• Journal of the korean society of oceanography
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• v.39 no.2
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• pp.119-135
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• 2004

A three-dimensional numerical model using POM (the Princeton Ocean Model) is established in order to understand the dispersion processes of the Yangtze River water in the Yellow and East China Seas. The circulation experiments for the seas are conducted first, and then on the bases of the results the dispersion experiments for the river water are executed. For the experiments, we focus on the tide effects and wind effects on the processes. Four cases of systematic experiments are conducted. They comprise the followings: a reference case with no tide and no wind, of tide only, of wind only, and of both tide and wind. Throughout this study, monthly mean values are used for the Kuroshio Current input in the southern boundary of the model domain, for the transport through the Korea Strait, for the river discharge, for the sea surface wind, and for the heat exchange rate across the air-sea interface. From the experiments, we obtained the following results. The circulation of the seas in winter is dependent on the very strong monsoon wind as several previous studies reported. The wintertime dispersion of the Yangtze River water follows the circulation pattern flowing southward along the east coast of China due to the strong monsoon wind. Some observed salinity distributions support these calculation results. In summertime, generally, low-salinity water from the river tends to spread southward and eastward as a result of energetic vertical mixing processes due to the strong tidal current, and to spread more eastward due to the southerly wind. The tide effect for the circulation and dispersion of the river water near the river mouth is a dominant factor, but the southerly wind is still also a considerable factor. Due to both effects, two major flow directions appear near the river mouth. One of them is a northern branch flow in the northeast area of the river mouth moving eastward mainly due to the weakened southerly wind. The other is a southern branch flow directed toward the southeastern area off the river mouth mostly caused by tide and wind effects. In this case, however, the tide effect is more dominant than the wind effect. The distribution of the low salinity water follows the circulation pattern fairly well.

• Clean Technology
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• v.24 no.1
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• pp.55-62
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• 2018

The adsorbent used in water-adsorption cooling system utilizing low-temperature heat of below $90^{\circ}C$ is required to exhibit high water uptake capacity at a relative humidity ($P/P_0$) between 0.1 and 0.3. Mesoporous silica (MCM-41) and MOF(MIL-101) exhibit quite large water adsorption capacity under saturated water vapor at $35^{\circ}C$. However, these adsorbents show small water adsorption capacity ($0.027{g_{water}\;g_{ads}}^{-1}$, $0.074{g_{water}\;g_{ads}}^{-1}$, respectively) in the relative humidity ($P/P_0$) range of 0.1 to 0.3. In this study, the surface properties of mesoporous silica and MOF were modified by simple methods to develop an adsorbent having a higher water uptake than the conventional water adsorbents at a relative humidity ($P/P_0$) of 0.1 ~ 0.3. In the case of mesoporous silica (MCM-41) exhibiting mainly water adsorption at $P/P_0=0.5{\sim}0.7$, aluminum species was functionalized on the mesopore walls and then cations existing near the aluminum were exchanged with various cations (e.g., $Na^+$, ${NH_4}^+$, and $(C_2H_5)_4N^+$). In addition, 20 wt% (to total weight of the composites) of hygroscopic inorganic salt ($CaCl_2$) was impregnated on the MCM-41. In the case of the MIL-101 (MOF), 20 wt% of hygroscopic inorganic salt ($CaCl_2$) was impregnated on the MIL-101. The MCM-41 which was ion-exchanged with various cations has main adsorption branch around 0.5 of $P/P_0$ which was slightly shifted with low-pressure direction in comparison with pristine MCM-41. However, tiny increases were observed on the adsorption in the range of $P/P_0$ between 0.1 and 0.3. After salt impregnation on the MCM-41, the adsorption capacity under $P/P_0=0.1{\sim}0.3$ at $35^{\circ}C$ was increased from $0.027{g_{water}\;g_{ads}}^{-1}$ to $0.152{g_{water}\;g_{ads}}^{-1}$. In the case of MIL-101, the amount of water adsorption at $35^{\circ}C$ under $P/P_0=0.1{\sim}0.3$ was increased from $0.074{g_{water}\;g_{ads}}^{-1}$ to $0.330{g_{water}\;g_{ads}}^{-1}$ after the salt impregnation.

• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1181-1191
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• 2013

This study have implemented finding the optimal water temperature parameter set for Hapcheon dam reservoir using CE-QUAL-W2 model. In particular the sensitivity analysis was carried out for four water temperature parameters of wind sheltering coefficient (WSC), radiation heat coefficient (BETA), light extinction coefficient (EXH2O), heat exchange coefficient at the channel bed (CBHE). Firstly, WSC, BETA, EXH2O shows relatively high sensitivity in common during April to September, and CBHE does during August to November. Secondly, as a result of identifying depth range of parameter influence, BETA and EXH2O show 0~9 m and 8~14 m which is thermocline layer close to water surface, CBHE is deep layer 12 m away from bottom. Finally, applying annual or monthly optimal parameter sets indicates that the bias between two sets does not show much differences for WSC and CBHE parameters, but BETA and EXH2O parameters show $0.20^{\circ}C$ and $0.51^{\circ}C$ of monthly average biases for two parameter sets. In particular the bias reveals to be $0.4^{\circ}C$ and $1.09^{\circ}C$ during May and August that confirms the necessity of use of monthly parameters during that season. It is claimed that the current operational custom use of annual parameters in calibration of reservoir water quality model requires the improvement of using monthly parameters.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.215-235
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• 1996

The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad ＆ fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH＋7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad ＆ fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).