AI-based design of a nuclear reactor core

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, a...

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Autores principales:	Vladimir Sobes, Briana Hiscox, Emilian Popov, Rick Archibald, Cory Hauck, Ben Betzler, Kurt Terrani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5cc19e46f7d146e78fd797a7dbd0cc5e
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spelling	oai:doaj.org-article:5cc19e46f7d146e78fd797a7dbd0cc5e2021-12-02T18:37:11ZAI-based design of a nuclear reactor core10.1038/s41598-021-98037-12045-2322https://doaj.org/article/5cc19e46f7d146e78fd797a7dbd0cc5e2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98037-1https://doaj.org/toc/2045-2322Abstract 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.Vladimir SobesBriana HiscoxEmilian PopovRick ArchibaldCory HauckBen BetzlerKurt TerraniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vladimir Sobes Briana Hiscox Emilian Popov Rick Archibald Cory Hauck Ben Betzler Kurt Terrani AI-based design of a nuclear reactor core
description	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.
format	article
author	Vladimir Sobes Briana Hiscox Emilian Popov Rick Archibald Cory Hauck Ben Betzler Kurt Terrani
author_facet	Vladimir Sobes Briana Hiscox Emilian Popov Rick Archibald Cory Hauck Ben Betzler Kurt Terrani
author_sort	Vladimir Sobes
title	AI-based design of a nuclear reactor core
title_short	AI-based design of a nuclear reactor core
title_full	AI-based design of a nuclear reactor core
title_fullStr	AI-based design of a nuclear reactor core
title_full_unstemmed	AI-based design of a nuclear reactor core
title_sort	ai-based design of a nuclear reactor core
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5cc19e46f7d146e78fd797a7dbd0cc5e
work_keys_str_mv	AT vladimirsobes aibaseddesignofanuclearreactorcore AT brianahiscox aibaseddesignofanuclearreactorcore AT emilianpopov aibaseddesignofanuclearreactorcore AT rickarchibald aibaseddesignofanuclearreactorcore AT coryhauck aibaseddesignofanuclearreactorcore AT benbetzler aibaseddesignofanuclearreactorcore AT kurtterrani aibaseddesignofanuclearreactorcore
_version_	1718377800301281280

AI-based design of a nuclear reactor core

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