Study on Onset of Flow Instability by Genetic Neural Network

LI Jingjing; ZHOU Tao; DUAN Jun; XIAO Zejun; HUANG Yanping

Feb. 2025

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LI Jingjing, ZHOU Tao, DUAN Jun, XIAO Zejun, HUANG Yanping. Study on Onset of Flow Instability by Genetic Neural Network[J]. Nuclear Power Engineering, 2014, 35(2): 63-66.

Citation:

LI Jingjing, ZHOU Tao, DUAN Jun, XIAO Zejun, HUANG Yanping. Study on Onset of Flow Instability by Genetic Neural Network[J]. Nuclear Power Engineering, 2014, 35(2): 63-66.

LI Jingjing, ZHOU Tao, DUAN Jun, XIAO Zejun, HUANG Yanping. Study on Onset of Flow Instability by Genetic Neural Network[J]. Nuclear Power Engineering, 2014, 35(2): 63-66.

Citation:

LI Jingjing, ZHOU Tao, DUAN Jun, XIAO Zejun, HUANG Yanping. Study on Onset of Flow Instability by Genetic Neural Network[J]. Nuclear Power Engineering, 2014, 35(2): 63-66.

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Study on Onset of Flow Instability by Genetic Neural Network

1. Nuclear Safety and Thermal Power Standardization Institute, North China Electric Power University, Beijing, 102206, China;
2. CNNC Key Laboratory on Nuclear Reactor Thermal Hydraulics Technology, Nuclear Power Institute of China, Chengdu, 610041, China

Received Date: 2012-12-25
Rev Recd Date: 2013-12-02

Available Online: 2025-02-15

Abstract

Abstract

The trend of OFI heat flux with the system parameters is studied by the method of genetic neural network. The test result shows that the results of GNN agree well with the results of experiments. The errors fall in the limits of ±10%. By using the GNN model to predict the effect of parameters on OFI, we can find that the heat flux of OFI grows with the increasing of system pressure, the inlet subcooled temperature and the mass flow. The effect of system pressure on OFI is smaller than that of the mass flow and the inlet subcooled temperature.
- Onset of flow instability,
- Flow instability,
- GNN