• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.168-171
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• 2008

In the present study, turbulent flows around cubic and L-shape buildings were simulated numerically. Standard ${\kappa}$-$\varepsilon$, RNG ${\kappa}$-$\varepsilon$, LES turbulence models were adopted for the present simulation. The wind pressure coefficients from these results were compared with the available experimental data. The result of RNG ${\kappa}$-$\varepsilon$ and LES turbulent models gave better prediction than that of standard ${\kappa}$-$\varepsilon$ turbulent model which is widely used in the turbulent flow simulation.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1141-1154
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• 2018

Numerical experiments were carried out to investigate the effect of data assimilation of observational data on weather and PM (particulate matter) prediction. Observational data applied to numerical experiment are aircraft observation, satellite observation, upper level observation, and AWS (automatic weather system) data. In the case of grid nudging, the prediction performance of the meteorological field is largely improved compared with the case without data assimilations because the overall pressure distribution can be changed. So grid nudging effect can be significant when synoptic weather pattern strongly affects Korean Peninsula. Predictability of meteorological factors can be expected to improve through a number of observational data assimilation, but data assimilation by single data often occurred to be less predictive than without data assimilation. Variation of air pressure due to observation nudging with high prediction efficiency can improve prediction accuracy of whole model domain. However, in areas with complex terrain such as the eastern part of the Korean peninsula, the improvement due to grid nudging were only limited. In such cases, it would be more effective to aggregate assimilated data.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.371-379
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• 2000

A noise prediction method of axial flow fan is developed by incorporating through-flow method and vortex shedding noise model. Fan noise is assumed to be generated due to the pressure fluctuation induced by wake vortices of fan blades and radiate as diploe distribution. The wake vortices are analyzed by combining Karman vortex street model and through-flow analysis results, and the vortex-induced fluctuating pressure on blade surface is calculated by thin airfoil theory. The predicted sound pressure levels and directivity patterns of fan noise by the present method are favorably compared with fan noise test data. Furthermore, the present method is shown to be very useful for predicting the aero-acoustic performance map of the fan operated at off-design point.

• Wind and Structures
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• v.19 no.6
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• pp.623-647
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• 2014

In this paper, a probability distribution which is consistent with the observed phenomenon at the roof corner and, also on other portions of the roof, of a low-rise building is proposed. The model is consistent with the choice of probability density function suggested by the statistical thermodynamics of open systems and turbulence modelling in fluid mechanics. After presenting the justification based on physical phenomenon and based on statistical arguments, the fit of alpha-stable distribution for prediction of extreme negative wind pressure coefficients is explored. The predictions are compared with those actually observed during wind tunnel experiments (using wind tunnel experimental data obtained from the aerodynamic database of Tokyo Polytechnic University), and those predicted by using Gumbel minimum and Hermite polynomial model. The predictions are also compared with those estimated using a recently proposed non-parametric model in regions where stability criterion (in skewness-kurtosis space) is satisfied. From the comparisons, it is noted that the proposed model can be used to estimate the extreme peak negative wind pressure coefficients. The model has an advantage that it is consistent with the physical processes proposed in the literature for explaining large fluctuations at the roof corners.

• Journal of Environmental Science International
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• v.21 no.5
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• pp.613-622
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• 2012

A pilot-scale pulse-jet bagfilter was designed, built and tested for the effects of four operating conditions (filtration velocity, inlet dust concentration, pulse pressure, and pulse interval time) on the total system pressure drop, using coke dust from a steel mill factory. Two models were used to predict the total pressure drop according to the operating conditions. These model parameters were estimated from the 180 experimental data points. The empirical model (EM) with filtration velocity, areal density, inlet dust concentration, pulse interval time and pulse pressure shows the best correlation coefficient (R=0.971) between experimental data and model predictions. The empirical model was used as it showed higher correlation coefficient (R=0.971) compared to that of the Multivariate linear regression(MLR) (R=0.961). The minimum pulse pressure predicted by empirical model (EM) was 5kg/$cm^2$.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.591-598
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• 2000

In the present study, the variation of settlement, pore water pressure and undrained shear strength through model tests were measured. Also, the variation of water content, unit weight and shear strength by the vane shear tests were observed. In this study, appropriate deposit time of construction equipments used in treatment of hydraulic fills is determined from the prediction curve of increased shear strength in dredged fills.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.7
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• pp.94-103
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• 1999

A model for the prediction of combustion and exhaust emissions of DI diesel engine has been formulated and developed . This model is a quasi-dimensional phenomenological one and is based on multi-zone combustion modelling concept. It takes into consideration, on a zonal basis ,detailed of fuel spray formation, droplet evaporation, air-fuel mixing, spray wall interaction, swirl , heat transfer, self ignition and burning rate . The emission model is considered with chemical equipment , as well as the kinetics of fuel. NO and soot reactions in order to calculate the pollutant concentrations within each zone and the whole of cylinder . The accuracy of prediction versus experimental data and the capability of the model in predicting engine heat release, cylinder pressure and all the major exhaust emissions on zonal and cumulative basis., is demonstrated. Detailed prediction results showing the sensitivity of the model bv various injection rates are presented and discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.2
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• pp.35-54
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• 2009

The recent economic crisis not only reduces the profit of department stores but also incurs the significance losses caused by the increasing late-payment rate of credit cards. Under this pressure, the scope of credit prediction needs to be broadened from the simple prediction of whether this customer has a good credit or not to the accurate prediction of how much profit can be gained from this customer. This study classifies the delinquent customers of credit card in a Korean department store into homogeneous clusters. Using this information, this study analyzes the repayment patterns for each cluster and develops the credit prediction system to manage the delinquent customers. The model presented by this study uses Kohonen network, which is one of artificial neural networks of data mining technique, to cluster the credit delinquent customers into clusters. Cox proportional hazard model is also used, which is one of survival analysis used in medical statistics, to analyze the repayment patterns of the delinquent customers in each cluster. The presented model estimates the repayment period of delinquent customers for each cluster and introduces the influencing variables on the repayment pattern prediction. Although there are some differences among clusters, the variables about the purchasing frequency in a month and the average number of installment repayment are the most predictive variables for the repayment pattern. The accuracy of the presented system leaches 97.5%.

• Transactions of Materials Processing
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• v.29 no.4
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Journal of Energy Engineering
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• v.25 no.1
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• pp.69-75
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• 2016

Bubble size is a key parameter for an accurate prediction of bubble behaviours in the multi-dimensional two-phase flow. In the current STAR CCM+ CFD code, a mechanistic bubble size model $S{\gamma}$ is available for the prediction of bubble size in the flow channel. As another model, Yun model is developed based on DEBORA that is subcooled boiling data in high pressure. In this study, numerical simulation for the gas-liquid two-phase flow was conducted to validate and confirm the performance of $S{\gamma}$ model and Yun model, using the commercial CFD code STAR CCM+ ver. 10.02. For this, local bubble models was evaluated against the air-water data from DEDALE experiments (1995) and Hibiki et al. (2001) in the vertical pipe. All numerical results of $S{\gamma}$ model predicted reasonably the two-phase flow parameters and Yun model is needed to be improved for the prediction of air-water flow under low pressure condition.