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Fiber-Optic Hydrophone Based on Michelson's Interferometer with Active Stabilization for Liquid Volume Measurement.

Welton Sthel Duque1, Camilo Arturo Rodríguez Díaz1, Arnaldo Gomes Leal-Junior1

  • 1Telecommunications Laboratory (LABTEL), Graduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitória 29075-910, ES, Brazil.

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Summary

This study introduces a fiber-optic hydrophone (FOH) system for precise liquid volume measurement in industrial tanks. Utilizing ultrasound waves and machine learning, the FOH sensor achieves 99.4% accuracy in determining liquid volumes.

Keywords:
Michelson’s interferometeractive stabilizationfiber-optic hydrophoneliquid volume measurementmachine learningultrasound acoustics

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Area of Science:

  • Optical Fiber Sensing
  • Acoustic Wave Detection
  • Machine Learning Applications

Background:

  • Optical fiber sensing has been utilized across various industries since the 1970s.
  • Continuous liquid volume measurement in production tanks requires reliable sensing solutions.
  • Fiber-optic hydrophone (FOH) sensors offer a potential method for detecting ultrasound acoustic waves.

Purpose of the Study:

  • To develop and evaluate a fiber-optic hydrophone (FOH) system for accurate liquid volume measurement.
  • To investigate the use of machine learning algorithms for predicting liquid volumes based on acoustic wave patterns.
  • To mitigate environmental noise interference in optical fiber sensing systems.

Main Methods:

  • A Michelson's interferometer (MI) based FOH system was designed using single mode fiber (SMF) coils.
  • An active stabilization mechanism with a piezoelectric actuator (PZT) was employed to reduce noise.
  • Ultrasound waves were used to generate acoustic patterns within a test tank, with amplitudes and phases measured.
  • Machine learning algorithms, including k-nearest neighbors (k-NN) and Gaussian process regression, were applied for volume prediction.

Main Results:

  • The FOH system demonstrated a liquid volume resolution of 1 mL.
  • High sensitivity was achieved with 340 mrad/mL and 70 mvolts/mL.
  • K-nearest neighbors (k-NN) classification achieved 99.4% accuracy in volume determination.
  • Gaussian process regression yielded a root mean squared error (RMSE) of 0.211 mL.

Conclusions:

  • The developed FOH system, coupled with machine learning, provides a highly accurate method for liquid volume measurement.
  • The system effectively addresses the challenges of nonlinear acoustic wave patterns and environmental noise.
  • This technology holds significant potential for continuous monitoring applications in various industrial sectors.