Nuclear Power Plant Burst Parameters Prediction During a Loss-of-Coolant Accident Using an Artificial Neural Network

2021 ◽  
pp. 407-418
Author(s):  
Priyanti Paul Tumpa ◽  
Md. Saiful Islam ◽  
Zazilah May ◽  
Md. Khorshed Alam
Author(s):  
Eltayeb Yousif ◽  
Zhang Zhijian ◽  
Tian Zhao-fei ◽  
A. M. Mustafa

To ensure effective operation of nuclear power plants, it is very important to evaluate different accident scenarios in actual plant conditions with different codes. In the field of nuclear safety, Loss of Coolant Accident (LOCA) is one of the main accidents. RELAP-MV Visualized Modularization software technology is recognized as one of the best estimated transient simulation programs of light water reactors, and also has the options for improved modeling methods, advanced programming, computational simulation techniques and integrated graphics displays. In this study, transient analysis of the primary system variation of thermo-hydraulics parameters in primary loop under SB-LOCA accident in AP1000 nuclear power plant (NPP) is carried out by Relap5-MV thermo-hydraulics code. While focusing on LOCA analysis in this study, effort was also made to test the effectiveness of the RELAP5-MV software already developed. The accuracy and reliability of RELAP5-MV have been successfully confirmed by simulating LOCA. The calculation was performed up to a transient time of 4,500.0s. RELAP5-MV is able to simulate a nuclear power system accurately and reliably using this modular modeling method. The results obtained from RELAP5 and RELAP5-MV are in agreement as they are based on the same models though in comparison with RELAP5, RELAP5-MV makes simulation of nuclear power systems easier and convenient for users most especially for the beginners.


2005 ◽  
Vol 127 (3) ◽  
pp. 230-236 ◽  
Author(s):  
Min-Rae Lee ◽  
Joon-Hyun Lee ◽  
Jung-Teak Kim

The analysis of acoustic emission (AE) signals produced during object leakage is promising for condition monitoring of the components. In this study, an advanced condition monitoring technique based on acoustic emission detection and artificial neural networks was applied to a check valve, one of the components being used extensively in a safety system of a nuclear power plant. AE testing for a check valve under controlled flow loop conditions was performed to detect and evaluate disk movement for valve degradation such as wear and leakage due to foreign object interference in a check valve. It is clearly demonstrated that the evaluation of different types of failure modes such as disk wear and check valve leakage were successful by systematically analyzing the characteristics of various AE parameters. It is also shown that the leak size can be determined with an artificial neural network.


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