• 한국지반환경공학회 논문집
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• 제7권6호
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• pp.133-143
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• 2006

본 논문에서는 저자가 제안한 유효응력모델을 이용하여 지진시 사면의 동적거동에 관한 수치해석을 수행하였다. 항복함수는 동일한 응력비를 가진 무한개의 방사선을 의미하며, 비관련 유동규칙을 가진 탄소성모델인 UBCSAND를 이용하였다. 이 모델은 FLAC내에 내장된 Mohr-Coulomb모델을 변형한 형태이다. UBCSAND모델을 이용하여 RPI에서 수행한 원심모형실험결과를 예측하였다. 먼저, UBCSAND모델을 Nevada모래를 사용한 반복 직접단순전단시험결과를 이용하여 검증하였으며, 액상화전후의 거동을 예측하였다. 이와 같이 검증된 모델을 원심모형실험에서 계측된 가속도, 과잉간극 수압, 변위와 서로 비교하였다. 일반적으로 계측치와 예측치가 일치하였다. 유효응력모델을 이용한 동적 수치해석기법은 서부 캐나다에서 댐, 교량, 터널, 파이프라인과 관련된 액상화 프로젝트에 실제 사용되고 있다.

• Nuclear Engineering and Technology
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• 제48권4호
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• pp.859-869
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• 2016

Component-scale modeling of boiling is predominantly based on the Eulerian-Eulerian two-fluid approach. Within this framework, wall boiling is accounted for via the Rensselaer Polytechnic Institute (RPI) model and, within this model, the bubble is characterized using three main parameters: departure diameter (D), nucleation site density (N), and departure frequency (f). Typically, the magnitudes of these three parameters are obtained from empirical correlations. However, in recent years, efforts have been directed toward mechanistic modeling of the boiling process. Of the three parameters mentioned above, the departure diameter (D) is least affected by the intrinsic uncertainties of the nucleate boiling process. This feature, along with its prominence within the RPI boiling model, has made it the primary candidate for mechanistic modeling ventures. Mechanistic modeling of D is mostly carried out through solving of force balance equations on the bubble. Forces incorporated in these equations are formulated as functions of the radius of the bubble and have been developed for, and applied to, low-pressure conditions only. Conversely, for high-pressure conditions, no mechanistic information is available regarding the growth rates of bubbles and the forces acting on them. In this study, we use direct numerical simulation coupled with an interface tracking method to simulate bubble growth under high (up to 45 bar) pressure, to obtain the kind of mechanistic information required for an RPI-type approach. In this study, we compare the resulting bubble growth rate curves with predictions made with existing experimental data.

• Bulletin of the Korean Chemical Society
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• 제32권1호
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• pp.286-290
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• 2011

Ginsenoside Rg3 was reported to have important biological activities. We demonstrate conjugation and quantification procedures of ginsenoside Rg3 to gold nanoparticles for future biological and medical applications. Ginsenoside Rg3 was conjugated to spherical gold nanoparticles using a bifunctional heptaethylene glycol linker. The sulfhydryl group of heptaethylene glycol was adsorbed onto gold nanoparticles, and carboxylic acid end of heptaethylene glycol was bonded through a hydroxyl group of Rg3 via ester bond formation. The conjugation of Rg3 was characterized with various spectroscopic techniques, high resolution-transmission electron microscopy, and using Rg3 monoclonal antibody. The Rg3- functionalized gold nanoparticles were $4.7{\pm}1.0$ nm in diameter with a surface charge of -4.12 mV. The total number of Rg3 molecules conjugated to a 3.6 mL solution of gold nanoparticle was determined to be $9.5{\times}10^{14}$ corresponding to ~6 molecules of Rg3/gold nanoparticle. These results suggest that ginsenoside Rg3 is successfully conjugated to gold nanoparticles via heptaethylene glycol linker. The quantification was performed by using Rg3 monoclonal antibody without interference of gold's intrinsic color.

• Nuclear Engineering and Technology
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• 제49권4호
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• pp.721-733
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• 2017

When temporary spent fuel storage pools at nuclear power plants reach their capacity limit, the spent fuel must be moved to an alternative storage facility. However, radioactive materials must be handled and stored carefully to avoid severe consequences to the environment. In this study, the risks of three potential accident scenarios (i.e., maritime transportation, an aircraft crashing into an interim storage facility, and on-site transportation) associated with the spent fuel transportation process were analyzed using a probabilistic approach. For each scenario, the probabilities and the consequences were calculated separately to assess the risks: the probabilities were calculated using existing data and statistical models, and the consequences were calculated using computation models. Risk assessment software was developed to conveniently integrate the three scenarios. The risks were analyzed using the developed software according to the shipment route, building characteristics, and spent fuel handling environment. As a result of the risk analysis with varying accident conditions, transportation and storage strategies with relatively low risk were developed for regulators and licensees. The focus of this study was the risk assessment methodology; however, the applied model and input data have some uncertainties. Further research to reduce these uncertainties will improve the accuracy of this model.

• Journal of Radiation Protection and Research
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• 제47권3호
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• pp.111-133
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• 2022

The exciting advancement related to the "modeling of digital human" in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.

• 한국항공우주학회지
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• 제31권6호
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• pp.23-29
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• 2003

얇은 익형 주위에 다양한 응축 과정을 수반하는 상대 습도가 100%인 포화 습공기 천음속 이상 유동에 대하여 연구하였다. 본 연구는 Rusak 과 Lee[11, 12]가 발전시킨 그리고 응축에 의한 열 증가의 효과를 포함하는 확장된 천음속 미 교란 모델을 사용하였고, 응축 과정은 서로 다른 두 가지 형태의 응축 과정을 고려한다. 먼저, 비 평형 균질 과정(nonequilibrium and homogeneous process)에서의 응축 질량비는 고전적 핵형성 이론과 작은 물방울 성장이론에 따라 계산되고, 평형과정(equilibrium process)에서의 응축 질량비는 등엔트로피 가정으로부터 계산된다. 유동 방정식과 응축 방정식들은 반복수치 계산법을 사용하여 그 해를 구하였다. 상류 유동 조건을 같게 하여 얻은 수치계산 결과들은 유동구조, 응축장, 그리고 익형 표면에서의 압력분포 등을 묘사한다. 유동특성, 즉 충격파의 위치와 강도 그리고 익형의 압력분포 등은 서로 다른 두 응축과정에서 각각 다른 유동특성을 나타냈다. 하지만, 각각의 응축과정에서 응축 결과로 생긴 열 증가는 유동거동에 상당한 변화를 야기 시키고 익형의 공력 성능에도 상당한 영향을 미친다.