Multi-dimensional acceleration sensors are used in important applications in the aerospace, weapon equipment, and nuclear fields and have strict requirements in terms of performance, volume, and mass. Fiber Bragg grating acceleration sensors use optical wavelength signals as a medium for information transmission to effectively eliminate the influence of electromagnetic interference between multi-dimensional sensors. In this study, we designed a composite flexure hinge three-dimensional acceleration sensor. To this end, we investigated the coupling mechanism between a new integrated elastomer structure and fiber grating to determine the influence of structural parameters on the static and dynamic characteristics, volume, and mass of the sensor. By optimizing the strain distribution, amplitude, and frequency and coupling characteristics between dynamic dimensions, a design theory and a method for integrating the three-dimensional acceleration sensor were developed. The size of the optimized accelerometer is only 25 mm × 25 mm × 30 mm. Performance testing revealed that, along the three spatial dimensions, the sensor had sensitivities of 51.9, 39.5, and 20.3 pm/g, respectively, resonance frequencies of 800, 1125, and 1750 Hz, respectively, and a measurable frequency range of 0–250 Hz.

Mediumfrequency fiber Bragg grating (FBG) acceleration sensors are used in important applications in mechanical, aerospace and weapon equipment, and have strict requirements in terms of resonance frequency and sensitivity. A novel medium-frequency accelerometer, based on fiber Bragg grating and flexible hinges, is proposed in this paper. The differential structure doubles the sensitivity of the sensor while avoiding temperature effects. The structure model and principle for the sensor are introduced, the sensor’s sensing characteristics are theoretically analyzed, and the structure parameters for the sensor are determined through numerical analysis. The sensing experiments show that the resonance frequency of the sensor is approximately 2800 Hz, the sensitivity is 21.8 pm/g in the flat frequency range of 50–1000 Hz, and the proposed sensor has a good temperature self-compensation function and lateral anti-interference capability.

FBG shape sensors based on soft substrates are currently one of the research focuses of wing shape reconstruction, where soft substrates and torque are two important factors affecting the performance of shape sensors, but the related analysis is not common. A high-precision soft substrates shape sensor based on dual FBGs is designed. First, the FBG soft substrate shape sensor model is established to optimize the sensor size parameters and get the optimal solution. The two FBG cross-laying method is adopted to effectively reduce the influence of torque, the crossover angle between the FBGs is 2α, and α = 30° is selected as the most sensitive angle to the torquer response. Second, the calibration test platform of this shape sensor is built to obtain the linear relationship among the FBG wavelength drift and curvature, rotation radian loaded vertical force and torque. Finally, by using the test specimen shape reconstruction test, it is verified that this shape sensor can improve the shape reconstruction accuracy, and that its reconstruction error is 6.13%, which greatly improves the fit of shape reconstruction. The research results show that the dual FBG high-precision shape sensor successfully achieves high accuracy and reliability in shape reconstruction.

Addressing the critical need for vibration measurement in extreme cryogenic environments, this work introduces a novel cryogenic accelerometer integrating dual fiber Bragg gratings (FBGs), a seismic mass, an elliptic hinge, and a fixed base. We systematically derived the theoretical foundations governing its dynamic response parameters: sensitivity, natural frequency, and temperature-responsive performance of overall structure. A comprehensive theoretical model was developed, enabling simulation-based optimization to define its dynamic and thermal response performance. The cryogenic FBG accelerometer underwent rigorous test and characterization at both room temperature (293 K) and cryogenic conditions (83 K), as well as across the extensive 83–293 K thermal range. Experimental results closely aligned with theoretical predictions; the sensitivity exhibited a positive correlation with decreasing temperature, while the natural frequency exhibited an inverse relationship. Within the 83–293 K operational envelope, the accelerometer-maintained functionality across the 30–250 Hz bandwidth. The sensitivity values were quantified at 64.87 pm/g (293 K) and 68.17 pm/g (83 K). Demonstrating robust resilience to cross-axis effects, the maximum transverse interference measured was only 9.82% (293 K) and 9.71% (83 K). Deviations between the experimental and theoretical sensitivities were minimal (1.59% at 293 K, 5.44% at 83 K), as were those for the natural frequency (5.30% and 8.60%). 4J36 Invar alloy serves as the core structural material, which features a stable face-centered cubic crystal structure, an extremely low coefficient of thermal expansion (CTE), low thermal conductivity, and outstanding ductility and toughness, demonstrating exceptional suitability for measurement elements. Furthermore, the proposed cryogenic accelerometer delivers commendable dynamic response characteristics. Its compact form factor (45 $\times$ 25 $\times$ 15 mm) and lightweight (50 g) are complemented by the distinct advantage of enabling remote, simultaneous multipoint acceleration monitoring within ultracryogenic settings.

Accurate measurement of pipeline flow is of great significance for industrial and environmental monitoring. Traditional intrusive methods have the disadvantages of high cost and damage to pipeline structure, while non-intrusive techniques can circumvent such issues. Although Taylor’s frozen hypothesis has a theoretical advantage in non-intrusive velocity detection, current research focuses on planar flow fields, and its applicability in turbulent circular pipes remains controversial. Moreover, there is no precedent for combining it with distributed acoustic sensing (DAS) technology. This paper constructs a circular pipe turbulence model through large eddy simulation (LES), revealing the spatiotemporal distribution characteristics of turbulent kinetic energy and the energy propagation rules of FK spectra. It proposes a dispersion feature enhancement algorithm based on cross-correlation, which combines a rotatable elliptical template with normalized cross-correlation coefficients to suppress interference from non-target directions. An experimental circulating pipeline DAS measurement system was set up to complete signal denoising and compare two principles of flow velocity verification. The results show that the vortex structure of turbulent flow in circular pipes remains stable in the convection direction, conforming to theoretical premises; the relative error of average flow velocity by this method is ≤3%, with significant improvements in accuracy and stability in high-flow zones. This study provides innovative methods and experimental basis for non-intrusive flow detection using DAS.

To address the technical challenges in urban buried gas pipeline leak detection, such as weak signal acquisition, insufficient identification accuracy, and significant positioning errors, this study proposes an intelligent detection method integrating helical sensing optical cable-based distributed acoustic sensing (DAS) technology with an improved YOLOv5 model. A helical sensing optical cable with precise pitch is adopted as the sensing unit, significantly enhancing the signal acquisition capability of the DAS system. At the algorithmic level, global feature modeling is achieved by introducing a Transformer module into the eighth layer of the backbone network, while channel-spatial attention mechanisms are incorporated in the ninth layer to optimize local feature extraction. Additionally, a data augmentation strategy combining positional translation and rotation is employed to substantially improve the model’s representation of leak signals. Experimental results demonstrate that under operational pressures ranging from 0.1 to 0.6 MPa, the proposed method achieves a mAP@0.5 of 96.7%, with precision and recall rates reaching 94.8% and 91.3%, respectively, while maintaining positioning errors below 0.07 meters. The findings validate the effectiveness of the synergistic architecture combining helical sensing optical cables with deep learning for pipeline leak detection, offering an innovative technical solution to overcome the limitations of traditional methods in sensitivity and positioning accuracy.