• 대한기계학회논문집A
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• 제26권11호
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• pp.2399-2408
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• 2002

A prediction procedure has been developed to evaluate the microtructures and material properties of heat affected zone (HAZ) in pressure vessel steel weld, based on temperature analysis, thermodynamics calculation and reaction kinetics model. Temperature distributions in HAE are calculated by finite element method. The microstructures in HAZ are predicted by combining the temperature analysis results with the reaction kinetics model for austenite grain growth and austenite decomposition. Substituting the microstructure prediction results into the previous experimental relations, the mechanical material properties such as hardness, yielding strength and tensile strength are calculated. The prediction procedure is modified and verified by the comparison between the present results and the previous study results for the simulated HAZ in reactor pressure vessel (RPV) circurnferential weld. Finally, the microstructures and mechanical material properties are determined by applying the final procedure to real RPV circumferential weld and the local weak zone in HAZ is evaluated based on the application results.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.88-93
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• 2020

Based on the capture-emission energy (CEE) maps of CMOS devices, a physics-informed machine learning model for the bias temperature instability (BTI)-induced threshold voltage shifts and low frequency noise is presented. In order to incorporate physics theories into the machine learning model, the integration of artificial neural network (IANN) is employed for the computation of the threshold voltage shifts and low frequency noise. The model combines the computational efficiency of IANN with the optimal estimation of Gaussian mixture model (GMM) with soft clustering. It enables full lifetime prediction of BTI under various stress and recovery conditions and provides accurate prediction of the dynamic behavior of the original measured data.

• 한국전산유체공학회지
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• 제10권4호통권31호
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• pp.12-17
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• 2005

A computational study of evaluation of current turbulence models is performed for a better prediction of thermal stratification in an upper plenum of a liquid metal reactor. The turbulence models tested in the present study are the two-layer model, the shear stress transport (SST) model, the v2-f model and the elliptic blending mode(EBM). The performances of the turbulence models are evaluated by applying them to the thermal stratification experiment conducted at JNC (Japan Nuclear Corporation). The algebraic flux model is used for treating the turbulent heat flux for the two-layer model and the SST model, and there exist little differences between the two turbulence models in predicting the temporal variation of temperature. The v2-f model and the elliptic blending model better predict the steep gradient of temperature at the interface of thermal stratification, and the v2-f model and elliptic blending model predict properly the oscillation of the ensemble-averaged temperature. In general the overall performance of the elliptic blending model is better than the v2-f model in the prediction of the amplitude and frequency of the temperature oscillation.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2006년도 추계 학술대회논문집
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• pp.77-82
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• 2006

Evaluation of the elliptic blending turbulence model (EBM) together with the two-layer model, shear stress transport (SST) model and elliptic relaxation model (V2-F) is performed for a better prediction of natural convection and thermal stratification. For a natural convection problem the models are applied to the prediction of a natural convection in a rectangular cavity and the computed results are compared with the experimental data. It is shown that the elliptic blending model predicts as good as or better than the existing second moment differential stress and flux model for the mean velocity and turbulent quantities. For thermal stratification problem the models are applied to the thermal stratification in the upper plenum of liquid metal reactor. In this analysis there exist much differences between the turbulence models in predicting the temporal variation of temperature. The V2-F model and EBM better predict the steep gradient of temperature at the interface of thermal stratification, and the V2-F model and EBM predict properly the oscillation of temperature. The two-layer model and SST model fail to predict the temporal oscillation of temperature.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 추계학술대회 논문집
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• pp.63-64
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• 2008

• 한국식품과학회지
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• 제22권6호
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• pp.690-695
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• 1990

커피의 저장 중 흡착에 가장 많은 영향을 미치는 인자로는 수분활성 및 온도이다. 따라서 커피의 제조공정 중 추출시 건조방법의 차이에 따른 세 종류의 제품에 대한 흡착 특성을 조사하고 수분활성, 온도 및 시간의 변화에 따른 흡착량을 측정하여 조건변수의 변화에 따른 흡착량의 변화를 예측할 수 있는 예측모델식을 수립하였다. 흡착거동은 전형적인 Sigmoid 형태를 나타냈으며 평형수분함량과 단분자층 수분함량은 동결 건조제품이 가장 높게 나타났으며 이는 건조방법에 의해 생성된 다공성구조에 기인된 것으로 사료된다. 기 발표된 여러 형태의 등온흡착곡선 모델식에 적용시켜 본 결과 Halsey 모델식의 상관계수 r값이 $0.98{\sim}0.99$로 가장 적합하였다. 또한 예측모델식은 SPSS COMPUTER PROGRAM을 이용하여 가장 오차가 적은 범위에서 수분활성, 온도 및 시간의 변화에 따른 흡착량의 변화를 예측할 수 있는 최종적인 모델식을 수립하였다.

• 한국해안·해양공학회논문집
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• 제33권6호
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• pp.238-245
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• 2021

본 연구에서는 LSTM 모델을 활용하여 낙동강 하구역의 DO 농도 예측을 위한 최적 모델 조건과 적합한 예측변수를 찾기 위한 Case study를 수행하였다. 모델 매개변수 case study 결과, Epoch = 300과 Sequence length = 1에서 상대적으로 높은 정확도를 보였다. 예측변수 case study 결과, DO와 수온을 예측변수로 했을 때 가장 높은 정확도를 보였으며, 이는 DO 농도와 수온의 높은 상관성에 기인한 것으로 판단된다. 상기 결과로부터 낙동강 하구역의 DO 농도 예측에 적합한 LSTM 모델 조건과 예측변수를 찾을 수 있었다.

• 한국재료학회지
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• 제34권7호
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• pp.363-369
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• 2024

In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module 'Matminer', 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the 'Fe' compositional ratio. The feature importance method provided by 'scikit-learn' was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

• 한국농공학회논문집
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• 제54권3호
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• pp.141-148
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• 2012

Food policy is considered as the most basic and central issue for all countries, while making efforts to keep each country's food sovereignty and enhance food self-sufficiency. In the case of Korea where the staple food is rice, the rice yield prediction is regarded as a very important task to cope with unstable food supply at a national level. In this study, Korean paddy Rice yield Prediction Model (KRPM) developed to predict the paddy rice yield using meteorological element and MODIS NDVI. A multiple linear regression analysis was carried out by using the NDVI extracted from satellite image. Six meteorological elements include average temperature; maximum temperature; minimum temperature; rainfall; accumulated rainfall and duration of sunshine. Concerning the evaluation for the applicability of the KRPM, the accuracy assessment was carried out through correlation analysis between predicted and provided data by the National Statistical Office of paddy rice yield in 2011. The 2011 predicted yield of paddy rice by KRPM was 505 kg/10a at whole country level and 487 kg/10a by agroclimatic zones using stepwise regression while the predicted value by KOrea Statistical Information Service was 532 kg/10a. The characteristics of changes in paddy rice yield according to NDVI and other meteorological elements were well reflected by the KRPM.

• 대한기계학회논문집
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• 제7권4호
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• pp.451-460
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• 1983

Computational four-equation turbulence model is developed and is applied to predict twodimensional unsteady thermal surface discharge into a reservoir. Turbulent stresses and heat fluxes in the momentum and energy equations are determined from transport equations for the turbulent kinetic energy (R), isotropic rate of kinetic energy dissipation (.epsilon.), mean square temperature variance (theta. over bar $^{2}$), and rate of destruction of the temperature variance (.epsilon. $_{\theta}$). Computational results by four-equation model are favorably compared with those obtained by an extended two-equation model. Added advantage of the four-equation model is that it yields quantitative information about the ratio between the velocity time scale and the thermal time scale and more detailed information about turbulent structure. Predicted time scale ratio is within experimental observations by others. Although the mean velocity and temperature fields are similarly predicted by both models, it is found that the four-equation model is preferably candidate for prediction of highly buoyant turbulent flows.