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The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Nuclear Science and Engineering
Fusion Science and Technology
Study indicates pilot facility could significantly reduce waste volumes
Waste disposal start-up Deep Isolation and fusion tech company SHINE Technologies have announced the completion of a collaborative study assessing the costs of disposing of radioactive byproducts from a pilot spent nuclear fuel recycling facility.
Stefano Terlizzi, Dan Kotlyar
Nuclear Science and Engineering | Volume 194 | Number 4 | April 2020 | Pages 280-296
Technical Paper | 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.