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Bumpy roads lead to beautiful places
Per Nuclear News tradition, this month’s issue is dedicated to highlighting our nuclear technology supply chain. U.S. nuclear suppliers have certainly seen their share of challenges in the last decade or so. The widely anticipated “Nuclear Renaissance” of the early 2000s gave way to Fukushima, then a wavelet of plant closures that ANS President Steve Nesbit addresses in his column on page 15 of the August 2021 issue of Nuclear News.
However, the nuclear narrative has taken on a more positive tone of late. Significant federal investments in advanced nuclear energy systems, coupled with a broader recognition of the need to decarbonize, has stoked excitement for a new generation of U.S. technology on the verge of scaled commercial deployment by the end of the decade. Hopefully, in the words of Washington Nationals manager Davey Martinez, whose team went from a 19–32 record to World Series champs in 2019, “Bumpy roads lead to beautiful places.”
Stefano Terlizzi, Dan Kotlyar
Nuclear Science and Engineering | Volume 194 | Number 4 | April 2020 | Pages 280-296
Technical Paper | dx.doi.org/10.1080/00295639.2019.1698239
Articles are hosted by Taylor and Francis Online.
Monte Carlo (MC) codes are widely used for the accurate modeling of nuclear reactors. However, efficient inclusion of thermal-hydraulic (TH) feedback within the MC calculation sequence is still an open problem. The issue is emphasized when coupled MC-TH calculations are needed to model the burnup evolution using multiple depletion steps. Among the techniques proposed to solve this problem is the utilization of stabilized Picard iteration in conjunction with a low-order prediction step. The latter is composed of a prediction block for cross sections and a fast deterministic solver that uses the cross sections to obtain a prediction of the power profile. The predicted power is then used as an improved guess for the next MC calculation, therefore leading to faster convergence for the overall algorithm. In this paper, we propose a new prediction block in which one-group cross sections are calculated through convolution of the TH scalar fields with MC-generated generalized transfer functions (GTFs). First-order perturbation theory is then utilized to calculate the power profile from the updated cross sections. A version of this prediction block using a simple fast Fourier transform–based approximation of the GTF is tested against a boiling water reactor unit-cell with realistic density profile and axial reflectors. The analysis was limited to the feedback between neutronics and coolant density variation. Good agreement was observed for both the spatial power and the one-group macroscopic cross-section profiles, which were compared to the reference MC results. This agreement was also preserved near the boundary, where the spatial flux gradients are maximum due to proximity to the axial reflectors.