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Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2025)
February 3–6, 2025
Amelia Island, FL|Omni Amelia Island Resort
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Christmas Night
Twas the night before Christmas when all through the houseNo electrons were flowing through even my mouse.
All devices were plugged in by the chimney with careWith the hope that St. Nikola Tesla would share.
Anthony L. Alberti, Todd S. Palmer
Nuclear Science and Engineering | Volume 194 | Number 10 | October 2020 | Pages 837-858
Technical Paper | doi.org/10.1080/00295639.2020.1758482
Articles are hosted by Taylor and Francis Online.
In this work, we attempt to overcome the “curse of dimensionality” inherent to neutron diffusion kinetics problems by employing a novel reduced-order modeling technique known as proper generalized decomposition (PGD). The novelty of this work is that it represents the first attempt at applying PGD reduced-order modeling to time-dependent multigroup neutron diffusion kinetics. The performance of PGD reduced-order models (ROMs) will be quantified by comparing PGD ROMs to reference high-fidelity solutions using Rattlesnake for the TWIGL problem, a standard reactor kinetics benchmark.
We show that for problems that exhibit sufficient spatial regularity, our proposed PGD algorithm computes accurate ROMs in less time than the reference high-fidelity calculation. By considering a variation of the TWIGL benchmark that maintains an analogous delayed supercritical behavior but has a smooth spatial solution, we compute PGD ROMs with a maximum relative difference in total power of less than 2.2% using 103 fewer degrees of freedom and a speedup of nearly 13× when compared to reference solutions. However, when introducing the stronger spatial irregularities of the reference benchmark, the accuracy and timing of the proposed PGD algorithm diminishes. We show that by using continuous finite elements, PGD ROMs are subject to undesirable numerical oscillations. In this paper, we motivate the use of PGD in neutron diffusion kinetics, discuss the adopted mathematical framework, and using our results, discuss the challenges and unique aspects of our implementation.