인삼은 반음지성 식물로 해가림을 위해 지붕 및 벽에 해가림막 시설이 필요하다. 하지만 해마다 강해지는 강풍이나 태풍으로 인해 많은 농가시설물이 피해를 입고 있으며, 특히 인삼재배시설의 경우 시설물이 길게 하나로 연결되어 있어 피해가 크다. 이러한 피해를 방지하기 위해서는 인삼재배시설에 가해지는 풍하중을 평가하여 그것에 견딜 수 있도록 내풍설계를 해야 한다. 이 연구에서는 관행식과 후주연결식 인삼재배시설의 구조골조에 대한 풍하중을 산정하기 위해 필요한 지붕 및 벽 해가림막의 순압력계수를 풍동실험을 통해 정량적으로 평가하였다. 이 연구결과는 인삼재배시설에 대한 내풍설계의 기초자료가 될 것이다.

인삼은 반음지성 식물로 해가림을 위한 해가림막 시설이 필요하다. 하지만 해마다 강해지는 강풍이나 태풍으로 인해 많은 시설물이 피해를 입고 있으며, 특히 인삼재배시설의 경우 시설물이 연결되어 하나의 단지를 이루고 있어 피해가 크다. 해가림막은 차광을 위한 것이지만 바람이 투과하는 재질로 이루어진 것도 있어 강풍에 의해 바람이 투과 하는지 아닌지를 판단할 필요가 있다. 따라서 본 연구에서는 인삼재배시설의 대표적인 두 가지의 설치유형(관행식, 후 주연결식)을 고려하여 모형을 재현하였다. 그리고 먼저 열선풍속계를 이용하여 투과실험을 선행한 후 다점풍압계를 이용하여 본 실험을 수행하여 인삼재배시설의 골조용 풍압분포 특성을 규명하였다. 실험 결과는 인삼재배시설의 설치유형에 따라 하방향 순압력계수와 상방향 순압력계수로 나누어 그래프로 정리하였다.

Dispersion conditions of exhaust gas emitted from the ship stack are varied due to the state of sea surface, ship velocity and external environment condition etc. In the certain circumstances, the exhaust gas flowing backward phenomena is appeared due to the shape of vessel structure. The negative pressure layers can appear at the rear part of deck house and cause the exhaust gas flowing backward phenomena. In this study, 1:100 scale model ship was manufactured and tested in the large wind tunnel test center to ensure the exhaust gas flow patterns. Measurement results showed that the greatest pressure point was x/H=1.55 at zone 6 and Case 2 was a relatively high wind pressure distribution.

This paper attempted to bridge this gap by identifying the number of flat-plate solar collectors. The characteristics of wind pressure coefficients acting on flat-plate solar collectors which are most widely used were investigated for various wind direction. Findings from this study found that the location where the maximum wind pressure coefficient occurred in the solar collector was the edge of the collector. Regarding the characteristics according to the number of collectors, the paper found that downward wind pressure coefficient of the lower edge of the collector was higher than the upward wind pressure coefficient of the upper edge of the collector in the basic module (1 piece). However, as the number of collectors increases, the upward wind pressure coefficient of the upper edge become higher than the downward wind pressure coefficient of the lower edge. Finally yet important, it was found that the location of the maximum wind pressure coefficient was changed according to the number of solar collectors.

One of the most destructive forces around greenhouses is wind. Wind loads can be obtained by multiplying velocity pressure by dimensionless wind force coefficient. Generally, wind force coefficients can be determined by wind tunnel experiments. The wind force coefficient distribution on a single - span arched greenhouse was estimated using experimental data and compared with reported values from various countries. The results obtained are as follows : 1. The coefficients obtained from this study agree with the values proposed by G. L. Nelson except about 0.5 of difference in the middle region of roof section. This discrepancy is mainly attributed to the dissimilarity of experimental conditions (or wind tunnel test such as Reynolds number, type of terrain, surface roughness of model, location of the lapping and measuring methods. 2. Considering that the wind force coefficients are varied along the height of a wall at wind direction perpendicular to wall, structural analysis using subdivided wind force coefficient distribution is more resonable for wall. 3. It is recommendable that wind force coefficient distribution on a roof should take more subdivision than the existing four equal divisions for more accurate structural design. 4. Structural design using wind forces close to real values is more advantageous in safety and expense.

The effects of high-resolution wind profiler (HWP) data on the wind distributions were evaluated in two different coastal areas during the study period (23-26 August, 2007), indicating weak-gradient flows. The analysis was performed using the Weather Research and Forecasting (WRF) model coupled with a three-dimensional variational (3DVAR) data assimilation system. For the comparison purpose, two coastal regions were selected as: a southwestern coastal (SWC) region characterized by a complex shoreline and a eastern coastal (EC) region surrounding a simple coastline and high mountains. The influence of data assimilation using the HWP data on the wind distributions in the SWC region was moderately higher than that of the EC region. In comparison between the wind speed and direction in the two coastal areas, the application of the HWP data contributed to improvement of the wind direction distribution in the SWC region and the wind strength in the EC region, respectively. This study suggests that the application of the HWP data exerts a large impact on the change in wind distributions over the sea and thus can contribute to the solution to lack of satellite and buoy data with their observational uncertainty.