This study details the creation and analysis of physics-based and data-driven digital twins (DT) for heat transport in the Full-scale Emplacement (FE) experiment at Mont Terri underground research laboratory, aimed at using continuously incoming temperature and humidity sensor data from a long-term heater experiment for a physics-based modeling digital twin (PBM-DT) and data-driven modeling digital twin (DDM-DT) to predict further temperature evolution in the near-field and highlighting their respective strengths. The PBM's allows mechanistic insights into thermal processes whereas the DDM's shows easy adaptability and high efficiency in temperature prediction. The predicted temperature evolution shows that both models can be used for repository monitoring and safety analysis in the early repository phase, underlining the potential of PBM-DT and DDM-DT approaches to support optimized repository design and safety. This research argues for the combined application of these methods to refine DT technologies in nuclear waste management.
Pfingsten et al. (Mon,) studied this question.