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Gas distribution mapping is important to have an accurate understanding of gas concentration levels in hazardous environments. A major problem is that in-situ gas sensors are only able to measure concentrations at their specific location. The gas distribution in-between the sampling locations must therefore be modeled. In this research, we interpret the task of spatial interpolation between sparsely distributed sensors as a task of enhancing an image's resolution, namely super-resolution. Because autoencoders are proven to perform well for this super-resolution task, we trained a convolutional encoder-decoder neural network to map the gas distribution over a spatially sparse sensor network. Due to the difficulty to collect real-world gas distribution data and missing ground truth, we used synthetic data generated with a gas distribution simulator for training and evaluation of the model. Our results show that the neural network was able to learn the behavior of gas plumes and outperforms simpler interpolation techniques.
Die laserbasierte aktive thermografische Prüfung als berührungslose Methode der zerstörungsfreien Werkstoffprüfung (NDT) basiert auf der aktiven Erwärmung des Testobjekts (OuT) und Messung des resultierenden Temperaturanstiegs mit einer Infrarotkamera. Dadurch bedingt können systematische Abweichungen vom vorhergesagten Erwärmungsverhalten Aufschluss über dessen innere Struktur geben. Jedoch ist das Auflösungsvermögen für innenliegende Defekte durch die diffusive Natur der Wärmeleitung in Festkörpern begrenzt. Thermografische Super-Resolution (SR)-Methoden zielen darauf ab, diese Limitation durch die Kombination mehrerer Messungen mit jeweils unterschiedlicher strukturierter Erwärmung und mathematischer Optimierungsmethoden zu überwinden.
Zur Rekonstruktion innerer Defekte mithilfe thermografischer SR-Rekonstruktionsmethodik wird für die Gesamtheit mehrerer Messungen ein schlecht gestelltes und stark regularisiertes inverses mathematisches Problem gelöst, was in einer dünnbesetzten Karte der internen Defektstruktur des OuTs resultiert.
Der vorliegende Vortrag gibt einen Überblick über die geleisteten Arbeiten in diesem Gebiet im Rahmen der hier mit dem Wissenschaftspreis der DGZfP 2024 prämierten Arbeit.
In infrared thermography, the interaction of the heat flow with the internal geometry or inhomogeneities in a sample and their effect on the transient temperature distribution is used, e.g., to detect defects non-destructively. An equivalent way of describing this is the propagation of thermal waves inside the sample. Although thermography is suitable for a wide range of inhomogeneities and materials, the fundamental limitation is the diffuse nature of thermal waves and the need to measure their effect radiometrically at the sample surface only. The crucial difference between diffuse thermal waves and propagating waves, as they occur, e.g., in ultrasound, is the rapid degradation of spatial resolution with increasing defect depth. This degradation usually limits the applicability of thermography for finding small defects on and below the surface.
A promising approach to improve the spatial resolution and thus the detection sensitivity and reconstruction quality of the thermographic technique lies in the shaping of these diffuse thermal wave fields using structured laser thermography.
Some examples are:
• Narrow crack-like defects below the surface can be detected with high sensitivity by superimposing several interfering thermal wave fields,
• Defects very close to each other can be separated by multiple measurements with varying heating structures,
• Defects at different depths can be distinguished by an optimized temporal shaping of the thermal excitation function,
• Narrow cracks on the surface can be found by robotic scanning with focused laser spots.
We present the latest results of this technology obtained with high-power laser systems and modern numerical methods.