Critical heat flux function approximation using genetic algorithms
Function approximation is the problem of finding a system that best explains the relationship between input variables and an output variable. We propose two hybrid genetic algorithms (GAs) of parametric and nonparametric models for function approximation. The former GA is a genetic nonlinear Levenbe...
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| Published in: | IEEE transactions on nuclear science Vol. 52; no. 2; pp. 535 - 545 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.04.2005
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9499, 1558-1578 |
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| Abstract | Function approximation is the problem of finding a system that best explains the relationship between input variables and an output variable. We propose two hybrid genetic algorithms (GAs) of parametric and nonparametric models for function approximation. The former GA is a genetic nonlinear Levenberg-Marquardt algorithm of parametric model. We designed the chromosomes in a way that geographically exploits the relationships between parameters. The latter one is another GA of nonparametric model that is combined with a feedforward neural network. The neuro-genetic hybrid here differs from others in that it evolves diverse input features instead of connection weights. We tested the two GAs with the problem of finding a better critical heat flux (CHF) function of nuclear fuel bundle which is directly related to the nuclear-reactor thermal margin and operation. The experimental result improved the existing CHF function originated from the KRB-1 CHF correlation at the Korea Atomic Energy Research Institute (KAERI) and achieved the correlation uncertainty reduction of 15.4% that would notably contribute to increasing the thermal margin of the nuclear power plants. |
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| AbstractList | The experimental result improved the existing CHF function originated from the KRB-1 CHF correlation at the Korea Atomic Energy Research Institute (KAERI) and achieved the correlation uncertainty reduction of 15.4% that would notably contribute to increasing the thermal margin of the nuclear power plants. Function approximation is the problem of finding a system that best explains the relationship between input variables and an output variable. We propose two hybrid genetic algorithms (GAs) of parametric and nonparametric models for function approximation. The former GA is a genetic nonlinear Levenberg-Marquardt algorithm of parametric model. We designed the chromosomes in a way that geographically exploits the relationships between parameters. The latter one is another GA of nonparametric model that is combined with a feedforward neural network. The neuro-genetic hybrid here differs from others in that it evolves diverse input features instead of connection weights. We tested the two GAs with the problem of finding a better critical heat flux (CHF) function of nuclear fuel bundle which is directly related to the nuclear-reactor thermal margin and operation. The experimental result improved the existing CHF function originated from the KRB-1 CHF correlation at the Korea Atomic Energy Research Institute (KAERI) and achieved the correlation uncertainty reduction of 15.4% that would notably contribute to increasing the thermal margin of the nuclear power plants. |
| Author | Sung-Deok Hong Yung-Keun Kwon Byung-Ro Moon |
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| CitedBy_id | crossref_primary_10_1109_TCAPT_2006_885944 crossref_primary_10_1016_j_anucene_2012_09_020 crossref_primary_10_1016_j_nucengdes_2011_07_029 crossref_primary_10_1016_j_rser_2017_10_040 crossref_primary_10_1016_j_ijheatmasstransfer_2013_03_025 crossref_primary_10_1016_j_nucengdes_2024_113587 crossref_primary_10_1016_j_pnucene_2013_07_004 crossref_primary_10_1016_j_ijheatmasstransfer_2024_125441 crossref_primary_10_1007_s11004_014_9577_3 |
| Cites_doi | 10.1007/3-540-58484-6_255 10.1109/72.774236 10.3327/jnst.39.564 10.1016/S0893-6080(03)00014-5 10.1016/0029-5493(95)01154-4 10.1016/0167-8191(90)90086-O 10.1080/01621459.1981.10477729 10.1109/3477.537319 10.1109/43.700718 10.1016/S0035-3159(97)87750-1 10.1016/S0735-1933(97)00078-X 10.1016/S0017-9310(98)00286-5 10.13182/NSE97-A1937 10.1109/TSMCB.2003.816922 10.1016/0029-5493(95)01178-1 10.1109/91.580801 10.1109/ICEC.1997.592288 10.1214/aos/1176347115 10.1137/0111030 10.1016/0167-8655(89)90037-8 10.13182/NSE68-A20912 10.1007/978-3-642-56927-2 10.13182/NT98-A2923 10.1109/4235.850656 10.1109/12.508322 |
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| SubjectTerms | Approximation Biological cells Correlation Critical heat flux (CHF) Energy research feature extraction Feedforward neural networks Function approximation genetic algorithm (GA) Genetic algorithms Heat flux Heat transfer Input variables Levenberg-Marquardt algorithm Mathematical analysis Mathematical models Neural networks Nuclear fuels Nuclear power generation Nuclear power plants Parametric statistics Studies system identification Testing Uncertainty |
| Title | Critical heat flux function approximation using genetic algorithms |
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