• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3879-3891
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• 2021

A heat pipe residual heat removal system is proposed to be incorporated into the reactor driven subcritical (RDS) facility, which has been proposed by MIT Nuclear Reactor Laboratory for testing and demonstrating the Fluoride-salt-cooled High-temperature Reactor (FHR). It aims to reduce the risk of the system operation after the shutdown of the facility. One of the main components of the system is an air-cooled heat pipe heat exchanger. The alkali-metal high-temperature heat pipe was designed to meet the operation temperature and residual heat removal requirement of the facility. The heat pipe model developed in the previous work was adopted to simulate the designed heat pipe and assess the heat transport capability. 3D numerical simulation of the subcritical facility active zone was performed by the commercial CFD software STAR CCM + to investigate the operation characteristics of this proposed system. The thermal resistance network of the heat pipe was built and incorporated into the CFD model. The nominal condition, partial loss of air flow accident and partial heat pipe failure accident were simulated and analyzed. The results show that the residual heat removal system can provide sufficient cooling of the subcritical facility with a remarkable safety margin. The heat pipe can work under the recommended operation temperature range and the heat flux is below all thermal limits. The facility peak temperature is also lower than the safety limits.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.109-115
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• 2022

The CNN (Convolution Neural Network) algorithm which combines a deep learning technique, and a computer vision technology, makes image classification feasible with the high-performance computing system. In this thesis, the CNN algorithm is applied to the classification problem, by using a typical deep learning framework of TensorFlow and machine learning techniques. The data set required for supervised learning is generated with the same type of bolts. some of which have undamaged threads, but others have damaged threads. The learning model with less quantity data showed good classification performance on detecting damage in a bolt image. Additionally, the model performance is reviewed by altering the quantity of convolution layers, or applying selectively the over and under fitting alleviation algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.407-407
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• 2023

최근 환경부에서 발표한 국가물관리기본계획에서 수자원 총량 중 생활·공업·농업·유지용수의 이용량은 365억m³/년으로 약 29.4%로 발표되었다. 유지용수를 제외한 농업용수 이용량의 비중은 약 60.5%이며, 이 중 약 80%가 논에서 활용되고 있다. 이러한 농업용수 이용량 중 사용되지 않고 하천으로의 방류량이 존재하는데 이를 관개회귀수량이라하며, 농업용수의 약 35%가 하천으로 회귀된다 발표하나 지역에 따른 편차가 존재하기에 정확한 회귀수량을 산정하기엔 미흡한 실정이다. 따라서 본 연구에서는 네트워크 모형을 통한 용배수로 구축 이후 회귀수 정량화를 하고자 하며, 정량화를 위한 네트워크 모형은 EPA-SWMM(Storm Water Management Model) 모형을 활용하였다. 해당 모형은 미국 환경 보호국(U.S. Environmental Protection Agency, EPA)에서 개발한 네트워크 물리모형으로 다양한 환경적 요소에 따른 수문 영향을 확인 가능한 모형이다. 해당 모형의 다양한 네트워크 기능을 통해 논배수로 네트워크를 구축하여 회귀수 정량화를 진행하고자 한다. 논배수로 네트워크를 구축하기 이전 현장조사를 진행하였다. 현장조사를 통한 용수계통도를 작성하였으며, 모형의 입력자료로 필요한 네트워크 용배수로관 표고값을 측량하였다. 이후 현장조사 및 측량 자료를 활용하여 네트워크 물리모형의 입력자료 구축을 진행하였으며, 해당 자료 구축은 지리 정보 시스템 중 ArcGIS와의 연계를 통해 구축하였다. 모형의 수리학적 입력자료는 해당지역의 계측자료를 활용하였으며, 필지 사이의 내리흐름 및 펌프를 통한 용수 또한 네트워크 물리모형의 기능을 활용하여 구축하였다. 이후 계측자료와의 비교를 통한 매개변수 보정을 진행하였으며, 전체 논배수로에 대한 농업용수의 흐름 및 회귀수량을 분석하였다. 해당 연구를 통해 농업용수의 회귀수 산정 및 지역 편차에 따른 회귀수 정량화 등의 연구에 활용될 것으로 기대한다.

• Journal of the Korean Geographical Society
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• v.46 no.6
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• pp.673-692
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• 2011

Advances in computer technology have enabled us to develop and use numerical landscape evolution models (NLEMs) for exploring the dynamics of geomorphic system from a variety of viewpoints which previously could have not been taken. However, as of yet there have been no trials using or developing NLEMs in Korea. The purpose of this research is to develop a 2 dimensional NLEM on a geological time scale and evaluate its usefulness. The newly developed NLEM (ND-NLEM) treats bedrock weathering as one of the major geomorphic processes and attempts to simulate the thickness of soil. As such it is possible to model the weathering-limited as well as the transport-limited environment on hillslopes. Moreover the ND-NLEM includes not only slow and continuous mass transport like soil creep, but also rapid and discrete mass transport like landslides. Bedrock incision is simulated in the ND-NLEM where fluvial transport capacity is large enough to move all channel bed loads, such that ND-NLEM can model the detachment-limited environment. Furthermore the ND-NLEM adopts the D-infinity algorithm when routing flows in the model domain, so it reduces distortion due to the use of the steepest descent slope flow direction algorithm. In the experiments to evaluate the usefulness of the ND-NLEM, characteristics of the channel network observed from the model results were similar to those of the case study area for comparison, and the hypsometry curve log during the experiment showed rational evidence of landscape evolution. Therefore, the ND-NLEM is shown to be useful for simulating landscape evolution on a geological time scale.

• Tunnel and Underground Space
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• v.25 no.2
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• pp.186-198
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• 2015

This paper aims for the ultimate goal to optimize the work place environment through assuring the optimal required ventilation rate based on the analysis of the airflow. The working environment is deteriorated due to a rise in temperature of a coal mine caused by increase of its depth and carriage tunnels. To improve the environment, the ventilation evaluation on J coal mine is carried out and the effect of a length of the tunnel on the temperature to enhance the ventilation efficiency in the subsurface is numerically analyzed. The analysis shows that J coal mine needs $17,831m^3/min$ for in-flow ventilation rate but the total input air flowrate is $16,474m^3/min$, $1,357m^3/min$ of in-flow ventilation rate shortage. The temperatures were predicted on the two developed models of J mine, and VnetPC that is a numerical program for the flowrate prediction. The result of the simulation notices the temperature in the case of developing all 4 areas of -425ML as a first model is predicted 29.30 at the main gangway 9X of C section and in the case of developing 3 areas of -425ML excepting A area as a second model, it is predicted 27.45 Celsius degrees.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.2
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• pp.103-112
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• 2011

This study aims to demonstrate the relationship between various factors and soil erosion or deposition, simulated from distributed rainfall-sediment-runoff model applications. We selected area, overland flow length, local slope as catchment representative characteristics among many important geographic factors and also used the grid-based accumulated rainfall as a representative hydro-climatic factor to assess the effect of these two different types of factors on erosion and deposition. The study catchment was divided based on the Strahler's stream order method for analysis of the relationship between area and erosion or deposition. Both erosion and deposition increased linearly as the catchment area became larger. Erosion occurred widely throughout the catchment, whereas deposition was observed at the grid-cells near the channel network with short overland flow lengths and mild slopes. In addition, the relationship results between grid-based accumulated rainfall and soil erosion or deposition showed that erosion increased gradually as rainfall amount increased, whereas deposition responded irregularly to variations in rainfall. Within the context of these results, it can be concluded that deposition is closely related with the geographic factors used in this study while erosion is significantly affected by rainfall.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.499-505
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• 2015

Trend Impact Analysis(: TIA) is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. An adaptive neuro-fuzzy inference system is a kind of artificial neural network that integrates both neural networks and fuzzy logic principles, It is considered to be a universal estimator. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using Adaptive Neuro-Fuzzy Inference System(: ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes. The trigger attributes can be calculated by a stochastic dynamic model; then different scenarios are generated using Monte-Carlo simulation. To compare the proposed method, a simple simulation is provided concerning the impact of river basin drought on the annual flow of water into a lake.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.83-97
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• 2000

The traffic management schemes through traffic signal control and information provision could be effective when the link-level data and trip-level data were used simultaneously in analysis Procedures. But, because the trip-level data. such as origin, destination and departure time, can not be obtained through the existing surveillance systems directly. It is needed to estimate it using the link-level data which can be obtained easily. Therefore the objective of this study is to develop the model to estimate O-D demand using only the link flows in highway network as a real time. The methodological approaches in this study are kalman filer, least-square method and normalized least-square method. The kalman filter is developed in the basis of the bayesian update. The normalized least-square method is developed in the basis of the least-square method and the natural constraint equation. These three models were experimented using two kinds of simulated data. The one has two abrupt changing Patterns in traffic flow rates The other is a 24 hours data that has three Peak times in a day Among these models, kalman filer has Produced more accurate and adaptive results than others. Therefore it is seemed that this model could be used in traffic demand management. control, travel time forecasting and dynamic assignment, and so forth.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.34-45
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• 2004

Existing Semangeum's water balance analysis simplifies whole basin to single basin and achieved volume of effluence that produce by Kajiyama way to foundation. But Semangeum is complicated and various rice-wine strainer supply system. And there is difficulty to apply as elastic when water balance element is changed at free point. Divided to unit possession station for suitable water balance analysis model application to Semangeum in this study. And developed basin water balance model of GIS base that can do details analysis is bite about development and transfer of an appropriation in the budget of basin water resources. Achieved study including abstraction and concept design that use UML (unified modeling language) diagram for details analysis, stream network composition for rice-wine strainer supply system application, preprocessing of GIS base and postprocessing module development, model revision and verification etc. Support of this water balance analysis model is available to establish efficient water resources administration plan through outward flow process analysis of water resources. And support is considered to be possible in more convenient and, reasonable water resources administration way establishment by minimizing manual processing in systematic water resources government official to user and support diversified analysis system.