• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.118-119
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• 2010

star formation is one of the hottest areas in astromy and increasing evidence is showing that star formation is actually a highly dynamic precess driven and strongly influenced by turbulent dynamics of molecular clouds. despite significant progress ir observation in process of star formation, earliest stage of star formation remains imcomplete. so, computer simulations are essential tool since the complex dynamics of star formation. We have performed simulation about the process of low mass star formation using the SPH simulation. we use the dragon-code, the most advanced star formation N-body Smoothed Particle Hydrodynamics (SPH) codes. We present how change the internal properties and how should evolve, while changing the values for Mass turbulence, central density and so on. ( mass range of values is 0.1 < M < $5\;M{\odot}$) based on this results, we discussed their circumstellar, characteristics they were borned and how they will evove while the Birth of low mass stars from interstellar cloud.

• Journal of the Korean GEO-environmental Society
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• v.25 no.4
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• pp.13-20
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• 2024

In this paper, a parallel analysis algorithm for Smoothed Particle Hydrodynamics (SPH), one of the numerical methods for fluidic materials, is introduced. SPH, which is a meshless method, can represent the behavior of a continuum using a particle-based approach, but it demands substantial computational resources. Therefore, parallel analysis algorithms are essential for SPH simulations. The domain decomposition algorithm, which divides the computational domain into partitions to be independently analyzed, is the most representative method among parallel analysis algorithms. In Discrete Element Method (DEM) and Molecular Dynamics (MD), the Cartesian coordinate-based domain decomposition method is popularly used because it offers advantages in quickly and conveniently accessing particle positions. However, in SPH, it is important to share particle information among partitioned domains because SPH particles are defined based on information from nearby particles within the smoothing length. Additionally, maintaining CPU load balance is crucial. In this study, a highly parallel efficient algorithm is proposed to dynamically minimize the size of orthogonal domain partitions to prevent excess CPU utilization. The efficiency of the proposed method was validated through numerical analysis models. The parallel efficiency of the proposed method is evaluated for up to 30 CPUs for fluidic models, achieving 90% parallel efficiency for up to 28 physical cores.

• Korean Chemical Engineering Research
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• v.57 no.4
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• pp.475-483
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• 2019

Special chemical warfare agents are lethal gases that attack the human respiratory system. One of such gases are blood agents that react with the irons present in the electron transfer system of the human body. This reaction stops internal respiration and eventually causes death. The molecular sizes of these agents are smaller than the pores of an activated carbon, making chemical adsorption the only alternative method for removing them. In this study, we carried out a Computational Fluid Dynamics simulation by passing a blood agent: cyanogen chloride gas through an SG-1 gas mask canister developed by SG Safety Corporation. The adsorption bed consisted of a Silver-Zinc-Molybdenum-Triethylenediamine activated carbon impregnated with copper, silver, zinc and molybdenum ions. The kinetic analysis of the chemical adsorption was performed in accordance with the test procedure for the gas mask canister and was validated by the kinetic data obtained from experimental results. We predicted the dynamic behaviors of the main variables such as the pressure drop inside the canister and the amount of gas adsorbed by chemisorption. By using a granular packed bed instead of the Ergun equation that is used to model porous materials in Computational Fluid Dynamics, applicable results of the activated carbon were obtained. Dynamic simulations and flow analyses of the chemical adsorption with varying gas flow rates were also executed.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.3
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• pp.137-145
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• 2014

We present a design sensitivity analysis(DSA) method for multiscale problems based on bridging scale decomposition. In this paper, we utilize a bridging scale method for the coupled system analysis. Since the analysis of full MD systems requires huge amount of computational costs, a coupled system of MD-level and continuum-level simulation is usually preferred. The information exchange between the MD and continuum levels is taken place at the MD-continuum boundary. In the bridging scale method, a generalized Langevin equation(GLE) is introduced for the reduced MD system and the GLE force using a time history kernel is applied at the boundary atoms in the MD system. Therefore, we can separately analyze the MD and continuum level simulations, which can accelerate the computing process. Once the simulation of coupled problems is successful, the need for the DSA is naturally arising for the optimization of macro-scale design, where the macro scale performance of the system is maximized considering the micro scale effects. The finite difference sensitivity is impractical for the gradient based optimization of large scale problems due to the restriction of computing costs but the analytical sensitivity for the coupled system is always accurate. In this study, we derive the analytical design sensitivity to verify the accuracy and applicability to the design optimization of the coupled system.