• 한국해양환경ㆍ에너지학회지
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• 제16권3호
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• pp.211-216
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• 2013

대부분의 사고선박 예인력 계산을 위한 선체저항 성능 추정은 매우 간단하고 오래된 방법을 사용하고 있는데, 특히 바지선의 경우는 미국 해군 예인 매뉴얼에서 사용하는 방법과 유사한 방법을 사용하고 있다. 본 논문에서는 미국 해군 예인 매뉴얼 방법과 국내 해양수산부 고시 방법을 검토한 결과, 이는 비합리적이고 부정확한 추정 방법임을 밝혔다. 나아가, 바지선에 적용할 수 있는 합리적이고도 정확한 저항 성능 추정 방법으로 새로운 Modified-Yamagata-Barge 방법을 도출하였다.

• 시스템엔지니어링학술지
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• 제16권1호
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• pp.9-17
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• 2020

Currently, Storage Reliability is analyzed when predicting the reliability of guided missile. However, Mission Reliability and Logistics Reliability should be analyzed according to the definition of reliability in MIL-STD-785B. Therefore, it is necessary to accurately predict the reliability of guided missile based on the definition of reliability. In this paper, we proposed improved the reliability procedure and model for guided missile based on which the definition of reliability considering the mission profile. The proposed model can calculate the final failure rate by applying the ratio of the dormant and storage according to the mission profile. The proposed model has been confirmed to be more accurate than the existing model compared to the actual failure rate value. The results of this study can be useful for applying the reliability prediction to any guided missile.

• Advances in materials Research
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• 제8권4호
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• pp.295-311
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• 2019

In this study, a three-dimensional Finite Element Model has been developed to estimate the size of the weakened zone in a bi-material a ceramic bonded to metal. The calculations results were compared to those obtained using Scanning Electron Microscope (SEM). In the case of elastic-plastic behaviour of the structure, it has been shown that the simulation results are coherent with the experimental findings. This indicates that Finite Element modeling allows an accurate prediction and estimation of the weakening effect of residual stresses on the bonding interface of Alumina. The obtained results show us that the three-dimensional numerical simulation used by the Finite Element Method, allows a good prediction of the weakened zone extent of a ceramic, which is bonded with a metal.

• 한국항공운항학회지
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• 제23권2호
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• pp.51-56
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• 2015

This paper presents the procedure of drag prediction for EAV-1, based on a numerical analysis correlated to an in-flight test. EAV-1, developed by Korea Aerospace Research Institute, is a small-sized UAV to test a hydrogen-fuel cell power system. The long-endurance test flight of 4.5 hours provides numerous in-flight data. The thrust and drag of EAV-1 during the flight test are estimated based on the wind-tunnel test results for EAV-1's propeller performance. In addition, the CFD analysis using a commercial Navier-Stokes code is carried out for the full-scale EAV-1. The computational result suggests that the initial CFD analysis substantially under-predicts the in-flight drag in that the discrepancy is up to 27.6%. Therefore, additional investigation for more accurate drag prediction is performed; the effect of propeller slipstream is included in the CFD analysis through "fan disk" modelling. Also, the additional drag from airplane trim and load factor that actually exists during the flight test in a circular path is considered. These supplemental analyses for drag prediction turn out to be effective since the drag discrepancy reduces to 2.3%.

• Nuclear Engineering and Technology
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• 제46권3호
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• pp.373-380
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• 2014

Safety-related parameters are very important for confirming the status of a nuclear power plant. In particular, the reactor vessel water level has a direct impact on the safety fortress by confirming reactor core cooling. In this study, the reactor vessel water level under the condition of a severe accident, where the water level could not be measured, was predicted using a fuzzy neural network (FNN). The prediction model was developed using training data, and validated using independent test data. The data was generated from simulations of the optimized power reactor 1000 (OPR1000) using MAAP4 code. The informative data for training the FNN model was selected using the subtractive clustering method. The prediction performance of the reactor vessel water level was quite satisfactory, but a few large errors were occasionally observed. To check the effect of instrument errors, the prediction model was verified using data containing artificially added errors. The developed FNN model was sufficiently accurate to be used to predict the reactor vessel water level in severe accident situations where the integrity of the reactor vessel water level sensor is compromised. Furthermore, if the developed FNN model can be optimized using a variety of data, it should be possible to predict the reactor vessel water level precisely.

• 소성∙가공
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• 제29권2호
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• pp.76-88
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• 2020

The need for advanced high strength steel (AHSS) forming technology is increasing as interest in light weight and safe automobiles increases. Multipoint dieless forming (MDF) is a novel sheet metal forming technology that can create any desired longitudinal and transverse curvature in sheet metal. However, since the springback phenomenon becomes larger with high strength metal such as AHSS, predicting the required MDF to produce the exact desired curvature in two directions is more difficult. In this study, a prediction model using artificial neural network (ANN) was developed to predict the springback that occurs during AHSS forming through MDF. In order to verify the validity of model, a fit test was performed and the results were compared with the conventional regression model. The data required for training was obtained through simulation, then further random sample data was created to verify the prediction performance. The predicted results were compared with the simulation results. As a result of this comparison, it was found that the prediction of our ANN based model was more accurate than regression analysis. If a sufficient amount of data is used in training, the ANN model can play a major role in reducing the forming cost of high-strength steels.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.1051-1055
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• 1997

Hot forging is widely used in the manufacturing of automotive component. The mechanical, thermal load and thermal softening which is happened by the high temperature die in hot forging. Tool life of hot forging decreases considerably due to the softening of the surface layer of a tool caused by a high thermal load and long contact time between the tool and workpieces. The service life of tools in hot forging process is to a large extent limited by wear, heat crack, plastic deformation. These are one of the main factors affecting die accuracy and tool life. It is desired to predict tool life by developing life prediction method by FE-simulation. Lots of researches have been done into the life prediction of cold forming die, and the results of those researches were trustworthy, but there have been little applications of hot forming die. That is because hot forming process has many factors influencing tool life, and there was not accurate in-process data. In this research, life prediction of hot forming die by wear analysis and plastic deformation has been carried out. To predict tool life, by experiment of tempering of die, tempering curve was obtained and hardness express a function of main tempering curve.

• Journal of Electrical Engineering and Technology
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• 제10권3호
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• pp.877-887
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• 2015

Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.

• 한국안전학회지
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• 제32권2호
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• pp.14-20
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• 2017

A numerical study was conducted to identify the predictive performance for the bare-bead thermocouple (TC) using FDS (Fire Dynamics Simulator) in simulated thermal environments of fire. A relative prediction bias of TC temperature calculated from reverse-radiation correction by FDS was evaluated with the comparison of previous experimental data. As a result, it was identified that the TC temperatures predicted by FDS were lower than the temperatures measured by bare-bead TC for the ranges of heat flux and gas temperature considered. The relative prediction bias of TC temperature by FDS was gradually increased with the increase in radiative heat flux and also significantly increased with the decrease in the gas temperature. Quantitatively, at the gas temperature of $20^{\circ}C$, the TC temperature predicted by FDS had the relative bias of approximately -20% with the radiative heat flux of $20kW/m^2$ corresponding to thermal radiation level of the flashover. It is predicted from the present study that more accurate validation of fire modeling will be possible with the quantitative prediction bias occurred in the process of reverse-radiation correction of temperature predicted by FDS.

• 대한기계학회논문집A
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• 제25권2호
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• pp.264-274
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• 2001

An integrated fatigue strength assessment system was computerized. The system developed consists of 9 modules: user interface, cycle counting, load history construction, data searching, fatigue properties estimation, fatigue data analysis, true stress and strain analysis, expert system for crack initiation life prediction, fatigue crack initiation and propagation life prediction. Fatigue strength database also was included in this system. The fatigue expert system helps a beginner to predict a fatigue crack initiation life in fatigue strength assessment. The expert system module in this system is developed on the personal computer by using C language and UNiK, an expert system developing tool. To evaluate the system, the results of test under variable loading of SAE and failure data from a field were analyzed. The evaluation show that the system provided fatigue life prediction within 3-scatter band and gave reasonable predictions. To get more accurate predictions of fatigue life without fatigue properties, we recommend utilizing the system along with the fatigue strength database.