• Journal of the Korean Society of Visualization
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• v.19 no.1
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• pp.62-67
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• 2021

Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.4
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• pp.222-230
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• 2023

In the present work, the scale effect on the Boil-Off Rate (BOR) was investigated based on an analytical method to systematically evaluate the thermal performance of a Liquefied Natural Gas (LNG) Cargo Containment System (CCS). A two-dimensional thermal resistance network model was developed to accurately estimate the heat ingress into the CCS from the outside. The analysis was performed for the KC-1 LNG membrane tank under the IGC and USCG design conditions. The ballast compartment of both the LNG tank and cofferdam was divided into six sections and a thermal resistance network model was made for each section. To check the validity of the developed model, the analysis results were compared with those from existing literature. It was shown that the BOR values under the IGC and USCG design conditions were agreed well with previous numerical results with a maximum error of 1.03% and 0.60%, respectively. A SDR, the scale factor of the LNG CCS was introduced and the BOR, air temperature of the ballast compartment, and the surface temperature of the inner hull were obtained to examine the influence of the SDR on the thermal performance. Finally, a correlation for the BOR was proposed, which could be expressed as a simple formula inversely proportional to the SDR. The proposed correlation could be utilized for predicting the BOR of a full-scale LNG tank based on the BOR measurement data of lab-scale model tanks.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.3
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• pp.681-689
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• 1994

A model to predict the onset of significant void (OSV) in vertical flow between parallel plates has been developed. The model was compared to the experimental data of Whittle and Forgan (1967) and Dougherty et al. (1990), showing excellent agreement. The model was also compared with the Saha-Zuber(1974) correlation, which has been widely used in computer codes for nuclear safety analysis. The present theory is more conservative than this correlation, and further shows that, contrary to this correlation, the Stanton number is not solely related to the Peclet number. This may explain the large error margins required for the Saha-Zuber correlation, and also the scatter beyond the error margins specified by the authors. The steady-state OSV heat fluxes for equal and unequal heating cases between parallel plates were compared. The arithmetic mean of heat fluxes for unequal heating cases is less than the heat flux for equal heating cases. The result may imply that OSV is controlled by local thermal parameters rather than bulk parameters.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.127-135
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• 2018

PURPOSES : This study develops various models that can estimate the pattern of road surface temperature changes using machine learning methods. METHODS : Both a thermal mapping system and weather forecast information were employed in order to collect data for developing the models. In previous studies, the authors defined road surface temperature data as a response, while vehicular ambient temperature, air temperature, and humidity were considered as predictors. In this research, two additional factors-road type and weather forecasts-were considered for the estimation of the road surface temperature change pattern. Finally, a total of six models for estimating the pattern of road surface temperature changes were developed using the MATLAB program, which provides the classification learner as a machine learning tool. RESULTS : Model 5 was considered the most superior owing to its high accuracy. It was seen that the accuracy of the model could increase when weather forecasts (e.g., Sky Status) were applied. A comparison between Models 4 and 5 showed that the influence of humidity on road surface temperature changes is negligible. CONCLUSIONS : Even though Models 4, 5, and 6 demonstrated the same performance in terms of average absolute error (AAE), Model 5 can be considered the optimal one from the point of view of accuracy.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2524-2533
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• 2024

When analyzing containment thermal-hydraulics, computational fluid dynamics (CFD) is a powerful tool because multi-dimensional and local analysis is required for some accident scenarios. According to the previous study, neglecting steam bulk condensation in the CFD analysis leads to a significant error in boundary layer profiles. Validating the condensation model requires the experimental data near the condensing surface, however, available boundary layer data is quite limited. It is also important to confirm whether the heat and mass transfer analogy (HMTA) is still valid in the presence of bulk condensation. In this study, the boundary layer measurements on the vertical condensing surface in the presence of air were performed with the rectangular channel facility WINCS, which was designed to measure the velocity, temperature, and concentration boundary layers. We set the laminar flow condition and varied the Richardson number (1.0-23) and the steam volume fraction (0.35-0.57). The experimental results were used to validate CFD analysis and HMTA models. For the former, we implemented a bulk condensation model assuming local thermal equilibrium into the CFD code and confirmed its validity. For the latter, we validated the HMTA-based correlations, confirming that the mixed convection correlation reasonably predicted the sum of wall and bulk condensation rates.

• Journal of the Semiconductor & Display Technology
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• v.3 no.3
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• pp.19-21
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• 2004

This paper presents a 3-dimensional diffusion simulation with adaptive solution strategy. The developed diffusion simulator VLSIDIF-3 was designed to re-refine areas. Refine scheme was calculated by the difference of doping concentration between any of two nodes. Each element is greater than tolerance and redo diffusion process until error is tolerable. Numerical experiment in low doping diffusion problem showed that this adaptive solution strategy is very efficient in both memory and time, and expected this scheme would be more powerful in complex diffusion model.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• Journal of Welding and Joining
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• v.17 no.6
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• pp.84-89
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• 1999

Most of ferrous b.c.c weld materials may experience martensitic transformation during rapid cooling after welding. It is well known that volume expansion due to the phase transformation could influence on the relaxation of welding residual stress. To apply this effect practically, it is a prerequisite to establish a numerical model which is able to estimate the effect of phase transformation on residual stress relaxation quantitatively. For this purpose, the analysis is carried out in two regions. i.e., heating and cooling, because the variation of material properties following a phase transformation in cooling is different in comparison with the case in heating, even at the same temperature. The variation of material properties following phase transformation is considered by the adjustment of specific heat and thermal expansion coefficient, and the distribution of residual stress in analysis is compared with that of experiment by previous study. consequently, in this study, simplified numerical procedures considering phase transformation, which based on a commercial finite element package was established through comparing with the experimental data of residual stress distribution by other researcher. To consider the phase transformation effect on residual stress relaxation, the transition of mechanical and thermal property such as thermal expansion coefficient and specific heat capacity was found by try and error method in this analysis.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.2
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• pp.85-92
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• 2023

Respiratory infections such as COVID-19 mainly occur within enclosed spaces. The presence or absence of abnormal symptoms of respiratory infectious diseases is judged through initial symptoms such as fever, cough, sneezing and difficulty breathing, and constant monitoring of these early symptoms is required. In this paper, image matching correction was performed for the RGB camera module and the thermal imaging camera module, and the temperature of the thermal imaging camera module for the measurement environment was calibrated using a blackbody. To detection the target recommended by the standard, a deep learning-based object recognition algorithm and the inner canthus recognition model were developed, and the model accuracy was derived by applying a dataset of 100 experimenters. Also, the error according to the measured distance was corrected through the object distance measurement using the Lidar module and the linear regression correction module. To measure the performance of the proposed model, an experimental environment consisting of a motor stage, an infrared thermography temperature screening system and a blackbody was established, and the error accuracy within 0.28℃ was shown as a result of temperature measurement according to a variable distance between 1m and 3.5 m.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.213-216
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• 2003

Efforts for better understanding of the interaction between groundwater recharge and thermal regime of the subsurface medium is gaining momentum for its diverse applications in water resources. A numerical model is developed to simulate temperature variations of the subsurface under time varying groundwater recharge. The model utilizes MacCormack scheme for finite difference approximation of the partial differential equation describing the conductive and advective heat transport. For the estimation of recharge rate, optimization of the model is realized by searching for the unknown parameters which minimize the root-mean-square error between simulated and measured temperatures. Simulation results for 22-year time series data of temperature measurements reveal that the proposed model can accurately simulate subsurface temperature variations resulting from the redistribution of the heat due to the movement of water and it can also estimate temporal variations of recharge. Seasonal variations of recharge and a linear relationship between precipitation and recharge are clearly reflected in the simulated results.