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Development and characterization of a passive, bio-inspired flow-tracking sensor.

J N Galler1, D E Rival1

  • 1Mechanical and Materials Engineering Department, Queen's University, Kingston, Ontario, Canada.

Bioinspiration & Biomimetics
|February 2, 2023
PubMed
Summary
This summary is machine-generated.

A new dynamic model corrects for inertial lag in large flow sensors, enabling accurate atmospheric measurements. This method improves flow tracking in turbulent environments using inertial measurement units (IMUs).

Keywords:
distributed sensingdynamic model correctioninertial flow tracers

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Area of Science:

  • Fluid dynamics
  • Atmospheric science
  • Sensor technology

Background:

  • Natural seed transport demonstrates effective dispersal in turbulent atmospheric flows.
  • Developing passive sensors for large-scale flow tracking faces challenges like inertial lag due to sensor size and mass, impacting measurement accuracy.
  • Inertial measurement units (IMUs) offer potential for beyond visual line-of-sight sensing but are limited by reduced flow fidelity.

Purpose of the Study:

  • To develop a flow-physics-based tracer correction method to extract inaccessible flow information from IMU-based sensors.
  • To overcome the limitations of inertial lag in passive flow sensors for atmospheric measurements.
  • To validate a novel dynamic model for accurate flow velocity estimation in turbulent conditions.

Main Methods:

  • A low-order unsteady aerodynamic model, inspired by the added-mass concept, was developed for tracer correction.
  • A spherical sensor platform equipped with an IMU and magnetometer was used for evaluation.
  • The dynamic model back-calculated instantaneous flow velocity using sensor velocity and acceleration, validated against optical tracking and particle image velocimetry.

Main Results:

  • The energized-mass-based dynamic model accurately estimated true wind speed in a 1D axial gust test case, with a maximum error of 10%.
  • In a cylinder wake test case, the model successfully extracted velocity oscillation amplitude and vortex-shedding frequency, previously inaccessible.
  • The correction technique demonstrated good agreement with particle image velocimetry measurements.

Conclusions:

  • Inertial IMU-based flow sensors are viable for Lagrangian tracking in large-scale, turbulent atmospheric environments.
  • Coupling inertial sensors with a robust dynamic model is crucial for accurate flow measurement and overcoming inertial lag.
  • The developed flow-physics-based correction method enhances the utility of IMU sensors for atmospheric flow tracking.