• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.12
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• pp.1065-1072
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• 2014

The shape of an impeller splitter for a dual-inlet centrifugal blower was optimized to enhance the blower performance. Two design variable, the normalized chord and pitch of a splitter, were used to evaluate the blower performance and internal flow fields based on the three-dimensional flow analysis. The blower performance obtained using this numerical simulation had a maximum error of 4 percent compared to that in an experiment at the design flow condition. The shape optimization of the splitter successfully increased the blower efficiency and pressure by 3.65 and 1.14 percent compared to the reference values. The blower performance was increased by reducing the flow separation near the blade suction surface by optimizing the shape of the splitter, which produced a pressure increase at the outlet of the volute casing.

• Journal of the Optical Society of Korea
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• v.20 no.3
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• pp.413-426
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• 2016

There has been much advancement in the field of aerial cameras for geographical features with the help of drones, image processing power and computer aided optical programs. In this study, we propose a new optical lens design technique which minimizes the amount of ‘the third order Seidel aberration’ for enhancing MTF. In addition, we suggest a new optical lens design which stabilizes the mass-production yield through R.S.M and has robustness secure through the Taguchi method. Eventually, the image processing algorithm of stereo matching is implemented in order to evaluate whether the proposed lens design result meets adequate specifications for the use of dual aerial photographs or not. This paper provides good guidance for the optical design by development of experiments.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.3
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.1
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• pp.9-15
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• 2019

Sensorless electric superchargers have recently been actively developed to provide a large amount of oxygen to engines in order assist the combustion process for miniaturizing the engines and improving fuel efficiency. The model-based sensorless method for surface-mounted permanent magnet synchronous motors has a disadvantage in that the system may become unstable due to parameter variations in low-speed operation and the rapid-acceleration section. An electric supercharger requires fast response to improve the engine response delay, such as the turbocharger turbo-rack. Therefore, the responsiveness must be improved to use the model-based sensorless system. The position compensation algorithm designed in this study is controlled by converting the position error into the beta, which is the angle formed by the d-axis and the stator current during sudden speed change. In this study, we improved the response of the model-based sensorless system through the algorithm and verified the algorithm validity by applying the algorithm to an actual dual-motor supercharger.

• Journal of the Society of Naval Architects of Korea
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• v.51 no.3
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• pp.212-219
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• 2014

As subsea production has been revived up, the demand of subsea equipment has also been increased. Among the equipment, subsea tree plays a major role in safety. The tubing hanger is one of the most important components in subsea tree. In this study structural reliability analysis on dual bore tubing hanger of subsea tree is performed. The target reliability which is introduced in ISO regulation is used for judging whether tubing hanger is safe or not. The considered loads are working pressure, working temperature and suspended tubing weight. Thermal-stress analysis on tubing hanger is performed and kriging model is created based on the results of FEM analysis. According to von Mises criterion, limit state equation can be estimated. Reliability analysis is performed by using level 2 method and the result is verified by that of Monte Carlo Simulation. For finding most probable failure point, enhanced HL-RF method is adopted. Because the reliability of model doesn't reach target reliability, an improvement measure should be considered. Thus, it is suggested to change the material of tubing hanger main body to AISI 4140.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.233-236
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• 2000

The acoustical response of fish depends on size and physical structure na, most important, on the presence or absence of a swimbladder. Acoustic scattering models for swimbladdered fish represent a fish by an ideal pressure-release surface having the size and shape as the swimbladder. Target strength experiments of red seabream (Chrysophrys major) have been conducted using 38 (split-beam), 120 (split-beam) and 200kHz (dual-beam) frequencies. At each start of each experiment, the live fish are placed in the cage at the surface, then the cage is lowed to about $4{\cal}m$ depth where it remains during the measurements. To test the acoustic models, predictions of target strength based on swimbladder morphometries of 10 red seabream offish total length from $103{\cal}mm{\;}to{\;}349{\cal}mm$ ($3 <$TL/\lambda$ < 45)are compared with conventional target strength measurements on the same, shock-frozen immediately after caged experiments. X-ray was projected along dorsal aspect to know the morphological construction of swimbladder. and fish body. At high frequencies, Helmholtz-kirchhoff(HK) approximation would greatly enhance swimbladdered fish modeling. Sound scattering model [HK-ray approximation model] for comparison to experimental target strength data was used to model backscatter measurements from individual fish. The scattering data can be used in the inverse method along with multiple frequency sonar systems to investigate the adequacy of classification and identification of fish

• Transactions of the KSME C: Technology and Education
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• v.3 no.1
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• pp.23-28
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• 2015

In this paper, we performed the design optimization dual-shell and tube heat exchanger on exhaust waste heat recovery for gas heat pump using CFD and RSM. CFD analysis is useful to design the complex structure such as double shell and tube heat exchanger. By computer simulation, engineers can assess the feasibility of the given design factors and change them to get a better design. But if one wishes to perform complex analysis on the simulation, such dual-shell and tube heat exchanger for GHP, the computational time can become overwhelming. CFD is powerful but it takes a lot of time for complex structure. Therefore, the CFD analysis is minimized by the optimization using the RSM method. As a result, the number of baffle and tube are optimized by 6 baffles and 25 tubes for heat transfer and flow friction. And then pressure drop and heat transfer is improved about 12.2%. We confirm the design optimization using CFD and RSM is useful on complex structure of heat exchanger.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.29-43
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• 2019

The calibration of microscopic traffic simulation models has received much attention in the simulation field. Although no standard has been established for it, a genetic algorithm (GA) has been widely employed in recent literature because of its high efficiency to find solutions in such optimization problems. However, the performance still falls short in simulation analyses to support fast decision making. This paper proposes a new calibration procedure using a dual GA and central composite design (CCD) in order to improve the efficiency. The calibration exercise goes through three major sequential steps: (1) experimental design using CCD for a quadratic response surface model (RSM) estimation, (2) 1st GA procedure using the RSM with CCD to find a near-optimal initial population for a next step, and (3) 2nd GA procedure to find a final solution. The proposed method was applied in calibrating the Gipps car-following model with respect to maximizing the likelihood of a spacing distribution between a lead and following vehicle. In order to evaluate the performance of the proposed method, a conventional calibration approach using a single GA was compared under both simulated and real vehicle trajectory data. It was found that the proposed approach enhances the optimization speed by starting to search from an initial population that is closer to the optimum than that of the other approach. This result implies the proposed approach has benefits for a large-scale traffic network simulation analysis. This method can be extended to other optimization tasks using GA in transportation studies.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1157-1169
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• 2008

This paper presents spectral element formulation which approximates Lamb wave propagation by PZT transducers bonded on a thin plate. A two layer beam model under 2-D plane strain condition is introduced to simulate high-frequency dynamic responses induced by a piezoelectric (PZT) layer rigidly bonded on a base plate. Mindlin-Herrmann and Timoshenko beam theories are employed to represent the first symmetric and anti-symmetric Lamb wave modes on a base plate, respectively. The Euler-Bernoulli beam theory and 1-D linear piezoelectricity are used to model the electro-mechanical behavior of a PZT layer. The equations of motions of a two layer beam model are derived through Hamilton's principle. The necessary boundary conditions associated with the electro-mechanical properties of a PZT layer are formulated in the context of dual functions of a PZT layer as an actuator and a sensor. General spectral shape functions of response field and the associated boundary conditions are obtained through equations of motions converted into frequency domain. Detailed spectrum element formulation for composing the dynamic stiffness matrix of a two layer beam model is presented as well. The validity of the proposed spectral element is demonstrated through numerical examples.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.2
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• pp.69-78
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• 1999

In this paper, a neural network approach to forecast Korean regional precipitation is presented. We first analyze the characteristics of the conventional models for time series prediction, and then propose a new model and its learning method for the precipitation forecast. The proposed model is a layered network in which the outputs of a layer are buffered within a given period time and then fed fully connected to the upper layer. This study adopted the dual connections between two layers for the model. The network behavior and learning algorithm for the model are also described. The dual connection structure plays the role of the bias of the ordinary Multi-Layer Perceptron(MLP), and reflects the relationships among the features effectively. From these advantageous features, the model provides the learning efficiency in comparison with the FIR network, which is the most popular model for time series prediction. We have applied the model to the monthly and seasonal forecast of precipitation. The precipitation data and SST(Sea Surface Temperature) data for several decades are used as the learning pattern for the neural network predictor. The experimental results have shown the validity of the proposed model.