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This work introduces a comprehensive and statistically robust framework for quantifying model parameter uncertainties in nuclear fuel performance predictions, ensuring that all sources of discrepancy (e.g., measurement noise, parameter variability, and model inadequacy) are systematically captured. Parameter uncertainties are essential inputs to safety analyses and are typically obtained through calibration; however, standard Bayesian calibration often underestimates uncertainty when the underlying model cannot fully reproduce experimental observations. To overcome this limitation, we employ a hierarchical probabilistic formulation in which model parameters vary according to distributions governed by both local experiment-specific and global hyperparameters, thereby explicitly accounting for unmodeled variability and structural deficiencies in the model. This paper demonstrates the practical implementation of this approach using an efficient Metropolis-Hastings-within-Gibbs sampling to calibrate a fission gas release model in the fuel performance code TRANSURANUS against Westinghouse's commercial database. The resulting posteriors provide uncertainty estimates that credibly span all code-to-measurement discrepancies, remain robust across linear and nonlinear models, and directly support safety evaluations by offering defensible uncertainty margins. This approach establishes a generalizable pathway for uncertainty quantification in fuel performance modeling and enhances confidence in predictive simulations to inform high-consequence decisions.

Small modular reactors (SMRs) have gained attention for their potential to reduce capital investment and enhance deployment flexibility. However, these benefits often come at the cost of reduced neutron economy and efficiency, which could increase the fuel share of the total levelized cost of electricity. This study evaluates the economic performance of a low power density pressurized water reactor–style SMR using the Westinghouse integrated levelized cost of electricity methodology. We investigate the impact of cycle length and core reload fraction on fuel and outage costs for equilibrium cycles. Additionally, a Monte Carlo–based sensitivity analysis is performed to assess the robustness of the results under uncertainty in the economic input parameters. The study also explores the economic viability of adopting low-enriched uranium plus for extended-cycle operation. The results provide insights into fuel utilization and cost optimization strategies for sustainable SMR deployment.

Fuel management optimization is a non-linear combinatorial problem characterized by multiple constraints and objectives. This study focuses on the optimization of equilibrium cycles, an idealized set of conditions frequently used in the development of long-term fuel management strategies. Due to the high dimensionality of the problem and the computational expense associated with simulating equilibrium behavior, a surrogate modeling methodology is introduced to improve computational efficiency. The approach employs the PyTorch-Geometric (PyG) library and utilizes a Graph Neural Network (GNN) Transformer encoder to predict equilibrium cycle safety margins. By leveraging the inherent graph structure of equilibrium cycles, the proposed framework enables accurate safety margin prediction while substantially reducing computational time, thereby supporting faster and more scalable fuel management optimization.

Fuel performance codes, such as Transuranus, predict fuel behavior and are used to ensure the safe operation of nuclear reactors. These codes are moderately time-consuming and affordable in many applications but may be limited in others, primarily when many fuel rods must be evaluated simultaneously. This work presents how the temporal neural network techniques, Temporal Convolutional Networks, and a Fourier Neural Operator can be combined to form a deep heterogeneous joint architecture as a surrogate model for fuel performance modeling in time-critical situations. We train the model using realistic power histories and corresponding outputs generated using the fuel performance code Transuranus. The ultimate result is a surrogate model for use in time-critical situations that take milliseconds to evaluate for thousands of fuel rods and have a mean test error of unseen data around a few percent.

In this work we present the economic performance of equilibrium cycles for a low power-density small modular reactor for long fuel residence time. The results of the selected pressurized light water reactor, suggesting no advances in long cycle operation, are presented. In addition, we present the strategy that will be adopted for the successive in-core fuel management optimization.

In this study, we applied the Total Monte Carlo (TMC) methodology in de-excitation simulations of primary fission fragments (FF) using the TALYS code. The goal was to develop and optimise a methodology to benchmark initial fission model assumptions on fission mass yield distributions, excitation energy sharing and angular momentum population. The study was performed on the thermal neutron induced fission of Pu-239(n(th),f). The work aimed at evaluating fission model deficiencies and parameter sensitivities. We systematically varied TALYS input data by generating 5000 random files through the GEF code, randomizing 94 model parameters that affect fission yields and energy distributions within 3% of their default values. This variation revealed significant changes in the fission observables, such as prompt neutron and gamma-ray multiplicities and energy spectra. The results indicate some systematic defects in the assumed excitation-energies and angular momenta. Another outcome from the study is the identification of a need for new correlation measurements on prompt neutrons and gamma-rays from the Pu-239(n(th),f) reaction, as well as an updated evaluation.