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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Vydané v:	IEEE transactions on nuclear science Ročník 52; číslo 2; s. 535 - 545
Hlavní autori:	Kwon, Yung-Keun, Moon, Byung-Ro, Hong, Sung-Deok
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2005 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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.
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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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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