• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of Radiation Protection and Research
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• v.29 no.3
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• pp.187-196
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• 2004

The health effects resulting from severe accidents of typical 1,000MWe KSNP(Korea Standard Nuclear Plant) PWR and typical 600MWe CANDU(CANada Deuterium Uranium) plants were estimated and compared. The population distribution of the site extending to 80km for both site were considered. The releaese fraction for various source term categories(STC) and core inventories were used in the estimation of the health effects risks by using the MACCS2(MELCOR Accident Consequence Code System2) code. Individuals are assumed to evacuate beyond 16km from the site. The health effects considered in this comparative study are early and cancer fatality risk, and the results are presented as CCDF(Complementary Cumulative Distribution Function) curves considering the occurrence probability of each STC's. According to the results, the early and cancer fatality risks of PHWR plants we lower than those of PWR plants. This is attributed the fact that the amount of radioactive mateials that released to the atmosphere resulting from the postulated severe accidents of PHWR plants are smaller than that of PWR plants. And, the dominating initiating event of STC that shows maximum early and cancer fatality risk is SGTR(Steam Generator Tube Rupture) for both plants. Therefore, the appropriated actions must be taken to reduce the occurrence probability and the amounts of radioactive materials released to the environment in order to protect the public for both PWR and PHWR plants.