• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집B
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• pp.288-293
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• 2001

A critical heat flux (CHF) prediction method using an artificial neural network (ANN) was evaluated for application to the high-heat-flux (HHF) subcooled flow boiling. The developed ANN predictions were compared with the experimental database consisting of a total of 3069 CHF data points. Also, the prediction performance by the ANN was compared with those by mechanistic models and a look up table technique. The parameter ranges of the experimental data are: $0.33{\leq}D{\leq}37.5mm$, $0.002{\leq}L{\leq}4m$, $0.37{\leq}G{\leq}134Mg/m^2s$, $0.1{\leq}P{\leq}20MPa$, $50\leq{\Delta}h_{sub,in}\leq1660kJ/kg$, and $1.1{\leq}q_{CHF}\leq276MW/m^2$. $276MW/m^2$. It was found that 91.5% of the total data points were predicted within $a{\pm}20%$ error band, which showed the best prediction performance among the existing CHF prediction methods considered.

• 한국전자통신학회논문지
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• 제16권6호
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• pp.1239-1248
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• 2021

본 논문은 학문적인 이해를 기반을 둔 예측을 수행하기 위해 FDNN(: Flood drought index neural network) 알고리즘을 제시한다. 데이터에 의존한 예측이 아닌 학문적인 이해를 기반을 둔 예측을 딥러닝에 적용하기 위해, 알고리즘을 수리, 수문학을 기반으로 구성하였다. 강수량의 입력으로 하천의 유량을 예측하는 모델을 구성하여 K-교차검증을 통해 모델의 성능을 측정한다. 제시한 알고리즘의 성능을 증명하기 위해 시계열 예측에서 가장 많이 사용되는 LSTM(: Long short term memory) 알고리즘의 예측 성능과 비교하여 제시한 알고리즘의 우수성을 나타낸다.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2003년도 The Fifth Asian Computational Fluid Dynamics Conference
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• pp.257-257
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• 2003

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2006년도 PARALLEL CFD 2006
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• pp.161-162
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• 2006

• 대한기계학회논문집B
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• 제36권3호
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• pp.363-369
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• 2012

CANDU형 원전의 칼란드리아 내부 감속재 열유동은 입구 노즐에서 나온 제트 유동에 의해 발생하는 관성력과 감속재로 전달되는 열부하에 의해 발생하는 부력의 상호작용으로 인해 복잡한 난류 특성을 나타낸다. 본 연구에서는 이러한 복잡한 감속재 열유동을 정확히 예측하기 위한 난류모델의 성능을 평가하기 위해 상용 유동해석 프로그램인 FLUENT에 탑재된 난류모델들을 사용해서 계산한 결과를 Sheridan Park Engineering Laboratory (SPEL)의 실험값과 비교하였다. 결론적으로 CANDU형 원전의 칼란드리아 내부 감속재 열유동을 신뢰할 수 있게 예측하기 위해서는 부력이 난류 유동에 미치는 영향을 고려해주는 생성항을 포함한 난류 모델이 사용되어야 한다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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• pp.260-260
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• 2017

This study aims to compare the performance of TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability model) and TiVaSS (Time-variant Slope Stability model) in the prediction of rainfall-induced shallow landslides. TRIGRS employs one-dimensional (1-D) subsurface flow to simulate the infiltration rate, whereas a three-dimensional (3-D) model is utilized in TiVaSS. The former has been widely used in landslide modeling, while the latter was developed only recently. Both programs are used for the spatiotemporal prediction of shallow landslides caused by rainfall. The present study uses the July 2011 landslide event that occurred in Mt. Umyeon, Seoul, Korea, for validation. The performance of the two programs is evaluated by comparison with data of the actual landslides in both location and timing by using a landslide ratio for each factor of safety class ( index), which was developed for addressing point-like landslide locations. In addition, the influence of surface flow on landslide initiation is assessed. The results show that the shallow landslides predicted by the two models have characteristics that are highly consistent with those of the observed sliding sites, although the performance of TiVaSS is slightly better. Overland flow affects the buildup of the pressure head and reduces the slope stability, although this influence was not significant in this case. A slight increase in the predicted unstable area from 19.30% to 19.93% was recorded when the overland flow was considered. It is concluded that both models are suitable for application in the study area. However, although it is a well-established model requiring less input data and shorter run times, TRIGRS produces less accurate results.

• 대한전자공학회논문지SP
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• 제41권5호
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• pp.23-34
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• 2004

본 논문에서는 사전학습이 필요 없는 능동 특징점 모델(non-prior training active feature model; NPT AFM) 기반에서 광류(optical flow)를 이용한 객체추적 기술을 제안한다. 제안한 알고리듬은 비정형 객체에 대한 분석[1]에 초점을 두고 있으며, 실시간에서 NPT-AFM을 사용한 강건한 추적을 가능하게 한다. NPT-AFM 알고리듬은 관심 객체의 위치를 파악하는 과정 (localization)과 이전 프레임 정보와 현재 프레임 정보를 이용하여, 객체의 위치를 예측(prediction), 보정(correction)하는 과정으로 나눌 수 있다 위치 파악 과정에서는 움직임 분할(motion segmentation)을 수행한 후 개선된 Shi-Tomasi의 특징점 추적 알고리듬[2]을 사용 하였다. 예측 및 보정 과정에서는 광류 정보를 사용하여 특징점을 추적하고[3] 만약, 특징점이 적절히 추적 되지 않거나 추적에 실패하면 특징점들의 시간(temporal), 공간(spatial)적 정보를 이용하여 예측, 보정하게 된다. 객체의 형태 (shape)대신 특징점을 사용하였으며, 객체를 추적하는 과정에서 특징점들은 능동 특징점 모델(active feature model; AFM)을 위한 학습 집합(training sets)의 요소로 갱신된다. 실험결과, 제안한 NPT-AF% 기반 추적 알고리듬은 실시간에서 비정형 객체를 추적하는데 강건함을 보석준다.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 춘계학술발표회논문집(1)
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• pp.356-361
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• 1997

To investigate parametric effect on CHF and to get CHF data, experimental study has been performed with vertical round tubes under the condition of low pressure and low flow (LPLF). Test sections are made of Inconel-625 tube and have the geometry of 8 and 10 mm in diameter, and 0.5 and 1.0 m in heated length. All experiments have been conducted at the pressure of under 9 bar, the mass flux of under 250 kg/$m^2$ and the inlet subcooling of 350 and 450 kJ/kg, for stable upward flow with water as a coolant. Flow regime analysis has been performed for obtained CHF data with Mishima's flow regime map, which reveals that most of the CHF occur in the annular-mist flow regime. General parametric trends of the collected CHF data are consistent with those of previous studies. However, for the pressure effect on CHF, two different are observed; For relatively high mass flux, CHF increases with pressure and far lower mass flux, CHF decrease with pressure. Using modern data regression tool, ACE algorithm, two new CHF correlations for LPLF condition are developed based on local condition and inlet condition, respectively. The developed CHF correlations show better prediction accuracy compared with existing CHF prediction methods.

• 소성∙가공
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• 제27권4호
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• pp.227-235
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• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.

• 한국복합재료학회:학술대회논문집
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• 한국복합재료학회 2003년도 춘계학술발표대회 논문집
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• pp.184-187
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• 2003

Complete prediction of second order permeability tensor for three dimensional circular braided preform is critical to understand the resin transfer molding process of composites. The permeability can be predicted by considering resin flow through the multi-axial fiber structure. In this study, permeability tensor for a 3-D circular braided preform is calculated by solving a boundary problem of a periodic unit cell. Flow field through the unit cell is obtained by using a 3-D finite volume method (FVM) and Darcy's law is utilized to obtain permeability tensor. Flow analysis for two cases that a fiber tow is regarded as impermeable solid and permeable porous medium is carried out respectively. It is found that the flow within the intra-tow region of the braided preform is negligible if inter-tow porosity is relatively high but the flow through the tow must be considered when the porosity is low. To avoid checkerboard pressure field and improve the efficiency of numerical computation, a new interpolation function for velocity variation is proposed on the basis of analytic solutions. Permeability of the braided preform is measured through a radial flow experiment and compared with the permeability predicted numerically.