• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.156-161
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• 2016

An accident of an ammonia tank pipeline at a storage plant resulted in one death and three injuries in 2014. Many accidents including toxic gas releases and explosions occur in the freezing and refrigerating systems using ammonia. Especially, the consequence can be substantial due to that the large amount of ammonia is usually being used in the refrigeration systems. In this study, offsite consequence analysis has been investigated when ammonia leaks outdoors from large storages. Both flammable and toxic effects are under consideration to calculate the affected area using simulation programs for consequence analysis. ERPG-2 concentration (150 ppm) has been selected to calculate the evacuation distance out of various release scenarios for their dispersions in day or night. For offsite residential, the impact area by flammability is much smaller than that by toxicity. The methodology consists of two steps as followings; 1. Calculation for discharge rates of accidental release scenarios. 2. Dispersion simulation using the discharge rate for different conditions. This proactive prediction for accidental releases of ammonia would help emergency teams act as quick as they can.

• Nuclear Engineering and Technology
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• v.51 no.6
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• pp.1525-1531
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• 2019

The indigenous nuclear power program of India is based mainly on a series of Pressurised Heavy Water Reactors (PHWRs). A burst correlation for Indian PHWR fuel claddings has been developed and empirical burst parameters are determined. The burst correlation is developed from data available in literature for single-rod transient burst tests performed on Indian PHWR claddings in inert environment. The heating rate and internal overpressure were in the range of 7 K/s-73 K/s and 3 bar-80 bar, respectively, during the burst tests. A burst criterion for inert environment, which assumes that deformation is controlled by steady state creep, has been developed using the empirical burst parameters. The burst criterion has been validated with experimental data reported in literature and the prediction of burst parameters is in a fairly good agreement with the experimental data. The burst criterion model reveals that increasing the heating rate increases the burst temperature. However, at higher heating rates, burst strain is decreased considerably and an early rupture of the claddings without undergoing considerable ballooning is observed. It is also found that the degree of anisotropy has significant influence on the burst temperature and burst strain. With increasing degree of anisotropy, the burst temperature for claddings increases but there is a decrease in the burst strain. The effect of anisotropy in the ${\alpha}$-phase is carried over to ${\alpha}+{\beta}$-phase and its effect on the burst strain in the ${\alpha}+{\beta}$-phase too can be observed.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.1052-1065
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• 2024

Advanced Pressurized Water Reactors (APWRs) and Boiling Water Reactors (BWRs) employ a suppression pool as a heat sink to prevent containment overpressure. Steam can be discharged into the pool through multi-hole spargers or blowdown pipes in both normal and accident conditions. Direct Contact Condensation (DCC) creates sources of momentum and heat. The competition between these two sources determines the development of thermal stratification or mixing of the pool. Thermal stratification is of safety concern as it reduces the cooling capability compared to a completely mixed pool condition. In this work we develop a scaling approach to prediction of the thermal stratification in a water pool induced by steam injection through spargers. Experimental data obtained from large-scale pool tests conducted in the PPOOLEX and PANDA facilities, as well as simulation results obtained using validated codes are used to develop the scaling. Two injection orientations, namely radial injection through multi-hole Sparger Head (SH) and vertical injection through Load Reduction Ring (LRR), are considered. We show that the erosion rate of the cold layer can be estimated using the Richardson number. In this work, scaling laws are proposed to estimate both the (i) transient erosion velocity and (ii) the stable position of the thermocline. These scaling laws are then implemented into a 1D model to simulate the thermal behavior of the pool during steam injection through the sparger.

• Journal of the Korean Institute of Gas
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• v.20 no.5
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• pp.17-26
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• 2016

Chemical plants and oil gas refinery facilities are intrinsically vulnerable to industrial hazards, such as explosion or fire. Especially, the fire is extremely dangerous to facility structures and plant personnel because of direct flame, radiant heat and smoke. In addition, it has the ripple effect of destroying infra-structures and polluting the environment. In an effort to tackle these potential industrial risks, the procedure of FRA techniques in chemical plants were investigated. The main focus was put on the time variation of physical properties of the main building, i.e. control rooms, warehouses and electrical substations, from a direct flame contact and radiant heat. The deformation of a building due to fire was monitored and modeled with respect to time variable. A variety of case studies, domestic and abroad, was tested in the model to verify the FRA procedure. The developed model was proven to be highly effective to reduce the possible risks at chemical plants. An accurate accident frequency prediction and damage quantification was made by the developed model.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.1
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• pp.94-101
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• 1994

This paper describes a short-term prediction model for the movement of an oil slick in shallow waters. Under the assumption that the initial movement of the oil slick is governed by spreading and advection, the model has been developed and applied to Kyungki-Bay near Incheon Harbor. The initial spreading is estimated by using an empirical formula. The depth-averaged momentum equations are solved numerically for the volume transport velocities, in which the $M_2$ tide is the main driving source. A staggered grid system is adopted fur spatial discretization and the half-time method is implemented for time marching. The numerical result is visualized with the help of animation and thus the contaminated area is displayed on a monitor in time sequence. The input data are the time, the location and the volume of spill accident as well as environmental data such as md and $M_2$ tide.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.603-610
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• 2018

The development of industry and the increase in the use of fossil fuels have accelerated the process of global warming and climate change, resulting in more frequent and intense natural disasters than ever before. Since electricity facilities are often installed outdoors, they are heavily influenced by natural disasters and the number of related accidents is increasing. In this paper, we analyzed the statistical status of domestic electrical fires, electric shock accidents, and electrical equipment accidents and hence analyzed the risk associated with climate change. Through the analysis of the electrical accidental data in connection with the various regional (metropolitan) climatic conditions (temperature, humidity), the risk rating and charts for each region and each equipment were produced. Based on this analysis, a basic electric risk prediction model is presented and a method of displaying an electric hazard prediction map for each region and each type of electric facilities through a website or smart phone app was developed using the proposed analysis data. In addition, efforts should be made to increase the durability of the electrical equipment and improve the resistance standards to prevent future disasters.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.113-120
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• 2020

Safety accidents, called industrial accidents in construction work, are causing a lot of casualties, property damage and social controversy in the event of an accident, causing the construction to lose public confidence. The risk of safety accidents at construction sites may continue to increase as the construction of high-rise, large-scale, and multi-purpose complex buildings has increased in recent years. In particular, the most frequently constructed apartment construction among reinforced concrete buildings is designed and constructed with a wall-like structure with no beams for each floor, while the lower floors are made of lamen with columns and beams. As a result, the transfer beam or transfer slab to withstand the upper load is installed on the upper part of the Ramen structure, so the system Dongbari, which is installed as a temporary material during concrete laying construction, may collapse at any time during plowing and curing. The purpose of this study is to apply IT convergence technology to prevent the collapse of the system Dongbari during concrete installation, and to apply many of the variables that may occur during construction on a case-by-case basis to check the stability of the system Dongbari and to propose a model of the anti-conducting prediction system.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.53-66
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• 2013

The major purpose of this study is to evaluate methodologies to predict the injury severity of pedestrian-vehicle collisions. Methodologies to be evaluated and compared in this study include Binary Logistic Regression(BLR), Ordered Probit Model(OPM), Support Vector Machine(SVM) and Decision Tree(DT) method. Valuable insights into applying methodologies to analyze the characteristics of pedestrian injury severity are derived. For the purpose of identifying causal factors affecting the injury severity, statistical approaches such as BLR and OPM are recommended. On the other hand, to achieve better prediction performance, heuristic approaches such as SVM and DT are recommended. It is expected that the outcome of this study would be useful in developing various countermeasures for enhancing pedestrian safety.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.2
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• pp.95-101
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• 2017

In this study, we derived theoretical equations for the zigzag movement of a leading vehicle, which is the most frequent behavior in train accidents, by using a simplified spring-mass model for the rolling stock. In order to solve the equations of motion, we applied the Runge-Kutta method, which is the typical numerical analysis method used for differential equations. Furthermore, the lateral displacement of the wheel-set at the wheel-rail interface was estimated using kinetic energy. In order to verify the derived equations, we compared the theoretical and simulated results under various collision conditions. The maximum relative deviations of the lateral displacements were 0.8 [%] ~ 4.7 [%] in light collisions and 0.6 [%] ~ 5.1 [%] under derailment conditions. When an accident is simulated, these theoretical equations can be used to predict the overall behavior and obtain the offset of the body-to-body link as the initial perturbation.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.1-17
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• 2015

In this paper, we report what we have observed with regards to a prediction model for the military based on enlisted men's internal(cumulative records) and external data(SNS data). This work is significant in the military's efforts to supervise them. In spite of their effort, many commanders have failed to prevent accidents by their subordinates. One of the important duties of officers' work is to take care of their subordinates in prevention unexpected accidents. However, it is hard to prevent accidents so we must attempt to determine a proper method. Our motivation for presenting this paper is to mate it possible to predict accidents using enlisted men's internal and external data. The biggest issue facing the military is the occurrence of accidents by enlisted men related to maladjustment and the relaxation of military discipline. The core method of preventing accidents by soldiers is to identify problems and manage them quickly. Commanders predict accidents by interviewing their soldiers and observing their surroundings. It requires considerable time and effort and results in a significant difference depending on the capabilities of the commanders. In this paper, we seek to predict accidents with objective data which can easily be obtained. Recently, records of enlisted men as well as SNS communication between commanders and soldiers, make it possible to predict and prevent accidents. This paper concerns the application of data mining to identify their interests, predict accidents and make use of internal and external data (SNS). We propose both a topic analysis and decision tree method. The study is conducted in two steps. First, topic analysis is conducted through the SNS of enlisted men. Second, the decision tree method is used to analyze the internal data with the results of the first analysis. The dependent variable for these analysis is the presence of any accidents. In order to analyze their SNS, we require tools such as text mining and topic analysis. We used SAS Enterprise Miner 12.1, which provides a text miner module. Our approach for finding their interests is composed of three main phases; collecting, topic analysis, and converting topic analysis results into points for using independent variables. In the first phase, we collect enlisted men's SNS data by commender's ID. After gathering unstructured SNS data, the topic analysis phase extracts issues from them. For simplicity, 5 topics(vacation, friends, stress, training, and sports) are extracted from 20,000 articles. In the third phase, using these 5 topics, we quantify them as personal points. After quantifying their topic, we include these results in independent variables which are composed of 15 internal data sets. Then, we make two decision trees. The first tree is composed of their internal data only. The second tree is composed of their external data(SNS) as well as their internal data. After that, we compare the results of misclassification from SAS E-miner. The first model's misclassification is 12.1%. On the other hand, second model's misclassification is 7.8%. This method predicts accidents with an accuracy of approximately 92%. The gap of the two models is 4.3%. Finally, we test if the difference between them is meaningful or not, using the McNemar test. The result of test is considered relevant.(p-value : 0.0003) This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of enlisted men's data. Additionally, various independent variables used in the decision tree model are used as categorical variables instead of continuous variables. So it suffers a loss of information. In spite of extensive efforts to provide prediction models for the military, commanders' predictions are accurate only when they have sufficient data about their subordinates. Our proposed methodology can provide support to decision-making in the military. This study is expected to contribute to the prevention of accidents in the military based on scientific analysis of enlisted men and proper management of them.