• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.3
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• pp.344-353
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• 2019

The wakes behind a square cylinder were simulated using two-equation turbulence models, $k-{\varepsilon}$ and RNG $k-{\varepsilon}$ models. For comparisons between the model predictions and analytical solutions, we employed three skill assessments:, the correlation coefficient for the similarity of the wake shape, the error of maximum velocity difference (EMVD) of the accuracy of wake velocity, and the ratio of drag coefficient (RDC) for the flow patterns as in the authors' previous study. On the basis of the calculated results, we discussed the feasibility of each model for wake simulation and suggested a suitable value for an eddy viscosity related constant in each turbulence model. The $k-{\varepsilon}$ model underestimated the drag coefficient by over 40 %, and its performance was worse than that in the previous study with one-equation and mixing length models, resulting from the empirical constants in the ${\varepsilon}-equation$. In the RNG $k-{\varepsilon}$ model experiments, when an eddy viscosity related constant was six times higher than the suggested value, the model results were yielded good predictions compared with the analytical solutions. Then, the values of EMVD and RDC were 3.8 % and 3.2 %, respectively. The results of the turbulence model simulations indicated that the RNG $k-{\varepsilon}$ model results successfully represented wakes behind the square cylinder, and the mean error for all skill assessments was less than 4 %.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2007-2016
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• 2005

An artificial neural network (ANN) is successfully presented for prediction acidity constant (pKa) of various benzoic acids and phenols with diverse chemical structures using a nonlinear quantitative structure-property relationship. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The polarizability term $(\pi_1)$, most positive charge of acidic hydrogen atom $(q^+)$, molecular weight (MW), most negative charge of the acidic oxygen atom $(q^-)$, the hydrogen-bond accepting ability $(\epsilon_B)$ and partial charge weighted topological electronic (PCWTE) descriptors are inputs and its output is pKa. It was found that properly selected and trained neural network with 205 compounds could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network was applied for prediction pKa values of 37 compounds in the prediction set, which were not used in the optimization procedure. Squared correlation coefficient $(R^2)$ and root mean square error (RMSE) of 0.9147 and 0.9388 for prediction set by the MLR model should be compared with the values of 0.9939 and 0.2575 by the ANN model. These improvements are due to the fact that acidity constant of benzoic acids and phenols in water shows nonlinear correlations with the molecular descriptors.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.545-552
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• 2006

Small-scale models have been frequently used for seismic performance tests because of limited testing facilities and economic reasons. However, there are not also enough studies on similitude law for analogizing prototype structures accurately with small-scale models, although conventional similitude law based on geometry similitude is not well consistent in their inelastic seismic behaviors. When fabricating prototype and small-scale model of reinforced concrete structures by using the same material, added mass is demanded from a volumetric change and scale factor could be limited due to aggregate size. Therefore, it is desirable to use different materials for small-scale model. In our recent study, a modified similitude law was derived depending on geometric scale factor, equivalent modulus ratio and ultimate strain ratio. And quasi-static and pseudo-dynamic tests on the specimens are carried out using constant and variable modulus ratios, and correlation between prototype and small-scale model is investigated based on their test results. In this study, tests on scaled model of different concrete compressive strength aye carried out. In shaking table tests, added mass can not be varied. Thus, constant added mass on expected maximum displacement was applied and the validity was verified in shaking table tests. And shaking table tests on non-artificial mass model is carried out to settle a limitation of acceleration and the validity was verified in shanking table tests.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.543-547
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• 2002

In this paper, we introduce a more improved camera modeling method for linear pushbroom images than the method proposed by Orun and Natarajan(ON). ON model shows an accuracy of within 1 pixel if more than 10 ground control points(GCPs) are provided. In general, there is high correlation between platform position and attitude parameters but ON model ignores attitude variation in order to overcome such correlation. We propose a new method that obtains an optimal solution set of parameters without ignoring the attitude variation. We first assume that attitude parameters are constant and estimate platform position's. Then we estimate platform attitude parameters using the values of estimated position parameters. As a result, we can set up an accurate camera model for a linear pushbroom satellite scene. In particular, we can apply the camera model to its surrounding scenes because our model provide sufficient information on satellite's position and attitude not only for a single scene but also for a whole imaging segment. We tested on two images: one with a pixel size 6.6m$\times$6.6m acquired from EOC(Electro Optical Camera), and the other with a pixel size 10m$\times$l0m acquired from SPOT. Our camera model procedures were applied to the images and gave satisfying results. We had obtained the root mean square errors of 0.5 pixel and 0.3 pixel with 25 GCPs and 23 GCPs, respectively.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.1
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• pp.43-50
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• 2012

Two typical animal waste management practices, composting and stockpiling, were evaluated for their effect on the degradation of three veterinary antibiotics (VAs), chlortetracycline (CTC), tylosin (TYL), and monensin (MNS). The VAs were applied to horse manure plots subject to composting or stockpiling, and core samples were collected over a period of time. Selected buffer solutions were used to extract the VAs and analysis for concentration was conducted with solid phase extraction (SPE) followed by high performance liquid chromatography tandem mass spectrometry (HPLC/MS/MS) technique. The VAs demonstrated rapid dissipation within ten days followed by a gradual decrease in concentration until the end of the experimental period (141 days). All three VAs degraded more rapidly in the composting samples than in the stockpiling samples, particularly between 20 and 60 days of the observation period. Degradation of the three VAs generally followed a first-order kinetic model, and a fitted model with a calculated rate constant was determined for each treatment. TYL in composting showed the fastest degradation, with a calculated rate constant of $0.91day^{-1}$; the slowest degradation was exhibited by MNS in stockpiling, with rate constant of $0.17day^{-1}$. Calculated correlation coefficients ranged from 0.89 to 0.96, indicating a strong correlation between measured concentrations and fitted values in this study. Although concentration of TYL in composting treatment showed below detection limit during the test period, this study suggests that composting can reduce animal waste contaminants prior to field application as fertilizer.

• ETRI Journal
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• v.16 no.2
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• pp.15-25
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• 1994

In this paper we present a new formula which can predict the exact detection probability of a generalized order statistics (GOS) constant false alarm rate (DFAR) detector for a partially correlated Rayleigh target model (0 < $ \rho$< 1) in a closed form, where $\rho$ is the correlation coefficient between returned pulses. By simply substituting a set of specific coefficient into the derived formula, one can obtain the detection probability of any kind of CFAR detector. Detectors may include the order statistics CFAR detector, the censored mean level detector, and the trimmed mean CFAR detector, but are not necessarily restricted to them. The numerical result for the first order Markov correlation model as applied to some of the detectors shows that as $\rho$ increases from zero to one, higher signal-to-noise ratio is required to achieve the same detection probability.

• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.17-35
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• 1994

A semi-empirical model for microwave polarimetric radar backscattering from bare soil surfaces was developed using polarmetric radar measurements and the knowledge based on the theoretical and numerical solutions. The microwave polarimetric backscatter measurements were conducted for bare soil surfaces under a variety of roughness and moisture conditions at L-, C-, and X-band frequencies at incidence angles ranging from 10` to 70`. Since the accrate target parameters as well as the radar parameters are necessary for radar scattering modeling, a complete and accurate set of ground truth data were also collected using a laser profile meter and dielectric probes for each surface condition, from which accurate measurements were made of the rms height, correlation length, and dielectric constant. At first, the angular and spectral dependencies of the measured radar backscatter for a wide range of roughnesses and moisture conditions are examined. Then, the measured scattering behavior was tested using theoretical and numerical solutions. Based on the experimental observations and the theoretical and numerical solutions, a semi-empirical model was developed for backscattering coeffients in terms of the surface roughness parameters and the relative dielectric constant of the soil surface. The model was found to yield very good agreement with the backscattering measurements of this study as well as with independent measurements.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.416-421
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• 2000

We have developed a simple model for describing the non-spherical particle growth phenomena using modified 1-dimensional sectional method. In this model, we solve simultaneously particle volume and surface area conservation sectional equations which consider particles' irregularities. From the correlation between two conserved properties of sections, we can predict the evolution of the aggregates' morphology. We compared this model with a simple monodisperse-assumed model and more rigorous two dimensional sectional model. For the comparison, we simulated silica and titania particle formation and growth in a constant temperature reactor environment. This new model shows a good agreement with the detailed two dimensional sectional model in total number concentration, primary particle size. The present model can also successfully predict particle size distribution and morphology without costing very heavy computation load and memory needed for the analysis of two dimensional aerosol dynamics.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.7
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• pp.1020-1027
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• 2000

A simple model for describing the non-spherical particle growth phenomena has been developed. In this model, we solve simultaneously particle volume and surface area conservation sectional equations that consider particles' non-sphericity. From the correlation between two conserved properties of sections, we can predict the evolution of the aggregates' morphology. This model was compared with a simple monodisperse-assumed model and more rigorous two-dimensional sectional model. For comparison, formation and growth of silica particles have been simulated in a constant temperature reactor environment. This new model showed good agreement with the detailed two-dimensional sectional model in total number concentration and primary particle size. The present model successfully predicted particle size distribution and morphology without costing very heavy computation load and memory needed for the analysis of two dimensional aerosol dynamics.

• Advances in Computational Design
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• v.4 no.4
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• pp.367-379
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• 2019

Multiple Inputs and Multiple Outputs (MIMO) Fuzzy logic model is developed to predict the engine performance and emission characteristics of pongamia pinnata biodiesel with hydrogen injection. Engine performance and emission characteristics such as brake thermal efficiency (BTE), brake specific energy consumption (BSEC), hydrocarbon (HC), carbon monoxide (CO), carbon dioxide ($CO_2$) and nitrous oxides ($NO_X$) were considered. Experimental investigations were carried out by using four stroke single cylinder constant speed compression ignition engine with the rated power of 5.2 kW at variable load conditions. The performance and emission characteristics are measured using an Exhaust gas analyzer, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends (Diesel, B10, B20 and B30) and engine load conditions. Fuzzy logic model uses triangular and trapezoidal membership function because of its higher predictive accuracy to predict the engine performance and emission characteristics. Computational results clearly demonstrate that, the proposed fuzzy model has produced fewer deviations and has exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.99136 to 1 with experimental values. The developed fuzzy logic model has produced good correlation between the fuzzy predicted and experimental values. So it is found to be useful for predicting the engine performance and emission characteristics with limited number of available data.