Optical Tracking in Hypersonic Wind Tunnels

An efficient way to measure hypersonic aerodynamics

I developed an algorithm to use two independent cameras (at different frame rates) to extract aerodynamics from free-flight models in the UniSQ TUSQ hypersonic wind tunnel.

All image processing and analysis is performed in open-source code, making use of open-cv image processing functions. The primary challenge with this project is reducing the measurement noise which gets amplified when the position and orientation of the body is differentiated twice to extract forces and moments. To resolve this, I use a custom-written Unscented (Sigma point) Kalman filter and Bayesian smoother for state estimation, which provide smooth second kinematic derivatives.

See some of our videos below, and check out the publications [1] [2] (awarded annual Best Paper by AIAA Ground Testing Technical Committee).

A 30mm cube dropped from near-stationary in Mach 6 flow. Video duration is approx 30 ms in real-time. Tracking result (white frame), expected marker locations (green and blue circles) and image-detected markers (red circles) overlaid.
A 30mm cube dropped with approximately 600 RPM rotation in Mach 6 flow. Video duration is approx 30 ms in real-time. Tracking result (white frame), expected marker locations (green and blue circles) and image-detected markers (red circles) overlaid.

References

2024

  1. Journal
    Aerodynamic Measurements of Hypersonic Free-Flight via Optical Tracking and Bayesian State Estimation
    Andrew Lock, Flynn Hack, Ingo Jahn, and 3 more authors
    AIAA Journal, Nov 2024
    DOI

2023

  1. Optical aerodynamic measurements of hypersonic free-flight using Bayesian state estimation
    Andrew Lock, Gerard Armstrong, Flynn Hack, and 3 more authors
    In AIAA AVIATION 2023 Forum, Jun 2023
    DOI