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Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
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Isolating fast and slow flows in three-dimensional fluid dynamics.

Donald Derrick1, Mark Jermy2, Jason Chen2

  • 1New Zealand Institute of Language, Brain & Behaviour, University of Canterbury, 20 Kirkwood Drive, Ilam, Christchurch, 8041, Canterbury, New Zealand. donald.derrick@canterbury.ac.nz.

Scientific Reports
|June 20, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel optical flow method to accurately capture complex air movements, like those from speech, by overcoming limitations of traditional algorithms. The technique enhances visualization and analysis of air velocity patterns near the speaker.

Keywords:
Air motion in speechFlow velocity analysisOptical flow field analysisOptical flow velocimetrySchlierenSignal processing

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Area of Science:

  • Fluid Dynamics
  • Acoustics
  • Biophysics

Background:

  • Conventional optical flow (OF) methods struggle with complex flows containing simultaneous high and low-speed motions.
  • The small-motion assumption in traditional OF algorithms leads to preferential tracking of slower flow components.
  • Distinguishing fast speech-generated airflows from slower buoyancy-driven flows is challenging for existing OF techniques.

Purpose of the Study:

  • To develop and validate an improved optical flow method capable of analyzing complex fluid dynamics with co-existing distinct speeds.
  • To accurately capture and analyze air velocity patterns generated by human speech.
  • To enable detailed spatial and temporal analysis of airflow dynamics at varying distances from the speaker.

Main Methods:

  • A novel optical flow technique was developed by balancing light intensity and applying per-pixel time-based high-pass filtering.
  • The method was demonstrated using schlieren video of air motion generated by speech.
  • Kymographs (space-time velocity plots) were generated and analyzed using Generalized Additive Mixed-effect Models (GAMM).

Main Results:

  • The enhanced OF method successfully extracted detailed air flow patterns from speech several centimeters from the mouth.
  • Peak velocities observed for the English "pa" sound correlated well with Computational Fluid Dynamics (CFD) simulations.
  • Statistical analysis at 30 cm from the lips showed significantly improved predictive power (2.64% to 7.57%) and model fit (89.3% residual reduction).

Conclusions:

  • The developed optical flow method effectively overcomes limitations of conventional techniques in complex flow scenarios.
  • This approach enables precise analysis of speech-induced airflow dynamics, offering insights into phonetics and aerodynamics.
  • The method is generalizable to various 3D optically accessible flows with co-located high and low-speed motions.