Changes in Chemical Structure and Mechanical Properties of Cross-linked Polyethylene Aged under Simulated Severe Accident Conditions of a Nuclear Power Plant

2020 ◽  
Vol 140 (9) ◽  
pp. 445-450
Author(s):  
Yu Miyazaki ◽  
Naoshi Hirai ◽  
Yoshimichi Ohki
Keyword(s):  
Mechanical Properties ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Chemical Structure ◽  
Severe Accident ◽  
Accident Conditions

Development and Verification of Severe Accident Simulation Capability for a NPP Simulator

2013 ◽  
Author(s):  
Zhifei Yang ◽  
Xiaofei Xie ◽  
Xing Chen ◽  
Shishun Zhang ◽  
Yehong Liao ◽  
...  
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Simulation System ◽  
Severe Accident ◽  
Normal Operation ◽  
Feed Water ◽  
Accident Simulation ◽  
Accident Conditions ◽  
Relap5 Code

It is reflected in the severe accident in Fukushima Daiichi that the emergency capacity of nuclear power plant needs to be enhanced. A nuclear plant simulator that can model the severe accident is the most effective means to promote this capacity. Until now, there is not a simulator which can model the severe accident in China. In order to enhance the emergency capacity in China, we focus on developing a full scope simulator that can model the severe accident and verify it in this study. The development of severe accident simulation system mainly includes three steps. Firstly, the integral severe accident code MELCOR is transplanted to the simulation platform. Secondly, the interface program must be developed to switch calculating code from RELAP5 code to MELCOR code automatically when meeting the severe accident conditions because the RELAP5 code can only simulate the nuclear power plant normal operation state and design basis accident but the severe accident. So RELAP5 code will be stopped when severe accident conditions happen and the current nuclear power plant state parameters of it should be transported to MELCOR code, and MELCOR code will run. Finally, the CPR1000 nuclear power plant MELCOR model is developed to analyze the nuclear power plant behavior in severe accident. After the three steps, the severe accident simulation system is tested by a scenario that is initiated by the station black out with reactor cooling pump seal leakage, HHSI, LHSI and auxiliary feed water system do not work. The simulation result is verified by qualitative analysis and comparison with the results in severe accident analysis report of the same NPP. More severe accident scenarios initiated by LBLOCA, MBLOCA, SBLOCA, SBO, ATWS, SGTR, MSLB will be tested in the future. The results show that the severe accident simulation system can model the severe accident correctly; it meets the demand of emergency capacity promotion.

Fundamental Experiments on Local Hydrogen Behaviors Under Severe Accident Conditions in a Rectangular Subcompartment of Nuclear Power Plant

2002 ◽  
Author(s):  
Jung-Jae Lee ◽  
Un-Jang Lee ◽  
Goon-Cherl Park
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Hydrogen Concentration ◽  
Concentration Distribution ◽  
Nuclear Power ◽  
Experimental Results ◽  
Severe Accident ◽  
Severe Accidents ◽  
Helium Gas ◽  
Accident Conditions

During severe accidents in nuclear power plant (NPP), the limit of local hydrogen concentration is regulated to ensure the integrity of containment. In this study hydrogen mixing experiments were conducted to investigate the hydrogen concentration distribution in a subcompartment of NPP. The mixing compartment is a vertical rectangular type with the dimension of 1×1×1.5 m3. The helium gas was used as a simulant of hydrogen. The goals of this study are to understand local hydrogen mixing phenomena and to examine the effects of the wall condensing, of the existence of obstacle and of the containment spray operation on local hydrogen concentration in NPP. Experimental results showed that the hydrogen might be locally accumulated in the subcompartment and the local hydrogen concentration could instantaneously rise during the spray operation.

Dissolution, Mechanical Properties, and Thermal Stability of Microparticles Containing Radioactive Cesium on Plant Litter Derived from the Fukushima Daiichi Nuclear Power Plant Accident,  and Soil Decontamination Trials 

2021 ◽  
Author(s):  
Ichiro Tanaka ◽  
Atsushi Yamaguchi ◽  
Kenji Kikuchi ◽  
Nobuo Niimura ◽  
Yume Saeki ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Thermal Stability ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Plant Litter ◽  
Radioactive Cesium ◽  
Soil Decontamination ◽  
Fdnpp Accident ◽  
Fukushima Daiichi

<p>The radioactive cesium (<sup>134</sup>Cs and <sup>137</sup>Cs), which originated from the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident, has remained in the soil and on plants as water-insoluble microparticles (termed as CsMPs) to some extent, and maintained relatively high radioactivity levels in the district. However, it has been reported that the radioactive Cs has been absorbed by plants. To interpret this phenomenon, the authors investigated CsMPs to determine if they become soluble during filtration and dialysis experiments. Moreover, other physical properties, such as mechanical properties and thermal stability, were observed during the course of the relevant experiments. These properties can be obtained by using carbonized charcoal litter with CsMPs. And simple and economic decontamination trials of the soil were performed by sieving after drying and roughly crushing.</p>

Study on Sensor with Mechanical Properties in Nuclear Power Plant with Application of BP Neural Network to Fault Tolerant Control

2013 ◽  
Vol 644 ◽  
pp. 56-59
Author(s):  
Jin Yang Li ◽  
Hong Xia ◽  
Shou Yu Cheng
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Bp Neural Network ◽  
Nuclear Power ◽  
Control Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Level Sensor

All kinds of sensor with mechanical properties often can go wrong in nuclear power plant. In this kind of situation, it puts forward a kind of active fault tolerant control method based on the improved BP neural network. Firstly, the method will train sensor by BP neural network. Secondly, it will be established dynamic model bank in all kinds of running state. The system will be detected by using BP neural network real time. When the sensor goes wrong, it will be controled by reconstruction. Taking pressurizer water-level sensor as the case, a simulation experiment was performed on the nuclear power plant simulator. The results showed that the proposed method is valid for the fault tolerant control of sensor in nuclear power plant.

Sign in / Sign up

Export Citation Format

Share Document