• 한국전산유체공학회:학술대회논문집
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• 1999.05a
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• pp.131-136
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• 1999

박리를 수반하는 진동하는 이차원 익형 주위의 비정상 유동장에 대해 SST 난류 모델을 이용하여 해석을 수행하였다. SST 모델은 정상 유동장 해석에서 기존의 난류 모델에 비해 우수한 성능을 보인다고 알려져 있으나 큰 박리영역에서 공력 계수의 진동 현상을 보이는 등 비정상 유동장 해석에 문제점을 보이고 있다. 본 논문에서는 이러한 공력 계수의 진동 현상이 SST 모델을 이차원으로 확장하는 과정에서 발생한 것임을 밝히고 이에 대한 보완을 통하여 수정된 SST 모델을 제시하고자 한다. SST 모델의 기본이 되었던 BSL 모델 및 SST 모델, 수정된 SST 모델을 사용하여 정상 유동장과 비정상 유동장 해석을 수행하여 각 모델의 난류 유동장 해석 특성을 비교하고 이를 통하여 수정된 SST 모델이 박리를 수반하는 비정상 유동장 해석에서 원래의 SST 모델에 비해 향상된 결과를 나타남을 알 수 있었다.

• Journal of computational fluids engineering
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• v.4 no.3
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• pp.44-51
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• 1999

A modified version of SST turbulence model is suggested to simulate unsteady separated flows over oscillating airfoils. The original SST model, which shows good performance in predicting various steady flows, often results in oscillatory behavior of aerodynamic loads in large separated flow regions. It is shown that this oscillatory behavior is due to the adoption of the absolute value of vorticity in generalizing the original model. As a remedy, a modification is made such that the vorticity in the original SST model is replaced by strain rate. The present model is verified for a mild separated airfoil flow at fixed angle of incidence and for unsteady flowfields about oscillating airfoils. The results are compared with BSL model and original SST model. It is illustrated that the present model gives a better agreement with the experimental results than other two models.

• Journal of the Korean earth science society
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• v.28 no.3
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• pp.343-356
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• 2007

This paper explored the characteristics of the interannual sea surface temperature (SST) variability in the equatorial Pacific by analyzing the simulated data from a newly coupled general circulation model (CGCM). The CGCM simulates well the realistic ENSO variability as well as the mean climatologies including SST, seasonal cycle, precipitation, and subsurface structures. It is argued that the zonal gradient of SST in the equatorial Pacific is responsible for the over-energetic SST variability near the equatorial western boundary in the model. This variability could also be related to the strong westward propagation of SST anomalies which resulted from the enhanced the zonal advection feedback. The simple two-strip model supports this by sensitivity tests. Analysis of the relationship between zonal mean thermocline depth and NINO3 SST index suggested that the ENSO variability is controlled by the recharge-discharge oscillator of the model. The lead-lag regression result reveals that heat buildup process in the western equatorial Pacific associated with the increase of the barrier layer thickness (BLT) is a precedent condition for El $Ni\widetilde{n}o$ to develop.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.8
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• pp.1-7
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• 2003

The numerical simulation of flow-filed around a square cylinder near a wall with $\varepsilon$-SST turbulence model is carried out in this study. The newly suggested $\varepsilon$-SST turbulence model that modifies the original SST turbulence model is proved to yield more accurate results than the other 2-equation turbulence models in large separation region around a bluff body. Therefore, $\varepsilon$-SST turbulence model can be effectively applied for predicting the flow-fields with large separation. And it is found that vortex shedding is suppressed below the critical gap height, the Strouhal number is affected by the gap height and the wall boundary layer thickness.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.1
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• pp.18-25
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• 2013

Assessment and validation of RANS turbulence models are conducted for the optimal analysis of supersonic converging-diverging nozzle through the comparison between computational results and experimental data. One/two equation turbulence closures such as Spalart-Allmaras, RNG k-${\varepsilon}$, and k-${\omega}$ SST are employed to simulate the two-dimensional nozzle flow. Computational results with the turbulence models mentioned fairly well predict shock structure of the nozzle-inside and pressure distribution along the wall. Especially, SST model among the employed ones shows the best agreement to experimental results.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• 한국해양학회지
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• v.27 no.1
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• pp.35-45
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• 1992

This paper is concerned with the development and application of a stochastic model for predicting E1 nino phenomenon. For this, first a general criterion for determining E1 nino phenomenon, including period and strength, which is based on partial sum of monthly sea surface temperatures (SST) anomalies, is proposed, Secondly, the annual fluctuations, periodicity and dependence of monthly mean of equatorial Pacific SST during the period 1951-1990 are analyzed. Based on these, time series nonlinear regression model for the prediction of SST have been derived. A statistical procedure for using the model to predict the SST have been derived. A statistical procedure for using the model to predict the SST level is also proposed.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.1
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• pp.1-10
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• 2016

Currently available surface seawater partial pressure carbon dioxide ($pCO_2$) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the $pCO_2$ measurements, we construct a neural network (NN) model based on satellite data to map $pCO_2$ for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where $pCO_2$ data are best available, to map and estimate the variability of $pCO_2$ based on in situ $pCO_2$ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted $pCO_2$ values. The RMSE (root mean square error) of the NN model output was $19.2{\mu}atm$ and much less than the variability of in situ $pCO_2$. The variability of $pCO_2$ with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of $pCO_2$ increases. When SST is lower than $15^{\circ}C$, $pCO_2$ variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than $15^{\circ}C$, the variability of $pCO_2$ is less sensitive to changes in SST and chlorophyll. The mean rate of the annual $pCO_2$ increase estimated by the NN model output in the Ulleung Basin is $0.8{\mu}atm\;yr^{-1}$ from 2003 to 2014. As NN model can successfully map $pCO_2$ data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.

• Journal of Advanced Marine Engineering and Technology
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• v.35 no.5
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• pp.616-624
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• 2011

The flow characteristics and heat transfer augment on the periodically arranged semi-circular ribs in a rectangular channel for turbulent flow has been investigated numerically. The aspect ratio of the rectangular channel was AR=5, the rib height to hydraulic diameter ratio were 0.07 and rib height to channel height ratio was set as e/H=0.117 for various PR(rib pitch-to-rib height rate) between 8~14, respectively. The SST k-${\omega}$ turbulence model and v2-f turbulence model were used to find out the heat transfer and the flow characteristics of near the wall which are suited to obtain realistic phenomena. The numerical analysis results show turbulent flow characteristics, heat transfer enhancement and friction factor as observed experimentally. The results predict that turbulent kinetic energy(k) is closely relative to the diffusion of recirculation flow. and v2-f turbulence model simulation results have a good agreement with experimental values.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.