FUEL PERFORMANCE CODE COSMOS FOR ANALYSIS OF LWR UO<sub>2</sub>AND MOX FUEL

Lee, Byung-Ho; Koo, Yang-Hyun; Oh, Jae-Yong; Cheon, Jin-Sik; Tahk, Young-Wook; Sohn, Dong-Seong

doi:10.5516/net.2011.43.6.499

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Cited by 5 publications

(2 citation statements)

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“…Enhancing the thermal conductivity of UO 2 fuel pellet is greatly attractive in the aspect of fuel performance [4][5][6]. The high thermal conductive pellet lowers the fuel temperature during operation, reducing the mobility of the fission gases [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Fabrication of micro-cell UO2–Mo pellet with enhanced thermal conductivity

Kim

Rhee

Kim

et al. 2015

Journal of Nuclear Materials

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Section: Introductionmentioning

confidence: 99%

Fabrication of micro-cell UO2–Mo pellet with enhanced thermal conductivity

Kim

Rhee

Kim

et al. 2015

Journal of Nuclear Materials

Self Cite

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“…Certain crowd simulation methods exist that simulate crowd escape behaviour on the basis of mass and energy conservation. Lighthill, Witham and Richards (LWR) (Lee et al, 2011) developed the LWR model, which is based on a fluid dynamics continuity equation. Using the finite element method, some researchers have proposed several improved LWR models (Al-nasur, 2006;Jiang et al, 2010) as well; these describe crowds evolution using mass conservation and velocity-density equations.…”

Section: 12mentioning

confidence: 99%

Abnormal Prediction of Dense Crowd Videos by a Purpose–Driven Lattice Boltzmann Model

Xue

Liu

Tao

et al. 2017

International Journal of Applied Mathematics and Computer Science

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In the field of intelligent crowd video analysis, the prediction of abnormal events in dense crowds is a well-known and challenging problem. By analysing crowd particle collisions and characteristics of individuals in a crowd to follow the general trend of motion, a purpose-driven lattice Boltzmann model (LBM) is proposed. The collision effect in the proposed method is measured according to the variation in crowd particle numbers in the image nodes; characteristics of the crowd following a general trend are incorporated by adjusting the particle directions. The model predicts dense crowd abnormal events in different intervals through iterations of simultaneous streaming and collision steps. Few initial frames of a video are needed to initialize the proposed model and no training procedure is required. Experimental results show that our purpose-driven LBM performs better than most state-of-the-art methods.

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