• Journal of computational fluids engineering
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• v.17 no.4
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• pp.41-48
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• 2012

A numerical analysis of thermal stratification in the upper plenum of the MONJU fast breeder reactor was performed. Calculations were performed for a 1/6 simplified model of the MONJU reactor using the commercial code, CFX-13. To better resolve the geometrically complex upper core structure of the MONJU reactor, the porous media approach was adopted for the simulation. First, a steady state solution was obtained and the transient solutions were then obtained for the turbine trip test conducted in December 1995. The time dependent inlet conditions for the mass flow rate and temperature were provided by JAEA. Good agreement with the experimental data was observed for steady state solution. The numerical solution of the transient analysis shows the formation of thermal stratification within the upper plenum of the reactor vessel during the turbine trip test. The temporal variations of temperature were predicted accurately by the present method in the initial rapid coastdown period (~300 seconds). However, transient numerical solutions show a faster thermal mixing than that observed in the experiment after the initial coastdown period. A nearly homogenization of the temperature field in the upper plenum is predicted after about 900 seconds, which is a much shorter-term thermal stratification than the experimental data indicates. This discrepancy is due to the shortcoming of the turbulence models available in the CFX-13 code for a natural convection flow with thermal stratification.

• The Korean Journal of Physiology and Pharmacology
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• v.9 no.2
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• pp.77-86
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• 2005

In this study, we examined the morphine-induced modulation of the nociceptive spinal dorsal horn neuronal activities before and after formalin-induced inflammatory pain. Intradermal injection of formalin induced time-dependent changes in the spontaneous activity of nociceptive dorsal horn neurons. In naive cats before the injection of formalin, iontophoretically applied morphine attenuated the naturally and electrically evoked neuronal responses of dorsal horn neurons. However, neuronal responses after the formalin-induced inflammation were significantly increased by morphine. Bicuculline, $GABA_A$ antagonist, increased the naturally and electrically evoked neuronal responses of dorsal horn neurons. This increase in neuronal responses due to bicuculline after the formalin-induced inflammation was larger than that in the naive state, suggesting that basal $GABA_A$ tone increased after the formalin injection. Muscimol, $GABA_A$ agonist, reduced the neuronal responses before the treatment with formalin, but not after formalin treatment, again indicating an increase in the GABAergic basal tone after the formalin injection which saturated the neuronal responses to GABA agonist. Morphine-induced increase in the spinal nociceptive responses after formalin treatment was inhibited by co-application of muscimol. These data suggest that formalin-induced inflammation increases $GABA_A$ basal tone and the inhibition of this augmented $GABA_A$ basal tone by morphine results in a paradoxical morphineinduced increase in the spinal nociceptive neuronal responses after the formalin-induced inflammation.

• Bulletin of the Korean Chemical Society
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• v.23 no.9
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• pp.1315-1327
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• 2002

The model compounds, 8-methoxypsoralen-CH2O(CH2)n-adenine (MOPCH2OCnAd, n=2, 3, 5, 6, 8, and 10) in which 5 position of 8-methoxypsoralen (8-MOP) is linked by various lengths of polymethylene bridge to N9 of adenine. UV absorption spectra are identical with the sum of MOPCH2OC3 and adenine absorption spectra. Solvent effects on the UV absorption and fluorescence emission spectra indicate that the lowest excited singlet state is the $(\pi${\rightarrow}$\pi*)$ state. The spectral characteristics of the fluorescence of MOPCH2OCnAd are strongly dependent upon the nature of the solvents. The fluorescence emission spectra in aprotic solvents are broad and structureless due to the excimer formation through the folded conformation accelerated by hydrophobic ${\pi}-{\pi}$ stacking interaction. Increasing polarity of the protic solvents leads to higher population of unfolded conformation stabilized through favorable solvation and H-bonding, and consequently to an increase in the fluorescence intensity, fluorescence lifetime, and a shift of fluorescence maximum to longer wavelengths. The decay characteristics of the fluorescence in polar protic solvents shows two exponential decays with the lifetimes of 0.6-0.8 and 1.6-1.9 ns in 5% ethanol/water, while MOPCH2OC3 shows 0.5 and 1.7 ns fluorescence lifetimes. The long-lived component of fluorescence can be attributed to the relaxed species (i.e., the species for which the solvent reorientation (or relaxation) has occurred), while the short-lived components can be associated with the unrelaxed, or only partially relaxed, species.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.245-250
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• 2014

Addiction can be largely divided into two categories. One is called medium addiction in which medium itself causes an addiction. Another is called cause addiction that brings addiction through combination of sensitive self and latent personal action. The medium addiction involves addiction phenomena directly caused by illegal drugs, alcohol and various other chemicals. The cause addiction is dependent on personal sensitivities as a sensitive problem of personal and includes cyber addictions such as shopping, work, game, internet, TV, and gambling. In this paper we propose two-dimensional addiction model that are equivalent to using an R-L-C series circuit of Electrical circuit and a Spring-Damper-mass of mechanical system. We also organize a Duffing equation that is added a nonlinear term in the proposed two-dimensional addiction model. We represent periodic motion and chaotic motion as time series and phase portrait according to parameter's variation. We confirm that among parameters chaotic motion had addicted state and periodic motion caused by change in control coefficient had pre-addiction state.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.24 no.2
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• pp.138-148
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• 2012

A stochastic reliability analysis model has been developed for evaluating the time-dependent stability performance of armor units of rubble-mound breakwaters subjected to the multiple loads of arbitrary magnitudes which could be occurred randomly. The initial structural capacities and the damage rates of armor units of rubble-mound breakwaters could be estimated as a function of the incident wave height with a given return period by using the modified Hudson's formula and Melby's formula. The structural stability performances of armor units of rubble-mound breakwaters could be analyzed in detail through the lifetime reliability investigations according to the limit states such as the serviceability or ultimate limit state and the conditions of multiple loads. Finally, repair intervals for the structural management of armor units of rubble-mound breakwaters could quantitatively be evaluated by a new approach suggested in this paper that has been based on the target probability for repair and the accumulated probabilities of failure obtained from the present stochastic reliability analysis model.

• Journal of the Korean Institute of Gas
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• v.14 no.4
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• pp.31-36
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• 2010

An alternative method is presented for predicting failure probability of pipelines with corrosion defects in this paper. The failure of corroded pipeline occurs when the operating pressure is grater than the remaining strength of the pipeline, and a limit state function can be defined as the differences between the remaining strength and the operating pressure. Then, based on structural reliability theory, we can estimate the failure probability of corroded pipeline, which is dependent on elapsed time of the pipeline with active corrosion defects. In this study, a root finding (RF) method has been adopted to solve the limit state function instead of Monte-Carlo simulation (MCS) method which traditionally has been employed to solve those kinds of problems. The calculation results shows that there are only small differences between the RF and the MCS method but the RF has higher efficiency in calculation than the MCS.

• Korean Journal of Materials Research
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• v.24 no.5
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• pp.236-242
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• 2014

Fabrication of reaction-bonded $Al_2O_3$ (RBAO) ceramics using Al-Zn-Mg alloy powder was studied in order to improve traditional RBAO ceramic processing using Al powder. The influence on reaction-bonding and microstructure, as well as on physical and mechanical properties, of the particulate characteristics of the $Al_2O_3$-Al alloy powder mixtures after milling, was revealed. Variation of the particulate characteristics of this $Al_2O_3$-Al alloy powder mixture with milling time was reported previously. To start, the $Al_2O_3$-Al alloy powder mixture was milled, reaction-bonded, post-sintered, and characterized. During reaction-bonding of the $Al_2O_3$-Al alloy powder mixture compacts, oxidation of the Al alloy took place in two stages, that is, there was solid- and liquid-state oxidation of the Al alloy. The solid-state oxidation exhibited strong dependence on the density of surface defects on the Al-alloy particles formed during milling. Higher milling efficiency resulted in less participation of the Al alloy in reaction-bonding. This was because of its consumption by chemical reactions during milling, and subsequent powder handling, and could be rather harmful in the case of over-milling. In contrast to very little dependence of oxidation of the Al alloy on its particle size after milling, the relative density, microstructure, and flexural strength were strongly dependent on particle size after milling (i.e., on milling efficiency). The relative density and 4-point flexural strength of the RBAO ceramics in this study were ~98% and ~365 MPa, respectively, after post-sintering at $1,600^{\circ}C$.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2230-2245
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• 2023

KANT (KAIST Advanced Nuclear Tachygraphy) is a PWR core simulator recently developed at Korea Advance Institute of Science and Technology, which solves three-dimensional steady-state and transient multigroup neutron diffusion equations under Cartesian geometries alongside the incorporation of thermal-hydraulics feedback effect for multi-physics calculation. It utilizes the standard Nodal Expansion Method (NEM) accelerated with various Coarse Mesh Finite Difference (CMFD) methods for neutronics calculation. For thermal-hydraulics (TH) calculation, a single-phase flow model and a one-dimensional cylindrical fuel rod heat conduction model are employed. The time-dependent neutronics and TH calculations are numerically solved through an implicit Euler scheme, where a detailed coupling strategy is presented in this paper alongside a description of nodal equivalence, macroscopic depletion, and pin power reconstruction. For validation of the steady, transient, and depletion calculation with pin power reconstruction capacity of KANT, solutions for various benchmark problems are presented. The IAEA 3-D PWR and 4-group KOEBERG problems were considered for the steady-state reactor benchmark problem. For transient calculations, LMW (Lagenbuch, Maurer and Werner) LWR and NEACRP 3-D PWR benchmarks were solved, where the latter problem includes thermal-hydraulics feedback. For macroscopic depletion with pin power reconstruction, a small PWR problem modified with KAIST benchmark model was solved. For validation of the multi-physics analysis capability of KANT concerning large-sized PWRs, the BEAVRS Cycle1 benchmark has been considered. It was found that KANT solutions are accurate and consistent compared to other published works.

• Wind and Structures
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• v.18 no.2
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• pp.195-213
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• 2014

Recently, the horizontal axis rotor performance optimizer (HARP_Opt) tool was developed in the National Renewable Energy Laboratory, USA. This innovative tool is becoming more popular in the wind turbine industry and in the field of academic research. HARP_Optwas developed on the basis of two fundamental modules, namely, WT_Perf, a performance evaluator computer code using the blade element momentum theory; and a genetic algorithm module, which is used as an optimizer. A pattern search algorithm was more recently incorporated to enhance the optimization capability, especially the calculation time and consistency of the solutions. The blade optimization is an aspect that is highly dependent on experience and requires significant consideration on rotor control strategies, wind data, and generator type. In this study, the effects of rotor control strategies including fixed speed and fixed pitch, variable speed and fixed pitch, fixed speed and variable pitch, and variable speed and variable pitch algorithms on optimal blade shapes and rotor performance are investigated using optimized blade designs. The effects of environmental wind data and the objective functions used for optimization are also quantitatively evaluated using the HARP_Opt tool. Performance indices such as annual energy production, thrust, torque, and roof-flap moment forces are compared.

• ETRI Journal
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• v.42 no.1
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• pp.90-100
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• 2020

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.