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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.

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.

Deep geological disposal is extensively studied for the long-term containment of high-level nuclear waste, which relies on the multi-barrier concept to achieve the required degree of isolation. This study focuses on assessing the hydro-chemical-mechanical (HCM) behavior of a liner concrete structure under repository conditions through the development of predictive models. We include models for the hydro-mechanical (HM) response of materials and hydro-chemical (HC) interaction at the concrete-rock interface, which will be later integrated into a coupled HCM model. The HM model reveals that porosity changes are negligible due to HM loads, while the altered concrete may affect the liner integrity. The HC model shows that porosity changes are controled by portlandite and calcium-silicate-hidrate (CSH) dissolution and precipitation of a secondary CSH phase. The maximum increased porosity occurs near de interface, reaching 0.23. The length of geochemical affection is 3 cm, with increased porosity from 0.15 to 0.20.

Deep geological disposal is extensively studied for the long-term containment of high-level nuclear waste. A disposal system should guarantee that the waste is not a hazard for the environment and remains isolated over long periods of time (millions or hundreds of thousands of years). Mined repositories are suitable to be implemented, as they rely on the multi-barrier concept to achieve the required degree of isolation. This study focuses on assessing the hydro-chemical-mechanical (HCM) behavior of cementitious materials, mainly used as liners or barrier systems, under repository conditions through the development of predictive models. The objective is to analyze the interaction between a support concrete structure and the surrounding clay rock in a deep waste-disposal facility. The elaborated methodology includes models for the hydro-mechanical (HM) response of materials and the hydro-chemical (HC) interaction at the concrete-rock interface, which will be later integrated into a coupled HCM model. The HM model is developed with the finite element method software CODE_BRIGHT and simulates the response of the concrete structure and surrounding rock. The two-dimensional plane-strain model consists of four stages: equilibration, simulating the excavation and alterations caused by drilling, constructing the support structure, and evaluating the long-term behavior of the system. Displacements, porosity variations and stress distributions are analyzed., The HC modeling focuses on the reactive transport modeling of the concrete-rock system. Retraso- CODE_BRIGHT is used to simulate the geochemical processes. The initial modeling stages considers a simplified one-dimensional system with a restricted number of mineral species involved in the concretesurrounding rock interaction. Data from earlier studies conducted at the Mont Terri Underground Rock Laboratory are used as the basis for the model, regarding the composition of concrete, the rock materials and pore-water compositions. The variation in pH, mineral precipitation/dissolution, volume fraction of minerals and concentrations of elements as a function of time and space are analyzed., The simulation time is 25 years. Preliminary HM results show a generalized compression state of stress, vertical y-displacements of 2 mm and horizontal x-displacements of 0.8 mm in the concrete structure. It is observed that porosity remains invariable because of the geomechanical effect. In the inner region of the lining, the failure limit is exceeded vertically. The excavation damage zone of the host rock extends 10 cm from the concrete-rock interface in the y-direction and 2 m away to the flanks in the x-direction. Preliminary HC results show significant dissolution of portlandite and calcium-silicate-hydrate (C-S-H) phases in the concrete-rock interface region. This results in an increase in the concrete porosity from 0.15 to a maximum value of 0.28 at the concrete-rock interface, and affects the concrete up to a distance of 1 cm from the interface. Future work will focus on determining an appropriate coupling method that integrates both models into an HCM model. Upscaling of concrete degradation with a correlation between porosity variations and changes in the Young’s modulus will be developed, ranging from the aggregate scale to continuum scale. In addition, model results will be compared with the experimental data obtained from the EURADMAGIC project.

A key component of risk assessment for radioactive waste repositories in deep geological formations is the prediction of potential radionuclide (RN) transport through the geosphere. One big challenge for such large-scale heterogeneous transport simulations is the integration of realistic geochemical models and their parameters at affordable computational costs. Sorption on minerals is an important retardation process and typically applying constant distribution coefficients (Kd) that can be easily included in reactive transport codes. One of the advantage of this approach is the computational efficiency, but it cannot reflect changes in geochemical conditions. On the other hand, mechanistic surface complexation models used for process understanding can be directly coupled to transport codes with geochemical solvers, but usually only at high computational costs. An alternative is provided by the smart Kd concept (www.smartkd-concept.de) [1], specifically developed to describe variable RN sorption in transport models as consequence of changing geochemical conditions over space and time. The philosophy behind this concept is to compute multidimensional look-up tables storing distribution coefficients - but here referred to as smart Kd as they are based on mechanistic sorption models. These tables can cover wide ranges of important geochemical parameters, e.g. pH, ionic strength, and concentrations of dissolved ions. The information stored in such a look-up table can be accessed by reactive transport codes at each simulation step. This approach was already implemented in the code d3f++ [2]. Here, an additional implementation and validation of the smart Kd-approach in the code OpenGeoSys OGS6 [3, 4] is presented. The application case selected is sorption of repository-relevant RNs and possible migration scenarios through a typical sedimentary rock system covering potential host rock formations in Northern Germany. This serves as a comprehensive proof-of-concept and demon