The Role of Continuous Gust and Prescribed Motion on Unsteady Aerodynamic Loads

 

Many aerodynamic vehicles operate at relatively low Reynolds numbers and unsteady flow conditions, which a continuous gust or prescribed motion can describe in a controlled environment in a wind tunnel. When an airfoil passes through the unsteady flow, pressure fluctuations are generated on its surface. Integrated results of these pressures are the generation of aerodynamic loads. The current study is devoted to exploring the combined effect of continuous gust and oscillatory pitching motion in a controlled environment with NACA 0018 airfoil in the range of Reynolds numbers. Miniature pressure sensors are embedded inside the airfoil to obtain instantaneous aerodynamic loads. Even though NACA 0018 airfoil in oscillatory pitching motion was extensively studied, not many performed a comparison of sectional lift to the aerodynamic theory of Wagner.

Rotor performance and noise measurement and prediction

 

The future of urban air mobility has a well-known challenge in the form of community acceptance due to noise pollution since the annoying sound produced by a multi-rotor vehicle can bring a severe acoustic nuisance to an urban soundscape. Hence, it is essential to find a solution to attenuate the acoustic signature of a multi-rotor platform for further development and acceptance of these vehicles by the public. In this project, we applied a phase control technique to attenuate an acoustic signature. This noise reduction technique can leverage destructive interference of the coherent acoustic source field between a system of propellers. Phase control maintains the relative angular blade positions of propellers rotating at equivalent rates. In doing so, destructive interference of the coherent acoustic source field can ultimately be leveraged to modify the overall sound directivity. Without phase control, satisfying a noise constraint in sensitive areas would likely necessitate a change in the flight path or operational state – the byproduct often a detriment to performance, by steering noise (more specifically, the aerodynamic contribution generated by rotating blades) away from these sensitive areas via phase control.

Wavelet-based Beamforming

 

As the use of rotor-based vehicles and UAVs has spread, the noise generated by the rotors has become a concern. Therefore, research and development of quieter propellers have become a matter of great significance. Therefore, it is desired to reduce and alter the acoustic signature of a propeller without compromising the aerodynamic performance and efficiency. Studying propeller acoustic behavior and the influence of different variables on that behavior is a first step in developing quieter geometry. A standard tool to collect the necessary information is a phased microphone array (PMA). Such a device is commonly used to determine acoustic sources' location and magnitude. By processing PMA measurements using a beamforming algorithm suitable for acoustic signals propagating from objects under motion, and understanding of the acoustic behavior of rotating sources can be achieved.

Propeller Tip Geometry Effect on the Acoustic Signature

 

Many aerodynamic vehicles operate at relatively low Reynolds numbers and unsteady flow conditions, which a continuous gust or prescribed motion can describe in a controlled environment in a wind tunnel. When an airfoil passes through the unsteady flow, pressure fluctuations are generated on its surface. Integrated results of these pressures are the generation of aerodynamic loads. The current study is devoted to exploring the combined effect of continuous gust and oscillatory pitching motion in a controlled environment with NACA 0018 airfoil in the range of Reynolds numbers. Miniature pressure sensors are embedded inside the airfoil to obtain instantaneous aerodynamic loads. Even though NACA 0018 airfoil in oscillatory pitching motion was extensively studied, not many performed a comparison of sectional lift to the aerodynamic theory of Wagner.

Quadrotor noise

 

Autonomous multi-rotor aerial vehicles (MAVs) are an emerging technology, which has a large number of current and potential applications in a wide range of industries. These airborne instruments are becoming growingly autonomous thanks to modern artificial intelligence technologies, with their navigation and interaction capabilities based predominantly on visual sensing. While vision has attracted considerable attention, it suffers from a poor performance in low light and direct sunlight conditions and is vulnerable to occlusions. This project targets to change this situation, endowing drones with “ears”. The proposed research aims at the development of novel algorithms based on a combination of machine learning and physical modeling, and real-time systems for acoustic-based autonomous mapping, localization, and interaction of MAVs.