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Digitalisation for nuclear waste management: predisposal and disposal

  • Data science (digitalisation and artificial intelligence) became more than an important facilitator for many domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly usable for applications related to the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilising the best models, input data and monitoring information to integrate multi-chemical-physical, coupledData science (digitalisation and artificial intelligence) became more than an important facilitator for many domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly usable for applications related to the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilising the best models, input data and monitoring information to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DTw) able to mirror or predict the performance of its corresponding physical twins. Therefore, the main target of the Topical Collection is exploring how the development of DTw can benefit the development of safe, efficient solutions for the pre-disposal and disposal of radioactive waste. A particular challenge for DTw in radioactive waste management is the combination of concepts from geological modelling and underground construction which will be addressed by linking structural and multi-physics/chemistry process models to building or tunnel information models. As for technical systems, engineered structures a variety of DTw approaches already exist, the development of DTw concepts for geological systems poses a particular challenge when taking the complexities (structures and processes) and uncertainties at extremely varying time and spatial scales of subsurface environments into account.zeige mehrzeige weniger

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Metadaten
Autor*innen:O. Kolditz, D. Jacques, F. Claret, J. Bertrand, S. V. Churakov, C. Debayle, D. Diaconu, K. Fuzik, D. Garcia, N. Graebling, B. Grambow, E. Holt, A. Idiart, P. Leira, V. Montoya, Ernst NiederleithingerORCiD, M. Olin, W. Pfingsten, N. I. Prasianakis, K. Rink, J. Sampier, I. Szöke, R. Szöke, L. Theodon, J. Wendling
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Environmental Earth Sciences
Jahr der Erstveröffentlichung:2023
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:Springer
Jahrgang/Band:82
Ausgabe/Heft:1
Aufsatznummer:42
Erste Seite:1
Letzte Seite:11
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik
Freie Schlagwörter:Data science; Digitalization; Disposal; Nuclear waste; Predisposal
Themenfelder/Aktivitätsfelder der BAM:Energie
Energie / Kerntechnische Entsorgung
DOI:10.1007/s12665-022-10675-4
URN:urn:nbn:de:kobv:b43-569387
ISSN:1866-6280
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:02.02.2023
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:02.02.2023
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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