• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.78-84
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• 2011

CFD simulation has been conducted on a small scaled 4 nozzle clustered engine operating with air. In the present paper, the effects of grid size, turbulence models, flux difference methods have been compared. The results show that the base flows are somewhat different as the turbulence models, while Roe and AUSM flux differences produced almost the same results. Spalart-Allmaras turbulence model produces more accurate results rather than famous SST k-w model. The calculated Mach number and pressure profile in the engine base reveal the complex base flow structure, which is somewhat different from the generally estimated flow fields.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.5
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• pp.327-333
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• 2013

The purpose of this study is to predict sloshing pressure of a actual tank by using measured pressure in scaled down tank and to evaluate with structural strength of LNG Corner Block. For this purpose, we performed sloshing analysis about 138K class tank by using Ansys CFX program, and were measured both average pressure and maximum peak pressure according to scaled tank ratio. Also, measured pressure was converted to pressure of the actual tank by Froude scale law, and we conducted the evaluation of structural strength about the conner block of actual size KC-1.

• Phonetics and Speech Sciences
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• v.13 no.3
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• pp.71-78
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• 2021

Most neural-network-based speech synthesis models utilize neural vocoders to convert mel-scaled spectrograms into high-quality, human-like voices. However, neural vocoders combined with mel-scaled spectrogram prediction models demand considerable computer memory and time during the training phase and are subject to slow inference speeds in an environment where GPU is not used. This problem does not arise in linear spectrogram prediction models, as they do not use neural vocoders, but these models suffer from low voice quality. As a solution, this paper proposes a Tacotron 2 and Transformer-based linear spectrogram prediction model that produces high-quality speech and does not use neural vocoders. Experiments suggest that this model can serve as the foundation of a high-quality text-to-speech model with fast inference speed.

• Journal of KSNVE
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• v.10 no.6
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• pp.1009-1016
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• 2000

This paper describes the procedure of increasing the efficiency of experimental modal analysis and updating the quality of FE model using the scaled commercial vehicle frame. In this study, it was found that the experimental modal analysis could be more efficient when the measurements were made on the areas with high kinetic energies. Such areas could be located with the aid of FE modal analysis. Also, the number of measurement points could be decided by considering the dynamic characteristics of full FE model. The correlation of FE model and experimental modal analysis was assessed by the differences between the natural frequencies and MAC matrix, which is based on normal modes. These differences of modal parameters were reduced through the sensitivity and optimization analysis of which objective function consisted of the errors of natural frequencies and the diagonal terms of MAC matrix.

• Journal of the Korean Society of Combustion
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• v.6 no.1
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• pp.14-19
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• 2001

Interests and importance of down-scale combustor is increasing with the emerging need for miniaturized power source which is now a bottleneck of micro system development. But in down scaled combustor increased heat loss compared to thermal energy generation inhibits the usability and application of the device, so as a preliminary work of down scaled combustor fabrication. Modeling tool for the device should be established, in this study modeling approach of closed vessel combustion phenomena that can express heat loss effect and resulting quenching is proposed and the result is compared with experiment data. From this model heat loss effect following combustor scale down can be further understood, and further more design parameter and analysis tool can be obtained.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.04a
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• pp.225-227
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• 2011

The geometrically sub-scaled wall-injection test model was employed to visualize interactions of internal flow of a solid rocket motor equipped fin/slot grain and submerged nozzle. Symmetric vortex and circumferential flow patterns were visualized.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1248-1254
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• 2010

Authors conducted a deduction of some parameters using the magnetic equivalent circuit method and a verification study of the thrust force control algorithm of a rotary-typed small-scaled linear induction motor for a railway transit. In a LIM, it is possible to express the parameters of the magnetic equivalent circuit into a function of the shape of the secondary aluminium plate and the airgap between the LIM primary core and the secondary aluminium plate. It means that the LIM properties can be changed considerably by the shape of the secondary aluminium plate and the airgap between the LIM primary core and the secondary aluminium plate. So, authors analyzed a tendency of changes of the magnetic equivalent circuit parameters and the LIM characteristics by changing of the airgap of the secondary aluminium plate of a rotary-typed small-scaled LIM. And authors conducted a verification study of the indirect vector control algorithm with constant slip frequency by using the rotary-typed small-scaled LIM tester set on the basis of the calculated LIM parameters. Finally authors accomplished a research on applicability for LIM railway transit.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.33-46
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• 2015

In this paper, we predicts the analog and digital circuit performance of FinFETs that are scaled down following the ITRS(International technology roadmap for semiconductors). For accurate prediction of the circuit performance of scaled down devices, accurate parasitic resistance and capacitance analytical models are developed and their accuracies are within 2 % compared to 3D TCAD simulation results. The parasitic capacitance models are developed using conformal mapping, and the parasitic resistance models are enhanced to include the fin extension length($L_{ext}$) with respect to the default parasitic resistance model of BSIM-CMG. A new algorithm is developed to fit the DC characteristics of BSIM-CMG to the reference DC data. The proposed capacitance and resistance models are implemented inside BSIM-CMG to replace the default parasitic model, and SPICE simulations are performed to predict circuit performances such as $f_T$, $f_{MAX}$, ring oscillators and common source amplifier. Using the proposed parasitic capacitance and resistance model, the device and circuit performances are quantitatively predicted down to 5 nm FinFET transistors. As the FinFET technology scales, due to the improvement in both DC characteristics and the parasitic elements, the circuit performance will improve.

• Journal of the Korean Magnetics Society
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• v.27 no.2
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• pp.63-69
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• 2017

Naval ships are particularly required to maintain acoustic and magnetic silence due to their operational characteristics. Among them, underwater magnetic field signals derived by ships are likely to be detected by threats such as surveillance systems and mine systems at close distance. In order to increase the survivability of the vessels, various techniques for reducing the magnetic field signal are being studied and it is necessary to consider not only the magnitude of the magnetic field signal but also the gradient of it. In this paper, we use the commercial electromagnetic finite element analysis tool to predict the induced magnetic field signal of ship's scaled model, and arrange the degaussing coil. And the optimum degaussing current of the coil was derived by applying the particle swarm optimization algorithm considering the gradient constraint. The validity of the optimal degaussing technique is verified analytically by comparing the magnetic field signals after the degaussing with or without gradient constraint.

• The Journal of Korean Institute of Information Technology
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• v.18 no.5
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• pp.55-64
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• 2020

Estimating the state of charge (SOC) of a battery is essential for increasing the stability and reliability of a photovoltaic system. In this study, battery SOC estimation methods were proposed using artificial neural networks (ANNs) with gradient descent (GD), Levenberg-Marquardt (LM), and scaled conjugate gradient (SCG), and an adaptive neuro-fuzzy inference system (ANFIS). The charge start voltage and the integrated charge current were used as input data and the SOC was used as output data. Four models (ANN-GD, ANN-LM, ANN-SCG, and ANFIS) were implemented for battery SOC estimation and compared using MATLAB. The experimental results revealed that battery SOC estimation using the ANFIS model had both the highest accuracy and highest convergence speed.