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  • 【Awards and Commendations】Sunghyon Jang, won “Encouragement Award” at Risk Science Technoplogy Division, AESJ.

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2021年03月24日

【Awards and Commendations】Sunghyon Jang, won “Encouragement Award” at Risk Science Technoplogy Division, AESJ.

【Awards and Commendations】Sunghyon Jang, won “Encouragement Award” at Risk Science Technoplogy Division, AESJ.

 

1.Name
Sunghyon Jang

2. Faculty/Graduate School, Department (Stream / Program) / Major
Assistant Professor, School of Engineering, Department of Nuclear Engineering and Management

3. Name of award and short explanation about the award
The 1st Encouragement Prize of Risk Science and Technology Division, Atomic Energy Society Japan

4. About awarded research
Title: Probabilistic Risk Assessment on Combined Event of Earthquake and Internal Flooding
with Effect of After shock
When a seismic-induced combined event occurs at a nuclear power plant, the aftershocks’ impact is expected. Thus, a method for quantitative evaluation of the impact of aftershocks is necessary. In this study, a combined method of numerical analysis and the probabilistic risk assessment is proposed to develop a method for risk assessment of the seismic-induced combined event considering the aftershock’s impact. As a result of the evaluation, it is found that the event progression is accelerated due to a sloshing event which is induced by the aftershocks.

5. Your impression & future plan
It is a great honor to receive the 1st Encouragement Prize of Risk Science and Technology Division, Atomic Energy Society Japan.
Probabilistic risk assessment is an important technology to identify and reduce potential risks in nuclear power plant systems. I am working on research to extend the possibility of probabilistic risk assessment technology by converging conventional probabilistic risk assessment and simulation technology.
I suppose that the encouragement award committee decided to give this prize to encourage the effort and show their expectation on this research. Thus, I would like to continue implementing the latest simulation technology and also A.I. and machine learning in probabilistic risk assessment technology.

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