• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.27-37
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• 2020

The Fukushima Daiichi accident in 2011 revealed some vulnerabilities of existing Nuclear Power Plants (NPPs) under extended Station Blackout (SBO) accident conditions. One of the key Severe Accident Management (SAM) strategies developed post Fukushima accident is the In-Vessel Retention (IVR) Strategy which aims to retain the structural integrity of the Reactor Pressure Vessel (RPV). RELAP/SCDAPSIM/MOD3.4 is selected to predict the thermal-hydraulic response of APR1400 undergoing an extended SBO. To assess the effectiveness of the IVR strategy, it is essential to quantify the underlying uncertainties. In this work, both the epistemic and aleatory uncertainties are considered to identify the success window of the IVR strategy. A set of in-vessel relevant phenomena were identified based on Phenomena Identification and Ranking Tables (PIRT) developed for severe accidents and propagated through the thermal-hydraulic model using Wilk's sampling method. For this work, a Systems Engineering (SE) approach is applied to facilitate the development process of assessing the reliability and robustness of the APR1400 IVR strategy. Specifically, the Kossiakoff SE method is used to identify the requirements, functions and physical architecture, and to develop a design verification and validation plan. Using the SE approach provides a systematic tool to successfully achieve the research goal by linking each requirement to a verification or validation test with predefined success criteria at each stage of the model development. The developed model identified the conditions necessary for successful implementation of the IVR strategy which maintains the vessel integrity and prevents a melt-through.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.102-114
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• 2021

Biofouling represents an important problem in the shipping industry since it causes the increase in surface roughness. The most of ships in the current world fleet do not have good coating condition which represents an important problem due to strict rules regarding ship energy efficiency. Therefore, the importance of the control and management of the hull and propeller fouling is highlighted by the International Maritime Organization and the maintenance schedule optimization became valuable energy saving measure. For adequate implementation of this measure, the accurate prediction of the effects of biofouling on the hydrodynamic characteristics is required. Although computational fluid dynamics approach, based on the modified wall function approach, has imposed itself as one of the most promising tools for this prediction, it requires significant computational time. However, during the maintenance schedule optimization, it is important to rapidly predict the effect of biofouling on the ship hydrodynamic performance. In this paper, the effect of biofilm on the ship hydrodynamic performance is studied using the proposed performance prediction method for three merchant ships. The applicability of this method in the assessment of the effect of biofilm on the ship hydrodynamic performance is demonstrated by comparison of the obtained results using the proposed performance prediction method and computational fluid dynamics approach. The comparison has shown that the highest relative deviation is lower than 4.2% for all propulsion characteristics, lower than 1.5% for propeller rotation rate and lower than 5.2% for delivered power. Thus, a practical tool for the estimation of the effect of biofouling with lower fouling severity on the ship hydrodynamic performance is developed.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2067-2076
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• 2022

The importance of safety culture has been emphasized to achieve a high level of safety. In this light, a systematic method to more properly deal with safety culture is necessary. Here, a decision-making tool that can apply a graded approach to the analysis of safety culture is proposed, called the F-D matrix, which determines the frequency and the difficulty of safety culture attributes recently defined by the IAEA. A hierarchical model of difficulty contributors was developed as a scoring standard, and its elements were weighted via expert evaluation using the analytic hierarchy process. The frequency of the attributes was derived by analyzing reported events from nuclear power plants in the Republic of Korea. Period-by-period comparisons with the F-D matrix can show trends in the change of the maturity level of an organization's safety culture and help to evaluate the effectiveness of previously implemented measures. In the evaluating the difficulty of the attributes in the recently developed harmonized safety culture model, the difficulties of Trending, Benchmarking, Resilience, and Documentation and Procedures were found to be relatively high, while the difficulties of Conflicts are Resolved, Ownership, Collaboration, and Respect is Evident were found to be relatively low. A case study was conducted with an analysis period of 10 years to attempt to reflect the many changes in safety culture that have been made following the Fukushima accident in March 2011. As a result of comparing two periods following the Fukushima accident, the overall frequency decreased by about 40%, providing evidence for the effects of the various improvements and measures taken following the increased emphasis on safety culture. The proposed F-D matrix provides a new analytical perspective and enables an in-depth analysis of safety culture.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.959-964
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• 2022

Lead is an important material, both for fusion or fission reactors. The cross sections of natural lead should be validated because lead is a main component of lithium-lead modules suggested for fusion power plants and it directly affects the crucial variable, tritium breeding ratio. The presented study discusses a validation of the lead transport libraries by dint of the activation of carefully selected activation samples. The high emission standard ²⁵²Cf neutron source was used as a neutron source for the presented validation experiment. In the irradiation setup, the samples were placed behind 5 and 10 cm of the lead material. Samples were measured using a gamma spectrometry to infer the reaction rate and compared with MCNP6 calculations using ENDF/B-VIII.0 lead cross sections. The experiment used validated IRDFF-II dosimetric reactions to validate lead cross sections, namely ¹⁹⁷Au(n, 2n)¹⁹⁶Au, ⁵⁸Ni(n,p)⁵⁸Co, ⁹³Nb(n, 2n)^92mNb, ¹¹⁵In(n,n')^115mIn, ¹¹⁵In(n,γ)^116mIn, ¹⁹⁷Au(n,γ)¹⁹⁸Au and ⁶³Cu(n,γ)⁶⁴Cu reactions. The threshold reactions agree reasonably with calculations; however, the experimental data suggests a higher thermal neutron flux behind lead bricks. The paper also suggests ²⁵²Cf isotropic source as a valuable tool for validation of some cross-sections important for fusion applications, i.e. reactions on structural materials, e.g. Cu, Pb, etc.

• 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.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2000.05a
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• pp.1-1
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• 2000

The results of the modification of metal materials treated by high temperature pulst:d plasma fluxes (HTlPPF) with a specific power of incident flux changing in the $(3...100)10^5{]\;}W/cm^2$ range and a pulse duration lying from 15 to $50{\;}\mu\textrm{s}$ have been presented. The results of HTPPF action were studied on the stainless steels of 18Cr-l0Ni, 16Cr- 15Ni, 13Cr-2Mo types; on the structural carbon steels of (13...35)Cr, St. 3, St. 20, St. 45 types; on the tool steels of U8, 65G, ShHI5 types, and others; on nickel and high nickel alloy of 20Cr-45Ni type; on zirconium- and vanadium-base alloys and other materials. The microstructure and properties (mechanical, tribological, erosion, and other properties) of modified materials and surface alloying of metals exposed to HTPPF action have been investigated. It was found that the modification of materials by HTPPF resulted in a simultaneous increase of several properties of the treated articles: microhardness of the surface and layers of 40...60 $\mu\textrm{m}$ in depth, tribological characteristics (friction coefficient, wear resistance), mechanical properties ({\sigma_y}, {\;}{\sigma_{0.2}}.{\;}{\sigma_r}) on retention of the initial plasticity ($\delta$), corrosion resistance, radistanation erosion under ion irradiation, and others. The determining factor of the changes observed is the structural-phase modification of the near-surface layers, in particular, the formation of the fine cellular structure in the near-surface layers at a depth of $20{\;}{\mu\textrm{m}}$ with dimension of cells changing in the range from 0.1 to $1., 5{\;}\mu\textrm{m}$, depending on the kind of material, its preliminary treatment, and the parameters of plasma fluxes. The remits obtained have shown the possibility of purposeful surface alloying of metals exposed to HTPPF action over a depth up to 20...45 $\mu\textrm{m}$ and the concentration of alloying element (Ni, Cr, V) up to 20 wt.%. Possible industrial brunches for using the treatment have been also considered, as well as some results on modifying the serial industrial articles by HTPPF.

• Structural Engineering and Mechanics
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• v.86 no.1
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• pp.119-137
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• 2023

The purpose of this research is to assess the performance of CBN and ceramic tools during the dry turning of gray cast iron EN GJL-350. During the turning operation, the variable machining parameters are cutting speed, feed rate, depth of cut and type of the cutting material. This contribution consists of two sections, the first one deals with the performance evaluation of four materials in terms of evolution of flank wear, surface roughness (2D and 3D) and cutting forces. The focus of the second section is on statistical analysis, followed by modeling and optimization. The experiments are conducted according to the Taguchi design L₃₂ and based on ANOVA approach to quantify the impact of input factors on the output parameters, namely, the surface roughness (Ra), the cutting force (Fz), the cutting power (Pc), specific cutting energy (Ecs). The RSM method was used to create prediction models of several technical factors (Ra, Fz, Pc, Ecs and MRR). Subsequently, the desirability function approach was used to achieve a multi-objective optimization that encompasses the output parameters simultaneously. The aim is to obtain optimal cutting regimes, following several cases of optimization often encountered in industry. The results found show that the CBN tool is the most efficient cutting material compared to the three ceramics. The optimal combination for the first case where the importance is the same for the different outputs is Vc=660 m/min, f=0.116 mm/rev, ap=0.232 mm and the material CBN. The optimization results have been verified by carrying out confirmation tests.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.140-146
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• 2023

Water quality is crucial for human health and the environment. Accurate measurement of the quantity of organic carbon in water is essential for water quality evaluation, identification of water pollution sources, and appropriate implementation of water treatment measures. Total organic carbon (TOC) analysis is an important tool for this purpose. Although other methods, such as chemical oxygen demand (COD) and biochemical oxygen demand (BOD) are also used to measure organic carbon in water, they have limitations that make TOC analysis a more favorable option in certain situations. For example, COD requires the use of toxic chemicals, and BOD is time-consuming and can produce inconsistent and unreliable results. In contrast, TOC analysis is rapid and reliable, providing accurate measurements of organic carbon content in water. However, common methods for TOC analysis can be complex and energy-intensive because of the use of high-temperature heaters for liquid-to-gas phase transitions and the use of acid, which present safety risks. This study focuses on a TOC analysis method using TiO₂ photocatalysis, which has several advantages over conventional TOC analysis methods, including its low cost and easy maintenance. For TiO₂, rutile and anatase powders are mixed with an inorganic binder and spray-coated onto a glass fiber substrate. The TiO₂ powder and inorganic binder solutions are adjusted to optimize the photocatalytic reaction performance. The TiO₂ photocatalysis method is a simple and low-power approach to TOC analysis, making it a promising alternative to commonly used TOC analysis methods. This study aims to contribute to the development of more efficient and cost-effective approaches for water quality analysis and management by exploring the effectiveness and reliability of the developed equipment.

• Journal of the Korean Society of Food Culture
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• v.25 no.6
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• pp.764-769
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• 2010

The aim of this study was to examine how customers perceive storytelling at a real local restaurant and to understand how consumption reflects their evaluation by understanding their values. Participants received information from a real local restaurant in the Gangneung area to examine the effects of storytelling and to evaluate their affective attitudes towards local restaurant stories, word-of-mouth intentions, and purchasing intentions using a seven-point Likert scale. A total of 310 consumers were used. A factor analysis was performed to identify consumption value characteristics and factor structure, which consisted economic value, emotional value, and epistemic value. To test whether local restaurant consumers could be classified into homogenous groups based on their consumption values, a three cluster solution was selected, and a Kmeans cluster analysis was performed. As a result, three groups were identified and labeled appropriate for their scores based on each of the consumption values; emotional value-oriented consumers to cluster 1, epistemic value-oriented consumers to cluster 2, and economic value-oriented consumers to cluster 3. An analysis of variance was used to examine the differences in the affective attitudes towards storytelling at the local restaurant, purchasing intentions, and word-of-mouth intentions across the three clusters. The epistemic value-oriented consumers had the highest score for all three variables. In contrast, economic value-oriented consumers had the lowest scores for the three variables. A regression analysis revealed that affective attitudes towards storytelling were significantly affected by these three consumption values. It also showed that positive affective attitudes towards storytelling were associated with higher purchasing intention and word-of-mouth. The significance of this study was to show how customers perceive storytelling at a real restaurant and reflect on their evaluation by understanding their consumption values. As a result, this study examined the potential power of storytelling as an effective marketing communication tool for local restaurants.