• 시스템엔지니어링학술지
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• 제16권2호
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• pp.67-77
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• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

• 시스템엔지니어링학술지
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• 제12권2호
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• pp.91-99
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• 2016

The safety of nuclear power plants has received much attention; this safety largely depends on the continuous availability of electrical energy source during all modes of nuclear power plant operation. A station blackout (SBO) describes the loss of the off-site electric power, the failure of the emergency diesel generators, and the unavailability of the alternate AC (AAC) power. Consequently, all systems that are AC powered such as the safety injection, shutdown cooling, component cooling water, and essential service water systems are unavailable. The aim of this study is to investigate the deficiencies of the existing alternatives for coping with an extended SBO for APR1400 design. The method is analyzing the existing deficiencies and proposing an optimal solution for the NPP design during the extended SBO. This study, established a new passive system, called passive decay heat removal system (PDHRS), using systems engineering approach.

• 한국안전학회지
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• 제27권6호
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• pp.192-204
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• 2012

For a severe accident of nuclear power plant, an approach to estimation of the radiological source term using a severe accident code(MELCOR) has been proposed. Although the MELCOR code has a capability to estimate the radiological source term, it has been hardly utilized for the radiological consequence analysis mainly due to a lack of understanding on the relevant function employed in MELCOR and severe accident phenomena. In order to estimate the severe accident source term to be linked with the radiological consequence analysis, this study proposes 4-step procedure: (1) selection of plant condition leading to a severe accident(i.e., accident sequence), (2) analysis of the relevant severe accident code, (3) investigation of the code analysis results and post-processing, and (4) generation of radiological source term information for the consequence analysis. The feasibility study of the present approach to an early containment failure sequence caused by a fast station blackout(SBO) of a reference plant (OPR-1000), showed that while the MELCOR code has an integrated capability for severe accident and source term analysis, it has a large degree of uncertainty in quantifying the radiological source term. Key insights obtained from the present study were: (1) key parameters employed in a typical code for the consequence analysis(i.e., MACCS) could be generated by MELCOR code; (2) the MELOCR code simulation for an assessment of the selected accident sequence has a large degree of uncertainty in determining the accident scenario and severe accident phenomena; and (3) the generation of source term information for the consequence analysis relies on an expert opinion in both areas of severe accident analysis and consequence analysis. Nevertheless, the MELCOR code had a great advantage in estimating the radiological source term such as reflection of the current state of art in the area of severe accident and radiological source term.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 추계학술발표회논문집(1)
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• pp.469-474
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• 1997

To provide tile information ell severe accident progression is very important for advanced or new type of nuclear power plant (NPP) design. A parametric study, therefore was performed to investigate the effect of thermal hydraulic design parameters ell severe accident progression of pressurized water reactors (PWRs), Nine parameters, which are considered important in NPP design or severe accident progression, were selected among the various thermal hydraulic design parameters. The backpropagation neural network (BPN) was used to determine parameters, which might more strongly affect the severe accident progression, among mile parameters. For training. different input patterns were generated by the latin hypercube sampling (LHS) technique and then different target patterns that contain core uncovery time and vessel failure time were obtained for Young Gwang Nuclear (YGN) Units 3&4 using modular accident analysis program (MAAP) 3.0B code. Three different severe accident scenarios, such as two loss of coolant accidents (LOCAs) and station blackout(SBO), were considered in this analysis. Results indicated that design parameters related to refueling water storage tank (RWST), accumulator and steam generator (S/G) have more dominant effects on the progression of severe accidents investigated, compared to tile other six parameters.

• 전자공학회논문지
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• 제51권3호
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• pp.10-21
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• 2014

후쿠시마 원전사고는 자연재해에 의한 중대사고 발생 시 전원공급 중단 및 극한 환경으로 인해 발전소 내부 상황을 정확하게 파악하지 못하였고, 대부분의 계측제어시스템이 그 기능을 제대로 발휘하지 못해 비상냉각기능이 상실되어 수소폭발 및 다량의 방사능이 누출된 사고였다. 본 논문에서는 중대사고 발생 시에도 발전소 내부 상황을 감시하고, 적절히 제어할 수 있는 비상대응시스템에 대하여 소개하고, 비상대응시스템에 사용되는 무선통신망의 성능요구사항에 대해서 논의하고, 요구사항을 만족시킬 수 있는 비상통신망의 성능을 마코프 모델을 이용하여 분석하였다.

• Nuclear Engineering and Technology
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• 제52권10호
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• pp.2221-2229
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• 2020

After the Fukushima Daiichi nuclear power plant (NPP) accident, level 3 probabilistic safety assessment (PSA) has emerged as an important task in order to assess the risk level of the multi-unit NPPs in a single nuclear site. Accurate calculation of the radionuclide concentrations and exposure doses to the public is required if a nuclear site has multi-unit NPPs and large number of people live near NPPs. So, there has been a great need to develop a new method or procedure for the fast and accurate offsite consequence calculation for the multi-unit NPP accident analysis. Since the multi-unit level 3 PSA is being currently performed assuming that all the NPPs are located at the same position such as a center of mass (COM) or base NPP position, radionuclide concentrations or exposure doses near NPPs can be drastically distorted depending on the locations, multi-unit NPP alignment, and the wind direction. In order to overcome this disadvantage of the COM method, the idea of a new multiple location (ML) method was proposed and implemented into a new tool MURCC (multi-unit radiological consequence calculator). Furthermore, the MURCC code was further improved for the multi-unit level 3 PSA that has the arbitrary number of multi-unit NPPs. The objectives of this study are to (1) qualitatively and quantitatively compare COM and ML methods, and (2) demonstrate the strength and efficiency of the ML method. The strength of the ML method was demonstrated by the applications to the multi-unit long-term station blackout (LTSBO) accidents at the four-unit Vogtle NPPs. Thus, it is strongly recommended that this ML method be employed for the offsite consequence analysis of the multi-unit NPP accidents.

• 시스템엔지니어링학술지
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• 제16권2호
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• pp.97-109
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• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.