Development of an ultrasonic hydrophone based on a fiber optic Michelsons interferometer to measure the volume of liquids
Name: WELTON STHEL DUQUE
Publication date: 29/07/2022
Advisor:
Name |
Role |
|---|---|
| ANSELMO FRIZERA NETO | Advisor * |
| CAMILO ARTURO RODRIGUEZ DIAZ | Co-advisor * |
Examining board:
| Name |
Role |
|---|---|
| ANSELMO FRIZERA NETO | Advisor * |
| CAMILO ARTURO RODRIGUEZ DIAZ | Co advisor * |
| MARIA JOSE PONTES | Internal Examiner * |
Summary: Sensing technologies with optical fibers have been studied and applied since the 1970s in oil and gas, industrial, medical, aerospace, and civil areas. Detecting ultrasound acoustic waves through fiber-optic hydrophones (FOH) sensors can be one solution for continuous measurement of volumes inside production tanks used by these industries. So, this work presents a FOH system composed of two optical fiber coils made with commercial SMF (Single Mode Fiber), working in the sensor head of a Michelson`s interferometer (MI), supported by an active stabilization mechanism that drives other optical coil wound around a piezoelectric actuator (PZT) in the reference arm, to mitigate external mechanical and thermal noises come from the environment. A graduated cylinder glass of 1000 ml is used as a test tank filled with water, inside which the sensor head and an ultrasound source are placed at. As a means of detection, amplitudes and phases are measured, and machine learning algorithms predict their respective liquid volumes. The acoustic waves create patterns electronically detected with resolution of 1 ml, and sensitivity of 340 mrad/ml and 70 mvolts/ml. The non-linear behavior of both measurands required the analysis of classification, distance metrics and regression algorithms to define an adequate model. The results show the system can decide liquid volumes with the accuracy of 99.4% using a k-NN (k Nearest Neighbors) classification with one neighbor and Manhattan`s distance. Moreover, a gaussian process regression using rational quadratic metrics presented and RMSE (Root Mean Squared Error) of 0.211 ml.
Keywords: liquid-volume measurements; fiber-optic hydrophone; Michelson`s interferometer; ultrasound acoustics; active stabilization; machine learning.
