• Journal of computational fluids engineering
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• v.19 no.4
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• pp.45-51
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• 2014

We develop a numerical method for solving the radiation hydrodynamic equations in one-dimensional spherical coordinates. The present method is validated through simulations of shock tube, thermal radiative diffusion and point explosion problems. The transient growth of the fireball is investigated by varying explosion yields. The present study clearly captures well-known breakaway phenomena related to the shock separation between pressure waves and thermal shock front. The fireball radius at the breakaway point is roughly increased by the yield to power of 0.4.

• 한국전산유체공학회:학술대회논문집
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• 2006.05a
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• pp.47-48
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• 2006

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.719-724
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• 2007

Many engineering problems often require the large amount of computing resources for iterative simulations of problems treating many parameters and input files. In order to overcome the situation, this paper proposes an e-Science based computational system. The system exploits the Grid computing technology to establish an integrated web service environment which supports distributed high throughput computational simulations and remote executions. The proposed system provides an easy-to-use parametric study service where a computational service includes real time monitoring. To verify usability of the proposed system, two kinds of applications were introduced. The first application is an Aerospace Integrated Research System (e-AIRS). The e-AIRS adapts the proposed computational system to solve CFD problems. The second one is design and optimization of protein 3-dimensional structures.

• Journal of computational fluids engineering
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• v.18 no.3
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• pp.34-41
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• 2013

We develop a novel immersed boundary (IB) method based on implicit direct forcing scheme for incompressible flows. The proposed IB method is based on an iterative procedure for calculating the direct forcing coupled with the momentum equations in order to satisfy no-slip boundary conditions on IB surfaces. We perform simulations of two-dimensional flows over a circular cylinder for low and moderate Reynolds numbers. The present method shows that the errors for estimated velocities on IB surfaces are significantly reduced even for low Reynolds number with a fairly large time step while the previous methods based on direct forcing failed to provide no-slip boundary conditions on IB surfaces.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2004.06a
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• pp.163-169
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• 2004

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.466-469
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• 2016

계산과학공학 및 여러 과학분야에서 컴퓨터 시뮬레이션을 통한 연구가 활발히 수행되고 있다. 하지만 정확도 및 복잡도가 높아짐에 따라 시뮬레이션을 수행하는 비용도 크게 증가하고 있다. 특히 여러 입력 변수를 변화시켜가며 시뮬레이션을 수행하는 경우, 시뮬레이션 수행 비용은 더욱 커진다. 하지만 지금까지는 이전에 수행된 시뮬레이션 결과를 재활용하는 연구는 거의 이루어지지 않았다. 본 논문에서는 이와 관련된 연구들을 살펴보고, 기존 시뮬레이션 결과를 사용하여 반복 요청된 시뮬레이션에 대한 결과를 즉시 반환하거나 유사 시뮬레이션에 대한 결과를 예측하는 시스템을 설계한다. 본 논문에서 설계한 시스템을 통해, 사용자는 시뮬레이션을 수행하지 않고도 반복 또는 유사 시뮬레이션에 대한 결과를 빠르게 얻을 수 있다.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2009.05a
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• pp.93-93
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• 2009

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.158-161
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• 2003

A cascade structured neural network called Sigma-Pi$_{t}$ Cascaded Hybrid Neural Network ($\sigma$$\pi$$_{t}$-CHNN) is Proposed. It is an extended version of the Sigma-Pi Cascaded extended Hybrid Neural Network ($\sigma$$\pi$-CHNN), where the classical multiplicative neuron ($\pi$-neuron) is replaced by the translated multiplicative ($\pi$$_{t}$-neuron) model. The learning algorithm of $\sigma$$\pi$$_{t}$-CHNN is composed of an evolutionary programming method, responsible for determining the network architecture, and of a Levenberg-Marquadt algorithm, responsible for tuning the weights of the network. The $\sigma$$\pi$$_{t}$-CHNN is evaluated in 2 pattern classification problems: the 2 spirals and the sonar problems. In the 2 spirals problem, $\sigma$$\pi$$_{t}$-CHNN can generate neural networks with 10％ less hidden neurons than that in previous neural models. In the sonar problem, $\sigma$$\pi$$_{t}$-CHNN can find the optimal solution for the problem i.e., a network with no hidden neurons. These results confirm the expanded information processing capabilities of $\sigma$$\pi$$_{t}$-CHNN, when compared to previous neural network models. network models.

• 한국전산유체공학회:학술대회논문집
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• 2008.08a
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• pp.29.1-29.1
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• 2008

• 한국전산유체공학회:학술대회논문집
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• 2008.08a
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• pp.36.3-36.3
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• 2008