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- 8.6 Faseroptische Sensorik (170) (entfernen)
We demonstrate (and are the first to do so) 63 km Brillouin Optical Frequency-Domain Analysis (BOFDA) for temperature and strain monitoring using a 100 km fiber loop. The use of BOFDA for long-range applications can be considered a novel approach, as previous investigations focused on the utilization of Brillouin Optical Time-Domain Reflectometry and Analysis (BOTDR and BOTDA, respectively). At 51.7 km, a 100 m hotspot (37 °C) was detected without using distributed Raman amplification or image processing.
We present, to our knowledge for the first time, a 100-km Brillouin Optical Frequency-Domain Analysis (BOFDA) employing a 200-km fiber loop. Compared to our previous publication, enhanced sensor length, sensor accuracy and spatial resolution are presented. The performance improvements are achieved by applying distributed Raman amplification (DRA) and a digital high-pass filter. We report on temperature measurements over sensing distances of 75 km and 100 km both with a 12.5-m spatial resolution. Temperature changes of 5 °C have been measured along 75 km sensing fiber. A temperature change of 30 °C has been detected at 99.5 km.
We present a temperature sensor based on a polymer exhibiting a Lower Critical Solution Temperature (LCST) in aqueous solution encapsulated in a capillary. Parameters are chosen such that the solution exhibits a cloud point in a temperature range of 30° C to 39°C. The characteristic of thermoresponsive polymers with an LCST, is that above that temperature phase separation of the polymer takes place which leads to a temperature-dependent formation of a cloudy suspension. An optical intensity measurement over the desired temperature range is established by an increase of optical attenuation inside the polymer solution caused by a rising temperature. For our purpose, the polymer capillary is connected to transmitter and receiver via a Polymer Optical Fibre (POF). Our intensity measurement is, to the best of our knowledge, a novel method and can be considered simple when compared to existing fibre-based temperature measurement techniques. Due to the lack of electrical components at the probe, this sensor is suitable for measurements in strong electromagnetic fields and environments for which flying sparks are hazardous, i.e., inflammable fluids or gases. Furthermore, all manufactured sensors share the same temperature dependence and, therefore, are well-suited for comparative measurement, e.g., flow measurement systems. With the given temperature range, a body temperature measurement is also suitable.
We demonstrate the use of a 3D printed radial collimator in X-ray powder diffraction and surface sensitive grazing incidence X-ray diffraction. We find a significant improvement in the overall Signal to background ratio of up to 100 and a suppression of more than a factor 3⋅10⁵ for undesirable Bragg reflections generated by the X-ray “transparent” windows of the sample environment.
The background reduction and the removal of the high intensity signals from the windows, which limit the detector’s dynamic range, enable significantly higher sensitivity in experiments within sample environments such as vacuum chambers and gas- or liquid-cells. Details of the additively manufactured steel collimator geometry, alignment strategies using X-ray fluorescence, and data analysis are also briefly discussed. The flexibility and affordability of 3D prints enable designs optimized for specific detectors and sample environments, without compromising the degrees of freedom of the diffractometer.
The influence of size effects on the thermoelectric properties of thin platinum films is investigated and compared to the bulk. Structural properties, like the film thickness and the grain size, are varied. We correlate the electron mean free path with the temperature dependence of the electrical conductivity and the absolute Seebeck coefficient SPt of platinum. A measurement platform was developed as a standardized method to determine SPt and show that SPt,film is reduced compared to SPt,bulk. Boundary and surface scattering reduce the thermodiffusion and the phonon drag contribution to SPt,film by nearly the same factor. We discuss in detail on behalf of a model, which describes the temperature dependence of the absolute Seebeck coefficient, the influence of size effects of electron-phonon and phonon-phonon interaction on SPt.
Monitoring of construction projects plays increasingly important role in the implementation of growing needs and requirements, especially in geologically difficult areas. This particularly concerns tunnelling and special civil engineering projects. The use of fiber optic sensors for structural health monitoring provides significant economic advantages regarding lower life-cycle costs of the civil infrastructure. We present our achievements in the development of distributed polymer optical fiber sensors based on Rayleigh and Brillouin scattering for early damage detection.
Acoustic emission testing
(2018)
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.
Application of Intensity-Based Coherent Optical Time Domain Reflectometry to Bridge Monitoring
(2022)
Although distributed fiber sensing techniques have been widely used in structural health monitoring, the measurement results of bridge monitoring, particularly under destructive testing, have rarely been reported. To the best of our knowledge, this paper is the first report of distributed vibration measurement results, which we obtained during a three-day destructive test on an abolished bridge. A coherent optical time domain reflectometry (COTDR) was used to acquire the vibration information while the bridge was being sawed. The obtained signal was analyzed in time and frequency domain. Some characteristics of the sawing-induced vibration were retrieved by the short-time Fourier transform; the vibration exhibited several high frequency components within the measured range up to 20 kHz and all the components appeared in the same time slot. Some unexpected signals were also detected. Thorough analysis showed that they are quite different from the sawing-induced vibration and are believed to originate from internal damage to the bridge (probably the occurrence of cracks).
In this study, a signal processing approach for heterodyne Փ-OTDR and C-OTDR systems that can obtain external perturbation and its frequency content in a faster way is proposed. We can detect vibrations with the same or better SNR using this processing approach, only with a single step and fast calculation. Fig. 12 shows the comparison of the normalized measurement time for the gamma matrix method and the conventional method throughout the three experiments. The speed of processing using a gamma matrix is ~35%–50% faster compared to a conventional method in high frequency test (PZT), low frequency test (walking) and street monitoring test. The processing speed in low frequency test is normally a bit lower than the similar high frequency one, since we must use higher number of time frames. Also, in street test we have higher speed because we can select wider gauges.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
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 novel DAS setup based on geometric phases in coherent heterodyne detection is applied for the first time to the characterisation of the Earth’s subsurface. In addition, an optimisation of the proposed setup in terms of its spatial resolution is also presented for the first time. The surface waves are generated by strong blasts of 25 kg of explosives at a dedicated test site. A 10 km dark fiber link in the vicinity of the test site connected to the test setup records the resulting strain signals. The spike-free and low-noise strain data thus obtained minimize post-processing requirements, making the setup a candidate for real-time seismic monitoring. An analysis of the dispersion characteristics of the generated surface waves is performed using a recently reported optimised seismic interferometric technique. Based on the dispersion characteristics, the shear wave velocities of the surface waves as a function of the depth profile of the Earth’s crust are determined using an optimised evolutionary algorithm.
We present our achievements in the development of distributed fiber optic sensing systems based on Brillouin optical frequency-domain analysis for structural health monitoring. The focus of the applications is on the gapless monitoring of geotechnical structures, large area infrastructures and electrical grids. The work includes the latest use of machine learning algorithms to reduce measurement time by coexistent increasing the measurement accuracy.
Glass-fibre-reinforced plastics (GFRP) are commonly used for the construction of pressure vessels and tanks for example in automotive and aerospace industries. Especially for pressure vessels used for the storage of natural gas or hydrogen with operation pressures up to 700 bar, an early material fatigue detection is of great interest. Measuring the distributed strain profile of GFRP structures helps to understand and detect material fatigue. In this article, we demonstrate the great potential of swept wavelength interferometry (SWI) based distributed strain sensing for the monitoring of pressure vessels made from GFRP. A resin transfer molding (RTM) process was used to manufacture GFRP-tube specimens. Commercial optical fibres with polyimid coating were glued to the surface externally in circumferential and axial direction. A cyclic load of up to 150 bar was applied to the samples using a servo-hydraulic test bench. Comparing the loaded and unloaded test conditions, we determined up to 2 % elongation in circumferential direction. We demonstrate reliable distributed strain measurements with sub-centimetre spatial resolution. By monitoring these high-resolution strain profiles, we were able to detect local material degradation that manifested itself as localized strain changes. Crucially, the material Degradation could be detected already after 75 % of dat the fatigue life before a crack appeared that led to leakage.