Chapter 4 Data System and Data Management in a Federation of HPC/ Cloud Centers

Artificial Intelligence, Deep Learning, Machine Learning, Supercomputin

HRTEM analysis of the high-temperature phases of the newly developed high-temperature Ni-base superalloy VDM 780 Premium

VDM 780 Premium is a recently developed Ni-based superalloy designed for working at high service temperatures (above 650 °C) while keeping the good workability of alloy 718. VDM 780 Premium is based on the austenitic matrix (gamma phase) strengthened by intermetallic Ni3Al-like precipitates (gamma’ phase, fcc L12 structure). Other co-precipitates may be formed in function of the applied heat treatment, such as Ni3Nb-based (delta phase, orthorhombic DOa structure) or Ni3Ti-based (eta phase, hexagonal DO24 structure) precipitates. The amount as well as the size and morphology of the different precipitates depend on the heat treatments performed on the alloy, playing an important role in improving the creep properties or the behavior during forging and recrystallization. This work contains a complex study using various techniques of analytical electron microscopy and synchrotron diffraction intended to clarify the structure of the high-temperature phase formed in the newly developed VDM 780 Premium alloy. The atomic structure of the high-temperature plate-like precipitates formed in VDM 780 Premium after two different thermal treatments has been investigated in relation with the surrounding matrix lattice, proving the stacked d/h structure of the precipitates

Chapter 4 Data System and Data Management in a Federation of HPC/ Cloud Centers

Artificial Intelligence, Deep Learning, Machine Learning, Supercomputin

Metadata4Ing: An ontology for describing the generation of research data within a scientific activity

The ontology Metadata4Ing is developed within the NFDI Consortium NFDI4Ing with the aim of providing a thorough framework for the semantic description of research data, with a particular focus on engineering sciences and neighbouring disciplines. This ontology allows a thorough description of the whole data generation process (experiment, observation, simulation), embracing the object of investigation, all sample and data manipulation procedures, a summary of the data files and the information contained, and all personal and institutional roles. The subordinate classes and relations can be built according to the two principles of inheritance and modularity. Inheritance means that a subclass inherits all properties of its superordinate class, possibly adding some new ones. Modularity means that all expansions are independent of each other; this makes possible for instance to generate expanded ontologies for any possible combinations of method × object of research

Metadata4Ing: An ontology for describing the generation of research data within a scientific activity.

Arndt S, Farnbacher B, Fuhrmans M, et al. Metadata4Ing: An ontology for describing the generation of research data within a scientific activity.The ontology Metadata4Ing is developed within the NFDI Consortium NFDI4Ing with the aim of providing a thorough framework for the semantic description of research data, with a particular focus on engineering sciences and neighbouring disciplines. This ontology allows a thorough description of the whole data generation process (experiment, observation, simulation), embracing the object of investigation, all sample and data manipulation procedures, a summary of the data files and the information contained, and all personal and institutional roles. The subordinate classes and relations can be built according to the two principles of inheritance&nbsp;and modularity. Inheritance&nbsp;means that a subclass inherits all properties of its superordinate class, possibly adding some new ones. Modularity&nbsp;means that all expansions are independent of each other; this makes possible for instance to generate expanded ontologies for any possible combinations of method &times; object of research. The new version 1.2.0 simplifies the specification of parameters by variables Allows to assign string values to variables Offers the possibility to model value ranges uses qudt terms for unit and quantity kind switches to prov:Agent and prov:Organisation (replacing foaf:Person and foaf:Organisation) updates&nbsp;JSON-LD context file, the documentation, the README and the metadata of the ontology adds a citation.cff file </ul

HPC-Cloud-Big Data Convergent Architectures and Research Data Management: The LEXIS Approach

International audienc