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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.
The container of high-level radioactive waste (HLRW) being in deep geological disposal, the backfill material is needed to serve as the second defense for HLRW and the highly compacted bentonite is generally selected. As the time goes, the underground water will infiltrate the backfill, causing the corrosion of materials for the building of containers in the formed electrolyte. Carbon steel, titanium and its alloy are the potential candidate materials for the fabrication of HLRW containers.
The current investigation aims at assessing the safety of HLRW container in deep geological disposal for hundreds of thousands of years and facilitating the material selection for future Container fabrication by estimating their corrosion behavior in compacted bentonite with a series of moisture content at different temperatures through electrochemical methods including open circuit potential (OCP), electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization curve (PC) measurements. The corrosion rates were estimated for a carbon steel, a pure titanium and a titanium alloy in compacted Gaomiaozi Bentonite infiltrated with simulated underground water in Beishan area of China over an expected disposal period up to 106 years respectively, showing that titanium and its alloy are more reliable materials for building HLRW containers than carbon steel.