Sensitivity and uncertainty analysis in pebble-bed reactors: A study using the High-Temperature Code Package (HCP)

The High Temperature Code Package (HCP) provides advanced modeling and simulation tools for High-Temperature Gas-Cooled Reactors (HTGRs). However, despite its capabilities, HCP currently lacks integrated methods for Uncertainty Quantification (UQ) and Sensitivity Analysis (SA). This research aims to implement a statistical framework within HCP by leveraging the DAKOTA toolkit and Python libraries, thereby enabling UQ/SA workflows to evaluate how uncertainties influence the performance of HTGR systems. DAKOTA provides state-of-the-art sampling and analysis methods, which are integrated with HCP's steady-state and transient multiphysics simulation environments. In this study, a UQ analysis was conducted for both steady-state and transient multiphysics scenarios for a the HTR-200 reactor design. Results demonstrate that the HTR-200 model exhibits robust performance under input uncertainties related to inlet gas temperature, mass flow rate, and reactor power, with variations in Quantities of Interest (QoIs) remaining within expected tolerances. A global SA was the primary focus for a Pressurized Loss of Forced Convection (PLOFC) scenario and a fuel depletion case to further explore the influence of key parameters. An innovative strategy was employed to efficiently compute Sobol sensitivity indices for time-dependent QoIs by using a Gaussian process emulator as a surrogate model for HCP, alongside principal component analysis to reduce the dimensionality of time-series data. The results identified reactor power as the most influential parameter for the PLOFC response, while the outer pebble radius and UO₂ density were found to have the most significant impact on fuel depletion and neutron population.