### Abstract

Original language | English |
---|---|

Pages (from-to) | 17-35 |

Number of pages | 19 |

Journal | Nuclear Engineering and Design |

Volume | 220 |

Issue number | 1 |

DOIs | |

Publication status | Published - 2003 |

MoE publication type | A2 Review article in a scientific journal |

### Fingerprint

### Keywords

- turbulent flow
- boiling water reactors
- nuclear reactors
- mass flow
- critical heat flux
- artificial neural networks
- neural networks

### Cite this

*Nuclear Engineering and Design*,

*220*(1), 17-35. https://doi.org/10.1016/S0029-5493(02)00304-7

}

*Nuclear Engineering and Design*, vol. 220, no. 1, pp. 17-35. https://doi.org/10.1016/S0029-5493(02)00304-7

**Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions : Applications of artificial neural network.** / Guanghui, Su. (Corresponding Author); Morita, K.; Fukuda, K.; Pidduck, Mark; Dounan, Jia; Miettinen, Jaakko.

Research output: Contribution to journal › Review Article › Scientific › peer-review

TY - JOUR

T1 - Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions

T2 - Applications of artificial neural network

AU - Guanghui, Su.

AU - Morita, K.

AU - Fukuda, K.

AU - Pidduck, Mark

AU - Dounan, Jia

AU - Miettinen, Jaakko

PY - 2003

Y1 - 2003

N2 - Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study. The input parameters of the ANN are pressure, mean mass flow rate, relative amplitude, inlet subcooling, oscillation period and the ratio of the heated length to the diameter of the tube, L/D. The output is a nondimensionalized factor F, which expresses the relative CHF under oscillation conditions. Based on the trained ANN, the influences of principal parameters on F for FC were analyzed. The parametric trends of the CHF under oscillation obtained by the trained ANN are as follows: the effects of pressure below 500 kPa are complex due to the influence of other parameters. F will increase with increasing mean mass flow rate under any conditions, and will decrease generally with an increase in relative amplitude. F will decrease initially and then increase with increasing inlet subcooling. The influence curves of mean mass flow rate on F will be almost the same when the period is shorter than 5.0 s or longer than 15 s. The influence of L/D will be negligible if L/D>200. It is found that the minimum number of neurons in the hidden layer is a product of the number of neurons in the input layer and in the output layer.

AB - Artificial neural networks (ANNs) for predicting critical heat flux (CHF) under low pressure and oscillation conditions have been trained successfully for either natural circulation or forced circulation (FC) in the present study. The input parameters of the ANN are pressure, mean mass flow rate, relative amplitude, inlet subcooling, oscillation period and the ratio of the heated length to the diameter of the tube, L/D. The output is a nondimensionalized factor F, which expresses the relative CHF under oscillation conditions. Based on the trained ANN, the influences of principal parameters on F for FC were analyzed. The parametric trends of the CHF under oscillation obtained by the trained ANN are as follows: the effects of pressure below 500 kPa are complex due to the influence of other parameters. F will increase with increasing mean mass flow rate under any conditions, and will decrease generally with an increase in relative amplitude. F will decrease initially and then increase with increasing inlet subcooling. The influence curves of mean mass flow rate on F will be almost the same when the period is shorter than 5.0 s or longer than 15 s. The influence of L/D will be negligible if L/D>200. It is found that the minimum number of neurons in the hidden layer is a product of the number of neurons in the input layer and in the output layer.

KW - turbulent flow

KW - boiling water reactors

KW - nuclear reactors

KW - mass flow

KW - critical heat flux

KW - artificial neural networks

KW - neural networks

U2 - 10.1016/S0029-5493(02)00304-7

DO - 10.1016/S0029-5493(02)00304-7

M3 - Review Article

VL - 220

SP - 17

EP - 35

JO - Nuclear Engineering and Design

JF - Nuclear Engineering and Design

SN - 0029-5493

IS - 1

ER -