Xiao Deng

As a key infrastructure for China’s “West-to-East Power Transmission” project, transmission lines (TL) face the threat of ice accretion under complex microclimatic conditions. This study proposes a plane principal strain sensing method based on a fiber Bragg grating circular array, achieving synchronous monitoring of 6 strains (ranging from -2000 to 2000 με) across the TL cross-section. Through finite element simulation experiments, a mapping relationship between the bending of TL and the plane principal strain has been established. After completing the sensor calibration, an experimental platform for the freeze-thaw process of ice accretion on the TL was built. The relationships between ice mass and bending strain, as well as the ice shape on the TL cross-section (C-shaped and circular ice) and plane principal strain, were studied. Furthermore, a BP neural network model was developed to determine the 4 states of the icing process (no ice/freeze/stable/thaw), achieving an accuracy of 91.23%. This study provides effective monitoring of the freeze-thaw process of ice accretion on the TL, offering important technical support for the prevention and control of ice accretion in power grid.

Single-pixel imaging (SPI) has significant advantages in detecting complex environments such as solid noise, high scattering, and long distances. In response to the contradiction between the field of view and resolution in traditional SPI systems, we propose a field of view expansion imaging method based on a digital micromirror device (DMD) and scanning mirror. Combining the advantages of traditional point scanning and SPI, we use high-precision control of the scanning mirror (with an error control of ± 3 mV) to scan and expand the reflected image on the target object onto the DMD, then using compressed sensing to achieve efficient sampling, thereby expanding the imaging field of view. We use the optimized TVAL3 algorithm for image recovery and reconstruction and stitching is performed. For second and third order imaging, results show that the imaging resolution of the individual and continuous targets have been improved by about 4 times and 9 times, the peak signal-to-noise ratio (PSNR) has been increased by about 2 times and 3 times, respectively. The experimental results indicate that the proposed imaging method may gradually adapt to existing beam scanning imaging systems. It will be beneficial for the widespread application of high-resolution imaging in large fields of view.

To perceive spatial principal strain in frozen environments, the 6-axis strain sensor was proposed in previous research. When monitoring ice instability, it is necessary to demodulate the cascaded 6 FBGs of the sensor over a wide wavelength range. The edge filtering demodulation method based on dual-peak resonance LPFG spectral control is proposed, which achieves accurate demodulation of the center wavelength of cascaded 6 FBGs over a large wavelength range (each demodulation range is 15 nm, and the total range is 90 nm). We have etched a dual-peak resonance LPFG using CO2 laser and designed a demodulation system based on piezoelectric ceramics (PZT). By precisely controlling the bending degree of LPFG through PZT, the spectrum of the LPFG can be regularly changed. The linear mathematical model of the demodulation system has been established, which theoretically verifies the feasibility of the proposed demodulation method. To solve the nonlinear error caused by the linearity poor at the edge of the demodulation interval, the BP-neural network demodulation model was established. The average error is smaller, which can reach 0.014 nm, with an MSE of 0.0005. This work can provide useful technical support for the widespread application of FBG multi-dimensional strain sensors.

Industrial equipment, such as wind turbine foundations and oil and gas pipelines in cold regions, may undergo extrusion/expansion deformation during freezing and thawing of frozen soil, which affects their power response and safe operation. Measuring the internal deformation of frozen soil can immediately reflect the strain situation of industrial equipment to reduce the risk of equipment operation. We designed a 6-dimensional strain sensor (6-S Sensor) based on Fiber Bragg Gratings (FBG) to obtain the spatial principal strain distribution. The strain range, linearity, and average error of the sensor were -4000~8000 με, 0.997, and 2.94%, respectively. The sensor accurately measured the frozen and thermal expansion of frozen soil at different temperatures in the laboratory. The maximum frozen expansion was 6471.38 με, which occurred in the X direction. The accuracy of spatial principal strain monitoring for the sensor was evaluated through uniaxial compression. The stability of the sensor was verified by the monitoring experiment under natural temperature for half a month. This study provided a pioneering method for monitoring the internal spatial principal strain of frozen soil.

In this work, a seawater salinity sensor based on dual resonance peaks long period fiber grating (LPFG) and back propagation neural network (BPNN) has been proposed. A LPFG working near dispersion turning point has been theoretically designed and actually fabricated, which has a refractive index (RI) sensitivity of 1304 nm/RIU in RI range of 1.33156 to 1.39947. Then, the sensor was investigated under different salinity and temperature conditions, in which the salinity changed from 5 to 40‰ and the temperature changed from 0 to 30°C. The highest seawater salinity sensitivity of 0.2662nm/‰ was achieved when the temperature was 30 °C, while the highest average temperature sensitivity was 0.482 nm/ ° C. The BPNN was employed for temperature compensation and salinity prediction because of nonlinear response to the variations of seawater salinity and temperature, whose mean absolute error and maximum absolute error are 2.1774 and 6.8901, respectively. Because the prediction accuracy is poor, the genetic algorithm has been employed for BPNN optimization. Then, BPNN based genetic algorithm method achieved the mean absolute error of 0.10139 and maximum absolute error of 0.19016, whose prediction accuracy was increased by an order of magnitude. This novel sensor based on optimized BPNN could be applied in the field of seawater salinity measurement.

Variations of seawater salinity often cause ocean internal waves, water masses and stratification, which affect the stability of the ocean environment. Therefore, the study of seawater salinity is significant for the prediction of changes in the ocean environment. However, existing methods for measuring seawater salinity generally have the disadvantages of low sensitivity and low accuracy. In this work, we proposed a seawater salinity sensor based on long period fiber grating (LPFG) in the dispersion turning point (DTP), which has demonstrated the possibility to fabricate LPFG with a shorter grating period by CO₂ laser in a thin single mode fiber (SMF) of 80 μm cladding diameter without etching. For obtaining higher sensitivity that could meet the measurement requirement in practice, the proposed sensor was optimized by combining etching cladding and DTP. After the LPFG working near DTP was fabricated by a CO₂ laser, the cladding diameter was reduced to 57.14 μm for making cladding mode LP_1,7 work near DTP by hydrofluoric acid (HF) solutions. The experimental results have demonstrated that a sensitivity of 0.571 nm/‰ can be achieved when the salinity increases from 5.001‰ to 39.996‰, and the sensor shows good repeatability and stability. Based on its excellent performance, the optimized LPFG is a prospective sensor to monitor seawater salinity in real time. Meanwhile, a low-cost way was provided to make LPFG work near DTP instead of ultraviolet exposure and femtosecond laser writing.

Three-dimensional strain (3D strain) and the principal strain in ice are very important for glacier movement research and glacier-related disaster prediction. The internal 3D strain of ice is sensitive to temperature, and it is very complex during the whole freezing and melting process including phase transformation. In this study, we present a 3D strain sensor (3D-S Sensor) based on fiber Bragg grating (FBG) and long period fiber grating (LPFG) that can measure the 3D strain in ice. The LPFG (the temperature sensitivity coefficient is 56.3 pm/°C) was fabricated by the laser etching method, which was used for the temperature compensation of FBGs to improve the accuracy of strain measurement. The 3D principal strain can be calculated by the value of 6 direction strains from FBGs. A freeze-melt experiment is designed to verify the performance of 3D-S Sensor. The experimental results show that the sensor can measure the complex 3D deformation of ice from a more precise angle. Among the 6 directions of strain measured by the sensor, the strain value in the Z direction is the largest (ε _z = 6232.9 με). The first principal strain in ice is close to the negative direction of the Z axis (θ 1 _Z = 160.7°), and the maximum value is 6638.2 με. The measured results are consistent with the actual deformation of ice, indicating that the sensor can effectively measure the 3D strain and principal strain inside the ice.

Aiming at the problem that the length of the temperature sensing area is smaller than the spatial resolution, which leads to inaccurate temperature measurement, a multi-point temperature correction scheme was proposed, and a device and method that could be applied to the measurement of temperature distribution in medium and small scale sections were introduced. Using the scheme based on multi-modal Gaussian fitting, the simultaneous correction of multiple inaccurate temperature points was realized, and the temperature accuracy of the system was improved. A prototype of the distributed Raman thermometry system was designed, and the measurement of temperature distribution in medium and small scale sections was realized by wrapping the fiber around the PPR tube. The experimental device improved the measurement accuracy of 2 m and 1.5 m temperature sensing areas to 1 °C, the thickness of the ice sheet of the Wanjiazhai Reservoir on the Yellow River in winter was measured to be 42.33 cm, which was 1.67 cm different from the artificial measurement results, and the measured temperature of 36 m deep river water profile fluctuated in the range of 0.32 ℃ ~ 0.90 ℃. The experimental results show that the distributed Raman thermometry system can monitor the temperature distribution of the river flow profile on a large scale in the field.