• Journal of Radiation Protection and Research
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• v.34 no.1
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• pp.37-42
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• 2009

Whole body counters (WBCs) are used to monitor radiation workers for internal contamination of radionuclides at domestic nuclear power plants (NPPs). A WBC is a scintillation detector using sodium iodide (NaI) and provides the identification of inhaled radionuclide and the measurement of its internal radioactivity in a short time. However, it is often possible to estimate external contamination as internal contamination due to radionuclides attached to the skin of radiation workers and this leads to an excessively conservative estimation of radioactive contamination. In this study, several experiments using a WBC and the Korean humanoid phantom were performed to suggest the more systematic method of discrimination between external and internal contamination. Furthermore, a WBC geometry experiment was conducted to suggest the optimal WBC geometry in consideration of deposited areas inside the body for dominant radionuclides at NPPs. The procedure of measurement and estimation of internal radioactivity for radiation workers at NPPs was improved on the basis of experimental results. Thus, it is expected to prevent from estimating internal exposure dose conservatively owing to the application of accurate whole body counting program to NPPs.

• Nuclear Engineering and Technology
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• v.52 no.4
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• pp.784-790
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• 2020

The accelerated corrosion by Flow Accelerated Corrosion (FAC) has caused unexpected rupture of piping, hindering the safety of nuclear power plants (NPPs) and sometimes causing personal injury. For the safety, it may be necessary to select some pipes in terms of condition monitoring and to measure the change in thickness of pipes in real time. Direct current potential drop (DCPD) method has advantages in on-line monitoring of pipe wall thinning. However, it has a disadvantage in that it is difficult to quantify thinning due to various thinning shapes and thus there is a limitation in application. The machine learning approach has advantages in that it can be easily applied because the machine can learn the signals of various thinning shapes and can identify the thinning using these. In this paper, finite element analysis (FEA) was performed by applying direct current to a carbon steel pipe and measuring the potential drop. The fundamental machine learning was carried out and the piping thinning model was developed. In this process, the features of DCPD to thinning were proposed.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.873-880
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• 2017

To identify the feasibility of disposing of spent activated carbon as a clearance level waste, we performed characterization of radioactive pollution for spent activated carbon through radioisotope analysis; results showed that the C-14 concentrations of about half of the spent activated carbon samples taken from Korean NPPs exceeded the clearance level limit. In this situation, we selected thermal treatment technology to remove C-14 and analyzed the moisture content and thermal characteristics. The results of the moisture content analysis showed that the moisture content of the spent activated carbon is in the range of 1.2-23.9 wt% depending on the operation and storage conditions. The results of TGA indicated that most of the spent activated carbon lost weight in 3 temperature ranges. Through py-GC/MS analysis based on the result of TGA, we found that activated carbon loses weight rapidly with moisture desorption reaching to $100^{\circ}C$ and desorbs various organic and inorganic carbon compounds reaching to $200^{\circ}C$. The result of pyrolysis analysis showed that the experiment of C-14 desorption using thermal treatment technology requires at least 3 steps of heat treatment, including a heat treatment at high temperature over $850^{\circ}C$, in order to reduce the C-14 concentration below the clearance level.

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2195-2206
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• 2021

In order to mitigate the damage caused by accidents in nuclear power plants (NPPs), evacuation strategies are usually managed on the basis of off-site effects such as the diffusion of radioactive materials and evacuee traffic simulations. However, the interactive behavior between evacuees and the accident environment has a significant effect on the consequential gap. Agent-based modeling (ABM) is a method that can control and observe such interactions by establishing agents (i.e., the evacuees) and patches (i.e., the accident environments). In this paper, a radiological emergency evacuation model is constructed to realistically check the effectiveness of an evacuation strategy using NetLogo, an ABM toolbox. Geographic layers such as radiation sources, roads, buildings, and shelters were downloaded from an official geographic information system (GIS) of Korea, and were modified into respective patches. The dispersion model adopted from the puff equation was also modified to fit the patches on the geographic layer. The evacuees were defined as vehicle agents and a traffic model was implemented by combining the shortest path search (determined by an A ^* algorithm) and a traffic flow model incorporated in the Nagel-Schreckenberg cellular automata model. To evaluate the radiological harm to the evacuees due to the spread of radioactive materials, a simple exposure model was established to calculate the overlap fraction between the agents and the dispersion patches. This paper aims to demonstrate that the potential of ABM can handle disaster evacuation strategies more realistically than previous approaches.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.607-617
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• 2019

Global warming and climate change are increasing the intensity of typhoons and hurricanes and thus increasing the risk effects of typhoon and hurricane hazards on nuclear power plants (NPPs). To reflect these changes, a new NPP should be designed to endure design-basis hurricane wind speeds corresponding to an exceedance frequency of $10^{-7}/yr$. However, the short typhoon and hurricane observation records and uncertainties included in the inputs for an estimation cause significant uncertainty in the estimated wind speeds for return periods of longer than 100,000 years. A logic-tree framework is introduced to handle the epistemic uncertainty when estimating wind speeds. Three key parameters of a typhoon wind field model, i.e., the central pressure difference, pressure profile parameter, and radius to maximum wind, are used for constructing logic tree branches. The wind speeds of the simulated typhoons and the probable maximum wind speeds are estimated using Monte Carlo simulations, and wind hazard curves are derived as a function of the annual exceedance probability or return period. A logic tree decreases the epistemic uncertainty included in the wind intensity models and provides reasonably acceptable wind speeds.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1146-1156
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• 2021

Single-unit probabilistic safety assessment (SUPSA) has complex Boolean logic equations for accident sequences. Multi-unit probabilistic safety assessment (MUPSA) model is developed by revising and combining SUPSA models in order to reflect plant state combinations (PSCs). These PSCs represent combinations of core damage and non-core damage states of nuclear power plants (NPPs). Since all these Boolean logic equations have complemented gates (not gates), it is not easy to generate exact Boolean solutions. Delete-term approximation method (DTAM) has been widely applied for generating approximate minimal cut sets (MCSs) from the complex Boolean logic equations with complemented gates. By applying DTAM, approximate conditional core damage probability (CCDP) has been calculated in SUPSA and MUPSA. It was found that CCDP calculated by DTAM was overestimated when complemented gates have non-rare events. Especially, the CCDP overestimation drastically increases if seismic SUPSA or MUPSA has complemented gates with many non-rare events. The objective of this study is to suggest a new quantification method named probability subtraction method (PSM) that replaces DTAM. The PSM calculates accurate CCDP even when SUPSA or MUPSA has complemented gates with many non-rare events. In this paper, the PSM is explained, and the accuracy of the PSM is validated by its applications to a few MUPSAs.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.386-392
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• 2021

MACCS (MELCOR Accident Consequence Code System), WinMACCS, and MelMACCS now facilitate a multi-unit consequence analysis. MACCS evaluates the consequences of an atmospheric release of radioactive gases and aerosols into the atmosphere and is most commonly used to perform probabilistic safety assessments (PSAs) and related consequence analyses for nuclear power plants (NPPs). WinMACCS is a user-friendly preprocessor for MACCS. MelMACCS extracts source-term information from a MELCOR plot file. The current development can combine an arbitrary number of source terms, representing simultaneous releases from a multi-unit facility, into a single consequence analysis. The development supports different release signatures, fission product inventories, and accident initiation times for each unit. The treatment is completely general except that the model is currently limited to collocated units. A major practical consideration for performing a multi-unit PSA is that a comprehensive treatment for more than two units may involve an intractable number of combinations of source terms. This paper proposes and evaluates an approach for reducing the number of calculations to be tractable, even for sites with eight or ten units. The approximation error introduced by the approach is acceptable and is considerably less than other errors and uncertainties inherent in a Level 3 PSA.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2844-2853
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• 2023

A sophisticated Bayesian multilevel model for estimating group bias was developed to improve the utility of the ASTM E900-15 embrittlement trend curve (ETC) to assess the conditions of nuclear power plants (NPPs). For multilevel model development, the Baseline 22 surveillance dataset was basically classified into groups based on the NPP name, product form, and notch orientation. By including the notch direction in the grouping criteria, the developed model could account for TTS differences among NPP groups with different notch orientations, which have not been considered in previous ETCs. The parameters of the multilevel model and biases of the NPP groups were calculated using the Markov Chain Monte Carlo method. As the number of data points within a group increased, the group bias approached the mean residual, resulting in reduced credible intervals of the mean, and vice versa. Even when the number of surveillance test data points was less than three, the multilevel model could estimate appropriate biases without overfitting. The model also allowed for a quantitative estimate of the changes in the bias and prediction interval that occurred as a result of adding more surveillance test data. The biases estimated through the multilevel model significantly improved the performance of E900-15.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2952-2965
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• 2023

Advanced reactors, such as Small Modular Reactors or existing Nuclear Power Plants, often use Field Programmable Gate Array (FPGA) based controllers in new Instrumentation and Control (I&C) system architectures or as an alternative to existing analog-based I&C systems. Compared to CPU-based Programmable Logic Controllers (PLCs), FPGAs offer better overall performance. However, programming functions on FPGAs can be challenging due to the requirement for a hardware description language that does not explicitly support the operation of real numbers. This study aims to implement the Reactor Trip (RT) functions of the existing analog-based Reactor Protection System (RPS) using FPGAs. The RT equations for Overtemperature delta Temperature and Overpower delta Temperature involve dynamic compensators expressed with the Laplace transform variable, 's', which is not directly supported by FPGAs. To address this issue, the trip equations with the Laplace variable in the continuous-time domain are transformed to the discrete-time domain using the Z-transform. Additionally, a new operation based on a relative value for the equation range is introduced for the handling of real numbers in the RT functions. The proposed approach can be utilized for upgrading the existing analog-based RPS as well as digitalizing control systems in advanced reactor systems.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.273-282
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• 2023

Probabilistic safety assessment (PSA) has been widely used to evaluate the seismic risk of nuclear power plants (NPPs). However, studies on seismic PSA for process plants, such as gas plants, oil refineries, and chemical plants, have been scarce. This is because the major disasters to which these process plants are vulnerable include explosions, fires, and release (or dispersion) of toxic chemicals. However, seismic PSA is essential for the plants located in regions with significant earthquake risks. Seismic PSA entails probabilistic seismic hazard analysis (PSHA), event tree analysis (ETA), fault tree analysis (FTA), and fragility analysis for the structures and essential equipment items. Among those analyses, ETA can depict the accident sequence for core damage, which is the worst disaster and top event concerning NPPs. However, there is no general top event with regard to process plants. Therefore, PSA cannot be directly applied to process plants. Moreover, there is a paucity of studies on developing fragility curves for various equipment. This paper introduces PSA for gas plants based on FTA, which is then transformed into Bayesian network, that is, a probabilistic graph model that can aid risk-informed decision-making. Finally, the proposed method is applied to a gas plant, and several decision-making cases are demonstrated.