• 전산구조공학
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• 제37권2호
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• pp.11-15
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• 2024

• 인터넷정보학회논문지
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• 제19권1호
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• pp.123-130
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• 2018

With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

• Smart Structures and Systems
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• 제31권2호
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• pp.113-130
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• 2023

Hybrid simulation (HS) has attracted community attention in recent years as an efficient and effective experimental technique for structural performance evaluation in size-limited laboratories. Traditional hybrid simulations usually take deterministic properties for their numerical substructures therefore could not account for inherent uncertainties within the engineering structures to provide probabilistic performance assessment. Reliable structural performance evaluation, therefore, calls for stochastic hybrid simulation (SHS) to explicitly account for substructure uncertainties. The experimental design of SHS is explored in this study to account for uncertainties within analytical substructures. Both computational simulation and laboratory experiments are conducted to evaluate the pseudo-random Sobol sequence for the experimental design of SHS. Meta-modeling through polynomial chaos expansion (PCE) is established from a computational simulation of a nonlinear single-degree-of-freedom (SDOF) structure to evaluate the influence of nonlinear behavior and ground motions uncertainties. A series of hybrid simulations are further conducted in the laboratory to validate the findings from computational analysis. It is shown that the Sobol sequence provides a good starting point for the experimental design of stochastic hybrid simulation. However, nonlinear structural behavior involving stiffness and strength degradation could significantly increase the number of hybrid simulations to acquire accurate statistical estimation for the structural response of interests. Compared with the statistical moments calculated directly from hybrid simulations in the laboratory, the meta-model through PCE gives more accurate estimation, therefore, providing a more effective way for uncertainty quantification.

• 반도체디스플레이기술학회지
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• 제22권1호
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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.

• Wind and Structures
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• 제37권4호
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• pp.255-274
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• 2023

The aim of this paper is to enhance the accuracy of predicting time-averaged external surface pressures on low-rise buildings by utilizing Computational Fluid Dynamics (CFD) simulations. To achieve this, benchmark studies of the Silsoe cube and the Texas Tech University (TTU) experimental building are employed for comparison with simulation results. The paper is structured into three main sections. In the initial part, an appropriate domain size is selected based on the precision of mean pressure coefficients on the windward face of the cube, utilizing Reynolds Averaged Navier-Stokes (RANS) turbulence models. Subsequently, recommendations regarding the optimal computational domain size for an isolated building are provided based on revised findings. Moving on to the second part, the Silsoe cube model is examined within a horizontally homogeneous computational domain using more accurate turbulence models, such as Large Eddy Simulation (LES) and hybrid RANS-LES models. For computational efficiency, transient simulation settings are employed, building upon previous studies by the authors at the Windstorm Impact, Science, and Engineering (WISE) Lab, Louisiana State University (LSU). An optimal meshing strategy is determined for LES based on a grid convergence study. Three hybrid RANS-LES cases are investigated to achieve desired enhancements in the distribution of mean pressure coefficients on the Silsoe cube. In the final part, a 1:10 scale model of the TTU building is studied, incorporating the insights gained from the second part. The generated flow characteristics, including vertical profiles of mean velocity, turbulence intensity, and velocity spectra (small and large eddies), exhibit good agreement with full-scale (TTU) measurements. The results indicate promising roof pressures achieved through the careful consideration of meshing strategy, time step, domain size, inflow turbulence, near-wall treatment, and turbulence models. Moreover, this paper demonstrates an improvement in mean roof pressures compared to other state-of-the-art studies, thus highlighting the significance of CFD simulations in building aerodynamics.

• 한국CDE학회논문집
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• 제13권4호
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• pp.296-304
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• 2008

Recently, various attempts are being done to apply off-line programming system to field of paint robot. But most commercial simulation softwares have problems that are slow simulation speed and not support various painting paramenters on simulation. This paper proposes enhanced paint simulation method for off-line programming system. For these, this method used the mathematical model of flux field from a previous research. The flux field has the flux distribution function, which reflects on the feature of paint spray. A previous research derived this flux distribution function for an integral function and calculated paint thickness function for an integral function. But if flux distribution function is defined as an integral function, it is inadequate to use for real-time simulation because a number of calculation is needed for estimation of paint thickness distribution. Therefore, we defined the flux distribution function by numerical method for reducing a mount of calculation for estimation of paint thickness. We derived the equation of paint thickness function analytically for reducing a mount of calculation from the paint distribution function defined by numerical method. In order to prove proposed paint simulation method this paper compares the simulated and measured thickness. From this comparison this paper show that paint thickness distribution is predicted precisely by proposed spray paint simulation process.

• 대한조선학회논문집
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• 제59권6호
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• pp.393-399
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• 2022

During the ship hull design process, resistance performance estimation is generally calculated by simulation using computational fluid dynamics. Since such hull resistance performance simulation requires a lot of time and computation resources, the time taken for simulation is reduced by CPU clusters having more than tens of cores in order to complete the hull design within the required deadline of the ship owner. In this paper, we propose a method for estimating resistance performance of ship hull by simulation using a graph neural network. This method converts the 3D geometric information of the hull mesh and the physical quantity of the surface into a mathematical graph, and is implemented as a deep learning model that predicts the future simulation state from the input state. The method proposed in the resistance performance experiment of simple hull showed an average error of about 3.5 % throughout the simulation.

• 한국전산유체공학회지
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• 제15권1호
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• pp.1-8
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• 2010

RCCS is a passive safety-related system that removes the decay heat of VHTR when normal decay heat removal systems are in failure. Understanding thermo-hydraulics of RCCS is important to design a safer VHTR. RCCS consists of 292 cooling panels, which are placed in the reactor cavity. The layout of RCCS gives an idea that, for CFD simulations, cooling panels can be assumed to be one annulus tube. This assumption can reduce significantly the computational time, especially for the unsteady simulation. To simulate RCCS in an axisymmetric manner, three models were suggested and compared. Each model has (1) the same outer radius, (2) the same cross-sectional area (3) the same pressure drop, respectively, as the RCCS cooling panels. The steady-state simulation was conducted with these three models and the DO radiation model. It is found that over 90% of the heat from the outer wall of the reactor pressure vessel is transported to the RCCS by radiative heat transfer. The simulation with the third model, which has the same pressure drop as the design, estimates the closest wall temperature profiles to a thermo-hydraulic code, GAMMA+, result.

• 한국전산구조공학회논문집
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• 제21권1호
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• pp.59-71
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• 2008

본 논문에서는 지진하중하의 대형구조물의 시뮬레이션을 위해 실험과 해석을 병합한 분산공유 하이브리드 해석 및 실험소프트웨어 framework를 개발하였다. 제안된 소프트웨어 framework은 별도의 동적 그리고 정적 해석을 위한 프로그램의 개발이 필요 없기 때문에 일반 범용 유한요소해석 프로그램을 개발된 해석 및 실험 제어 프로그램과 interface API를 이용하여 사용할 수 있는 장점이 있다. 본 논문에서 개발된 소프트웨어 framework은 독자적 기능을 가진 module로 구성이 되어 있을 뿐만 아니라 개체지향형 프로그램 개념을 바탕으로 개발되었다. 예제를 통하여 개발된 시스템의 기능과 분산공유하이브리드 해석 및 실험에서의 유용성을 증명하였다.

• 한국시뮬레이션학회논문지
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• 제27권3호
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• pp.109-116
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• 2018

견고표적을 무력화하기 위해서는 높은 침투/관통성능을 가진 침투탄의 개발이 필수적이다. 침투탄의 설계를 위해, 본 논문에서는 노즈 형상 인자들이 침투자의 콘크리트 침투/관통성능에 미치는 영향을 분석하였다. 전산해석은 상용 전산해석 프로그램인 AUTODYN-2D를 사용하여 수행하였다. Forrestal의 시험결과를 사용하여 전산해석의 신뢰성을 검증하였으며, 침투자의 노즈 형상보다는 노즈 길이가 침투/관통성능에 더 큰 영향을 미치는 것을 확인하였다. 또한, 침투자의 노즈 길이가 일정할 경우, 노즈 팁 직경을 특정값까지 증가시켜 침투/관통성능을 향상시킬 수 있음을 확인하였다.