Kerntechnische Entsorgung
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- 2023 (2) (entfernen)
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- Data science (1)
- Digitalization (1)
- Disposal (1)
- Nuclear waste (1)
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Organisationseinheit der BAM
Characterization is a very important step in dealing with materials and waste streams generated during the operational and decommissioning phases of nuclear installations, including nuclear power plants. Characterization allows differentiation between materials that can be released from regulatory control and those that require further treatment and conditioning to become a stable waste form suitable for future storage and final disposal according to its waste classification. Characterization is also needed in the pre-disposal stages of radioactive waste management to demonstrate compliance with the waste acceptance criteria of the facilities that will accept the different waste forms. This work will present the strategies developed and implemented by the three projects for in-depth and accurate waste characterization and investigation of the different radioactive waste packages considered. CHANCE, MICADO, and PREDIS will present their goals, the methods developed, the technologies used and the (preliminary) results contributing to the improvement of the safety and the data and information quality of the waste packages analyzed at the different stages of the waste management process. Special emphasis will also be given to complementary approaches highlighting the usability of the technologies, the accessibility of the data, and the problem-solving of the three projects within the European panorama.
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.