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Erscheinungsjahr
- 2019 (471) (entfernen)
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- Optical Tomography (2)
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- Ortsaufgelöste Brillouin-Sensorik (2)
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- PFGI-POF (2)
- POD (2)
- Perfluorinated graded-index polymer optical fiber (2)
- Phased array probes (2)
- Phononischer Kristall (2)
- Photon counting detector (2)
- Plasma (2)
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- thermografisches Verfahren (2)
- 1D-Simulation (1)
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Eingeladener Vortrag
- nein (221)
Fatigue is one of the most prevalent issues, which directly influences the service life expectancy of concrete structures. Fatigue has been investigated for years for steel structures. However, recent findings suggest that concrete structures may also be significantly subjected to fatigue phenomena that could lead to premature failure of certain structural elements. To date, fatigue of reinforced concrete has been given little focus. Knowledge on the influence factors and durability/capacity effects on this material should be improved. Current technological means to measure fatigue in civil structures like bridges and wind turbines (both onshore and offshore) are outdated, imprecise and inappropriate.
Meanwhile, this topic has got much more attention as time-variant loading on concrete structures plays an increasing role, e.g. in bridges with increasing traffic and heavier trucks, and for wind turbines for renewable energy production, e.g. for offshore wind turbine support structures affected by wind and waves.
The European Innovative Training Networks (ITN) Marie Skłodowska-Curie Actions project INFRASTAR (Innovation and Networking for Fatigue and Reliability Analysis of Structures - Training for Assessment of Risk) provides research training for 12 PhD students. The project aims to improve knowledge for optimizing the design of new structures as well as for more realistic verification of structural safety and more accurate prediction of the remaining fatigue lifetime of existing concrete structures.
First, the INFRASTAR research framework is detailed. Then it will be exemplified through the presentation of the major results of the four PhD students involved in the work package dealing with auscultation and monitoring. This includes the development and improvement of Fiber Optics (FO) and Coda Wave Interferometry (CWI) for crack sizing and imagery, new sensor technologies and integration, information management, monitoring strategy for fatigue damage investigation and lifetime prediction.
The separation of two closely spaced defects in fields of Thermographic NDE is very challenging. The diffusive nature of thermal waves leads to a fundamental limitation in spatial resolution. Therefore, super resolution image reconstruction can be used. A new concerted ansatz based on spatially structured heating and joint sparsity of the signal ensemble allows for an improved reconstruction of closely spaced defects. This new technique has been studied using a 1D laser array with randomly chosen illumination pattern.
This paper presents the results after applying super resolution algorithms, such as the iterative joint sparsity (IJOSP) algorithm, to our processed measurement data. Different data processing techniques before applying the IJOSP algorithm as well as the influence of regularization parameters in the data processing techniques are discussed. Moreover, the degradation of super resolution reconstruction goodness by the choice of experimental parameters such as laser line width or number of measurements is shown.
The application of the super resolution results in a spatial resolution enhancement of approximately a factor of four which leads to a better separation of two closely spaced defects.
The separation of two closely located defects in fields of Thermographic NDE is very challenging. The diffusive nature of thermal waves leads to a fundamental limitation in spatial resolution. Therefore, super resolution image reconstruction can be used. A new concerted ansatz based on spatially structured heating and joint sparsity of the signal ensemble allows an improved reconstruction of closely located defects. This new technique has also been studied using 1D laser arrays in active thermography.
The post-processing can be roughly described by two steps: 1. Finding a sparse basis representation using a reconstruction algorithm such as the Fourier transform, 2. Application of an iterative joint sparsity (IJOSP) method to the firstly reconstructed data. For this reason, different methods in post-processing can be compared using the same measured data set.
The focus in this work was the variation of reconstruction algorithms in step 1 and its influence on the results from step 2. More precise, the measured thermal waves can be transformed to virtual (ultrasound) waves that can be processed by applying ultrasound reconstruction algorithms and finally the super resolution algorithm. Otherwise, it is also possible to make use of a Fourier transform with a subsequent super resolution routine. These super resolution thermographic image reconstruction techniques in post-processing are discussed and evaluated regarding performance, accuracy and repeatability.
The diffusive nature of heat propagation complicates the separation of two closely spaced defects. This results in a fundamental limitation in spatial resolution. Therefore, super resolution (SR) image reconstruction can be used. SR processing techniques based on spatially structured heating and joint sparsity of the signal ensemble allows for an improved reconstruction of closely spaced defects. This new technique has been studied using a 1D laser array with randomly chosen illumination pattern.
This paper presents the results after applying SR algorithms such as the iterative joint sparsity (IJOSP) algorithm, to our processed measurement data. Two different data processing strategies are evaluated and discussed regarding their influence on the reconstruction goodness as well as their complexity. Moreover, the degradation of the SR reconstruction by the choice of regularization parameters in data processing is discussed.
The application of both SR techniques that are evaluated in this paper results in a spatial resolution enhancement of approximately a factor of four which leads to a better separation of two closely spaced defects. The fundamental difference between both SR techniques is their complexity.
The separation of two closely located defects in fields of Thermographic NDE is very challenging. The diffusive nature of thermal waves leads to a fundamental limitation in spatial resolution. Therefore, super resolution image reconstruction can be used.
The measured thermal waves can be transformed to virtual (ultrasound) waves that can be processed by applying ultrasound reconstruction algorithms and finally the super resolution algorithm. Otherwise, it is also possible to make use of a Fourier transform with a subsequent super resolution routine.
These super resolution thermographic image reconstruction techniques in post-processing are discussed and evaluated regarding performance, accuracy and repeatability.
Thermographic nondestructive evaluation (NDE) is based on the interaction of thermal waves with inhomogeneities. These inhomogeneities are related to sample geometry or material composition. Although thermography is suitable for a wide range of inhomogeneities and materials, the fundamental limitation is the diffusive nature of thermal waves and the need to measure their effect radiometrically at the sample surface only. The propagation of the thermal waves from the heat source to the inhomogeneity and to the detection surface results in a degradation in the spatial resolution of the technique. A new concerted ansatz based on a spatially structured heating and a joint sparsity of the signal ensemble allows an improved reconstruction of inhomogeneities. As a first step to establish an improved thermographic NDE method, an experimental setup was built based on structured 1D illumination using a flash lamp behind a mechanical aperture. As a follow-up to this approach, we now use direct structured illumination using a 1D laser array. The individual emitter cells are driven by a pseudo-random binary pattern and are additionally shifted by fractions of the cell period. The repeated measurement of these different configurations enables to illuminate each spot of the sample surface in lateral direction. This allows for a reconstruction that makes use of joint sparsity.
The measured data set is processed using super resolution image reconstruction algorithms such as the iterative joint sparsity (IJOSP) algorithm. Using this reconstruction technique and 150 different illumination patterns results in a spatial resolution enhancement of approximately a factor of four compared to the resolution of 5.9 mm for homogenously illuminated thermographic reconstruction.
Further, new data processing techniques have been studied before applying the IJOSP algorithm that are more performant or less prone to errors regarding image reconstruction. The choice of regularization parameters in data processing as well as experimental parameters such as the illumination pattern as a variable heat flux density (i.e., the Neumann boundary condition for convolution with the constant Green's function) have a big influence on the reconstruction goodness. With analytical-numerical modelling and numerical FEM simulations, we studied the influence of the experimental parameters on the result of the non-linear IJOSP reconstruction. This has also been investigated experimentally e.g. using different laser line widths or more measurements per position. These studies are used to derive optimal conditions for a certain measurement image reconstruction technique.
The separation of two closely spaced defects in fields of Thermographic NDE is very challenging. The diffusive nature of thermal waves leads to a fundamental limitation in spatial resolution. Therefore, super resolution image reconstruction can be used. A new concerted ansatz based on spatially structured heating and joint sparsity of the signal ensemble allows for an improved reconstruction of closely spaced defects. This new technique has been studied using a 1D laser array with randomly chosen illumination pattern.
This paper presents the results after applying super resolution algorithms, such as the iterative joint sparsity (IJOSP) algorithm, to our processed measurement data. Different data processing techniques before applying the IJOSP algorithm as well as the influence of regularization parameters in the data processing techniques are discussed. Moreover, the degradation of super resolution reconstruction goodness by the choice of experimental parameters such as laser line width or number of measurements is shown.
The application of the super resolution results in a spatial resolution enhancement of approximately a factor of four which leads to a better separation of two closely spaced defects.
In various kinds of radiography, deficient transmission imaging may occur due to backlighting inside the detector itself arising from light or radiation scattering. The related intensity mismatches barely disturb the high resolution contrast, but its long range nature results in reduced attenuation levels which are often disregarded. Based on X-ray observations and an empirical formalism, a procedure is developed for a first order correction of detector backlighting. A backlighting factor is modeled as a function of the relative detector coverage by the sample projection. Different cases of sample transmission are regarded at different backlight factors and detector coverage. The additional intensity of backlighting may strongly affect the values of materials’ attenuation up to a few 10%. The presented scenario provides a comfortable procedure for corrections of X-ray or neutron transmission imaging data.
Construction methods, environmental stressors, and aging factors are the main causes for defects of reinforced concrete in nuclear power plants (NPP). These defects are typically occurred as corrosion of reinforcement steel, delamination, cracks, malfunction of post-tensioning or steel composite systems etc. Some of the challenges for assessing the performance of these structures by nondestructive testing methods (NDT) are that the assessment could be performed only during the annual overhauls when testing is time-limited and uncertainty of the accuracy and reliability of the available NDT testing devices combined with the lack of the international uniformity of the methods used for NDT tests. To overcome these challenges, a mock-up wall representing a section of the concrete containment of the NPP was built. The mock-up wall included simulated defects, which mimic the most common types of defects in NPP concrete structures such as dimensional errors, honeycombing, delamination, defects adjacent to the steel liner and voids in grouted tendon ducts for the post-tensioned structures. This paper introduces the design and construction of the wall including the concrete properties, reinforcement, tendon ducts and the types of the simulated defects. The paper also introduces the NDT methods and techniques that are suitable for assessing the condition of the mock-up wall under the real environmental conditions. These techniques include mechanical methods as rebound hammer, ultrasonic and electromagnetic methods. This mock-up wall will enable to investigate reliably available NDT methods and experts’ skills providing also an important and very much need educational platform for future NDE experts.
The process of ensuring reliability of NDT applications contains various aspects, such as determining the performance and probability of success, the uncertainty in measurement, the provision of clear and functional procedures and ensuring the correct application accordingly. Test specimens have become powerful elements in supporting many of these aspects. Within the committee for NDT in Civil Engineering (NDT-CE) of the German Society for Nondestructive Testing (DGZfP), the subcommittee on Quality Assurance (UA-QS) therefore addresses the design and the integration of test specimens in the quality assurance process. Depending on the specific purpose, the requirements on test specimens can vary significantly based on the defined simulated scenario. The most prominent purposes of test specimens might be seen in providing references for inspection systems in regard to function control, calibration and validation. Further aspects can be parametric studies, basic investigation of physical principles related to NDT or a simplified and therefore comprehensive demonstration of inspection concepts (e.g. for teaching purposes). The specific purpose of a test specimen dictates the requirements regarding its conception, including the exact design, the material or the fabrication accuracy and the conditioning. In the development of a general guideline by the UA-QS for application-specific procedures and their validation, the use of test specimens is addressed and specific concepts for the design of test specimens are made. This includes the analysis of the measurement process regarding any given application, deriving an adequate calibration approach for it and designing test specimens (calibration specimens) accordingly. Furthermore, it includes the validation of the procedure taking into account all conditions related to the specific application in the field. The validation requires a statistically sufficient number of trials. Thorough evaluation of each trial can only be established if the ground-truth is known. Therefore, test specimens providing a realistic but controlled simulation of the inspection problem are valuable and indispensable elements in the validation process. The requirement of being fully realistic will often not be possible to fulfill due to practical restrictions. Any aspect that cannot be included in the simulation realistically needs to be simulated conservatively. This again, requires a sufficient understanding of the inspection principle and technique to ensure conservativeness. Among other quality-assurance-related aspects, the UA-QS establishes concepts and guidelines regarding sound and efficient approaches for the specific purposes of test specimens. This subcommittee brings together representatives of different Groups along the entire value chain of NDT-CE, including researchers, practitioners, manufacturers and clients. They all work together in establishing a common understanding and level of quality assurance in the industry.