• Nuclear Engineering and Technology
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• v.34 no.3
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• pp.242-249
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• 2002

The relationship between the tritium release rate from the nuclear power plant and tritium concentration in the environment around the Kori site was modeled. The tritium concentration in the atmosphere was calculated by multiplying the release rates and $\chi$/Q values, and the d3V deposition rate at each sector according to the direction and the distance was obtained using a dry deposition velocity. The area around Kori site was divided into 6 zones according to the deposition rate. The six zones were divided into 14 compartments for the numerical simulation. Transfer coefficients between the compartments were derived using site characterization data. Source terms were calculated from the dry deposition rates. Tritium concentration in surface soil water and groundwater was calculated based upon a compartment model. The semi-analytical solution of the compartment model was obtained with a computer program, AMBER. The results showed that most of tritium deposited onto the land released into the atmosphere and the sea. Also, the estimated concentration in the top soil agreed well to that measured. Using the model, tritium concentration was predicted in the case that the tritium release rates were doubled.

• Fire Science and Engineering
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• v.12 no.2
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• pp.29-42
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• 1998

The use of computer supported fire safety engineering calculations has grown significantly in recent years and will be increased rapidly. In this study, in order to examine for fire dynamics of the enclosed compartment with window glass(3mm, 4mm thickness) when the window glass breaks, we conducted numerical computer simulations about foam sofa fire with the zone type computer mode, FASTLite package(version 1.1.2) and the Berkeley algorithm for breaking window glass in a compartment fire, BREAK1 program (version 1.0). The analysis of the results in this paper shows that there are differences of fire dynamics between open-or enclosed-state compartment fire and the enclosed compartment fire with window glass breaking. It is also shown in this study that backdraft phenomenum occurs due to accumulated unburned combustible fuel when the glass of 4mm thickness breaks, and that temperature differences between the inner-and outer-surfaces of 3mm and 4mm thick glasses are appreciable. This study will help fire fighter to establish fire suppression or occupant's refuge strategies and fire safety engineer to enhance simulation techniques about the five dynamics of compartment fire.

• Fire Science and Engineering
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• v.15 no.2
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• pp.46-52
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• 2001

To evaluate the usability of compartment fire models for predicting sprinkler response time, fire experiment was conducted and measured sprinkler response time. The experimental data was compared with zone model "FASTLite"and field model "FDS"and field Model "SMARTFIRE" A Compartment fire conducted in a 2.4 m by 3.6 m by 2.4 m ISO 9705 room and measured H.R.R was approximately 100.3 kW. In test, Sprinkler activation temperature used is $72^{\circ}c$ and responded at 198s. The output of FASTLite, SMARTFIRE and, FDS for this fire scenario were 209s, 183s, and 192s, respectively. As a results, prediction using FDS model approached to that of test very closely and other models showed good approximated results also.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.3
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• pp.152-160
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• 2024

In the event of a maritime accident, search plans have traditionally been planned using experiential methods. However, these approaches cannot guarantee safety when the scale of a maritime accident increases. Therefore, this study proposes a model utilizing discrete event simulation (DES) to predict the diving time for compartment searches of a ship located on the seabed. The discrete event simulation model was created by applying the DEVS formalism. The M/V Sewol sinking was used as an example to simulate how to effectively navigate compartments of different sizes. The simulation results showed the optimal dive time with the number of decompression chambers needed to navigate the compartment as a variable. Based on this, we propose a methodology for efficient navigation planning while ensuring diver safety.

• Korean Journal of Veterinary Research
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• v.37 no.2
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• pp.291-299
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• 1997

In order to establish optimal dosage schedules and withdrawal times for sulfamethazine(SMZ) in pigs, pharmacokinetic and tissue distribution experiments were conducted in pigs. For comparative purposes, tissue depletion kinetics are also studied in rats. From three pigs administered with SMZ i.v., the pharmacokinetic profile of SMZ in two pigs was adequately described by a one-compartment open model whereas that in one pig was patterned after a two-compartment open model. Volume of distribution(Vd) was 0.48~0.57 L/kg and biological half-life($t_{1/2}$) was 11.8-16.8 h. From three pigs dosed with SMZ p.o., pharmacokinetic profile was explainable with a one-compartment open model. Time to reach maximum SMZ concentration in serum (Tmax) was 2.8 h, 3.2 h and 7.5 h. Elimination half-life was 2.8-7.5 h. The descending order in concentration of SMZ was plsama > kidney > liver > lung > heart > pancreas > spleen > duodenum > ileum > brain > adipsoe tissue from three pigs sacrificed at 5h, 29h and 54h after the administration of SMZ, p.o.. The protein binding of SMZ in pigs was 55.2%($2.5{\mu}g/ml$), 71.5% ($5{\mu}g/kg$) and 71.5%($10{\mu}g/ml$). The mean systemic bioavailability (F) of SMZ p.o. was 49.1 %. Meanwhile the pharmacokinetic profile of SMZ in rats was adequately described by a one-compartment open model. Absorption of SMZ p.o. in the rat was very rapid. In conclusion, the oral optimal dosage regimen of SMZ for pigs was the initial dose of 45.7 mg/kg followed by the maintenance dose of 30.2 mg/kg for high specific pathogens to SMZ. The time to reach below the stipulated residual allowable concentration (0.1 ppm) was calculated 93 h after oral administration of 200 mg/kg recommended by manufactureres.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.4
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• pp.234-241
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• 2002

In this study, the car crash analysis that simulates the crushing behavior of car forestructure during a frontal impact is carried out. The analysis model for front impact of a car consists of the lumped mass and the spring model. The characteristics value of masses and springs is obtained from the static analysis of a target car. The deceleration-time curve obtained from the simulation are compared with NCAP test data from the NHTSA. They show a good agreement with frontal crash test data. The deceleration-time curve of passenger compartment is classified into 3 stages; beginning stage, middle stage, and last stage. And the behavior of masses at each stage is explained. The effect of stiffness variation on deceleration of passenger compartment is resolved. The maximum loaded peak-time of torque box and dash is the main factor to control the passenger compartment's maximum deceleration.

• Journal of Environmental Health Sciences
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• v.31 no.4 s.85
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• pp.260-265
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• 2005

Indoor air quality might be affected by source strength of indoor pollutants, ventilation rate, decay rate, outdoor level, and so on. Although technologies measuring these factors exist directly, direct measurements of all factors are not always practical in most field studies. The purpose of this study was to develop an alternative method to estimate the source strength and deposition constant by application of multiple measurements. For the total duration of 60 days, indoor and outdoor $NO_2$ concentrations every 3 days were measured in 30 houses in Seoul, Asan and Daegu. Using a compartment model by mass balance and linear regression analysis, penetration factor (ventilation divided by sum of air exchange rate and deposition constant) and source strength factor (emission rate divided by sum of air exchange rate and deposition constant) were calculated. Subsequently, the source strength and deposition constant were estimated. Natural ventilation was $1.80{\pm}0.42\;ACH,\;1.11{\pm}0.50\;ACH,\;0.92{\pm}0.26\;ACH$ in Seoul, Asan and Daegu, respectively. Calculated deposition constant(K) and source strength of $NO_2,$ in this study were $0.98{\pm}0.28\;hr^{1}\;and\;16.28{\pm}7.47\;ppb/h,$ respectively.

• Journal of the Korean Society of Visualization
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• v.7 no.1
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• pp.3-8
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• 2009

Most vehicles have a heating, ventilating and air conditioning (HVAC) device to control the thermal condition and to make comfortable environment in the passenger compartment. The improvement of ventilation flow inside the passenger compartment is crucial for providing comfortable environment. For this, better understanding on the variation of flow characteristics of ventilation air inside the passenger compartment with respect to various ventilation modes is strongly required. Most previous studies on the ventilation flow in a car cabin were carried out using computational fluid dynamics (CFD) analysis or scale-down water-model experiments. In this study, whole ventilation flow discharged from the air vent of a real passenger car was measured using a special PIV (particle image velocimetry) system for large-size FOV (field of view). Under real recirculation ventilation condition, the spatial distributions of stream-wise turbulence intensity and mean velocity were measured in the vortical panel-duct center plane under the panel ventilation mode. These experimental data would be useful for understanding the detailed flow structure of real ventilation flow and validating numerical predictions.

• Fire Science and Engineering
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• v.32 no.1
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• pp.7-15
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• 2018

The prediction performance of combustion models in the Fire Dynamics Simulator (FDS) were evaluated by comparing with experiment for compartment propane gas fires. The mixture fraction model in the FDS v5.5.3 and Eddy Dissipation Concept (EDC) model in the FDS v6.6.3 were adopted in the simulations. Four chemical reaction mechanisms, such as 1-step Mixing Controlled, 2-step Mixing Controlled, 3-step Mixing Controlled and 3-step Mixed (Mixing Controlled + finite chemical reactions) reactions, were implemented in the EDC model. The simulation results with each combustion model showed similar level for the temperature inside the compartment. The prediction performance of FDS with each combustion model showed significant differences for the CO concentration while no distinguished differences were identified for the $O_2$ and $CO_2$ concentrations. The EDC 3-step Mixing Controlled largely over-predicted the CO concentration obtained by experiment and the mixture fraction model under-predicted the experiment slightly. The EDC 3-step Mixed showed the best prediction performance for the CO concentration and the EDC 2-step Mixing Controlled also predicted the CO concentration reasonably. The EDC 1-step Mixing Controlled significantly under-predict the experimental CO concentration when the previously suggested CO yield was adopted. The FDS simulation with the EDC 1-step Mixing Controlled showed difficulties in predicting the $CO_2$ concentration when the CO yield was modified to predict the CO concentration reasonably.

• Journal of fish pathology
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• v.21 no.2
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• pp.119-127
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• 2008

Oxytetracycline (OTC) has been widely used in eel culture as a therapeutic and prophylactic agent because of its broad-spectrum activity against gram-positive and -negative bacteria. The oral treatment dosage of OTC approved for the treatment of edwardsiellosis, furunculosis and vibriosis in eel is 50 mg/kg/day for 3-7 days in Korea. To determine new optimum dose of OTC in eel, the pharmacokinetics of OTC after single oral administration (100 mg/kg B.W., 200 mg/kg B.W.) in cultured eel, Anguilla japonica was examined. In oral dosage of 100 and 200 mg/kg body weight, the highest plasma concentrations of OTC were 1.19±0.42 ㎍/㎖ and 2.69±0.57 ㎍/㎖, respectively. Plasma concentrations of OTC were not detected after 720 h post-dose in all experiments. The kinetic profile of absorption, distribution and elimination of OTC in plasma wwas calculated fitting to a 1- and 2-compartment model by WinNonlin program. The following parameters were obtained for a single dosage of 100 and 200 mg/kg respectively: 1-compartment model, AUC= 82.48 and 432.68 ㎍*h/㎖, Tmax= 3.93 and 14.24 hr, Cmax= 0.94 and 2.34 ㎕/㎖; 2-compartment model, AUC= 448.73 and 530.65 ㎍*h/㎖, Tmax= 6.37 and 8.96 hr, Cmax= 0.90 and 3.21 ㎕/㎖.