• Nuclear Engineering and Technology
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• 제56권9호
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• pp.3717-3729
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• 2024

Accurately predicting evacuation time in a ventilated main control room (MCR) during fire emergencies is crucial for ensuring the safety of personnel at nuclear power plants. This study proposes to use neural networks alongside consolidated fire and smoke transport (CFAST) simulations to serve as a surrogate model for physics-based simulation tools. Our neural networks can promptly predict the evacuation time in MCRs, proving to be a valuable asset in fire emergencies and eliminating the need for time-consuming rollouts of the CFAST simulations. The CFAST model simulates fire and evacuation scenarios in a ventilated MCR with variations in input parameters such as door conditions, ventilation flow rate, leakage area, and fire propagation time. Target output parameters, such as hot gas layer temperature (HGLT), heat flux (HF), and optical density (OD), are used alongside standardized evacuation variables to train a machine learning model for predicting evacuation time. The findings suggest that high ventilation flow rates help to dilute smoke and discharge hot gas, leading to lower target output parameters and quicker evacuation. Standardized evacuation variables exceed the required abandonment criteria for all door conditions, indicating the importance of proper evacuation procedures. The results show that neural networks can generate evacuation times close to those obtained from CFAST simulations.

• The Journal of Asian Finance, Economics and Business
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• 제7권9호
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• pp.315-328
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• 2020

In this study, we examined various aspects of discretionary accruals. We compared the power of Jones Model (JM), Modified Jones Model (MJM) and Performance Matched Model (PMM). Furthermore, we tested whether accruals derived from cash flow approach or balance sheet approach provide better results and we investigated the significance of country and industry control variables in models. In order to perform these tests, we constructed thirty equations. The data consists of 319 non-financial companies over five years in the GCC region. We used panel data regression models, and testing suggests us to use random effect model as the most suitable one. The results show that PMM has the highest explanatory power among models and it is followed by JM and MJM, consecutively. Secondly, results reveal that accruals derived from cash flow approach provide more accurate results. Moreover, country dummies are significant in models with cash flow approach and they lose significance in balance sheet approach. We differentiated industries due to two different classifications: the first group with higher number of industries is more precise compared to the second group with a narrower scope and lower number of industries. The model including both industrial and country-wise dummies scores highest in significance.

• 한국분무공학회지
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• 제18권1호
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• pp.9-15
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• 2013

Wall thinning of pipeline in power plants occurs mainly by flow acceleration corrosion (FAC), cavitation erosion (C/E), liquid droplet impingement erosion (LDIE). Wall thinning by FAC and C/E has been well investigated; however, LDIE in plant industries has rarely been studied due to the experimental difficulty of setting up a long injection of highly-pressurized air. In this study, we designed a long-term experimental system for LDIE and investigate the behavior of LDIE for three kinds of materials (A106B, SS400, A6061). The main control parameter was the air-water ratio (${\alpha}$), which was defined as the volumetric ratio of water to air (0.79, 1.00, 1.72). In order to clearly understand LDIE, the spraying velocity (${\nu}$) of liquid droplets was controled larger then 160 m/s and the experiments were performed for 15 days. Therefore, this research focuses relation between erosion rate and air-water ratio on the various pipe-flow materials. NPP(nuclear power plant)'s LDIE prediction theory and management technique were drawn from the obtained data.

• 전기학회논문지
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• 제59권2호
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• pp.232-241
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• 2010

This paper describes the development of robust contingency analysis program for Korean Energy Management System. The important function of contingency analysis is to determine the bus/branch model for contingency, and to calculate the state of the power network based on the network model and topology output. In the proposed method, the bus/branch models for contingencies are determined exactly using a fast linked-list method based on the application common model database. To calculate the state of the power system included contingency, the full-decoupled powerflow approach, the partial powerflow method for contingencies and the proposed contingency screening algorithm are also used to contingency analysis. To verify the performance of the developed processor, we performed a file-based test using several structured input data and online test using the database which resides on memory. The results of these comprehensive tests showed that the developed processors can accurately calculate the power system contingency state from online data and can be applied to Korea Power Exchange system.

• Journal of Advanced Marine Engineering and Technology
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• 제35권5호
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• pp.582-589
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• 2011

선박에서 배출되는 온실가스를 저감하기 위한 기술로 연료전지 기술이 고려되어지고 있다. 본 연구에서는 메탄을 연료로 사용한 내부개질형 500kW급 고체산화물형 연료전지의 선박 적용을 가정한 연료전지 시스템을 모델링하여 시스템의 구성에 따른 공기, 메탄, 물의 공급 유량 및 시스템 운전 압력이 연료전지 스택의 입구 및 출구에서의 가스 온도, 스택 출력 및 시스템 효율 등에 미치는 영향에 관하여 검토하였다. 그 결과 공기와 메탄의 공급 유량이 연료전지 스택 입구 및 출구 가스 온도에 직접적인 영향을 주었다. 공기와 물의 공급 유량이 증가하면 스택 출력 및 시스템 효율이 증가하고, 메탄의 경우 스택 출력은 증가하나 시스템 효율은 낮아진다.

• Journal of Electrical Engineering and Technology
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• 제1권1호
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• pp.1-7
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• 2006

In an electricity market, the spot market is normally integrated with a forward or future market. The advantage of the forward market is to allow the market participants to deal in a part or the whole trading portfolio at a fix price in advance and to avoid risk associated to the uncertain price of the spot market. Japan has introduced a continuous auction base forward market from April 2005. This paper analyzes the Japanese forward market rules and operations, and introduces a new algorithm that may improve the efficiency of the market itself. The proposed algorithm enables us to give consideration to the specific characteristics of the power system and to integrate them in the auction mechanism. The benefits of the proposed algorithm are verified on an electronic simulation platform and the results described in this paper.

• 품질경영학회지
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• 제19권2호
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• pp.96-107
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• 1991

At the design stage of a plant, the plausible causes and pathways of release of hazardous materials are not clearly known. Thus there exist large amount of uncertainties on the consequences resulting from the operation of a fusion plant. In order to better handle such uncertain circumstances, we utilize the Probabilistic Risk Assessment(PRA) for the safety analyses on fusion power plant. In this paper, we concentrate on the tritium release accident. We develop a simple model that describes the process and flow of tritium, by which we figure out the locations of tritium inventory and their vulnerability. We construct event tree models that lead to various levels of tritium release from abnormal initiating events. Branch parameters on the event tree are assessed from the fault tree analysis. Based on the event tree models we construct influence diagram models which are more useful for the parameter updating and analysis. We briefly discuss the parameter updating scheme, and finally develop the methodology to obtain the predictive distribution of consequences resulting from the operating a fusion power plant. We also discuss the way to utilize the results of testing on sub-systems to reduce the uncertain ties on over all system.

• 한국산학기술학회논문지
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• 제8권5호
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• pp.1020-1025
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• 2007

전력시스템을 안정적이고 경제적으로 운영하기 위하여 EMS(energy management system)와 SCADA(supervisory control and data acquisition) 시스템이 사용되고 있다. EMS 내의 조류계산, 상정고장해석, 안전도해석과 같은 다양한 기능들의 신뢰성을 높이기 위해서는 정확한 데이터의 확보가 필수적이다. EMS 내에서 상태추정이 이와 같은 역할을 수행할 수 있으며, 본 논문에서는 정확한 상태추정을 위한 가관측성 해석 및 불량데이터 처리 프로그램을 개발하였다. 기본적인 알고리즘을 설명하고 사례연구를 통해 제안된 기법의 타당성을 검증하였다.

• 디지털산업정보학회논문지
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• 제17권3호
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• pp.9-15
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• 2021

In this paper, the Embedded MEMS system to the power apparatus used Edge Impulse machine learning tools and therefore an improved predictive system design is implemented. The proposed MEMS embedded system is developed based on nRF52840 system and the sensor with 3-Axis Digital Magnetometer, I2C interface and magnetic measurable range ±120 uT, BM1422AGMV which incorporates magneto impedance elements to detect magnetic field and the ARM M4 32-bit processor controller circuit in a small package. The MEMS embedded platform is consisted with Edge Impulse Machine Learning and system driver implementation between hardware and software drivers using SensorQ which is special queue including user application temporary sensor data. In this paper by experimenting, TensorFlow machine learning training output is applied to the power apparatus for analyzing the status such as "Normal, Warning, Hazard" and predicting the performance at level of 99.6% accuracy and 0.01 loss.

• 반도체디스플레이기술학회지
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• 제23권2호
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• pp.114-122
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• 2024

NF₃, Chamber cleaning gas, has a high Global Warming Potential (GWP) of 17,000, causing significant greenhouse effects. Reducing gas usage during the cleaning process is crucial while increasing the cleaning Rate and reducing cleaning standard deviation (Stdev). In a previous study with a 6-inch PECVD chamber, a multiple linear regression analysis showed that Power and Pressure had no significant effect on the cleaning Rate because of their P-values of 0.42 and 0.68. The weight for Flow is 11.55, and the weights for Power and Pressure are 1.4 and 0.7. Due to the limitations of the research equipment, which differed from those used in actual industrial settings, it was challenging to assess the effects in actual industrial environment. Therefore, to show an actual industrial environment, we conducted the cleaning process on a 12-inch PECVD chamber, which is production-level equipment, and quantitatively analyzed the effects of each variable. Power, Pressure, and NF3 Flow all had P-values close to 0, indicating strong statistical significance. The weight for Flow is 15.68, and the weights for Power and Pressure are 4.45 and 5.24, respectively, showing effects 3 and 7 times greater than those with the 6-inch equipment on the cleaning rate. Additionally, we analyzed the cleaning Stdev and derived that there is a trade-off between increasing the cleaning Rate and reducing the cleaning Stdev.