Simulation of nuclear fuel cycle options is essential for evaluating the sustainability of future nuclear systems and supporting strategic decisions on the back-end of the fuel cycle and nuclear energy development. Recently announced policy changes suggest that the global installed nuclear electricity-generating capacity may triple by 2025. The challenge of such growth is that it must rely on the well-proven once-through cycle with thermal reactors, while the sustainability of the growth would require advanced reactors and a closed fuel cycle. It is therefore crucial to understand, based on scenario studies, when and what advanced technologies need to be introduced and to what extent. 

The main challenge of fuel cycle studies is that evaluating different strategies requires detailed knowledge of the recycled fuel's composition, which in turn requires tracking a large number of isotopes throughout the fuel cycle and accurately determining the spent fuel composition. 

For this purpose, fast and accurate burnup models are needed, such as the FITXS method developed at the Institute of Nuclear Techniques of BME (BME NTI), which is based on the parametrization of one-group microscopic cross-sections as functions of the detailed fuel composition. This model is implemented in the JOSETTE scenario code of BME NTI and the SITON code of the Center for Energy Research (Budapest, Hungary).

The doctoral research aims to contribute to developing the FITXS method and to perform scenario analysis for advanced fuel cycle options that support the growth of installed capacity. In this context, the following tasks are foreseen for the candidate:

- Investigate the applicability of artificial neural networks and especially physics-informed neural networks in the cross-section parametrization method, select the best-suited methods, implement a neural network-based parametrization, and compare with the original FITXS method.

- Extend the reactor models implemented in the JOSETTE and SITON codes with small and advanced modular reactor (SMR and AMR) designs.

- Extend the capabilities of the FITXS method for accelerator-driven systems (ADS) and breeding blankets of fast reactor cores.

- Perform in-depth fuel cycle studies on published nuclear growth scenarios at regional and global levels, to investigate the role of different reactor technologies and fuel cycle facilities in supporting or limiting the potential growth, and try to find optimized scenarios from the fuel cycle aspect.