• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.2
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• pp.91-99
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• 2014

The common-rail injectors are the most critical component of the CRDI diesel engines that dominantly affect engine performances through high pressure injection with exact control. Thus, from now on the advanced combustion technologies for common-rail diesel injection engine require high performance fuel injectors. Accordingly, the previous studies on the numerical and experimental analysis of the diesel injector have focused on a optimum geometry to induce proper injection rate. In this study, computational predictions of performance of the diesel injector have been performed to evaluate internal flow characteristics for various needle lift and the spray pattern at the nozzle exit. To our knowledge, three-dimensional computational fluid dynamics (CFD) model of the internal flow passage of an entire injector duct including injection and return routes has never been studied. In this study, major design parameters concerning internal routes in the injector are optimized by using a CFD analysis and Response Surface Method (RSM). The computational prediction of the internal flow characteristics of the common-rail diesel injector was carried out by using STAR-CCM+7.06 code. In this work, computations were carried out under the assumption that the internal flow passage is a steady-state condition at the maximum needle lift. The design parameters are optimized by using the L16 orthogonal array and polynomial regression, local-approximation characteristics of RSM. Meanwhile, the optimum values are confirmed to be valid in 95% confidence and 5% significance level through analysis of variance (ANOVA). In addition, optimal design and prototype design were confirmed by calculating the injection quantities, resulting in the improvement of the injection performance by more than 54%.

• Reproductive and Developmental Biology
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• v.32 no.3
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• pp.141-146
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• 2008

To search a new porcine pheromonal odorant, the models of four type (2D-QSAR, HQSAR, CoMFA & CoMSlA) were derived from quantitative structure-activity relationship (QSAR) between tetrahydrofuran-2-yl family compounds and their observed binding affinity constants (Obs.p$[Od]_{50}$). The optimized CoMFA model (predictability; $r^{2}_{cv.}(q^2)$=0.886 & correlation coefficient: $r^{2}_{ncv.}$=0.984) from ligand based approaches was confirmed as the best model among them. The $N^{1}$-allyl-$N^{2}$-(tetrahydrofuran-2-yl)methyl)oxalamide (P1), 2-(4-trimethylammoniummethylcyclohexyloxy)tetrahydrofurane (P5) and 2-(3-trimethylammoniummethylcyclohexyloxy)tetrahydrofurane (P6) molecules predicted as porcine pheromonal odorant by the CoMFA model were showed relatively high binding affinity constant values (Pred.p$[Od]_{50}=8{\sim}10$) and very lower toxicity values against some sorts of toxicity.

• Tunnel and Underground Space
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• v.25 no.2
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• pp.155-167
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• 2015

The thermal-hydrological-mechanical (T-H-M) behavior of rock mass surrounding a high-temperature cavern thermal energy storage (CTES) operated for a period of 30 years has been investigated by TOUGH2-FLAC3D simulator. As a fundamental study for the development of prediction and control technologies for the environmental change and rock mass behavior associated with CTES, the key concerns were focused on the hydrological-thermal multiphase flow and the consequential mechanical behavior of the surrounding rock mass, where the insulator performance was not taken into account. In the present study, we considered a large-scale cylindrical cavern at shallow depth storing thermal energy of $350^{\circ}C$. The numerical results showed that the dominant heat transfer mechanism was the conduction in rock mass, and the mechanical behavior of rock mass was influenced by thermal factor (heat) more than hydrological factor (pressure). The effective stress redistribution, displacement and surface uplift caused by heating of rock and boiling of ground-water were discussed, and the potential of shear failure was quantitatively examined. Thermal expansion of rock mass led to the ground-surface uplift on the order of a few centimeters and the development of tensile stress above the storage cavern, increasing the potential of shear failure.

• Journal of Korea Water Resources Association
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• v.41 no.2
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• pp.149-162
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• 2008

The Soil and Water Assessment Tool (SWAT) developed by the USDA-Agricultural Research Service for the prediction of land management impact on water, sediment, and agricultural chemical yields in a large-scale basin was applied to Daecheong Reservoir basin to estimate the amount of soil losses from different land uses. The research outcomes provide important indications for reservoir managers and policy makers to search alternative watershed management practices for the mitigation of reservoir turbidity flow problems. After calibrations of key model parameters, SWAT showed fairly good performance by adequately simulating observed annual runoff components and replicating the monthly flow regimes in the basin. The specific soil losses from agricultural farm field, forest, urban area, and paddy field were 33.1, $2.3{\sim}5.4$ depending on the tree types, 1.0, and 0.1 tons/ha/yr, respectively in 2004. It was noticed that about 55.3% of the total annual soil loss is caused by agricultural activities although agricultural land occupies only 10% in the basin. Although the soil erosion assessment approach adopted in this study has some extent of uncertainties due to the lack of detailed information on crop types and management activities, the results at least imply that soil erosion control practices for the vulnerable agricultural farm lands can be one of the most effective alternatives to reduce the impact of turbidity flow in the river basin system.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.697-706
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• 2013

In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.

• Economic and Environmental Geology
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• v.42 no.4
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• pp.335-342
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• 2009

The effects of bacterial lysis on the rate of quartz dissolution were investigated under pH 7 condition using Bacillus subtilis cells which were either irradiated or non-irradiated with gamma ray. The amount of dissolved organic carbon (DOC) which resulted from bacterial lysis increased in slurries of quartz and bacteria mixture over experimental period. Lysis of non-irradiated bacteria led to the elevated concentration of dissolved silicon when compared with abiotic control. Concomitant increase in the amounts of DOC and dissolved silicon over time indicated that lixiviation of silicon from quartz was due to bacterial lysis. Higher amounts of DOC and dissolved silicon were present in the irradiated bacterial slurries than those of non-irradiated bacteria. The enhancement of quartz dissolution in the irradiated bacterial slurries was likely attributed to disruption of organic molecules in the bacterial cells by gamma ray and formation of effective ligands for quartz dissolution. The results suggest that the effects of bacterial lysis on mineral weathering rate should be considered for prediction of time for released radionuclides to migrate to surface biosphere in high level radioactive waste disposal site.

• Journal of The Korean Society of Grassland and Forage Science
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• v.35 no.3
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• pp.257-263
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• 2015

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

• Journal of Energy Engineering
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• v.26 no.4
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• pp.100-106
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• 2017

The materials of SUS304 and SS400 are adopted for prediction of workpiece temperature variation according to ambient temperature in a reheating furnace. Five thermocouples were installed in a depth direction inside the material, and the ambient temperature was raised to 1200 Celsius degrees. As a result, the material average temperature reached more than 1150 Celsius degrees, and the surface and inside of workpiece locally showed a temperature difference of more than 10K. In order to verify the experimental results, numerical analysis was conducted by applying a thermal model, and the error of numerical simulation compared with the experimental results was within the range of 15K at the average outlet temperature. Also, the error was relatively higher in the SS400 material, which has a larger specific heat change than the SUS304 material. In conclusion, the workpiece temperature in the reheating furnace can be achieved through the atmospheric temperature control, and it is experimentally proved that the material temperature change according to the atmospheric temperature can be estimated within about 3% error range at the outlet position using a thermal model.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.1-7
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• 2020

Deep neural networks(DNN), which are used as approximation functions in reinforcement learning (RN), theoretically can be attributed to realistic results. In empirical benchmark works, time difference learning (TD) shows better results than Monte-Carlo learning (MC). However, among some previous works show that MC is better than TD when the reward is very rare or delayed. Also, another recent research shows when the information observed by the agent from the environment is partial on complex control works, it indicates that the MC prediction is superior to the TD-based methods. Most of these environments can be regarded as 5-step Q-learning or 20-step Q-learning, where the experiment continues without long roll-outs for alleviating reduce performance degradation. In other words, for networks with a noise, a representative network that is regardless of the controlled roll-outs, it is better to learn MC, which is robust to noisy rewards than TD, or almost identical to MC. These studies provide a break with that TD is better than MC. These recent research results show that the way combining MC and TD is better than the theoretical one. Therefore, in this study, based on the results shown in previous studies, we attempt to exploit a random balance with a mixture of TD and MC in RL without any complicated formulas by rewards used in those studies do. Compared to the DQN using the MC and TD random mixture and the well-known DQN using only the TD-based learning, we demonstrate that a well-performed TD learning are also granted special favor of the mixture of TD and MC through an experiments in OpenAI Gym.

• Korean Journal of Poultry Science
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• v.47 no.3
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• pp.127-133
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• 2020

Chibby family member 2 (CBY2), also known as SPERT or NURIT, is a gene with Chibby-like super family domain, whose function is not well known. In this study, the quail CBY2 gene was cloned, its sequences were analyzed, and its role in the myogenesis of QM7 quail muscle cells was characterized. Quail CBY2 has 978 nucleotides, which are translated into 325 amino acids, and the amino acid sequences are highly similar to those of chicken CBY2. Avian CBY2 diverted from mammalian CBY2 during early evolutionary history. According to the protein domain prediction analysis, quail CBY2 has a Chibby-like superfamily domain consisting of 83 amino acids at the N-terminal of the protein, although compared to mammalian CBY2, many of the amino acids were different. CBY2 was highly expressed in the adipose tissue and moderately expressed in the liver, heart, and kidney, whereas rarely expressed in the muscle tissue in quail. To characterize the role of CBY2 in myogenesis, CBY2 was overexpressed in QM7 cells. The overexpression of CBY2 inhibited myotube formation as shown that the myotube area was approximately only 25% that of the control. Taken together, quail CBY2 has a Chibby-like superfamily domain and inhibits myogenesis. Further studies should focus on the identification of the inhibitory mechanism of CBY2 on myogenesis.