Search Results (409)

The integration of Artificial Intelligence (AI) into the design, manufacturing, and lifecycle management of Composite Overwrapped Pressure Vessels (COPVs) is transforming the pathway toward autonomous and adaptive composite systems. This paper presents a comprehensive review and roadmap for AI-enabled COPVs development, bridging materials design, process optimisation, and predictive maintenance. The study synthesises over a decade of research on data-driven composite manufacturing, combining technology intelligence, PESTEL-SWOT environmental assessment, and cross-sectoral analysis of industrial and academic advances. A unified workflow is proposed to illustrate AI integration across the COPVs lifecycle, highlighting data feedback loops for continuous optimisation through digital twins and intelligent process control. Structural Health Monitoring (SHM) plays a central role in this ecosystem by providing real-time high-fidelity data on damage evolution and environmental interactions in COPVs. Through embedded sensing technologies such as fibre optic sensors and acoustic emission systems, SHM enhances digital twin fidelity, supports AI-based anomaly detection, and strengthens model validation in safety-critical hydrogen storage applications. Critical challenges are identified, including limited hydrogen-exposure datasets, lack of real-time adaptability, explainability in safety-critical design, and sustainability of AI-intensive workflows. These challenges highlight the need for tighter SHM-AI integration to enable reliable condition assessment and prognostics under multi-physics loading conditions. Based on these findings, the paper outlines actionable research directions to enable reliable, transparent, and sustainable AI adoption in composite manufacturing under the Industry 4.0 and hydrogen-economy paradigms. Full article

Optical fibres are considered for applications in various nuclear environments in the presence of radiation exposure. Under irradiation, the properties of the optical fibres are modified. In the present paper we investigate the influence of gamma radiation on the magneto-optical properties of the Corning SMF-28e optical fibre. The stability of the Verdet constant is an important requirement for performing current measurements under radiation, for example, in magnetic fusion installations during nuclear (deuterium–tritium) plasma operation, where radiation at MGy dose levels can be accumulated. Our results demonstrate that radiation-induced changes in the Verdet constant are within its measurement accuracy (0.56%) for gamma radiation doses up to 770 kGy. Full article

Carbon fibre reinforced polymers (CFRPs) are increasingly used in biomedical and safety-critical applications, where embedded and real-time non-destructive testing (NDT) is essential to ensure structural integrity. This paper presents a cost-effective, AI-assisted thermographic inspection system designed from an embedded electronics and circuit-level perspective. The proposed platform integrates a long-wave infrared (LWIR) sensor, dedicated signal conditioning and power management circuits, and a Raspberry Pi-based processing unit within a unified hardware–software co-design approach. Infrared data acquired under surface heating conditions are processed on-board using a convolutional neural network based on a U-Net architecture, enabling automatic localisation and classification of subsurface defects in CFRP samples. Particular attention is devoted to embedded design constraints, including sensor interfacing, acquisition timing, end-to-end latency, and real-time processing scalability. Experimental results confirm the feasibility of real-time surface heat assessment and the robustness of the proposed architecture in detecting delaminations and voids. The presented system contributes to the development of intelligent embedded inspection electronics and provides a reference design for edge AI-enabled NDT systems in industrial and biomedical applications. Full article

This paper outlines the design of the space radiation detection experiment RADS to demonstrate new shielding materials in space during the Athene-1 mission, as well as the Gena-OT1 CubeSat precursor mission. The experiment compares new materials in the form of functional layers integrated into fibre-reinforced composite structures against traditional aluminium shielding. Trapped-particle motion is considered to maximise the exposure of the experiment in space. The radiation sensing units are based on off-the-shelf electronic components. Dosimeters based on a floating-gate MOSFET architecture are used to represent the damage mechanism in electronic devices exposed to space radiation. To account for particle- and energy-specific dose enhancement effects in the silicon of the dosimeters, the concept of a Cobalt-60 equivalent dose is introduced to serve as a calibration baseline. The structural design and software aspects are considered to increase ease of use for future satellite missions. Full 3D radiation simulations were conducted using FastRAD to validate the measurement concept of the sensor units in conjunction with the housing unit and the new shielding material. The experimental design has been verified, showcasing a unique method for evaluating new shielding materials in space. Full article

Magnetic monitoring of maritime environments is an important problem for monitoring and optimising shipping, as well as national security. New developments in compact, fibre-coupled quantum magnetometers have led to the opportunity to critically evaluate how best to create such a sensor network. Here we explore various magnetic sensor network architectures for target identification. Our modelling compares networks of scalar vs. vector magnetometers. We implement an unscented Kalman filter approach to perform target tracking, and we find that vector networks provide a significant improvement in target tracking, specifically tracking accuracy and resilience compared with scalar networks. Full article

Defects such as gaps, delamination, and the misalignment of fibres impair the performance of carbon fibre-reinforced composites and can lead to structural failure during operation. Eddy current testing has proven to be a suitable method for detecting these defects early in the manufacturing process. However, validated electromagnetic modelling techniques are required to develop new eddy current sensors and gain a better understanding of the eddy current signals caused by different defect sizes. This paper proposes a novel finite element modelling approach to better account for fibre heterogeneity using spline approximation. Further, adaptive mesh refinement is used to reduce FEM solution errors. A defect in the form of a gap is modelled by adjusting the spline approximation accordingly. Finally, the model also accounts for inter-laminar current paths between carbon fibre layers, which are determined by four-terminal resistance measurement. The results show that the electromagnetic properties of the structure can be successfully modelled. The simulation is validated by comparing the virtual scans with eddy current scans of dry carbon fibre fabric with and without artificially manufactured gaps. Full article

Surface plasmon resonance (SPR)-based optical fibre sensors have transformed label-free biosensing; however, single-interface evanescent field interactions continue to limit their sensitivity. This study presents a novel π-configuration optical fibre-based surface plasmon resonance sensor that greatly increases sensitivity by enabling dual plasmonic excitation on two symmetrically polished surfaces coated with optimized metallic thin films (Ag, Au, or Cu). We show, using finite element method simulations in COMSOL Multiphysics v6.3, that the π-configuration increases the interaction volume between the analyte and guided light, resulting in an enhanced sensitivity of 3300 nm/RIU for silver at refractive index (RI) 1.37–1.38, which is a 120% improvement over traditional D-shaped sensors (1500 nm/RIU). The maximum field norm for the π-configuration sensor is approximately 1.4 times greater than the maximum observed for the D-shaped SPR sensor at an analyte RI of 1.38. The sensor’s performance is evaluated using full-width half-maximum, wavelength sensitivity, and wavelength interrogation metrics. For the π-configuration sensor at an analyte RI of 1.38, the values of the FWHM, figure of merit, detection accuracy, and confinement loss were 36 nm, 94.29 RIU⁻¹, 0.94, and 38.5 dB/cm, respectively. The results obtained are purely simulated using COMSOL. With the support of electric field confinement analysis, a thorough theoretical framework describes the crucial coupling regime that causes ultra-high sensitivity at low RI. This design provides new opportunities for environmental monitoring, low-abundance biomarker screening, and early-stage virus detection, where it is necessary to resolve minute RI changes with high precision. Full article

Compression garments are widely employed in medical and sports contexts for their ability to promote venous return, manage oedema, support musculoskeletal function, and enhance athletic recovery. Advances in textile-based compression systems have been driven by innovations in fibres, yarn structures, fabric structure engineering, and design methods. This review critically examines the current literature on compression garments, highlighting the influence of raw materials and yarn architectures on performance, durability, and wearer comfort. Attention is given specially to fabric structures and manufacturing methods, where the evolution from traditional cut-and-sew methods to advanced seamless, flatbed, and circular knitting technologies is highlighted, along with their impact on pressure distribution and overall garment efficacy. The integration of 3D body scanning, finite element analysis, and predictive modelling, which enables more personalised and precise garment design, is also speculated upon. Moreover, the review highlights testing and evaluation methodologies, spanning both in vivo and in vitro based assessments, pressure sensor studies for real-time monitoring, and theoretical models mostly based on Laplace’s law. This literature survey provides a foundation for future innovations aimed at optimising compression garment design for both therapeutic and athletic use. Full article

The compression deformation of seed cotton has been identified as a key factor affecting the working reliability of the baling device and the quality of bale molding. However, due to the complex working conditions of seed cotton in the continuous compression process in a confined space, it has proven to be difficult to study the compression molding mechanism of machine-harvested seed cotton in the baling process. The present study employs a universal testing machine to compress the seed cotton. In addition, pressure sensors are utilised to ascertain the internal axial load transfer law of the seed cotton. Furthermore, the internal density distribution equation of the seed cotton is established. Moreover, the Fiber model is employed to establish a spatial helix structure model of the cotton fibre. Finally, the compression simulation test is conducted to calibrate its material parameters. The results of the study indicate that seed cotton exhibits hysteresis in its internal stress–strain transfer. Through the polynomial fitting of the compression–displacement curve, it has been demonstrated that as the seed cotton approaches the compressed side, the rate of change in compression increases. The internal density distribution of the seed cotton must be calculated when it is compressed to a density of 220 kg·m⁻³. It is found that the density of the upper layer of the seed cotton is slightly greater than that of the lower layer of the seed cotton. The density distribution equation must then be obtained through regression fitting. The parameters of the compression model must be calibrated by means of uniaxial compression tests. Finally, the density distribution equation of the cotton fibre must be obtained through the compression test. The parameters of the simulation model, as determined by the uniaxial compression test calibration, are of significant importance. This is particularly evident in the context of the Poisson’s ratio of cotton fibre and the cotton fibre elastic modulus under pressure. The regression equation was obtained through analysis of variance, and the simulation of contact parameter optimisation. The optimal parameter combination was determined to be 0.466, and the pressure at this time. The relative error was found to be 2.96%, and the compression of specific performance was determined to be 10.14%. These findings serve to validate the simulation model. The findings of this study have the potential to provide a theoretical foundation and simulation assistance for the design and optimisation of cotton picker baling devices. Full article

Developments in the textile industry occur both as a consequence of increased awareness among users and various requirements in terms of human and environmental safety. Modifications are aimed at improving performance parameters, using natural substances, moving away from synthetic materials, and improving ergonomics. In order to achieve this, various fibre-production techniques are used, as is the addition of substances, including nanosubstances, into the structure or onto the surface of a given material. In the case of fire-resistant fabrics, which primarily must meet thermal protection requirements, efforts are also being made to reduce weight and eliminate harmful chemicals (e.g., polycyclic aromatic hydrocarbons PAHs), and to create smart materials with sensors. However, it is necessary to further develop not only the materials themselves but also cleaning and decontamination techniques that will allow the fire resistance parameters that have been developed to be maintained. Full article

Distributed Acoustic Sensing (DAS) transforms conventional optical fibres into large-scale acoustic sensor arrays. While existing telecommunication cables are increasingly considered for DAS-based monitoring, their performance depends strongly on cable construction and strain transfer efficiency. In this study, the relative DAS signal amplitudes of three commercial telecommunication optical cables were experimentally compared using a benchtop Rayleigh backscattering-based interrogator under controlled laboratory conditions. By maintaining a constant temperature and ensuring no additional strain changes from the outside environment, we guaranteed that only strain-induced variations from acoustic excitations were measured. The results show clear differences in signal amplitude and signal-to-noise ratio (SNR) among the tested cables. The Microcable consistently produced the highest spatial peak amplitude (up to 0.029 a.u.) and SNR (up to 79), while the Duct cable reached 0.00268 a.u. with mean SNR ≈ 32. The Anti-Rodent cable showed low signal amplitude (0.0018 a.u.) but exhibited a high mean SNR (≈111) driven by an exceptional low noise floor in one of the runs. These findings reflect the variations in mechanical coupling between the fibre core and external perturbations and provide practical insights into the suitability of different telecom cable types for DAS applications, supporting informed choices for future deployments. Full article

Structures with slender cylindrical geometries, such as subsea power cables are critical components of infrastructure systems. These structures are prone to bending deformation under load, which can ultimately cause structural failure. In this study, distributed optical fibre sensors are used to monitor the bending deformation in slender cylindrical structures. Brillouin optical time-domain reflectometry-based strain sensing was used to experimentally study three-point bending and approximately constant curvature bending of a 6 m long circular hollow section (CHS). Optical fibres were attached to the outer surface of the CHS in two different configurations: parallel to the longitudinal axis and helically wound around the CHS. Strain responses due to changing magnitudes of deformation and changing orientation of the optical fibre around the circumference of the CHS were studied. A finite element model was employed to simulate and interpret the observed strain responses. A strain response inverse analysis was conducted using the strain data obtained from the experimental study to reconstruct the deformed shapes of the CHS. Both the longitudinally aligned and helically wound fibres showed distinct strain profiles that differentiate the three-point bending and constant curvature bending behaviours. The results revealed the ability of optical fibre sensing to evaluate the type; magnitude; and orientation of the bending deformations. This fundamental understanding supports the design of sensing systems for critical cylindrical infrastructure. Full article

Efficient soil moisture monitoring is fundamental to precision agriculture, enabling improved irrigation management, enhanced crop productivity, and sustainable water use. This review comprehensively evaluates soil moisture sensing technologies, classifying them into invasive and non-invasive approaches. The underlying operating principles, strengths, and limitations, as well as documented practical applications, are critically discussed for each technology. Invasive methods, including dielectric sensors, matric potential devices, heat-pulse sensors, and microstructured optical fibres, offer high-resolution data but require careful installation and calibration to account for environmental and soil-specific variables such as texture, salinity, and temperature. Non-invasive technologies—such as microwave remote sensing, electromagnetic induction, and ground-penetrating radar—enable large-scale monitoring without disturbing the soil profile; however, they face challenges in terms of resolution, cost, and data interpretation. Key performance factors across all sensor types include installation methodology, environmental sensitivity, spatial representativeness, and integration with decision-support systems. The review also addresses recent innovations such as biodegradable and Micro–Electro–Mechanical Systems sensors, the incorporation of Internet of Things platforms, and the application of artificial intelligence for enhanced data analytics and sensor calibration. While sensor deployment has demonstrated tangible benefits for irrigation efficiency and yield improvement, widespread adoption remains constrained by technical, economic, and infrastructural barriers, particularly for smallholder farmers. The analysis concludes by identifying research gaps and recommending strategies to facilitate the broader uptake of soil moisture sensors, with a focus on cost reduction, calibration standardisation, and integration into climate-resilient agricultural frameworks. Full article

Fibre Bragg grating (FBG) grid sensors are an underexplored technology with potential to benefit nuclear thermal hydraulics experiments. This paper presents a new FBG grid sensor consisting of 38 FBGs across 8 flow-crossing chords. Using this sensor, experiments determined for the first time that an FBG grid can detect large air bubbles rising in standing liquids—demonstrated in both columns of water and 20W50 automotive oil. The instrument’s sensitivity was quantified by comparing its measurements to high-speed camera recordings. Analysis of Bragg wavelength shift timings on each chord enabled the surface of a bubble to be reconstructed using the air–oil data. Finally, the increase in Bragg wavelength when bubbles interact with the FBG grid suggests a variant sensing principle different from that reported in the literature for FBG grids in flowing liquids. Full article

This review critically surveys the emerging integration of Surface-Enhanced Raman Scattering (SERS) with photonic-crystal fibers (PCFs) for chemical and biological detection, an area still scarce in the literature. SERS exploits electromagnetic and chemical enhancement to overcome the intrinsic weakness of Raman scattering, while PCF offers low transmission loss and a strong evanescent field that further amplify the signal. The structural designs of PCF, encompassing solid-core and hollow-core variants, are discussed and their respective advantages in different sensing scenarios are presented. Applications in chemical detection, biomedicine, and explosive identification are detailed, demonstrating the versatility and potential of PCF-SERS sensors. Future efforts will focus on robust PCF geometries that guarantee stable and reproducible signals, AI-driven spectral algorithms, hybrid fibre architectures and scalable manufacturing. These advances are expected to translate PCF-SERS from bench-top demonstrations to routine deployment in environmental monitoring, clinical diagnostics and food-safety control. Full article