• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.224-228
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• 2012

According to the results of Probabilistic Safety Assessment(PSA) for a Nuclear Power Plant(NPP), an Emergency Power Supply(EPS) system has been considered as one of the most important safety system. Especially, the interests in the reliability of the EPS system have been increased after the severe accidents of Fukushima Daiichi. Firstly, we performed the risk assessment and the importance analysis of the EPS system based on the PSA models of the reference plant, which is the Korean standard NPP type. Considering a portable Diesel Generator(DG) system as the reliability reinforcement of the EPS system, we modified the PSA models and performed the risk impact assessment and the importance analysis. Although the reliability of the potable DG could be about 20% of the reliability of the alternative AC DG, we identified that Core Damage Frequency(CDF) was decreased by at least 4.6%. In addition, the risk impacts due to the unavailability of the EPS system on CDF were decreased.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.981-988
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• 2023

A seismic event caused an accident at the Fukushima Nuclear Power Plant, which further resulted in simultaneous accidents at several units. Consequently, this incident has aroused great interest in the safety of nuclear power plants worldwide. A reasonable safety evaluation of such an external event should appropriately consider the correlation between SSCs (structures, systems, and components) and the probability of failure. However, a probabilistic safety assessment in current nuclear industries is performed conservatively, assuming that the failure correlation between SSCs is independent or completely dependent. This is an extreme assumption; a reasonable risk can be calculated, or risk-based decision-making can be conducted only when the appropriate failure correlation between SSCs is considered. Thus, this study analyzed the effect of the failure correlation of SSCs on the safety of the system to realize rational safety assessment and decision-making. Consequently, the impact on the system differs according to the size of the failure probability of the SSCs and the AND and OR conditions.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Korean Journal of Plant Taxonomy
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• v.51 no.1
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• pp.10-17
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• 2021

Invasive alien species management is pivotal for biodiversity conservation. Commelina caroliniana Walter, from the family Commelinaceae, is an alien plant native to the Himalayas and India, but it has been widely introduced around the world, including in the United States, Brazil, Philippines, and Japan. In Korea, the first population was found growing adjacent to agricultural land and farm roads on Jejudo Island, and field observations confirmed the presence of at least nine populations there. It is similar morphologically to C. diffusa Burm. f. but can be distinguished by involucral bracts that are ciliate at the base, hairs on the peduncle and obsolete upper cincinnus, brown spots on its 4-lobed antherode, and seed surfaces that are smooth to slightly alveolate. It was determined to have an invasiveness low score of 8 according to the Korean 'Invasive Alien Plant Risk Assessment', suggesting that it may spread to natural habitats. Although the current distribution of C. caroliniana is restricted to Jeju-si, it has spread dramatically in many other areas of the world. At present, it has had a limited impact on the local environment, but local and regulatory authorities should pay close attention to this plant and take measures to prevent its expansion in the future.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.10
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• pp.1291-1296
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• 2013

Risk-based inspection (RBI) is a well-known method that is used to optimize inspection activities based on risk analysis in order to identify the high-risk components of major facilities such as power plants. RBI, when implemented and maintained properly, improves plant reliability and safety while reducing unplanned outages and repair costs. Risk is given by the product of the probability of failure (POF) and the consequence of failure (COF). A semi-quantitative method is generally used for risk assessment. Semi-quantitative risk assessment complements the low accuracy of qualitative risk assessment and the high expense and long calculation time of quantitative risk assessment. The first step of RBI is to identify important failure modes and causes in the equipment. Once these are defined, the POF and COF can be assessed for each failure. During POF and COF assessment, an effective inspection method and range can be easily found. In this paper, the calculation of the POF is improved for accurate risk assessment. A modified semi-quantitative risk assessment was carried out for boiler facilities of thermal power plants, and the next maintenance schedules for the equipment were decided.

• Journal of Radiation Protection and Research
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• v.26 no.3
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• pp.197-201
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• 2001

A risk analysis consists of a triplet, , where Si is the scenario identification; Pi is the probability of each scenario; and Xi is the consequences of each scenario. A new computing framework, OMAM (ORIGEN-MAAP4-MMCS), has been developed and applied for assessing the risk of a reference plant as well as radiation source terms using the concept of risk triplet. The result of this study using the OMAM framework presented in this paper, can contribute to producing domestic nuclear power plant's risk data base as well as to establishing severe accident management plans.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2010.05b
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• pp.103-108
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• 2010

The purpose of this study is to analyze of the risk factors for oversea plants construction projects. For this study, risk factors data from related literature review, research organization and construction company was researched and classified under each EPC phases. In addition, a questionnaire survey by plant experts was conducted for analysis of risk weight and costs and time impact on each EPC phases. The results of this study are as follows: First, a detail design errors(engineering phase), a equipment procurement plan(procurement phase), and exchange rate fluctuations(construction phase) were analyzed the highest weight factors. Second, a financing plan(engineering phase), quantity take-off bill(procurement phase), and exchange rate fluctuations(construction phase) were analyzed the highest cost impact factors. Third, detail design errors(engineering phase), a equipment procurement plan(procurement phase), and schedule management errors(construction phase) were analyzed the highest time impact factors.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.426-433
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• 2017

This paper describes the development process, the innovative techniques used and insights gained from the latest integrated, full scope, multistate Level 2 PSA analysis conducted at the Leibstadt Nuclear Power Plant (KKL), Switzerland. KKL is a modern single-unit General Electric Boiling Water Reactor (BWR/6) with Mark III Containment, and a power output of $3600MW_{th}/1200MW_e$, the highest among the five operating reactors in Switzerland. A Level 2 Probabilistic Safety Assessment (PSA) analyses accident phenomena in nuclear power plants, identifies ways in which radioactive releases from plants can occur and estimates release pathways, magnitude and frequency. This paper attempts to give an overview of the advanced modeling techniques that have been developed and implemented for the recent KKL Level 2 PSA update, with the aim of systematizing the analysis and modeling processes, as well as complying with the relatively prescriptive Swiss requirements for PSA. The analysis provides significant insights into the absolute and relative importances of risk contributors and accident prevention and mitigation measures. Thanks to several newly developed techniques and an integrated approach, the KKL Level 2 PSA report exhibits a high degree of reviewability and maintainability, and transparently highlights the most important risk contributors to Large Early Release Frequency (LERF) with respect to initiating events, components, operator actions or seismic component failure probabilities (fragilities).

• Journal of the Korean Society of Safety
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• v.15 no.2
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• pp.126-135
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• 2000

Probabilistic Safety Assessment (PSA) is an engineering analysis method to identify possible contributors to the risk from a nuclear power plant and now it has become a standard tool in safety evaluation of nuclear power plants. PSA consists of three phases named as Level 1, 2 and 3. Level 2 PSA, mainly focused in this paper, uses a step-wise approach. At first, plant damage states (PDSs) are defined from the Level 1 PSA results and they are quantified. Containment event tree (CET) is then constructed considering the physico-chemical phenomena in the containment. The quantification of CET can be assisted by a decomposition event tree (DET). Finally, source terms are quantitatively characterized by the containment failure mode. As the main benefit of PSA is to provide insights into plant design, performance and environmental impacts, including the identification of the dominant risk contributors and the comparison of options for reducing risk, this technique is expected to be applied to the industrial safety area.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2004.05a
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• pp.47-60
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• 2004

Higher plants can be valuable genetic assay systems for monitoring environmental pollutants and evaluating their biological toxicity. Two assays are considered ideal for in situ monitoring and testing of soil, airborne and aqueous mutagenic agents; the Tradescantia stamen hair assay for somatic cell mutations and the Tradescantia micronucleus assay for chromosome aberrations. Both assays can be used for in vivo and in vitro testing of mutagens. Since higher plant systems are now recognized as excellent indicators and have unique advantages over in situ monitoring and screening, higher plant systems could be accepted by regulatory authorities as an alternative first-tier assay system for the detection of possible genetic damages resulting from the pollutants or chemicals used and produced by industrial sectors. It has been concluded that potential mutagen and carcinogen such as the heavy metals among indoor air particulates, volatile compounds in the working places, soil, and water pollutants contribute to the overall health risk. This contribution can be considerable under certain circumstances. It is therefore important to identify the level of genotoxic activity in the environment and to relate it to the biomarkers of a health risk in humans. The results from the higher plant bioassays could make a significant contribution to assessing the risks of pollutants and protecting the public from agents that can cause mutation and/or cancer. The plant bioassays, which are relatively inexpensive and easy to handle, are recommended for the scientists who are interested in monitoring pollutants and evaluating their environmental toxicity to living organisms.