• 한국전산유체공학회:학술대회논문집
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• 1998.11a
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• pp.114-120
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• 1998

The present numerical study investigates flow characters and heat transfer enhancement by the viscoelastic-driven secondary flow and buoyancy effect in a 2:1 rectangular duct. Three versions of thermal boundary conditions involving difference combination of heated walls and adiabatic walls are analyzed in this study. The Reiner-Rivlin model is adopted as a viscoelastic fluid model to simulate the secondary flow and temperature-dependent viscosity model is used. Calculated Nusselt numbers are very good agreement with experimental results for reported viscoelastic fluids. It is found that the heat transfer enhancement is mainly caused by the viscoelastic-driven secondary flow and buoyancy-induced secondary flow play a role of promoting this effect.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1980-1985
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• 2003

Two nonlinear ${\kappa}-{\epsilon}$ models with the wall function method are applied to the fully developed turbulent flow in a square duct. Typical predicted quantities such as axial and secondary velocities, turbulent kinetic energy and Reynolds stresses are compared in details both qualitatively and quantitatively with each other. A nonlinear ${\kappa}-{\epsilon}$ model with the wall function method capable of predicting accurately duct flows involving turbulence-driven secondary motion is presented in the present paper. The nonlinear ${\kappa}-{\epsilon}$ model adopted in a commercial code is found to be unable to predict accurately duct flows with the prediction level of secondary flows one order less than that of the experiment.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.6
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• pp.821-827
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• 2003

Fully developed turbulent flow in a square duct is numerically predicted with two nonlinear low-Reynolds-number ${\kappa}-{\varepsilon}$ models. Typical predicted quantities such as axial and secondary velocities, turbulent kinetic energy and Reynolds stresses are compared in detail with each other. It is found that the nonlinear low-Reynolds-number ${\kappa}-{\varepsilon}$ model adopted in a commercial code is unable to predict accurately duct flows involving turbulence-driven secondary motion with the prediction level of secondary flows one order less than that of the experiment.

• Journal of computational fluids engineering
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• v.8 no.1
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• pp.23-29
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• 2003

Two nonlinear κ-ε models with the wall function method are applied to the fully developed turbulent flow in a square duct. Typical predicted quantities such as axial and secondary velocities, turbulent kinetic energy and Reynolds stresses are compared in details both qualitatively and quantitatively with each other. A nonlinear κ-ε model with the wall function method capable of predicting accurately duct flows involving turbulence-driven secondary motion is presented in the present paper. The nonlinear κ-ε model of Shih et al.[1] adopted in a commercial code is found to be unable to predict accurately duct flows with the prediction level of secondary flows one order less than that of the experiment.

• Ocean Science Journal
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• v.40 no.2
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• pp.101-107
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• 2005

A simple analytical model is considered in an attempt to demonstrate a formation mechanism of the abyssal current in the East Sea. In this model, the abyssal currents are driven by wind through an outcrop region and flow along closed geostrophic contours. A rough estimate of the abyssal currents, arrived at by applying this model to the region of deep mixing in the East Sea, gives currents comparable to those observed, although there is an uncertainty in the surface area of the outcrop region. It seems that the spin-up of deep water by wind forcing through the region of deep winter mixing is, at least partly, an important contribution to the formation of the abyssal currents in the East Sea.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.666-668
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• 1998

In this paper we illustrate the design of a node label data flow machine based on self-timed paradigm. Data flow machines differ from most other parallel architectures, they are based on the concept of the data-driven computation model instead of the program store computation model. Since the data-driven computation model provides the excution of instructions asynchronously, it is natural to implement a data flow machine using self timed circuits.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.4
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• pp.611-617
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• 2008

In ubiquitous environments, the content adaptable services can be dynamically provided to adapt the frequent changes of contexts. These services have common things that the kinds of context factors are limited to ubiquitous environment, though the contexts are flexible. To reuse service architecture can be reasonable for effective adaptable service. In this paper, we design a software architecture with product line techniques for content adaptable applications in ubiquitous environment. Description of product line is to define variation points and their variants, to find out the dependencies between them and to keep the model based architecture, their alternatives.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.253-253
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• 2015

This research evaluated the effect of two main sources on the results of the ocean regional circulation model (ORCMs) during downscaling and nesting the results from the coarse data. The two sources should be the domain size, and temporal and spatial resolution different between driving and driven data. The Big-Brother Experiment is applied to examine the impact of them on the results of the ORCMs separately. Within resolution of 3km grid point ORCMs applying in the Big-Brother Experiment framework, it showed that the simulation results of the ORCMs depend on the domain size and specially the spatial and temporal resolution of lateral boundary conditions (LBCs). The domain size can be selected at 9.5 times larger than the interest area, and the spatial resolution between driving data and driven model can be up to 3 of ratio resolution and updating frequency of the LBCs can be up to every 6 hours per day.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.1-8
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• 2021

Cloud computing has proven its efficiency, especially after the increasing number of cloud services offered by a wide range of cloud providers, from different domains. Despite, these cloud services are mostly heterogeneous. Consequently, and due to the rising interest of cloud consumers to adhere to a multi-cloud environment instead of being locked-in to one cloud provider, the need for semantically interconnecting different cloud services from different cloud providers is a crucial and important task to ensure. In addition, considerable research efforts proposed interoperability solutions leading to different representation models of cloud services. In this work, we present our solution to overcome this limitation, precisely in the IAAS service model. This solution is a framework permitting the semantic interoperability of different IAAS resources in a multi-cloud environment, in order to assist cloud consumers to retrieve the cloud resource that meets specific requirements.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.225-230
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• 2023

With the development of AI technology, there is a growing discussion about Text-to-Image Generative AI. We presented a Generative AI video production method and delineated a methodology for the production of personalized AI-generated videos with the objective of broadening the landscape of the video domain. And we meticulously examined the procedural steps involved in AI-driven video production and directly implemented a video creation approach utilizing the VQGAN-CLIP model. The outcomes produced by the VQGAN-CLIP model exhibited a relatively moderate resolution and frame rate, and predominantly manifested as abstract images. Such characteristics indicated potential applicability in OTT-based video content or the realm of visual arts. It is anticipated that AI-driven video production techniques will see heightened utilization in forthcoming endeavors.