built for bike fitters and coaches. He outlines why traditional wind-tunnel and field-testing approaches struggle with cost, complexity, and repeatability, and explains how recent advances in computing and AI now make high-fidelity CFD practical at the fit-studio level. The discussion highlights AiRO’s emphasis on breadth of exploration—rapidly testing wide parameter ranges—rather than pursuing ever-higher fidelity in isolated conditions.

Key Points

  • Ingmar’s background spans wind-tunnel work, velodrome testing, CFD development, and elite projects with Specialized, the German Cycling Federation, and U.S. Speed Skating.
  • Conventional aero tools—wind tunnels, track testing, field testing, PIV, and ultrasonic tomography—are limited by scalability, environmental control, rider repeatability, and cost.
  • AiRO’s core components include:
    a parametric human model fitted from simple photographs and basic anthropometrics,
    a digital-twin rider posture controlled through ~18 intuitive sliders,
    fast, cloud-based CFD simulations.
  • AiRO’s strategic focus is breadth over depth: enabling broad parameter sweeps, positional permutations, and large-scale exploration that would be infeasible in a tunnel or on a track.
  • Digital repeatability eliminates rider-movement noise, one of the main sources of error in physical aero testing.
  • Current limitations include the absence of textured-surface modelling (aero socks, advanced skinsuits) and single-yaw simulations, though both are technically feasible as compute economics improve.

Links & Resources

  continue reading

176 episodes

Artwork
iconShare
 
Manage episode 520215357 series 3694264
Content provided by Michael Liberzon and Andrew Buckrell. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Michael Liberzon and Andrew Buckrell or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

Sports aerodynamicist Ingmar Jungnickel joins the show to discuss AiRO, a new CFD-based aero analysis tool built for bike fitters and coaches. He outlines why traditional wind-tunnel and field-testing approaches struggle with cost, complexity, and repeatability, and explains how recent advances in computing and AI now make high-fidelity CFD practical at the fit-studio level. The discussion highlights AiRO’s emphasis on breadth of exploration—rapidly testing wide parameter ranges—rather than pursuing ever-higher fidelity in isolated conditions.

Key Points

  • Ingmar’s background spans wind-tunnel work, velodrome testing, CFD development, and elite projects with Specialized, the German Cycling Federation, and U.S. Speed Skating.
  • Conventional aero tools—wind tunnels, track testing, field testing, PIV, and ultrasonic tomography—are limited by scalability, environmental control, rider repeatability, and cost.
  • AiRO’s core components include:
    a parametric human model fitted from simple photographs and basic anthropometrics,
    a digital-twin rider posture controlled through ~18 intuitive sliders,
    fast, cloud-based CFD simulations.
  • AiRO’s strategic focus is breadth over depth: enabling broad parameter sweeps, positional permutations, and large-scale exploration that would be infeasible in a tunnel or on a track.
  • Digital repeatability eliminates rider-movement noise, one of the main sources of error in physical aero testing.
  • Current limitations include the absence of textured-surface modelling (aero socks, advanced skinsuits) and single-yaw simulations, though both are technically feasible as compute economics improve.

Links & Resources

  continue reading

176 episodes

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