Machine learning based system performance prediction model for reactor control

Zeng, Yuyun; Liu, Jingquan; Sun, Kaichao; Hu, Lin-wen

doi:10.1016/j.anucene.2017.11.014

Title: Machine learning based system performance prediction model for reactor control

Journal Article · 28 February 2018 · Annals of Nuclear Energy

DOI: https://doi.org/10.1016/j.anucene.2017.11.014 · OSTI ID:1510111

;

; Sun, Kaichao; Hu, Lin-wen

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Sponsoring Organization:: USDOE

OSTI ID:: 1510111

Journal Information:: Annals of Nuclear Energy, Journal Name: Annals of Nuclear Energy Journal Issue: C Vol. 113; ISSN 0306-4549

Publisher:: ElsevierCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Implicit Treatment of Technical Specification and Thermal Hydraulic Parameter Uncertainties in Gaussian Process Model to Estimate Safety Margin Fynan, Douglas A.; Ahn, Kwang-Il Nuclear Engineering and Technology, Vol. 48, Issue 3 https://doi.org/10.1016/j.net.2016.01.016	journal	June 2016
Uncertainty analysis of Transportable Fluoride-salt-cooled High-temperature Reactor (TFHR) using coupled DAKOTA with RELAP-3D method Wang, Chenglong; Sun, Kaichao; Zhang, Dalin Nuclear Engineering and Design, Vol. 324 https://doi.org/10.1016/j.nucengdes.2017.08.021	journal	December 2017
Evolutionary fuzzy clustering for the Classification of transients in nuclear components Zio, E.; Baraldi, P. Progress in Nuclear Energy, Vol. 46, Issue 3-4 https://doi.org/10.1016/j.pnucene.2005.03.010	journal	January 2005
On-line monitoring applications in nuclear power plants Hashemian, H. M. Progress in Nuclear Energy, Vol. 53, Issue 2 https://doi.org/10.1016/j.pnucene.2010.08.003	journal	March 2011
Comparison of bootstrapped artificial neural networks and quadratic response surfaces for the estimation of the functional failure probability of a thermal–hydraulic passive system Pedroni, N.; Zio, E.; Apostolakis, G. E. Reliability Engineering & System Safety, Vol. 95, Issue 4 https://doi.org/10.1016/j.ress.2009.11.009	journal	April 2010
Particle filtering prognostic estimation of the remaining useful life of nonlinear components Zio, Enrico; Peloni, Giovanni Reliability Engineering & System Safety, Vol. 96, Issue 3 https://doi.org/10.1016/j.ress.2010.08.009	journal	March 2011
An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability Cadini, F.; Santos, F.; Zio, E. Reliability Engineering & System Safety, Vol. 131 https://doi.org/10.1016/j.ress.2014.06.023	journal	November 2014
A particle filtering and kernel smoothing-based approach for new design component prognostics Hu, Yang; Baraldi, Piero; Di Maio, Francesco Reliability Engineering & System Safety, Vol. 134 https://doi.org/10.1016/j.ress.2014.10.003	journal	February 2015
A tutorial on support vector regression Smola, Alex J.; Schölkopf, Bernhard Statistics and Computing, Vol. 14, Issue 3 https://doi.org/10.1023/B:STCO.0000035301.49549.88	journal	August 2004
Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations Mercer, J. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 209, Issue 441-458 https://doi.org/10.1098/rsta.1909.0016	journal	January 1909
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking Arulampalam, M. S.; Maskell, S.; Gordon, N. IEEE Transactions on Signal Processing, Vol. 50, Issue 2 https://doi.org/10.1109/78.978374	journal	January 2002
Hybrid Identification of Nuclear Power Plant Transients With Artificial Neural Networks Embrechts, M. J.; Benedek, S. IEEE Transactions on Industrial Electronics, Vol. 51, Issue 3 https://doi.org/10.1109/TIE.2004.824874	journal	June 2004
Neutronic Design Features of a Transportable Fluoride-Salt-Cooled High-Temperature Reactor Sun, Kaichao; Hu, Lin-Wen; Forsberg, Charles Journal of Nuclear Engineering and Radiation Science, Vol. 2, Issue 3 https://doi.org/10.1115/1.4032873	journal	June 2016
LIBSVM: A library for support vector machines Chang, Chih-Chung; Lin, Chih-Jen ACM Transactions on Intelligent Systems and Technology, Vol. 2, Issue 3 https://doi.org/10.1145/1961189.1961199	journal	April 2011
Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual Adams, Brian; Ebeida, Mohamed; Eldred, Michael https://doi.org/10.2172/1177048	report	May 2014

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