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Digitalisation for nuclear waste management: predisposal and disposal

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Samper-J_2023_EES_82-42.pdf (1.851Mb)
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http://hdl.handle.net/2183/32797
Atribución 3.0 España
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Título
Digitalisation for nuclear waste management: predisposal and disposal
Autor(es)
Kolditz, Olaf
Jacques, Diederik
Claret, Francis
Bertrand, Johan
Churakov, Sergey V.
Debayle, Christophe
Diaconu, Daniela
Fuzik, Kateryna
García-Cobos, D.
Graebling, Nico
Grambow, Bernd
Holt, Erika
Idiart, Andrés
Leira, Petter
Montoya, Vanessa
Niederleithinger, Ernst
Olin, Markus
Pfingsten, Wilfried
Prasianakis, Nikolaos
Rink, Karsten
Samper, Javier
Szöke, István
Szöke, Réka
Theodon, Louise
Wendling, Jacques
Data
2023
Cita bibliográfica
Kolditz, O., Jacques, D., Claret, F. et al. Digitalisation for nuclear waste management: predisposal and disposal. Environ Earth Sci 82, 42 (2023). https://doi.org/10.1007/s12665-022-10675-4
Resumo
[Abstract:] 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.
Palabras chave
Data science
Digitalisation
Artificial intelligence
Nuclear waste management
Numerical simulations
Digital Twins
DTw
 
Versión do editor
https://doi.org/10.1007/s12665-022-10675-4
Dereitos
Atribución 3.0 España

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