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, espec...

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Vydané v:Environmental earth sciences Ročník 82; číslo 1; s. 42
Hlavní autori: Kolditz, Olaf, Jacques, Diederik, Claret, Francis, Bertrand, Johan, Churakov, Sergey V., Debayle, Christophe, Diaconu, Daniela, Fuzik, Kateryna, Garcia, David, Graebling, Nico, Grambow, Bernd, Holt, Erika, Idiart, Andrés, Leira, Petter, Montoya, Vanessa, Niederleithinger, Ernst, Olin, Markus, Pfingsten, Wilfried, Prasianakis, Nikolaos I., Rink, Karsten, Samper, Javier, Szöke, István, Szöke, Réka, Theodon, Louise, Wendling, Jacques
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023
Springer Nature B.V
Springer
Predmet:
Artificial Intelligence
Biogeosciences
Computer applications
Computer Science
computer simulation
Construction
Data science
Digital twins
Digitalisation for Nuclear Waste Management: Predisposal and Disposal
Digitization
Earth and Environmental Science
Earth Sciences
Editorial
Environmental Science and Engineering
Geochemistry
Geology
Hydrology/Water Resources
Image processing
industry
Information processing
Machine learning
Mathematical models
Modelling
Numerical simulations
Object recognition
Pattern recognition
Physics
radioactive waste
Radioactive waste disposal
Radioactive wastes
Technology for Human Learning
Terrestrial Pollution
Underground construction
Virtual reality
Waste management
ISSN:1866-6280, 1866-6299
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Popis
Shrnutí: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, 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.
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ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-022-10675-4