윙세일(Wing-Sail)은 친환경 풍력 보조 추진 장치 중 하나로써 해운 분야의 온실가스 감축을 위해 실선에 적용이 증가하는 추세이다. 윙세일을 실제 선박에 적용하기 위해서는 정확한 보조 추진력의 산정과 공역학적 성능에 관한 연구가 필수적이다. 본 논문 에서는 중형 선박의 상부 구조 형상을 고려한 윙세일의 성능 평가를 수행하였다. 상부 구조 형상에는 선박의 갑판 및 거주구를 포함하 고 있으며, 윙세일의 공역학적 성능 평가를 위해 전산유체역학(Computational Fluid Dynamics, CFD)을 사용하였다. 윙세일의 공역학적 성 능은 선박의 상부 구조 형상을 고려하지 않은 단독 윙세일의 성능과 비교하였으며, 윙세일의 받음각(Angle Of Attack, AOA)과 풍향 (Apparent Wind Angle, AWA)의 변화를 고려하였다. 또한 본 논문에서는 윙세일의 항력 및 양력을 선박의 보조 추진력으로 변환하여 성 능 평가를 수행하였으며, 이때 최소 31%에서 최대 72%까지 추력이 감소함을 확인하였다. 본 연구를 통해서 윙세일의 추력 산정에 선 박의 갑판 및 거주구의 영향을 필수적으로 고려해야 할 것으로 판단되며, 윙세일의 적용을 위한 설계 및 엔지니어링에 도움을 줄 수 있을 것으로 기대한다.

Despite having enabled insects to become the most abundant and successful group on Earth, wings have been lost in numerous insect lineages, including Orthoptera. Melanoplinae, a subfamily that includes over 100 genera and more than 800 species in Acrididae, exhibits various wing-types and dispersal abilities. Some species possess extensive flight capabilities with long wings, while many groups that inhabit alpine environments tend to reduce their wings and dispersal ability. In order to infer the evolutionary history of Melanoplinae and their wings, we conducted molecular phylogenetic research. We established the phylogeny using seven mitochondrial (Cox1, Cox2, CytB, Nad2, Nad5, 12S and 16S) and two nuclear genes (H3 and Wg) for 139 taxa. By investigating the wing types in Melanoplinae, we estimated the ancestral state of the wings and traced their evolutionary history. Our results present that loss and recovery of wings occurred multiple times within Melanoplinae, showing distinct histories across inner taxa within the subfamily.

In this study, the flow rate at the drone and the pressure around the drone were investigated by carrying out the flow analysis due to the wing shape of drone. At models 1, 2 and 3, the positions of areas with the maximum flow rate around the drone according to the shape of the wing were seen to be same at the rear wing of drone. Model 2 has the fastest flow rate, followed by model 1 and model 3. At the distribution of flow pressure by model around the drone according to the wing shape of drone, models 1, 2 and 3 had the same highest pressures at the center of drone. In comparison with the maximum pressures of models near the wing shape of the drone, the flow pressure at model 2 was higher compared with models 1 and 3. At the wing shape of the drone, model 2 is considered to carry the flow performance better than models 1 and 3. So, the result of this study is thought to be useful for designing the wing shape of the drone. Without the test of flow performance due to the shape of drone wing, the flow performance can be seen as the flow rate and pressure are investigated through the flow analysis.

In this study, the flow analyses were carried out on three kinds of front wing models. The down forces of front wings which influence the stability, cornering at driving were investigated with three models. At model 1, the maximum pressure shown on the main plate of front wing is 3177.539Pa. The maximum pressures at models 2 and 3 are shown to be 3429.677Pa and 3506.494Pa, respectively. The higher the pressure, the more resistance. So, the lower the pressure, the less resistance the model gets. At model 1, the maximum velocity of stream that flows under the front wing was shown to be the smallest among three models. In case of all three models, the pressure at which the air passes through the front wing is high in the upper part of the front wing. Among three models, model 1 is thought to be the most appropriate model to give the effect of the down force while reducing the flow resistance at driving. By utilizing this study result, the flow velocity and pressure are investigated without the flow experiment at driving due to the configuration of automotive front wing, and the flow resistance can be seen.