1. A geometrical model of alpha and beta-radiation fields across spent nuclear fuel/water interfaces
- Author
-
Siberry, Angus, Scott, Tom, and Springell, Ross
- Subjects
Alpha-radiation ,Beta-radiation ,Dosimetry ,Radiation ,Spent Nuclear Fuel ,Radiolysis ,Dissolution ,Finite Element Modelling ,Nuclear Waste ,Nuclear Physics ,Nuclear Power ,Computational Physics - Abstract
Understanding the behaviour of nuclear materials in the event of exposure to water, is an essential element for improving efficiency, reliability and safety throughout the nuclear fuel cycle. The most radioactive of these materials is irradiated nuclear fuel, so much so that after use in a reactor it will remain highly radioactive for thousands of years. In order maintain safe containment of these materials over this timeline, great effort is being made to understand these materials and the interactions they have with their surroundings. A particular interest of the nuclear industry is the reaction between spent nuclear fuel and water. The highest risk of this occurring is during interim storage and, over time, in a Geological Disposal Facility (GDF). Although GDF sites are designed to be watertight, when considering the timescales required to sustain safe containment, the risks may become significant. A significant characteristic of a spent fuel/water interface is the chemical altering of the water molecules due to radiation (radiolysis) that occurs at and near the fuel surface. This process causes oxidising species to form that can cause oxidative dissolution of the fuel material. This process can be seen in the degradation of legacy fuel which is still held in wet storage at Sellafield. These fuels have degraded into particulate sludges alongside cladding and structural materials forming complex waste forms, creating huge decommissioning challenges for the UK nuclear industry. In order to understand the risks posed by each scenario involving these waste forms in water, modelling tools are required to aid our understanding of the likely mechanisms taking place, especially when considering events potentially thousands of years into the future. Due to the large quantity of chemical reactions, radionuclides, geometries and external conditions that influence these interactions the computational demand is high, and many assumptions are often required. In order to reduce the number of assumptions, computational efficiency of these models must improve. Chemical reaction and diffusion models require large computational loads to spatially resolve concentrations and reactions as a function of time. Due to these large computational loads the radioactive dose rates, that determine the concentrations of chemical species, are often analytically approximated leading to larger uncertainties within the model. Another common approach to determining dose rates is via comprehensive Monte-Carlo simulators that model radiation transport via stochastic decays to predict the likely dose rates across interfaces. The issue with this approach is that the computational effort and build times to produce these results dramatically increase the complexity of the model, which reduces the computational efficiency of the complete model. In this thesis a reconciliation of these approaches is attempted. Utilising simple geometries and exploiting the behaviour of α and β-radiation through matter, a computationally efficient approach has been developed. This work also utilises this approach to mimic real-world systems such as the radiolysis from and dissolution of particulates inside a GDF and interim storage setting. This work presents a framework where more complex and comprehensive models can be developed without the need for analytical approximations or Monte-Carlo simulators to provide dosimetry data. In conclusion, this thesis demonstrates the potential of geometrical modelling of radiation fields to bridge the gap between analytical approximations and comprehensive Monte-Carlo methods. Using this approach, advancements can be made on chemical modelling capabilities throughout the nuclear fuel cycle.
- Published
- 2022