Fluidic Flow Measurement Using Single Mode–Multimode–Single Mode Optical Fiber Sensor

Morshed, Ahmed Hisham; Atta, Raghied M.; Packirisamy, Muthukumaran

doi:10.1109/jsen.2021.3069363

Cited by 8 publications

(4 citation statements)

References 69 publications

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“…However, we can provide a few examples of widely utilized MMF sensors to illustrate the boundless applicability of MMF sensors. These include magnetic field sensing [112][113][114][115][116], distributed acoustic sensing [117], fluidic flow measurement [118], steel corrosion monitoring [119], relative humidity (RH) sensing [120][121][122], gas pressure sensing [103,123,124], and volatile organic compound (VOC) gas detection [125,126]. Table 6 provides a summary of the key parameters for the sensors discussed.…”

Section: Other Sensorsmentioning

confidence: 99%

Multimode optical fiber sensors: from conventional to machine learning-assisted

Wang,

Mizuno,

Dong

et al. 2023

Meas. Sci. Technol.

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Multimode fiber (MMF) sensors have been extensively developed and utilized in various sensing applications for decades. Traditionally, the performance of MMF sensors was improved by conventional methods that focused on structural design and specialty fibers. However, in recent years, the blossom of machine learning techniques has opened up new avenues for enhancing the performance of MMF sensors. Unlike conventional methods, machine learning techniques do not require complex structures or rare specialty fibers, which reduces fabrication difficulties and lowers costs. In this review, we provide an overview of the latest developments in MMF sensors, ranging from conventional methods to those assisted by machine learning. This article begins by categorizing MMF sensors based on their sensing applications, including temperature and strain sensors, displacement sensors, refractive index sensors, curvature sensors, bio/chemical sensors, and other sensors. Their distinct sensor structures and sensing properties are thoroughly reviewed. Subsequently, the machine learning-assisted MMF sensors that have been recently reported are analyzed and categorized into two groups: learning the specklegrams and learning the spectra. The review provides a comprehensive discussion and outlook on MMF sensors, concluding that they are expected to be utilized in a wide range of applications.

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Section: Other Sensorsmentioning

confidence: 99%

Multimode optical fiber sensors: from conventional to machine learning-assisted

Wang,

Mizuno,

Dong

et al. 2023

Meas. Sci. Technol.

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“…studies have reported advancements in sensor designs incorporating multimode-no-core-multimode [13], single-modeno-core-single-mode structures [14][15][16], and single-modephotonic crystal fiber-single-mode (SPCS) structures [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Research on sensing characteristics of microfluidic sensor based on photonic crystal fiber

Wang,

Luo

et al. 2024

Meas. Sci. Technol.

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To address the challenges associated with sample injection into the air hole of photonic crystal fiber and collimation, in this paper, we assemble a single-mode photonic crystal single-mode fiber structure sensor chip based on the Mach-Zehnder interference principle using microfluidic chip processing technology. The sensing principle is analyzed mathematically and the sensing characteristics are verified theoretically and experimentally. The temperature sensitivity of the sensor is −1.3325 nm/℃, and the refractive index sensitivity is 1666.2 nm/RIU. This structure solves the difficulty of filling and coupling photonic crystal fibers. Furthermore, it introduces a novel research methodology for the design and assembly of high-performance biosensors based on photonic crystal fibers.

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“…Flow rate measurement in real time plays an important role in the field of biochemistry, vascular medicine, pipeline monitoring, etc. Recently, fiber-optic flow sensors [1][2][3] based on various principles have been investigated owing to their distinctive advantages, such as tiny size, high sensitivity and accuracy, corrosion resistance, and, especially, immunity to electromagnetic interference. The multimode fiber detection method [4,5] achieves rapid measurements of flow through noninvasive detection.…”

Section: Introductionmentioning

confidence: 99%

A Miniature Liquid Flowmeter Using All-Fiber Fabry–Perot Cavity for Real-Time Measurement

Ding,

Lu,

Kong

et al. 2024

Photonics

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A miniature and highly sensitive fiber-optic liquid flowmeter based on Fabry–Perot interferometry (FPI) is proposed and demonstrated for fluid-flow micro-channel testing. The diaphragm deformation and pressure of the proposed sensor for flow rate detection are obtained from numerical and finite element method simulations of the theoretical model. The FPI flowmeter can be applied in real time to measure the ultra-wide dynamic range from 0 mL/min to 90 mL/min, with a response time of hundreds of milliseconds, controlling the flow rate with a resolution of 1.08 mL/min, which is 1.2% of the full scale. The quadratic functional relation between dip wavelength shifts and flow rates is verified by the flow calibration curves of the FPI flowmeter under dynamic pressure conditions. In addition, the effective temperature compensation is realized by connecting an FBG temperature sensor for variable temperature flow detection, and the measured error is reduced by nearly 25-times. The proposed sensor has the potential to measure the liquid flow rate in various applications.

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Fluidic Flow Measurement Using Single Mode–Multimode–Single Mode Optical Fiber Sensor

Cited by 8 publications

References 69 publications

Multimode optical fiber sensors: from conventional to machine learning-assisted

Multimode optical fiber sensors: from conventional to machine learning-assisted

Research on sensing characteristics of microfluidic sensor based on photonic crystal fiber

A Miniature Liquid Flowmeter Using All-Fiber Fabry–Perot Cavity for Real-Time Measurement

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