AI-based design of a nuclear reactor core (SEE FULL PAPER ATTACHED)Vladimir Sobes, Briana Hiscox, Emilian Popov, Rick Archibald,&...
Published on by Ben Betzler, Reactor Physics Engineer
Vladimir Sobes, Briana Hiscox, Emilian Popov, Rick Archibald, Cory Hauck, Ben Betzler & Kurt Terrani
Scientific Reports volume 11, Article number: 19646 (2021) Cite this article
Abstract
The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature peaking factor. The rapid development of advanced, and specifically, additive manufacturing (3-D printing) and its introduction into advanced nuclear core design through the Transformational Challenge Reactor program have presented the opportunity to explore the arbitrary geometry design of nuclear-heated structures. The primary challenge is that the arbitrary geometry design space is vast and requires the computational evaluation of many candidate designs, and the multiphysics simulation of nuclear systems is very time-intensive. Therefore, the authors developed a machine learning-based multiphysics emulator and evaluated thousands of candidate geometries on Summit, Oak Ridge National Laboratory’s leadership class supercomputer. The results presented in this work demonstrate temperature distribution smoothing in a nuclear reactor core through the manipulation of the geometry, which is traditionally achieved in light water reactors through variable assembly loading in the axial direction and fuel shuffling during refueling in the radial direction. The conclusions discuss the future implications for nuclear systems design with arbitrary geometry and the potential for AI-based autonomous design algorithms.
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Taxonomy
- Artificial Intelligence