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Machine learning-assisted surrogate construction for full-core fuel performance analysis

Journal Article · · Annals of Nuclear Energy
Not provided.
Research Organization:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Organization:
USDOE Office of Nuclear Energy (NE)
DOE Contract Number:
NE0008752
OSTI ID:
1976816
Journal Information:
Annals of Nuclear Energy, Journal Name: Annals of Nuclear Energy Journal Issue: C Vol. 168; ISSN 0306-4549
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

References (11)

Lookup Tables for Predicting CHF and Film-Boiling Heat Transfer: Past, Present, and Future journal October 2005
The Virtual Environment for Reactor Applications (VERA): Design and architecture journal December 2016
Coupled fuel performance calculations in VERA and demonstration on Watts Bar unit 1, cycle 1 journal September 2020
Probabilistic view of SiC/SiC composite cladding failure based on full core thermo-mechanical response journal August 2018
Pellet-clad mechanical interaction screening using VERA applied to Watts Bar Unit 1, Cycles 1–3 journal February 2018
The PROMETRA Program: Fuel Cladding Mechanical Behavior under High Strain Rate journal March 2007
Light water reactor fuel performance modeling and multi-dimensional simulation journal August 2011
Modeling the tension–compression asymmetric yield behavior of β-treated Zircaloy-4 journal August 2014
Physics-informed reinforcement learning optimization of nuclear assembly design journal February 2021
Lookup tables of critical heat fluxes journal July 1991
Application of the Critical Heat Flux Look-Up Table to Large Diameter Tubes journal January 2013

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