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Eingeladener Vortrag
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Carbon Capture Utilization and Storage (CCUS) is a promising technology to reach the target for reduction of CO2 emissions, e.g. from fossil-fuel operated power plants or cement mills. Crucial points for a sustainable and future-proof CCUS procedure are reliability and cost efficiency of the whole process chain, including separation of CO2 from the source, compression of CO2, its subsequent transportation to the injection site and injection into geological formations, e.g. aquifers.
Most components that are in contact with CO2-stream consist of steel. Depending on the operating conditions (e.g. temperature, pressure, and CO2-stream composition) specific suitable steels should be used. The compressed CO2-stream is likely to contain process specific impurities; small amounts of SO2 and NO2 in combination with oxygen and water are most harmful.
One approach, as currently preferred by pipeline operators, is to clean the CO2-stream to such levels, acceptable for carbon steel, commonly used as pipeline material. Another consideration would be, to use more corrosion resistant alloys for CO2-streams with higher amounts of impurities.
Due to the absence of certified benchmarks for upper limits, systematic experiments with impurities in the CO2-stream were carried out reflecting mainly transport and injection conditions.
Within the COORAL project (German acronym for “CO2 purity for capture and storage”) levels of impurities in the CO2-stream, being acceptable when using specific steels, were evaluated. Material exposure to dense or multiphase carbon dioxide (CO2) containing specific amounts of water vapor, oxygen (O2) sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) can be a challenge to steels. In some situations, condensation of impurities and reaction products from the CO2 stream can occur.
CO2 saturated brine is supposed to rise in the well when the injection process is interrupted. The material selection shall ensure that neither CO2 nor brine or a combination of both will leak out of the inner tubing.
This COORAL-work was extended by a follow-up project, called CLUSTER. Here the additional influence of impurities was investigated when merging CO2 streams from different sources, combined within a “so-called” cluster.
Results are summarized within the following table regarding suitability for different parts of the process chain.
Une transition mondiale vers des systèmes énergétiques plus durables, abordables et fiables a été initiée par l’accord de Paris et l’Agenda 2030 des Nations unies pour un développement durable. Il s’agit là d’un défi industriel majeur car les systèmes et infrastructures énergétiques résilients au changement climatique exigent de se positionner pour le long terme. Se pencher sur le comportement dans la durée des matériaux structurels - principalement des métaux et des alliages - s’impose alors comme une nécessité. Dans cette optique, « La corrosion : un défi pour une société durable »présente une série de cas montrant l’importance de la tenue à la corrosion et de la protection anticorrosion des métaux et des alliages pour le développement de systèmes durables, économiques et fiables de production d’énergie.
In failure analysis, micro-fractographic analysis of fracture surfaces is usually performed based on practical knowledge which is gained from available studies, own comparative tests, from the literature, as well as online databases. Based on comparisons with already existing images, fracture mechanisms are determined qualitatively. These images are mostly two-dimensional and obtained by light optical and scanning electron imaging techniques. So far, quantitative assessments have been limited to macrocopically determined percentages of fracture types or to the manual measurement of fatigue striations, for example. Recently, more and more approaches relying on computer algorithms have been taken, with algorithms capable of finding and classifying differently structured fracture characteristics. For the Industrial Collective Research (Industrielle Gemeinschaftsforschung, IGF) project “iFrakto” presented in this paper, electron-optical images are obtained, from which topographic information is calculated. This topographic information is analyzed together with the conventional 2D images. Analytical algorithms and deep learning are used to analyze and evaluate fracture characteristics and are linked to information from a fractography database. The most important aim is to provide software aiding in the application of fractography for failure analysis. This paper will present some first results of the project.
The research focus of this study was set on the corrosion process of condensate as droplets on the surface of carbon steels (X52, X70) martensitic steel UNS S41500, and super austenite UNS N08031 in CO2 atmosphere with impurities at 278 K (to simulate the offshore transportation condition in a buried pipeline). The possibility of dew/droplet formation on the steel surface and wetting behavior of corresponding materials were evaluated by contact angle measurement in dense CO2 at 278 K. To observe the effect of impurities (SO2 and O2) on droplet corrosion process, exposure tests were carried out in the mixed atmosphere with a drop, 1 ‑ 10 µL in volume, of CO2 saturated ultra-pure water on steel surface. Comparable exposure tests were carried out with the same gas mixture and the same volume of water, as vapor, to observe the droplet formation and the corrosion process that follows. Effects of surface roughness on the droplet formation and its corrosion process were further studied and showed no significant role upon long time exposure. The results from droplet experiments were compared to those from the bulk electrolyte for the further recommendation on the quality control of gas stream along with the use of carbon steels as transport pipelines in CCS - Carbon Capture and Storage system.
Die AG Fraktographie im DVM/DGM-Gemeinschaftsgremium „Elektronenmikroskopie in der Materialforschung“ betreibt seit 2013 eine fraktographische online-Datenbank („FractoDB“), die für das interessierte Fachpublikum kostenfrei zur Verfügung steht. Die Analyse und Bewertung von Bruchflächen und der darauf befindlichen Bruchmerkmale ist ein wichtiger Teilaspekt der Schadensanalyse bei der Suche nach Schadensmechanismen und Schadensursachen. Risse und Brüche an realen Bauteilen können nur dann bewertet werden, wenn gut dokumentierte Vergleichsbrüche aus Laborversuchen zur Verfügung stehen, sei es an Proben oder an Vergleichs-Bauteilen. Daher trägt die AG Fraktographie Bildmaterial zusammen, führt systematisch Laborversuche und Ringversuche durch und analysiert Brüche aus Schadensfällen. Die gewonnenen Daten werden zu Datensätzen zusammengestellt und über die Datenbank zugänglich gemacht. Derzeit ist ein Bestand von über 400 Datensätzen mit insgesamt über 4.500 Bildern verfügbar, welcher durchsucht werden kann und in Anlehnung an die VDI 3822 organisiert ist. Weitere Aktivitäten der AG Fraktographie, die in der FractoDB abgebildet werden, sind u.a. die Entwicklung einer fraktographischen Symbolik und die Analyse von Bruchmerkmalen mittels Machine Learning. Über die aktuellen Ergebnisse und Planungen wird berichtet.
Fractography plays a critical role in failure analysis of engineering components and has a considerable importance for safety investigations. Usually, the interpretation of fracture surfaces is done by experts with the help of literature and experimental data, that requires a lot of experience. The use of deep learning (DL) with neural networks in failure analysis becomes more and more relevant with the rapidly developing possibilities. Especially, the modern network architectures can assist fractographers in determining various fracture features on SEM images of the fracture surfaces. The basis for the best possible evaluation is the understanding of the influence of the input data used for training deep neural networks (DNN). Therefore, this study discusses the influence of the selection of the input data used for the prediction quality of these networks in order to take this into account for future data acquisition. Specimens of various metallic materials were subjected to fatigue cracking experiment under laboratory conditions. The fractured surfaces were then imaged using various modes or detectors (such as SE, BSE and topography) in SEM, and those captured images were used to create a training data set. The relevance of the individual data for the quality of the prediction is determined by a specific combination of the different detector data. For the training, the well-established architecture of a UNet-ResNet34 with a fixed set of hyperparameters is used. It has been found in this present study that the combination of all input data significantly increases the prediction accuracy, whereby even the combination of SE and BSE data provides considerable advantages over the exclusive use of SE images.
Bei der Untersuchung von Bruchflächen werden zunächst Beobachtungen gemacht, d.h. makroskopische und mikroskopische Merkmale analysiert und in Bildern dokumentiert. Im zweiten Schritt werden anhand der gefundenen Merkmale Rissverlauf und Bruchmechanismen ermittelt. Abschließend sollen diese Ergebnisse in einem Bericht oder Gutachten zusammengefasst werden. Hierbei ist eine skizzenhafte Darstellung der fraktographischen Befunde hilfreich für das Verständnis des Lesers. Ein vorgefertigter fraktographischer Symbolsatz würde dies erheblich vereinfachen.
Skizzen von fraktographischen Befunden sind in der Literatur recht häufig zu finden, z.B. in der VDI 3822 oder dem ASM Handbook Fractography, allerdings gibt es hierzu keine umfassende Systematik, wie sie z.B. aus der Meteorologie (Wetterkarten), Geographie (Landkarten, Seekarten) oder Geologie (Bodenkarten) seit langer Zeit bekannt sind. Deshalb wird hier der Versuch unternommen, einen Symbolsatz zu entwickeln und zur Verfügung zu stellen, mit dessen Hilfe fraktographische Befunde in eindeutiger und übersichtlicher Weise veranschaulicht werden können. Der fraktographische Symbolsatz ("Fracto Graphics") befindet sich in Entwicklung, ist offen zur Diskussion sowie für Verbesserungen.
Many microstructural features exhibit non-trivial geometries, which can only be derived to a limited extent from two-dimensional images. E.g., graphite arrangements in lamellar gray cast iron have complex geometries, and the same is true for additively manufactured materials and three-dimensional conductive path structures. Some can be visualized using tomographic methods, but some cannot be due to weak contrast and/or lack of resolution when analyzing macroscopic objects. Classic metallography can help but must be expanded to the third dimension. The method of reconstructing three-dimensional structures from serial metallographic sections surely is not new. However, the effort required to manually assemble many individual sections into image stacks is very high and stands in the way of frequent application. For this reason, an automated, robot-supported 3D metallography system is being developed at BAM, which carries out the steps of repeated preparation and image acquisition on polished specimen.
Preparation includes grinding, polishing and optionally etching of the polished surface. Image acquisition comprises autofocused light microscopic imaging at several magnification levels. The image stacks obtained are then pre-processed, segmented, and converted into 3D models, which in the result appear like microtomographic models, but with high resolution at large volume. Contrasting by classical chemical etching reveals structures that cannot be resolved using tomographic methods. The integration of further imaging and measuring methods into this system is underway. Some examples will be discussed in the presentation.
Die Fraktographische Online-Datenbank der AG Fraktographie – Entwicklungsstand und weitere Planung
(2023)
Vortrag zum aktuellen Stand der fraktographischen online-Datenbank. Darstellung des AGM/DVM Gemeinschaftsausschusses REM in der Materialforschung und seiner Arbeitsgruppen Fraktographie, EBSD und 2D/3D-Prüfung. Ziele und Inhalte der fraktographischen Datenbank, Aufruf zum Mitmachen. Durchführung von Vergleichsversuchen, Entwicklung einer fraktographischen Symbolik, Ringversuch, Vorhaben iFrakto mit Entwicklung einer KI-gestützten Bruchflächenanalyse.