Fiber Curvature Research Articles

Distributed curvature sensing using long period fiber grating and machine learning numerical analysis.

In this numerical study, we propose a fiber distributed curvature sensor based on the analysis of the spectral transmission of a long period fiber grating (LPG) with a neural network. A simulation of the optical transmissions of a proposed 6-cm LPG structure for different curvature profiles is first performed using EigenMode Expansion and a coupled-mode theory algorithm. Both fiber curvature profiles and their corresponding optical transmission spectra are then injected into a four dense layer neural network which, after training, leads to a 0.40% relative median estimation error in the bending profiles. This paper demonstrates the efficiency of neural network-based optical sensors to analyze non-uniform perturbations, while also revealing long-period gratings to be promising candidates for such systems.

Negative curvature fiber for suppressing high-order radial OAM modes transmission

The combination of terahertz (THz) wave and the orbital angular momentum (OAM) multiplexing technology can further improve the communication capacity. Compared to other outer cladding structures, the negative curvature structure has been proven to provide stronger confinement effect on electromagnetic waves. Here, we propose a novel polymer (COC TOPAS) fiber which consists a central hollow-core, annular region and single layer negative curvature circle tubes as outer cladding for terahertz OAM modes transmission. The mode map is established by mathematical analysis of cut-off conditions for the vector modes, and the excitation of high-order radial modes in the fiber is successfully suppressed. In addition, the effective refractive index, confinement loss, effective mode area, mode purity and dispersion characteristics of the fiber are investigated under the condition of ‘single mode’ operation in the frequency range of 2.0–3.0 THz.

Scraper conveyor shape sensing technology based on orthogonal optical fiber strain

Intelligent perception of a scraper conveyor straightness and attitude monitoring of mechanical supporting equipment in the stope have practical and theoretical values for mining. This study proposed an optical fiber curvature sensor and a scraper conveyor’s curve reconstruction method. The optical fiber curvature sensor comprises the fiber grating strain sensing optical cables, the flexible substrate, and the packaging material. The coordinate positions of each monitoring point are obtained through the strain–curvature conversion relationship and the slope recurrence algorithm, and then the reconstruction curve is obtained by fitting. The finite element simulation verifies the feasibility of the curve reconstruction method used for the deformation monitoring via optical fiber curvature sensors. The reconstruction error analysis results show that the root mean square error of reconstructions for two kinds of 2D plane bending and 3D space bending are 2.98%, 1.89%, and 3.13%, respectively. Their mean absolute errors are 8.9, 3.56, and 9.82 mm, respectively, verifying the feasibility and high accuracy of the proposed curve reconstruction equation. The research results provide a theoretical basis for the shape perception and straightening control of scraper conveyors in the intelligent working surface.

Deep learning for highly efficient curvature recognition using fiber scattering speckles

A flexible fiber-optic sensor enabled by deep learning is proposed and experimentally demonstrated for highly efficient curvature sensing application. This sensing modulation system combines a deep optical neural network based on a small training dataset, aiming to simplify speckle data capture and sensor model evaluation. The multimode fiber concatenated with a section of single stress-applying fiber serves as a sensing unit as well as an image transport medium. A type of hybrid scattering speckle images is collected and employed to provide more freedom to identify the bending curvature with and without external disturbances. In a perturbed environment, the trained optical classification model is suitable for the speckle dataset recognition with high accuracy rate of 98.3%. Moreover, the deep-learning-enabled fiber curvature sensor shows great potential for practical applications in real-time structural safety test, including studies on health monitoring of infrastructure equipment and aerospace wings.

Unraveling the biomechanical properties of collagenous tissues pathologies using synchrotron-based phase-contrast microtomography with deep learning

Mechanical stimuli are regulators not only in cells but also of the extracellular matrix activity, with special reference to collagen bundles composition, amount and distribution. Synchrotron-based phase-contrast computed tomography was widely demonstrated to resolve collagen bundles in 3D in several body districts and in both pre-clinical and clinical contexts. In this perspective study we hypothesized, supporting the rationale with synchrotron imaging experimental examples, that deep learning semantic image segmentation can better identify and classify collagen bundles compared to common thresholding segmentation techniques. Indeed, with the support of neural networks and deep learning, it is possible to quantify structures in synchrotron phase-contrast images that were not distinguishable before. In particular, collagen bundles can be identified by their orientation and not only by their physical densities, as was made possible using conventional thresholding segmentation techniques. Indeed, localised changes in fiber orientation, curvature and strain may involve changes in regional strain transfer and mechanical function (e.g., tissue compliance), with consequent pathophysiological implications, including developmental of defects, fibrosis, inflammatory diseases, tumor growth and metastasis. Thus, the comprehension of these kinetics processes can foster and accelerate the discovery of therapeutic approaches for the maintaining or re-establishment of correct tissue tensions, as a key to successful and regulated tissues remodeling/repairing and wound healing.

Hollow Core Double-Clad Fiber Coupler for Nonlinear Micro-Endoscopy

We report the design, fabrication and characterization of the first hollow core double-clad fiber coupler for nonlinear micro-endoscopy. It is made of a pure silica negative curvature hollow core double-clad fiber and a highly multimode fiber fused along each other. The fabrication of such a coupler was made possible thanks to the use of a soft (borosilicate) glass multimode fiber which can be fused along the negative curvature fiber without altering its geometry, thanks to its much lower fusion temperature. A 60% power transfer from the double-clad fiber to the multimode one was measured at 500 nm. Demonstration of two-photon fluorescence and second harmonic generation imaging was performed on unlabeled biological tissues with an endoscopic head mounted on the hollow core double-clad fiber coupler to demonstrate its applicability. This device greatly simplifies the integration of nonlinear micro-endoscopes based on hollow core double clad fibers.

Highly sensitive SPR curvature sensor based on graded-index fiber.

At present, fiber curvature sensors based on surface plasmon resonance (SPR) are mostly of the multimode fiber core type or cladding type. These types have many SPR modes, resulting that the sensitivity cannot be adjusted and is difficult to improve. In this Letter, a highly sensitive SPR curvature sensor based on graded-index fiber is proposed. The light-injecting fiber is eccentrically connected with the graded-index fiber to inject single-mode light. Due to the self-focusing effect, the light beam propagates in the graded-index multimode fiber with a cosine trajectory, and the cosine beam contacts the flat grooved sensing region fabricated on the graded-index fiber to generate SPR. Due to the single transmission mode of the proposed fiber SPR sensor, the curvature sensing sensitivity is greatly improved. By changing the light injection position of the graded-index multimode fiber, the sensitivity can be adjusted. The proposed curvature sensing probe has a high sensitivity and can identify the bending direction. When bending in the X direction, the sensitivity reaches 5.62 nm/m-1, and when bending in the - X direction, the sensitivity reaches 4.75 nm/m-1, which provides a new scheme for highly sensitive and directionally identifiable curvature measurement.

Age-related changes in human skeletal muscle microstructure and architecture assessed by diffusion-tensor magnetic resonance imaging and their association with muscle strength.

Diffusion-tensor magnetic resonance imaging (DT-MRI) offers objective measures of muscle characteristics, providing insights into age-related changes. We used DT-MRI to probe skeletal muscle microstructure and architecture in a large healthy-aging cohort, with the aim of characterizing age-related differences and comparing these to muscle strength. We recruited 94 participants (43 female; median age = 56, range = 22-89 years) and measured microstructure parameters-fractional anisotropy (FA) and mean diffusivity (MD)-in 12 thigh muscles, and architecture parameters-pennation angle, fascicle length, fiber curvature, and physiological cross-sectional area (PCSA)-in the rectus femoris (RF) and biceps femoris longus (BFL). Knee extension and flexion torques were also measured for comparison to architecture measures. FA and MD were associated with age (β = 0.33, p = 0.001, R2 = 0.10; and β = -0.36, p < 0.001, R2 = 0.12), and FA was negatively associated with Type I fiber proportions from the literature (β = -0.70, p = 0.024, and R2 = 0.43). Pennation angle, fiber curvature, fascicle length, and PCSA were associated with age in the RF (β = -0.22, 0.26, -0.23, and -0.31, respectively; p < 0.05), while in the BFL only curvature and fascicle length were associated with age (β = 0.36, and -0.40, respectively; p < 0.001). In the RF, pennation angle and PCSA were associated with strength (β = 0.29, and 0.46, respectively; p < 0.01); in the BFL, only PCSA was associated with strength (β = 0.43; p < 0.001). Our results show skeletal muscle architectural changes with aging and intermuscular differences in the microstructure. DT-MRI may prove useful for elucidating muscle changes in the early stages of sarcopenia and monitoring interventions aimed at preventing age-associated microstructural changes in muscle that lead to functional impairment.

Effects of Fiber Shape on Mechanical Properties of Fiber Assemblies

The effects of fiber shape on the mechanical responses of fiber assemblies under compression, tension, and shear deformations are numerically investigated using the discrete element method (DEM). Simulations of the compression of ring-shaped fibers are consistent with experimental results, verifying the discrete element method code. In the compressive tests of S-shaped fibers, pressure exhibits a nonmonotonic dependence on fiber curvature; while in the tensile tests, yield tensile stress generally decreases with increasing fiber curvature. In the shear tests, yield shear stress decreases with increasing fiber curvature for the S-shaped fibers, and the smallest yield shear stresses and the smallest coordination numbers are obtained for U-shaped and Z-shaped fibers. It is interesting to observe that for the assemblies of various fiber shapes, yield shear stress increases with increasing maximum Feret diameter of the fibers, which characterizes the largest dimension of a fiber between two parallel tangential lines. These novel observations of the effects of fiber shape provide some guidelines for material designs with the fibers.

The mechanical behaviors of random curved fiber networks by numerical simulations

Random curved fiber networks (RCFNs) are ubiquitous in biological materials and artificial composites, attracting much attention because of their unique mechanical properties and functionalities. Based on numerical simulations, the effects of fiber morphology, fiber aspect ratio and relative density on mechanical properties of two dimensional (2D) RCFNs are investigated in terms of the elastic modulus, yield strength as well as energy ratio of stretching to bending deformation. Based on the conditional probability density function (CPDF), a general theoretical model is extended for calculating the crosslink density of fiber networks, which elucidates the independence of crosslink density on fiber shape in RCFNs. According to numerical results, the elastic modulus and yield strength decrease with rising fiber curvature and are enhanced by increasing of aspect ratio and relative density. The influence of fiber aspect ratio in RCFNs exhibits thresholds, beyond which the mechanical properties will reach upper limits for constant curvature. The scaled yield strength to modulus of RCFNs versus curvature is found to collapse into a nonlinear curve regardless of aspect ratios. The energy ratio is influenced by fiber curvature more vigorously which is explained by deformations of curved segments and load paths deviations. This paper is expected to contribute to deep understanding of relationships between overall mechanical properties and structural parameters of RCFNs.

Effect of fiber content and curvature on probabilistic free vibration analysis of cylindrical shallow shells reinforced by short natural fibers

In the present paper, the effect of the fiber contents and the curvature on the probabilistic analysis of free vibration of Short Alpha Fibers (SAF) reinforced Cylindrical Shallow Shells has been presented. Firstly, an experimental study is carried out to manufacture the cylindrical shallow shells with different fiber contents from Alfa (Wf = 5%, 10% and 15%) impregnated with Medapoxy STR resin. Using a hammer, free vibration testing is made to predict damping coefficients, frequencies and mode shapes versus Wf and cylinder curvature (k). A numerical model was developed by ANSYS software to compute the natural frequencies and the corresponding shapes and compare them with the experimental results. Finally, a Monte Carlo simulation (MCS) is developed to study the probabilistic free vibration of cylindrical shallow shells modeled by finite element (FE). Material and physical properties are considered uncertain parameters and are represented by different Coefficients of Variation (CoV). The results show that reinforcing the epoxy matrix with SAF makes the cylindrical shallow shells more flexible and deformable due to high strain values and low stiffness. In fact, it can also reduce the effect of high resonance by considering the damping phenomenon. The reliable design of cylindrical shallow shells can only be ensured by intensive control of Young's modulus CoV (Ec).

Evaluation of focal adhesion mediated subcellular curvature sensing in response to engineered extracellular matrix.

Fibril curvature is bioinstructive to attached cells. Similar to natural healthy tissues, an engineered extracellular matrix can be designed to stimulate cells to adopt desired phenotypes. To take full advantage of the curvature control in biomaterial fabrication methodologies, an understanding of the response to fibril subcellular curvature is required. In this work, we examined morphology, signaling, and function of human cells attached to electrospun nanofibers. We controlled curvature across an order of magnitude using nondegradable poly(methyl methacrylate) (PMMA) attached to a stiff substrate with flat PMMA as a control. Focal adhesion length and the distance of maximum intensity from the geographic center of the vinculin positive focal adhesion both peaked at a fiber curvature of 2.5 μm-1 (both ∼2× the flat surface control). Vinculin experienced slightly less tension when attached to nanofiber substrates. Vinculin expression was also more affected by a subcellular curvature than structural proteins α-tubulin or α-actinin. Among the phosphorylation sites we examined (FAK397, 576/577, 925, and Src416), FAK925 exhibited the most dependance on the nanofiber curvature. A RhoA/ROCK dependance of migration velocity across curvatures combined with an observation of cell membrane wrapping around nanofibers suggested a hybrid of migration modes for cells attached to fibers as has been observed in 3D matrices. Careful selection of nanofiber curvature for regenerative engineering scaffolds and substrates used to study cell biology is required to maximize the potential of these techniques for scientific exploration and ultimately improvement of human health.

Eigenfrequency optimization of variable stiffness manufacturable laminates using spectral Chebyshev approach and lamination parameters

This study presents a meshless modeling approach to design variable-stiffness laminates considering manufacturing constraints. The governing equations are derived using lamination parameters and first-order shear deformation theory. The solution approach uses Chebyshev polynomials and Galerkin’s method to obtain the discretized equations of motion. The developed framework was used to maximize the fundamental frequency of composite plates. The variable-stiffness designs provided up to 28.4% higher frequencies compared to optimum constant-stiffness laminates, although the actual level of improvement depends on the number of layers. Finally, manufacturable fiber paths were obtained considering the allowed fiber curvature, which can also reduce the frequency values.

Recessive mutation for low fibre curvature in the wool of sheep

Two mating experiments suggest a recessive gene for lustre in the birth coat of the lamb and low fibre curvature of the yearling fleece. Suffolk sheep normally have high wool fibre curvature, but a sire was found that had very low fibre curvature (21.0°/mm). In the first experiment, no lambs were born with a lustrous birth coat among the initial progeny from this sire (n = 32). Fleece samples at weaning (70.0°/mm) and yearling shearing (67.6°/mm) had high fibre curvature with no sign of segregation. All second-generation progeny of this sire (n = 4) were born with a lustrous birth coat, yet none survived to lamb shearing. Breeding was discontinued.In a second experiment, two male lambs were born with lustrous birth coats and low fibre curvature at weaning (29.0°/mm, 20.8°/mm). First generation progeny (n = 17, n = 18) from these sires showed no evidence of lustre of their birth coat, with fleece samples that were relatively high in fibre curvature at weaning (43.1°/mm, 43.8°/mm) and yearling (43.4°/mm, 45.4°/mm) respectively. When one sire was crossed back to the ewe progeny of both (n = 10) there was strong evidence for segregation of lustre of the birth coat. Lustrous lambs (n = 9) had low fibre curvature when shorn at weaning (21.0°/mm) in comparison with lambs (n = 8) with a wild-type birth coat (46.5°/mm) (P < 0.001). Fibre curvature had increased at yearling shearing of those recorded as lustrous at birth (41.6°/mm) but remained significantly lower (P = 0.001) than those that were recorded as wild-type (56.9°/mm). There was also a large difference in medullation between the two phenotypes, where the lustrous birth coat was associated with greater medullation in the fleece at weaning (54.5 %) and again as yearlings (22.5 %), compared with wild-type progeny (9.5 % and 4.0 %) respectively. There was no difference between birth coat types for weaning weight, yearling weight or fibre diameter variation at either age of fleece sampling. Fleece weight was considerably lower (P = 0.001) for yearlings that had lustrous birth coats (1.22 kg) than those with wild-type birth coats (1.76 kg), and though there was no significant difference in fibre diameter at this age, fleece samples taken at weaning were around 3 µm finer in wild-type lambs (P = 0.008). These results suggest a recessive gene for low fibre curvature was segregating in this population.

Experimental study of an intensity-modulated curvature sensor with high sensitivity based on microstructured optical fiber.

Surface Plasmon Resonance (SPR) based fiber optic curvature sensors have the advantage of being insensitive to temperature and axial strain. However, they have the disadvantage of low sensitivity and small curvature detection range. To improve the performance of SPR curvature sensors, we propose an intensity-modulated microstructured optical fiber (MOF) curvature sensor. In this sensor, two no-core fibers (NCFs) are used as input-output couplers, and MOF with silver film deposited is used as sensing arms. The light in the cladding is used to excite the SPR, and the exciting resonant valley is extremely sensitive to slight bending changes. The performance of this sensor is investigated theoretically and experimentally. Numerical results show that its cladding pattern is more favorable in the excitation of SPR effects. Experimental results show that the cladding mode of MOF is very sensitive to curvature changes, thus giving it a great advantage in bending measurements. Its sensitivity reaches 0.18 dB/m-1, and linearity reaches 0.995 in the curvature range of 0-30 m-1. The sensor has the advantages of high sensitivity, low temperature and axial strain crosstalk, compact structure, and easy fabrication, which make it attractive in the field of bending sensing.

Experimental study on the temperature measurement of melt blowing fibers through use of infrared thermography

ABSTRACT The temperature of melt blowing (MB) and melt spun fibers is frequently measured using infrared thermography (IRT). However, measurements are affected by the size and curvature of the fiber and by reflected radiation from the nozzle. A procedure was developed to obtain accurate measurements of fiber temperature, which corrects fiber size, fiber curvature, and reflected radiation. This procedure is more general than the ones used so far to correct fiber temperature because it corrects the three abovementioned factors using a single correction formula, this being the first time that fiber temperature correction for reflected radiation has been accounted for. It is also more accurate because it uses the local fiber diameter to correct fiber curvature, instead of mean fiber diameter. The procedure was used to measure the temperature of MB adhesive fibers produced by two multi-hole nozzle designs, the results indicating that, in melt blowing applications, the distortion caused by fiber curvature and reflected radiation on the measurement proves to be far more significant than that caused by fiber size, while correction generally leads to an increase in fiber temperature. Considerations as to the effect of different parameters on temperature correction, as well as the quality of thermographic images, are also provided throughout the text. The conclusions emerging from this study allow more accurate temperature measurements of melt blown and melt spun fibers to be obtained.

Bartnik–Hilbert manifold structure on fibers of the scalar curvature and the constraint operator

Bartnik–Hilbert manifold structure on fibers of the scalar curvature and the constraint operator

Percolation threshold and electrical conductivity of conductive polymer composites filled with curved fibers in two-dimensional space.

Quantifying the influence of fiber curvature on the percolation behavior of flexible conductive fiber and further on the electrical conductivity of conductive polymer composites (CPCs) is crucial for the design of CPCs. This study considers CPCs as a random packing of soft curved discorectangles (CDCRs) in a polymer matrix. The geometry of CDCR is developed, and an inter-CDCR contact detection algorithm is used to generate a random packing structure of CDCRs. The effects of aspect ratio α and bending central angles θ of CDCR on the percolation threshold ϕc of the overlapped CDCR system in a two-dimensional plane are then investigated using the finite-size scaling method. The result reveals that ϕc decreases monotonically as α grows and increases monotonically as θ rises. A shape-independent power law formula, denoted as ϕc = 2.2015 A-0.8172dex is developed to quantify the relationship between the Adex and ϕc. A comparison of our numerical simulations, published data, and predictions verifies the reliability and universality of the fitting model. Subsequently, a resistor network searching algorithm (RNSA) is proposed to construct the random resistor network model (RRNM). A power law model, denoted as is developed to evaluate the effects of the normalized reduced density (η - ηc)/ηc on the effective conductivity σeff of CPC. Comparing our predictions with data from the literature and our simulation verifies the reliability of our RNSA and the fitting model. This paper's methodology and findings may provide a theoretical hint for the CPC's design.

Miniature optical fiber curvature sensor via integration with GaN optoelectronics

Optical fiber curvature sensors have been considered as a promising option for human motion detection due to its good toughness, bending flexibility and anti-electromagnetic interference. However, for wearable devices, the miniature configuration is preferred, and a high integration of the light emitter, receiver and guided fiber is essential to configure the miniaturized sensing system. Here, we present a miniaturized curvature sensing system by integrating a GaN-based optoelectronic chip with the plastical optical fiber (POF). The light emitter and detector are fabricated on a GaN-on-sapphire wafer to form a tiny chip sized at 2.5 times 1.5 mm2. The on-chip photodetector (PD) effectively senses the reflected light intensity, extracting information on the fiber bending deformation. A compact curvature sensing system is demonstrated for finger motion detection with movement angles of 30–90° and frequencies of 0.4, 1, and 1.6 Hz. The results show that the monolithically integrated LED and PD chip can be combined with the POF with reliable operation. The demonstration of the monolithically integrated optoelectronic device suggests a promising potential technology for future wearable fiber optical sensor system.

Hollow-core negative curvature fiber with high birefringence for temperature sensing based on surface plasmon resonance effect

A hollow-core negative curvature fiber (HC-NCF) with high birefringence for temperature sensing based on surface plasmon resonance effect is proposed. For the HC-NCF, the cladding layer is composed of six silica tubes, and the thickness and radius of the two silica tubes in the y-axis direction are different from those of other ones. Moreover, the two silica tubes in the y-axis direction are filled with the gold wires, and the ethanol to be tested is filled in other silica tubes and air area. As the temperature changes, the coupling state between the x-pol core mode and surface plasmon polarization mode will change. The temperature sensitivity can be up to 3.1 nm / ° C in the range of 20°C to 40°C. Moreover, the proposed HC-NCF temperature sensor simultaneously achieves the maximum R of 3.333 × 10 − 2 ° C and maximum FOM of 269 ° C − 1, which indicates that it has high sensitivity, good linearity, high R, and large FOM. The proposed HC-NCF temperature sensor is expected to be useful in the field of the environmental monitoring.