Prediction of and genetic algorithm optimization on data induced uncertainty reduction with the use of an integral experiment

DePillis, Alexander Amedeo; Bogetic, Sandra; Sobes, Vladimir

doi:10.1016/j.nucengdes.2024.113570

Title: Prediction of and genetic algorithm optimization on data induced uncertainty reduction with the use of an integral experiment

Journal Article · 01 December 2024 · Nuclear Engineering and Design

DOI: https://doi.org/10.1016/j.nucengdes.2024.113570 · OSTI ID:2440754

DePillis, Alexander Amedeo; Bogetic, Sandra;

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Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003996

OSTI ID:: 2440754

Journal Information:: Nuclear Engineering and Design, Journal Name: Nuclear Engineering and Design Journal Issue: C Vol. 429; ISSN 0029-5493

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

References (12)

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A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis Kumar, Akansha; Tsvetkov, Pavel V. Annals of Nuclear Energy, Vol. 85 https://doi.org/10.1016/j.anucene.2015.04.028	journal	November 2015
Ab initio path to heavy nuclei Binder, Sven; Langhammer, Joachim; Calci, Angelo Physics Letters B, Vol. 736 https://doi.org/10.1016/j.physletb.2014.07.010	journal	September 2014
Evaluating nuclear data and their uncertainties Talou, Patrick EPJ Nuclear Sciences & Technologies, Vol. 4 https://doi.org/10.1051/epjn/2018032	journal	January 2018
Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design Bevins, James E.; Slaybaugh, R. N. Nuclear Technology, Vol. 205, Issue 4 https://doi.org/10.1080/00295450.2018.1496692	journal	August 2018
Genetic Algorithm Design of a Coupled Fast and Thermal Subcritical Assembly Pevey, John; Chvála, Ondřej; Davis, Sarah Nuclear Technology, Vol. 206, Issue 4 https://doi.org/10.1080/00295450.2019.1664198	journal	October 2019
Nuclear Resonances, Scattering, and Reactions from First Principles: Progress and Prospects Quaglioni, Sofia; Navrátil, Petr Nuclear Physics News, Vol. 30, Issue 4 https://doi.org/10.1080/10619127.2020.1752089	journal	October 2020
Adjustment to Cross Section Data to fit Integral Experiments by Least Squares Method Kuroi, Hideo; Mitani, Hiroshi Journal of Nuclear Science and Technology, Vol. 12, Issue 11 https://doi.org/10.1080/18811248.1975.9733172	journal	November 1975
A fast and elitist multiobjective genetic algorithm: NSGA-II Deb, K.; Pratap, A.; Agarwal, S. IEEE Transactions on Evolutionary Computation, Vol. 6, Issue 2 https://doi.org/10.1109/4235.996017	journal	April 2002
Use of Integral Experiments in the Assessment of Large Liquid-Metal Fast Breeder Reactor Basic Design Parameters Palmiotti, G.; Salvatores, M. Nuclear Science and Engineering, Vol. 87, Issue 3 https://doi.org/10.13182/NSE87-333	journal	July 1984
Development of a New Fixed-source Sensitivity Tally Capability in the MCNP^® Code [Slides] Rising, Michael; Clark, Alexander 15.International Conference on Nuclear Data for Science and Technology (ND2022), Held Virtually, Sacramento, CA (United States), 21-29 Jul 2022 https://doi.org/10.2172/1898128	conference	July 2022
Validation of Nuclear Data Sensitivity Calculations by the MCNP PERT Card [Poster] Lamproe, Juliann; Cutler, Theresa; Hua, Michael 2021 MTV Virtual Workshop, Consortium for Monitoring, Technology, and Verification, Held Virtually, Ann Arbor, MI (United States), 29 - 31 March 2021 https://doi.org/10.2172/1906290	conference	March 2021

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