• Nuclear Engineering and Technology
• /
• v.53 no.12
• /
• pp.4060-4066
• /
• 2021

Flow-accelerated corrosion (FAC) of carbon steel piping is a significant problem in nuclear power plants. The basic process of FAC is currently understood relatively well; however, the accuracy of prediction models of the wall-thinning rate under an FAC environment is not reliable. Herein, we propose a methodology to construct pipe wall-thinning rate prediction models using artificial neural networks and a convolutional neural network, which is confined to a straight pipe without geometric changes. Furthermore, a methodology to generate training data is proposed to efficiently train the neural network for the development of a machine learning-based FAC prediction model. Consequently, it is concluded that machine learning can be used to construct pipe wall thinning rate prediction models and optimize the number of training datasets for training the machine learning algorithm. The proposed methodology can be applied to efficiently generate a large dataset from an FAC test to develop a wall thinning rate prediction model for a real situation.

• Nuclear Engineering and Technology
• /
• v.52 no.12
• /
• pp.2836-2846
• /
• 2020

During severe nuclear power plant (NPP) accidents, a H₂/CO mixture can be generated in the reactor pressure vessel by core degradation and in the containment as well by molten corium-concrete interaction. In spite of its importance, a state-of-the-art methodology predicting H₂/CO combustion risk relies predominantly on empirical correlations. It is therefore necessary to develop a proper methodology for flammability evaluation of H₂/CO mixtures at ex-vessel phases characterized by three factors: CO concentration, high temperature, and diluents. The developed methodology adopted Le Chatelier's law and a calculated non-adiabatic flame temperature model. The methodology allows the consideration of the individual effect of the heat transfer characteristics of hydrogen and carbon monoxide on low flammability limit prediction. The accuracy of the developed model was verified using experimental data relevant to ex-vessel phase conditions. With the developed model, the prediction accuracy was improved substantially such that the maximum relative prediction error was approximately 25% while the existing methodology showed a 76% error. The developed methodology is expected to be applicable for flammability evaluation in chemical as well as NPP industries.

• Journal of the Korea Safety Management & Science
• /
• v.26 no.1
• /
• pp.39-44
• /
• 2024

In order to prevent accidents via defective ammunition, this paper analyzes recent research on ammunition life prediction methodology. This workanalyzes current shelf-life prediction approaches by comparing the pros and cons of physical modeling, accelerated testing, and statistical analysis-based prediction techniques. Physical modeling-based prediction demonstrates its usefulness in understanding the physical properties and interactions of ammunition. Accelerated testing-based prediction is useful in quickly verifying the reliability and safety of ammunition. Additionally, statistical analysis-based prediction is emphasized for its ability to make decisions based on data. This paper aims to contribute to the early detection of defective ammunition by analyzing ammunition life prediction methodology hereby reducing defective ammunition accidents. In order to prepare not only Korean domestic war situation but also the international affairs from Eastern Europe and Mid East countries, it is very important to enhance the stability of organizations using ammunition and reduce costs of potential accidents.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.11
• /
• pp.1041-1045
• /
• 2010

Clusters can provide scalable and flexible architectures for parallel computing servers and data centers. Their performance prediction has been a very challenging issue. Existing performance measurement methodologies are able to measure the performance of servers already constructed. Thus they cannot provide a way to predict the overall system performance in advance when designing the system at the initial phase or adding more nodes for more capacity. Therefore, the performance modeling and prediction methodology for large-scale clusters is highly required. In this paper, we suggest a methodology to predict the performance of large-scale clusters, which consists of measurement, modeling and prediction steps. We apply the methodology to a real cluster server and show its usefulness.

• Journal of the Korean Society of Safety
• /
• v.13 no.4
• /
• pp.192-198
• /
• 1998

MRSM(modified response surface methodology)-2 model is presented for the prediction of boiling points in combustible solution of quaternary systems. This model requires only normal boiling points of pure substances and group-group parameters which are based on the group-group concepts without the use of experimental data under consideration. By means of this methodology, it is possible to predict the boiling points of the combustible mixture of quaternary systems by plotting of isothermal lines using computer graphics. The proposed methodology has been tested and compared successfully with reported boiling points in journals for the combustible solution of quaternary systems. It is hoped eventually that this methodology will permit prediction of the flash point and flammability limit for the combustible mixture of multicomponent systems.

• Journal of Korean Society of Forest Science
• /
• v.95 no.6
• /
• pp.778-783
• /
• 2006

Price prediction is essential to decisions of investment and shipment in oak mushroom cultivation. But predicting the prices of oak mushroom is very difficult because there are so many uncertain factors affecting the demand and the supply in the market. The Box-Jenkins methodology is one of strong tools in price prediction especially for the short-term using historical observations of time series. In this paper, the Box-Jenkins methodology is applied to find a model to forecast future oak mushroom prices. And out-of-sample test was conducted to check out the prediction accuracy. The result shows the high accuracy except for market disturbance period affected by unexpected weather change and reveals the usefulness of the model.

• Journal of the Korean Society of Safety
• /
• v.12 no.3
• /
• pp.76-82
• /
• 1997

Flash points ire used to classify flammable liquids according to their relative flammability. Such a classification is important for the safe handling of flammable liquids which constitute the solvent mixtures. MRSM(modified response surface methodology)-1 and MRSM-2 models we suggested for the prediction of the flash points in the flammable ternary system. By means of this methodology, it is possible to predict the flash points of the flammable mixtures system using computer graphics in the triangular coordinate for the ternary system. The proposed methodology(MRSM) has been tested and compared successfully with previously reported flash points in journal for the ternary system.

• Journal of Industrial Technology
• /
• v.29 no.A
• /
• pp.27-36
• /
• 2009

Reliability plays a pivotal role in products safety and quality. DoD RIAC recently developed a new reliability prediction methodology, $217Plus^{TM}$, for electronic systems. It officially replaces the well-known MIL-HDBK-217 and is expected to be widely used. Although theoretic study about $217Plus^{TM}$ and its application towards field systems seem to be attractive, it is also desirable to understand the general background of its development. In this paper, we performed a historical review of the arenas related to reliability prediction. Due to the vast of materials, our scope was limited to the development of $217Plus^{TM}$. We first reviewed Rome Laboratory and RIAC. We then explained the development course of reliability methods, MIL-HDBK-217, PRISM, and 217-Plus. This review will form not only a good understanding of the methodology but a basis for future study. We conclude this study with provision of future research areas.

• Nuclear Engineering and Technology
• /
• v.53 no.4
• /
• pp.1199-1209
• /
• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• KIEE International Transactions on Power Engineering
• /
• v.11A no.4
• /
• pp.15-20
• /
• 2001

Power system stability is correlated with system structure, disturbances and operating conditions, and power flows on transmission lines are closely related with those conditions. This paper proposes a methodology to identify correlative power flows for power system transient and small-signal stability prediction. In transient stability sense, the Critical Clearing Time is used to select some dominant contingencies, and Transient Stability Prediction index is proposed for the quantitative comparison. For small-signal stability discusses a methodology to identify crucial transmission lines for stability prediction by introducing a sensitivity factor based on eigenvalue sensitivity technique. On-line monitoring of the selected lines enables to predict system stability in real-time. Also, a procedure to make a priority list of monitored transmission lines is proposed. The procedure is applied to a test system, and it shows capabilities of the proposed method.