The quality of airflow generated by fans is essential for users directly exposed to it. We developed a fan inspired by the flapping motion of bird wings to produce natural-like airflow. Our prototype has two vertically oscillating wings that create airflow. Measurements were performed to analyze wind direction, wind velocity distribution, and power spectrum, and compared with those of conventional propeller fans. The power spectrum of airflow generated by flapping wings closely approximates that of natural wind. This approach offers a promising alternative to traditional fan designs, enhancing user comfort and well-being.

It is known that when fish swim in schools, the fish behind can achieve efficient swimming due to the reverse Kármán vortices generated by the fish in front. Theoretical calculations and experimental demonstrations have been conducted on this phenomenon to date. Apart from the tendency of fish to swim in schools, fish in a water flow have a tendency to stay near objects. On the downstream side of an object in a water flow, regions of recirculation (or flow stagnation) occur, and once the water flow exceeds a certain velocity, Kármán vortices are generated downstream. If fish utilize reverse Kármán vortices for efficient swimming, it can be hypothesized that they may also take advantage of the Kármán vortices generated downstream of objects. In this study, we aim to demonstrate this by conducting CFD (Computational Fluid Dynamics) analysis and experiments using a fish robot.

The swimming of a water strider is a typical hydrodynamic phenomenon strongly governed by surface tension. In this study, we numerically studied the swimming dynamics of a water strider to elucidate the propulsion mechanism. To compute the interaction between its legs and the water surface, we performed gas-liquid two-phase flow simulation including the motion of moving objects using a weakly compressible scheme and a direct-forcing immersed boundary method. We also used a tree-based Adaptive Mesh Refinement (AMR) method for simulation to reduce computational load. Our simulation revealed that swimming propulsion arises from an imbalance of the capillary force.

Integrating olfaction into flying robots enhances their utility in tasks such as search and rescue and hazard detection. Bio-hybrid sensors based on insect antennae have a high sensitivity; however, the effects of drone-induced airflow on odor detection remain unclear. In this study, we used computational fluid dynamics and experiments with a tethered drone to examine the influence of sensor placement on detection performance. We observed that propeller-induced flow draws in air over a wide area, and the sensor position affects the detectable odor region and signal strength. These findings highlight the importance of sensor placement for accurate odor source localization in drones and possibly in animals.

This study presents a biomimetic fan design inspired by the avian flapping flight. The design can generate airflow with reduced spatial non-uniformity. The influence of the wing pitch angle on airflow distribution was numerically examined, with effective flow area (EFA) and coefficient of variation (CV) adopted as the principal performance indices. The simulation results set the basis for developing a prototype that achieved an EFA of 78% and a CV of 0.12. These findings demonstrate that flapping-based fans can generate airflow with superior velocity, directionality, and uniformity, offering a promising pathway toward enhanced thermal comfort and improved fan technologies.

Avian wingtips comprise a series of primary feathers. When birds such as raptors glide, these feathers form slotted wing tips. In this study, we modeled avian wings as a series of rectangular elements and evaluated their aerodynamic performance using both two- and three-dimensional numerical simulations. We compared slotted wings with those without slotted wing tips and found that the slotted configuration weakened the wingtip vortices, resulting in an increased lift-to-drag ratio. These findings offer valuable insights into the design of bioinspired flying robots and the evolution of avian wing morphology.

The seeds of Dipterocarpaceae trees, known as samaras, possess wing-like structures that generate autorotation during descent, reducing terminal speed and enabling wide dispersal. In this study, the optimal geometric characteristics of samara-inspired wings were investigated using aerodynamic optimization based on the Quasi-Minimum Induced Loss (QMIL) method combined with a genetic algorithm (GA). The optimization targeted maximum thrust generation with minimal energy loss while considering the specific weight of the prototype. The resulting blade geometry closely resembled the natural wing shapes of Dipterocarpaceae samaras, suggesting that their wing geometry achieves maximum thrust and minimum descent speed, thereby indicating the aerodynamic optimality for efficient passive flight and confirming its suitability as a blade design for autorotating flying vehicles.