• 한국전산유체공학회지
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• 제19권3호
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• pp.52-61
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• 2014

The present paper deals with the efficient computation of higher-order CFD methods for compressible flow using graphics processing units (GPU). The higher-order CFD methods, such as discontinuous Galerkin (DG) methods and correction procedure via reconstruction (CPR) methods, can realize arbitrary higher-order accuracy with compact stencil on unstructured mesh. However, they require much more computational costs compared to the widely used finite volume methods (FVM). Graphics processing unit, consisting of hundreds or thousands small cores, is apt to massive parallel computations of compressible flow based on the higher-order CFD methods and can reduce computational time greatly. Higher-order multi-dimensional limiting process (MLP) is applied for the robust control of numerical oscillations around shock discontinuity and implemented efficiently on GPU. The program is written and optimized in CUDA library offered from NVIDIA. The whole algorithms are implemented to guarantee accurate and efficient computations for parallel programming on shared-memory model of GPU. The extensive numerical experiments validates that the GPU successfully accelerates computing compressible flow using higher-order method.

• 항공우주시스템공학회지
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• 제12권2호
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• pp.15-22
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• 2018

7개의 방정식으로 구성된 DIM을 사용하여 압축성 다상 유동에 대해 연구하였다. 액체와 기체의 상세한 경계면 유동 구조를 얻기 위해 5 차의 MLP와 변형된 HLLC 근사 리만 해법을 포함하는 고차 수치기법이 구현되었다. 수치 방법의 유효성 검증을 위해 물과 공기로 구성된 다양한 1차원 충격관 문제를 해석하였고, 불연속면에 대해 뛰어난 해상도를 얻을 수 있었다. 마하수 1.22의 충격파 조건에서의 2차원 공기-헬륨 기포에 대한 충격파 상호 작용을 수치 해석하였고, 충격파 현상들을 잘 모사하였으며 실험결과와 비교 검증하였다.

• 한국소음진동공학회논문집
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• 제18권12호
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• pp.1262-1269
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• 2008

Though multilayered foam-panel structures have been widely used to reduce sound transmission in various fields, most of the previous works to design them were conducted by repeated analyses or experiments based on initially given configurations or sequences. Therefore, it was difficult to obtain an optimal sequence of multilayered foam-panel structure yielding superior sound isolation capability. In this work, we propose a new design method to sequence a multi-panel structure lined with a poroelastic material having maximized sound transmission loss. Being formulated as a one-dimensional topology optimization problem fur a given target frequency, the optimal sequencing of panel-poroelastic layers is systematically carried out in an iterative manner. In this method, a panel layer is expressed as a limiting case of a poroelastic layer to facilitate the optimization process. This means that main material properties of a poroelastic material are treated as interpolated functions of design variable. The designed sequences of panel-poroelastic multilayer were shown to be significantly affected by the target frequencies; more panels were obtained at higher target frequency. The sound transmission loss of the system was calculated by the transfer matrix derived from Biot's theory.

• Nuclear Engineering and Technology
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• 제55권12호
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• pp.4607-4616
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• 2023

We implement machine learning regression models to predict peak pressures of primary and secondary systems, a major safety concern in Loss Of Condenser Vacuum (LOCV) accident. We selected the Multi-dimensional Analysis of Reactor Safety-KINS standard (MARS-KS) code to analyze the LOCV accident, and the reference plant is the Korean Optimized Power Reactor 1000MWe (OPR1000). eXtreme Gradient Boosting (XGBoost) is selected as a machine learning tool. The MARS-KS code is used to generate LOCV accident data and the data is applied to train the machine learning model. Hyperparameter optimization is performed using a simulated annealing. The randomly generated combination of initial conditions within the operating range is put into the input of the XGBoost model to predict the peak pressure. These initial conditions that cause peak pressure with MARS-KS generate the results. After such a process, the error between the predicted value and the code output is calculated. Uncertainty about the machine learning model is also calculated to verify the model accuracy. The machine learning model presented in this paper successfully identifies a combination of initial conditions that produce a more conservative peak pressure than the values calculated with existing methodologies.