Ingenieurwissenschaften und zugeordnete Tätigkeiten
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- 2022 (2) (entfernen)
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- 12%Cr steel (1)
- Cyclic steam oxidation (1)
- Data infrastructures (1)
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- Digital workflows (1)
- Knowledge graphs (1)
- Materials informatics (1)
- Ontologies (1)
- Specimen geometry (1)
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Organisationseinheit der BAM
- 5.1 Mikrostruktur Design und Degradation (2) (entfernen)
The steam side oxidation of ferritic–martensitic VM12-SHC steel was investigated under thermo-cyclic conditions in water steam at 620/320 °C and 30 bar with a focus on assessing the influence of pre-oxidation time, specimen geometry and surface finish. The specimens were pre-oxidized under isothermal conditions in water steam at 620 °C and 30 bar for 500 h or 1500 h. After pre-oxidation treatment, all specimens were subjected up to 258 thermal cycles. Three different geometries—rectangular coupons, U-shaped ring segments and ring samples—were investigated to evaluate the influence of open/closed shape, and flat/curved surface on corrosion rate. At the same time, two types of surface finish were considered: “as received” and “ground.” The formation of a protective scale by pre-oxidation was investigated. EBSD and ESMA analyses revealed that the Cr-content of the alloy appeared to be insufficient for obtaining a protective oxide scale under studied conditions, at the same time the anayses confirmed that initial oxidation depends on presence of minor alloying elements as Si and Mn, strong oxide formers which can alter the kinetics and morphology of the corrosion reaction. Moreover, rectangular coupons with small wall thickness and flat surface exhibited the highest corrosion rate, while “ground” curved samples showed only local oxidation. This indicates that for same pre-oxidation time, oxidation kinetics is controlled by curvature.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.