Digitalisation for nuclear waste management: predisposal and disposal

Olaf Kolditz, Diederik Jacques, Francis Claret, Johan Bertrand, Sergey Churakov, Debayle Christophe, Daniela Diaconu, Kateryna Fuzik, David Garcia, Nico Graebling, Bernd Grambow, Erika Holt, Andrés Idiart, Petter Leira, Vanessa Montoya, Ernst Niederleithinger, Olin Markus, Wilfried Pfingsten, Nikolaos I. Prasianakis, Karsten RinkJavier Samper, István Szöke, Réka Szoke, Louise Theodon, Jacques Wendling

Research outputpeer-review


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.
Original languageEnglish
Article number42
Number of pages11
JournalEnvironmental Earth Sciences
Issue number1
StatePublished - 2 Jan 2023

ASJC Scopus subject areas

  • Water Science and Technology
  • Earth-Surface Processes
  • Pollution
  • Geology
  • Soil Science
  • Global and Planetary Change
  • Environmental Chemistry

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