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Kim, Young-Ho;Kwon, Chi-Ho;Yoo, Seoung-Chool
- Proceedings of the KSME Conference
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- 2001.06e
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- pp.162-168
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- 2001
In the present study, flow visualizations, hot-wire velocity measurements and computational fluid dynamics were performed in order to determine complicated air flow characteristics in a neonate incubator. In this study, following conclusions can be made: (1) The flow visualization technique developed in the present study revealed an enough qualitative information for the flow field in the neonate incubator. Flow structures in a neonate incubator with a realistic three-dimensional shape was successfully visualized the present study. (2) Results from the flow visualization were relatively in good agreements with those obtained from the computational fluid dynamics. (3) Velocities very near the neonate measured by the hot-wire anemometer were relevant to those obtained from the computational fluid dynamics. (4) Temperatures were higher at the neck region and the medial aspect of both thighs, but lower in both extremities. (5) Small vortices between the neonate and the mattress might interfere with convective and evaporative heat transfers on the neonate's surface. In the fluid dynamic aspect, it is important to eliminate the formation of these small vortices for the design of incubator chamber.
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Noh, K.H.;Park, M.Y.;Park, S.H.;Lee, J.W.;Byun, Y.H.
- 한국전산유체공학회:학술대회논문집
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- 2007.10a
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- pp.111-114
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- 2007
Computational Fluid Dynamics (CFD) and the Finite Element Method (FEM) are used to perform aerodynamics analysis and structure analysis. For the fluid-structure interaction analysis, each technology should be considered as well. The process of aerodynamics-structure coupled analysis can be applied to various integrated analyses from many research fields. In this study, the aerodynamics-structure coupled analysis is performed for the missile at high angle of attack condition through the use of Computational Fluid Dynamics (CFD) and the Finite Element Method (FEM). For this purpose, the aerodynamics-structure coupled analyses procedure for the missile are established. The results of the integrated analysis are compared with rigid geometry of the missile and the effect of the deformation will be addressed.
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Hwang, Seung Tae;Han, Chang Kyun;Im, Yong Taek;Kim, Jong Rok;Bae, Youngmin;Moon, Young J.
- International Journal of Aeronautical and Space Sciences
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- v.18 no.2
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- pp.197-205
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- 2017
We investigate the slat noise generation mechanism by using large-eddy simulation (LES) and simple source modeling based on linearized Euler equations. An incompressible LES of an MD 30P30N three-element airfoil in the high-lift configuration is conducted at $Re_c=1.7{\times}10^6$. Using the total derivative of the hydrodynamic pressure (DP/Dt) acquired from the incompressible LES, representative noise sources in the slat cove region are characterized in terms of simple sources such as frequency-specific monopoles and dipoles. Acoustic radiation around the 30P30N multi-element airfoil is effectively computed using the Brinkman penalization method incorporated with the linearized Euler equation. The directivity pattern of $p^{\prime}_{rms}$ at $r=20c_{slat}$ in the multiple sources is closely compared to that obtained by the application of the LES/Ffowcs-Williams and Hawking's methods to the entire flow field. The power spectrum of p' at ${\theta}=290^{\circ}$ is in good agreement with the data reported in BANC-III, especially the broadband part of the spectrum with a decaying slope ${\propto}f^{-3}$.
https://doi.org/10.5139/IJASS.2017.18.2.197 인용 PDF KSCI

Joohwan Ha;Seokyoon Shin;Junyoung Kim;Changwoo Byun
- Journal of the Semiconductor & Display Technology
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- v.22 no.1
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- pp.134-138
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- 2023
This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.
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