Interoperability architecture for bridging computational tools: application to steel corrosion in concrete

Mir, Zahid; Friis, Jesper; Hagelien, Thomas F.; Svenum, Ingeborg-Helene; Ringdalen, Inga Gudem; Konchakova, Natalia; Zheludkevich, Mikhail L.; Höche, Daniel

doi:10.1088/1361-651x/ab6209

Cited by 5 publications

(7 citation statements)

References 20 publications

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“…The technology to enable semantic interoperability has a strong foundation in concepts and abstractions that includes vocabularies, taxonomies, relations, restrictions and a set of rules and axioms.These are the main ingredients of ontologies. Ontologies in computational science are usually constructed by a set of semantic triples or Resource Description Framework (RDF) triples, as specified by the W3C Consortium 1,2 . In order to transmit information between two computer systems, a set communication protocols must be established.…”

Section: Technology Stack For Semantic Interoperabilitymentioning

confidence: 99%

“…A well known challenge in setting up a complex modelling workflows is the interrelated calculation of different aspects of materials behaviour and optimization, where the output from the first model (A) is not represented in the way expected by the second model (B) [2]. However, if the output of model A is semantically equivalent to the input needed by model B, it supports the successful transformation by post-processing of the output of A to be used by model B, and hence establish a robust workflow for analysis and optimization of considered materials and business KPIs [3].…”

Section: Introductionmentioning

confidence: 99%

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A Practical Approach to Ontology-Based Data Modelling for Semantic Interoperability

Hagelien¹,

Preisig²,

Friis³

et al. 2021

14th WCCM-ECCOMAS Congress

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Efforts to provide a standard representational framework based on current materials modelling and characterization knowledge facilitating collaboration, digital data representation, knowledge systems and semantic interoperability has been the main agenda for the European Materials Modelling Council (EMMC). One challenge in adopting the technology is related to the on-boarding process, particularly regarding the availability of ontologies and practical aspects to linking data to the ontologies, which requires deep insights in the current knowledge organization system. Latter challenges we address by constructing an interoperability framework based on data-models and explicit ontological mappings. Data models allow for building a representation of a computer system from different perspectives. The conceptual view identifies what real-world concepts the data represents. The logical perspective defines the rules and structures of how to implement the strategy. The physical perspective establishes the relationship between the data model and a specific database system. We show the ontology-based data modelling approach. It addresses the separation of the logical and the conceptual perspective, and allows for the development of the data-models. This approach also puts the data-models directly into production before applying the ontological mappings or developing the domain ontologies. Finally, we show a framework for information exchange that connects data models to physical storage and allows software applications to eliminate the need to support specific input-output operations, file conversion, file versioning, etc.

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Section: Technology Stack For Semantic Interoperabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Practical Approach to Ontology-Based Data Modelling for Semantic Interoperability

Hagelien¹,

Preisig²,

Friis³

et al. 2021

14th WCCM-ECCOMAS Congress

View full text Add to dashboard Cite

show abstract

“…For example, the electric potential can be described as a 2D scalar field with the shape corresponding to the grid, with the unit Volt. The semantic interoperability platform SOFT [1] allows for instantiating the data model and manages data input/output to and from various sources, while ensuring a consistent internal representation.…”

Section: Efficient Data Centric Architecturementioning

confidence: 99%

“…Advanced simulation techniques provide the access to different software and data, which should be available and exchangeable in numerical analysis of the complex non-liner multi-scale modelling problem of corrosion, bridging different simulation tools on different simulation steps and levels [4]. Moreover, the computation challenge includes different issues with homogenization, accuracy, real-time validation and other functional complexities [1]. Unfortunately, exchange of data provided by different computational solvers is very often strong non-uniform due to use of different programming languages and application of different modelling stiles.…”

Section: Introductionmentioning

confidence: 99%

“…The real world objects and their properties are related to concepts in a domain ontology for galvanic corrosion based on the European Materials & Modelling Ontology (EMMO) [12]. This study will give a base for further investigations and offer some possibilities to implement the involved chemical and physical processes on a "volumetric" surface [1].…”

Section: Introductionmentioning

confidence: 99%

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Ontology Assisted Modelling of Galvanic Corrosion of Magnesium

Hoeche¹,

Konchakova²,

Zheludkevich³

et al. 2021

14th WCCM-ECCOMAS Congress

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Multi-physics and multiscale modelling frameworks, especially for the computer based analysis of surface damage and corrosion, requires not only robust computational tools but also an efficient data-centric architecture for handling information exchange at different modelling scales. The issue to exchange data provided by different computational solvers as well as required and used in different programming languages forms a request in specific formats signifying a strong non-uniformity for an easy nexus with other solvers. This non-uniformity has created a need to focus on intermittent state-of-theart data-centric software tools, which aim to bridge data exchange technology, to ensure heterogeneity across a wide range of solvers [1]. Moreover, data organization in the form of ontological representation and metadata structures are necessary to be prepared as a standard for a coherent representation of information regardless of the diverse nature of data formats specific to a scientific discipline. A domain ontology based on the European Materials & Modelling Ontology (EMMO) for galvanic corrosion is outlined and connected to corresponding concepts in a galvanic corrosion model. This fundamental work provides and discusses the concept, underlying terminology and working mechanism of a data-centric architecture for exchanging and interfacing data-flow between data sources/sinks and solvers. It realizes an ontological representation of physics and chemistry of galvanic corrosion. Thus, it is a nuclei for further improvement's of interoperability between complex corrosion related phenomena and models. It paves the way for accurate computational corrosion engineering.

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Toward a Unified Description of Battery Data

Clark

Bleken

Stier

et al. 2021

Advanced Energy Materials

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need batteries that are cheaper, safer, and more energy dense. [2] The World Economic Forum projects that the annual battery production revenue will grow to 300 billion dollars per year by 2030. [3] This demand from the market is compounded by the ambitious goals of the Paris Agreement on climate change, which require the transition to renewable energy conversion and storage technologies. [4] The development and large-scale production of sustainable high-performance batteries is one of the most intensely pursued technical research topics in the world today. Bold action is needed to meet this challenge. A coordinated and comprehensive effort spanning research and industry must be undertaken to develop battery technologies that achieve the strict performance standards set by the market in a sustainable and cost-effective way.Battery data plays an essential role in accelerating the development of new materials, cell designs, models, operating protocols, and manufacturing processes. [5] Recent advances in artificial intelligence (AI) methods such as, machine learning (ML) promise to greatly accelerate and improve insights into battery manufacturing, performance, and lifetime. [6][7][8][9] AI methods require vast amounts of data to Battery research initiatives and giga-scale production generate an abundance of diverse data spanning myriad fields of science and engineering. Modern battery development is driven by the confluence of traditional domains of natural science with emerging fields like artificial intelligence and the vast engineering and logistical knowledge needed to sustain the global reach of battery Gigafactories. Despite the unprecedented volume of dedicated research targeting affordable, high-performance, and sustainable battery designs, these endeavours are held back by the lack of common battery data and vocabulary standards, as well as, machine readable tools to support interoperability. An ontology is a data model that represents domain knowledge as a map of concepts and the relations between them. A battery ontology offers an effective means to unify battery-related activities across different fields, accelerate the flow of knowledge in both human-and machine-readable formats, and support the integration of artificial intelligence in battery development. Furthermore, a logically consistent and expansive ontology is essential to support battery digitalization and standardization efforts, such as, the battery passport. This review summarizes the current state of ontology development, the needs for an ontology in the battery field, and current activities to meet this need.

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Interoperability architecture for bridging computational tools: application to steel corrosion in concrete

Cited by 5 publications

References 20 publications

A Practical Approach to Ontology-Based Data Modelling for Semantic Interoperability

A Practical Approach to Ontology-Based Data Modelling for Semantic Interoperability

Ontology Assisted Modelling of Galvanic Corrosion of Magnesium

Toward a Unified Description of Battery Data

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