Digitalisation for nuclear waste management: predisposal and disposal

Olaf Kolditz (1, 2) , Diederik Jacques (3) , Francis Claret (4) , Johan Bertrand (5) , Sergey Churakov (6) , Christophe Debayle (7) , Daniela Diaconu (8) , Kateryna Fuzik (9) , David Garcia (10) , Nico Graebling (1) , Bernd Grambow (11) , Erika Holt (12) , Andrés Idiart (10) , Petter Leira (13) , Vanessa Montoya (3) , Ernst Niederleithinger (14) , Markus Olin (12) , Wilfried Pfingsten (6) , Nikolaos Prasianakis (6) , Karsten Rink (1) , Javier Samper (15) , István Szöke (13) , Réka Szöke (13) , Louise Theodon (5) , Jacques Wendling (5)

1 UFZ - Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research
2 TU Dresden - Technische Universität Dresden = Dresden University of Technology
3 SCK-CEN - Centre d'Etude de l'Energie Nucléaire
4 BRGM - Bureau de Recherches Géologiques et Minières (BRGM)
5 ANDRA - Agence Nationale pour la Gestion des Déchets Radioactifs
6 PSI - Paul Scherrer Institute
7 IRSN/PSE-ENV/SEDRE - Service des déchets radioactifs et des transferts dans la géosphère
8 ICN - Institute for Nuclear Research Pitesti
9 Vasylya Stusa St, 35-37, Kyiv, Ukraine - SSTC NRS
10 AMPHOS 21 Consulting
11 SUBATECH - Laboratoire de physique subatomique et des technologies associées
12 VTT - VTT Technical Research Centre of Finland
13 IFE - Institute for Energy Technology
14 BAM Bundesanstalt für Materialforschung u. -prüfung
15 Faculty of Sciences, Centre for Advanced Scientific Research (CICA)

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.

Domaines: Intelligence artificielle [cs.AI]; Environnements Informatiques pour l'Apprentissage Humain

Olaf Kolditz, Diederik Jacques, Francis Claret, Johan Bertrand, Sergey Churakov, et al.. Digitalisation for nuclear waste management: predisposal and disposal. Environmental Earth Sciences, 2023, 82 (1), pp.42. ⟨10.1007/s12665-022-10675-4⟩. ⟨irsn-03923989⟩