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The improvement potential of ultrafast all-optical switching by soliton self-trapping, using all-solid dual-core fibres with high index contrast, was analyzed numerically. The study of the femtosecond nonlinear propagation was performed based on coupled generalised nonlinear Schrödinger equations considering three fibre architectures: homogeneous cladding all-solid, photonic crystal air-glass, and photonic crystal all-solid. The structural geometries of all three architectures were optimised in order to support high-contrast switching performance in the C-band, considering pulse widths at the 100 fs level. Comparing the three structural alternatives, the lowest switching energies at common excitation parameters (1700 nm and 70 fs pulses) were predicted for the homogeneous cladding dual-core structure. Further optimization of the excitation wavelength and pulse width resulted in lower switching energies and simultaneous improvement of the switching contrasts at the combination of 1500 nm, 75 fs pulses and a fibre length of 43 mm. The spectral aspect in this optimised case expresses a broadband and uniform switching character with a span of over 200 nm and a contrast exceeding 30 dB at more frequency channels.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
A Kramers-Kronig (KK) receiver is applied to a phase-sensitive optical time domain reflectometry based on direct detection. An imbalanced Mach-Zehnder interferometer with a 2× 2 coupler is used in sensing system to encode the phase information into optical intensity. The directly obtained signal is treated as the in-phase component, and the KK receiver provides the quadrature component by Hilbert transform of the obtained signal, so that the optical phase can be retrieved by IQ demodulation. The working principle is well explained, and the obtained phase variance is theoretically analyzed. The experiment demonstrates the functionality of the sensor and validates the theoretical analysis.
In this thesis, a distributed Brillouin sensor in perfluorinated polymer optical fibers utilizing BOFDA is presented. These commercially available polymer fibers offer beneficial characteristics for sensing applications such as higher break down strain up to 100 %, minimal bending radii below 2 mm, higher sensitivity to temperature and lower sensitivity to strain compared to their silica equivalent.
The chosen wavelength of operation at 1319 nm corresponds to lower fiber propagation loss (< 37 dB/km) compared to other approaches at 1550 nm (150 - 250 dB/km). A 86 m PFGI-POF was successfully measured by BOFDA with spatial resolution of 3.4 m.
The findings related to humidity influences can serve as a basis for future distributed humidity sensors not only limited to stimulated Brillouin backscattering.
Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis.
A long distance range over tens of kilometers is a prerequisite for a wide range of distributed fiber optic vibration sensing applications. We significantly extend the attenuation-limited distance range by making use of the multidimensionality of distributed Rayleigh backscatter data: Using the wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) technique, backscatter data is measured along the distance and optical frequency dimensions. In this work, we develop, train, and test deep convolutional neural networks (CNNs) for fast denoising of these two-dimensional backscattering results. The very compact and efficient CNN denoiser “DnOTDR” outperforms state-of-the-art image denoising algorithms for this task and enables denoising data rates of 1.2 GB/s in real time. We demonstrate that, using the CNN denoiser, the quantitative strain measurement with nm/m resolution can be conducted with up to 100 km distance without the use of backscatter-enhanced fibers or distributed Raman or Brillouin amplification.
Short presentation of the PhD project in machine learning based Brillouin distributed sensing. Machine learning can be used to enhance the performance of BOFDA and reduce considerably the measurement time. Apart from this, ML can also be used to extract more information from the Brillouin gain spectrum in order to render the temperature and strain discrimination possible
We present the results of distributed fiber optic strain sensing for condition monitoring of a hybrid type IV composite fully wrapped pressure vessel using multilayer integrated optical fibers. Distributed strain sensing was performed for a total number of 252,000 load cycles until burst of the vessel. During this ageing test material fatigue could be monitored and spatially localized. Critical material changes were detected 17,000 cycles before material failure. Results have been validated by acoustic emission analysis.
The ever more ambitious strategic goals of meeting the requirements in ensuring technical safety and security of civil structures have resulted in flourishing development of innovative structural health monitoring (SHM) technologies for early damage diagnosis and prognosis. At the same time, implementing SHM systems provides tangible economic benefits derived from lower life-cycle costs associated with reduction in the maintenance, repair and insurance expenses. Due to the large size and harsh environmental conditions common to most civil structures, the broad range of favorable physical-mechanical properties of POFs allow for customized monitoring solutions for a wide variety of applications.
In addition to common SHM-related advantages of optical fibers including their electromagnetic immunity, small size, lightweight as well as spark-free and non-conductive characteristics, POFs offer better bending and fracture resistance then their glass-counterparts. Particularly, the improved robustness of POFs, their ease of handling, low Young’s Modulus and high elastic limit of 10% compared to 1% in silica glass [1] are relevant to practical applications. Depending on the composition, dopants, drawing process and geometry [2], strain measurement up to 45% [3] or even above 100% [4,5] has been demonstrated with standard POFs. Therefore, the dominant market expected for POF sensors includes monitoring of high-strain-rate deformations in earthwork structures, crack detection in concrete and masonry structures [6] or overstressing in high-rise steel structures exposed to moisture, corrosion, leakage, fatigue, vibration, fire, overflow, earthquake and intentional damage. For fracture monitoring within concrete structures, the sensory usage of POFs becomes especially favorable since the extremely alkaline environment of concrete mixtures is well known to be corrosive to standard silica glass optical fibers (GOFs) [1,7].
Most of advanced distributed sensing techniques are commonly based on Rayleigh backscatter reflectometry using commercially available multimode (MM) POFs. Such typical MM POFs range from a step-index (SI) poly(methyl methacrylate) (PMMA) POF having a core diameter of 1 mm to a low-loss graded-index (GI) perfluorinated (PF) POF based on poly(perfluorobutenyl-vinylether) also known as CYTOP [8] with a 50 µm core diameter. The relatively low optical attenuation value of 30 dB/km at 1.3 µm [9,10] makes PFGI POFs also interesting for distributed Brillouin sensing [11-14]. Compared to GOFs, PFGI POFs offer better potential for temperature measurement and have comparably low theoretical attenuation limit [15]. Therefore, POF-based distributed Brillouin sensing is expected to play an important role in the future of SHM, especially at high-strain ranges. The significance of the Brillouin measurment technique can be also enhanced by further development of the single-mode (SM) POFs which are still subject of research and are used for coherent detection techniques [6]. Furthermore, SM PMMA POFs have been characterized in a Mach-Zehnder interferometer setup for strain values up to 15.8% [16,17].
The current development of SM perfluorinated and microstructured POFs (mPOFs) represents an immense promise for quasi-distributed dynamic measurement at high strain levels based on fiber Bragg grating (FBG) technology. While SM mPOFs with optical losses of about 1 dB/m can be fabricated [6], the SM PF POFs presented by Zhou et al. feature low attenuation of even less than 0.2 dB/m in the wavelength range of 1.41 µm to 1.55 µm [18]. At the same time, the SM PF POFs have the potential for improved thermal stability compared to their PMMA counterparts [19].
This whole chapter provides a comprehensive overview on current POF-based sensing principles and SHM technologies, highlighting their diverse applications in civil engineering structures. In the application-related context, close attention is paid to the development of smart sensor-based geotextiles and geogrids. Such geosynthetics-integrated distributed POF sensors have proven to be a promising solution for two- or even tree-dimensional monitoring of critical high mechanical deformations in both geotechnical and masonry structures. Moreover, geosynthetics in the form of nonwoven geotextiles as well as polymer-based geogrids used as carrier materials for POF sensors enable optimized load transfer from the monitored structure to the measuring fiber without losing their original functionality. In other words, smart geosynthetics provide a cost-efficient dual solution for, on the one hand, well-established increase of structure stability and decrease of erosion effects, on the other hand, early-warning and detection capabilities in the prevention and elimination of potential hazards and lasting damages.