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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Veröffentlicht in:	Environmental earth sciences Jg. 82; H. 1; S. 42
Hauptverfasser:	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
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023 Springer Nature B.V Springer
Schlagworte:	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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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.
AbstractList	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.
ArticleNumber	42
Author	Debayle, Christophe Idiart, Andrés Prasianakis, Nikolaos I. Jacques, Diederik Holt, Erika Rink, Karsten Szöke, Réka Graebling, Nico Pfingsten, Wilfried Montoya, Vanessa Garcia, David Olin, Markus Grambow, Bernd Leira, Petter Claret, Francis Szöke, István Theodon, Louise Samper, Javier Kolditz, Olaf Bertrand, Johan Churakov, Sergey V. Wendling, Jacques Diaconu, Daniela Fuzik, Kateryna Niederleithinger, Ernst
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SubjectTerms	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
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