Digitalization and Digital Twins in Long Term Management of Radioactive Waste

Abstract Digitalization and Artificial Intelligence is the fastest emerging paradigms in engineering and natural science applications. As an example data-driven and physics-inspired machine learning methods have been developed and evaluated to accelerate numerical simulations; evaluating their usability for applications related to the radioactive waste management cycle is therefore of high relevance. Under the umbrella of the European Joint Programme on Radioactive Waste Management (EURAD) and on Pre-disposal Management of Radioactive Waste (PREDIS), different initiatives have been established to facilitate evaluation and implementation of digitalization technologies (and specifically digital twins mirroring their corresponding physical assets) for long-term radioactive waste management. Previous studies indicate that digitalization is an important tool, to improve processes throughout the whole waste management cycle including pre-disposal activities like waste treatment, conditioning, storage and aspects related to the operation and long-term performance of disposal systems. However, before implementation, several challenges related to different facets of digital twins and digitalization need further research. At this stage, the specific potential and role of different aspects of digital transformation for different topics of waste management is still somewhat vague. In this contribution, an overview will be given addressing selected research developments s based on activities that are ongoing in EURAD and PREDIS or in closely related activities.