• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.219-223
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• 2012

For an off-site consequence analysis at nuclear power plant, MELCOR Accident Consequence Code System(MACCS) II code is widely used as a software tool. In this study, the algorithm of web-based off-site consequence analysis program(OSCAP) using the MACCS II code was developed for an Integrated Leak Rate Test (ILRT) interval extension and Level 3 probabilistic safety assessment(PSA), and verification and validation(V&V) of the program was performed. The main input data for the MACCS II code are meteorological, population distribution and source term information. However, it requires lots of time and efforts to generate the main input data for an off-site consequence analysis using the MACCS II code. For example, the meteorological data are collected from each nuclear power site in real time, but the formats of the raw data collected are different from each site. To reduce the efforts and time for risk assessments, the web-based OSCAP has an automatic processing module which converts the format of the raw data collected from each site to the input data format of the MACCS II code. The program also provides an automatic function of converting the latest population data from Statistics Korea, the National Statistical Office, to the population distribution input data format of the MACCS II code. For the source term data, the program includes the release fraction of each source term category resulting from modular accident analysis program(MAAP) code analysis and the core inventory data from ORIGEN. These analysis results of each plant in Korea are stored in a database module of the web-based OSCAP, so the user can select the defaulted source term data of each plant without handling source term input data.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.1185-1192
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• 2004

Recently heavy traffic is getting worse because increasing population of transportation in urban area. In order to solve this problem. subway is realized with high speed, high density, highly efficiency. When fire accident happened in Dae-Gu subway in February 2003, there happened loss of people and lots of damage because of not being able to control even though fire alarm which was set up in the station rang. This thesis has constructed a simulation integrating operation system using a Database construction, operating program analysis in order to build up the most efficient train operation system. The result of simulation integrating operation system with emergency virtual situation like station and train fire, train failure, power line failure, all trains running on the rail were secured safety by train operation control system. With integrating operation of each train control system, train system, power system, machine facility system, the most efficient integrating operation system should have been constructed at the time the subway fire and power failure broke out.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.714-717
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• 2007

This paper presents the findings that may be useful for establishing safety policies involved with safety plan, program, and control relative to fatal accidents in construction job site. The fatal accident investigation database maintained by Korean Production Household Mascot Safety Industrial Complex is accessed. The 5 years fatal accidents occurred in 2002 to 2006 were analyzed. The statistic relative to the fatal accidents in different project types is obtained using different classifications such as occupation, years of experience, accident type, and time in a day. This research would contribute to ensure the effectiveness of safety policy so as to enhance worker safety.

• Safety and Health at Work
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• v.9 no.1
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• pp.30-41
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• 2018

Background: The purpose of the work is to develop a system that allows processing of information for analysis and industrial risk management, to monitor the level of industrial safety and to perform necessary measures aimed at the prevention of accidents, casualties, and development of professional diseases for effective management of industrial safety at hazardous industrial sites. Methods: Risk assessment of accidents and incidents is based on expert evaluations. Based on the lists of criteria parameters and their possible values, provided by the experts, a unified information and analytical database is compiled, which is included in the final interrogation questionnaires. Risk assessment of industrial injuries and occupational diseases is based on statistical methods. Results: The result of the research is the creation of Guidelines for risk management on hazardous industrial sites of the Republic of Kazakhstan. The Guidelines determine the directions and methods of complex assessment of the state of industrial safety and labor protection and they could be applied as methodological basis at the development of preventive measures for emergencies, casualties, and incidents at hazardous industrial sites. Conclusion: Implementation of the information-analytical system of risk level assessment allows to analyze the state of risk of a possible accident at industrial sites, make valid management decisions aimed at the prevention of emergencies, and monitor the effectiveness of accident prevention measures.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.349-359
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• 2017

During the past 25 years, in the context of probabilistic safety assessment, efforts have been directed towards establishment of comprehensive pipe failure event databases as a foundation for exploratory research to better understand how to effectively organize a piping reliability analysis task. The focused pipe failure database development efforts have progressed well with the development of piping reliability analysis frameworks that utilize the full body of service experience data, fracture mechanics analysis insights, expert elicitation results that are rolled into an integrated and risk-informed approach to the estimation of piping reliability parameters with full recognition of the embedded uncertainties. The discussion in this paper builds on a major collection of operating experience data (more than 11,000 pipe failure records) and the associated lessons learned from data analysis and data applications spanning three decades. The piping reliability analysis lessons learned have been obtained from the derivation of pipe leak and rupture frequencies for corrosion resistant piping in a raw water environment, loss-of-coolant-accident frequencies given degradation mitigation, high-energy pipe break analysis, moderate-energy pipe break analysis, and numerous plant-specific applications of a statistical piping reliability model framework. Conclusions are presented regarding the feasibility of determining and incorporating aging effects into probabilistic safety assessment models.

• Journal of Trauma and Injury
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• v.30 no.3
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• pp.80-86
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• 2017

Purpose: To analyze the data of patients who suffered trauma in a cultivator accident and visited the trauma center in rural Gyeongbuk Province. Methods: We retrospectively reviewed the medical records and Korean Trauma Data Bank data of 120 patients who suffered cultivator-related traumas and visited the rural regional trauma center in Gyeongbuk Province from January to December 2015. Results: The age of the patients ranged from 35 to 96 years (mean, 70 years). Ninety-one (75.8%) patients were men, and twenty-nine (24.2%) were women. Most of the patients were in their 70s (46 men [50.5%] and 13 women [44.8%]). In total, 113 patients (94.1%) arrived at the regional trauma center by ground transport and 7 (5.9%) arrived by air transport. Ninety-eight patients (81.7%) were transported to the regional trauma center directly from the scene of the accident, and twenty-two (18.3%) were transferred from another medical institute. The mean time from the accident to arrival at the emergency department was 139 minutes, and only 46 patients (38.3%) arrived within 1 hour. Twelve (10.0%) patients died, including two deaths on arrival and two post- cardiopulmonary resuscitation deaths in the emergency department. All deaths were of male cultivator operators. The causes of death were shock (hypovolemic, traumatic, or septic), subdural hematoma (open), hemothorax, rhabdomyolysis, and pneumonia. Conclusions: As the government - led regional trauma center project is on process, it would be clinically important to summarize the initial outcome of cultivator injuries, which are characteristically found more in regional trauma centers in the rural area, and have high mortality. Based on this study, in the future, it will be necessary to follow up and analyze more number of patients and to construct accurate database about trauma cases related to cultivator in Gyeongbuk region.

• Nuclear Engineering and Technology
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• v.43 no.1
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• pp.25-44
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• 2011

KAERI has been operating an integral effect test facility, ATLAS (Advanced Thermal-Hydraulic Test Loop for Accident Simulation), for accident simulations of advanced PWRs. Regarding integral effect tests, a database for major design basis accidents has been accumulated and a Domestic Standard Problem (DSP) exercise using the ATLAS has been proposed and successfully performed. The ATLAS DSP aims at the effective utilization of an integral effect database obtained from the ATLAS, the establishment of a cooperative framework in the domestic nuclear industry, better understanding of thermal hydraulic phenomena, and an investigation of the potential limitations of the existing best-estimate safety analysis codes. For the first ATLAS DSP exercise (DSP-01), integral effect test data for a 100% DVI line break accident of the APR1400 was selected by considering its technical importance and by incorporating comments from participants. Twelve domestic organizations joined in this DSP-01 exercise. Finally, ten of these organizations submitted their calculation results. This ATLAS DSP-01 exercise progressed as an open calculation; the integral effect test data was delivered to the participants prior to the code calculations. The MARS-KS was favored by most participants but the RELAP5/MOD3.3 code was also used by a few participants. This paper presents all the information of the DSP-01 exercise as well as the comparison results between the calculations and the test data. Lessons learned from the first DSP-01 are presented and recommendations for code users as well as for developers are suggested.

• Journal of Environmental Impact Assessment
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• v.3 no.2
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• pp.25-32
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• 1994

This study was carried out as the Anyang creek water quality management using Geographic Information System (GIS) is the purpose of this pilot project to apply a GIS to environmental management field. Analysis of water quality data has been investigated using GIS with modeling of water quality management for the Anyang creek. The results of this study are summarized as follows: 1. The concentration of Mercury in sediment was increased rapidly nearby A26(Nightsoil Treatment Plant) and maximum was showed at A18 (Imgok bridge). Cadmium was increased rapidly at A35(Chulsan bridge). 2. River water quality management using visible computer system as GIS is effective to make decision for water quality management plan and database of environmental factors should be completed before applying GIS. 3. When water pollution accident is occurred in the river water system, pollutant source can be traced and analysed systematically using GIS to manage pollutants discharged into the river water system.