Optimized fuel cycle and burnup analysis for pebble-bed reactors

In a pebble-bed reactor (PBR) core, hundreds of thousands of densely packed fuel pebbles flow slowly downward. This complicates fuel movement and extends computational time for fuel cycle analysis. To overcome this problem, a quasi-static pebble flow is combined with batch-wise refueling. Achieving an equilibrium core state requires a burnup sensitivity analysis to assess how methods for coupling neutronics with depletion impact the accuracy of burnup calculations. Numerical burnup calculations face a fundamental challenge of nonlinearity: the burnup matrix, which determines fuel depletion, varies over time due to its dependence on the neutron flux, which itself is influenced by the evolving nuclide density distributions. While the explicit Euler method is commonly used for coupling neutronics with fuel depletion, its low accuracy can be problematic in PBR applications. In contrast, the predictor–corrector method enhances accuracy but requires twice as many transport calculations, increasing computational demands. To address these challenges, this study performed a time-step optimization using the SERPENT Monte Carlo code on the HTR200 design under the once-through-then-out (OTTO) scheme. The study highlighted significant runtime reductions, from approximately 10 h to about 4 h, while analyzing the effective multiplication factor (k-eff) and key isotopes, such as Xe-135, U-235, and Pu-239.