Yeong-Ju Go

The cavitation phenomenon not only reduces hydrodynamic performance but also generates vibrations and noise, significantly compromising the operational stability of the system. In this study, we investigate the efficiency of air injection in controlling cavitation patterns and reducing noise on hydrofoil, both experimentally and numerically. The focus is to assess how the location of air injection on the suction side of the hydrofoil, the rate of air injection, and the cavitation number affect the cavitating flow. The hydrofoil has a span and chord length (C) of 100 mm. The air is injected from a column of multi-holes positioned at x/C = 0.05, 0.10, 0.30, and 0.40 separately and controlled through a flow meter. The cavitation number ranges from 3.65 to 1.62, while the air injection rates are set at 1, 3, and 5 standard liters per minute. The experiments are conducted at Chungnam National University's high-speed cavitating tunnel. Simultaneously, a high-speed camera is used to observe cavitating flow, and a pressure transducer is employed to measure noise levels. The results indicate that injecting air closer to the leading edge has the most significant impact on reducing vapor cavitation and noise. Injecting air at x/C = 0.05 reduces the length of the vapor sheet cavity by 27% compared to cases without air injection. Increasing the air injection rate increased the volume of ventilated cavitation. Noise reduction is primarily noticeable in the high-frequency region (>2 kHz) at a high cavitation number of 2.22. As the cavitation number decreases to 1.62, the noise reduction shifts mainly to the low-frequency region, and the effectiveness of air injection in suppressing noise is reduced.

The demand for new propeller designs has increased alongside the development of new technology, such as urban aircraft and large unmanned aerial vehicles. In order to experimentally identify the performance of a propeller, a wind tunnel that provides the operating flow is essential. However, in the case of a meter class or larger propeller, a large wind tunnel is required and the related equipment becomes heavy; therefore, it is difficult to implement in reality. For this reason, propeller studies have been conducted via reduced models. In this case, it is necessary to investigate the different performance outputs between the full- and model-scale propellers due to the size difference. In the current study, a method is proposed to investigate the difference in the aerodynamic performance caused by the difference in propeller scale using VLM and RANS calculations, and the differences are analyzed. The wind tunnel test also verified the propeller performance prediction method. The boundary of aerodynamic performance independent of the Reynolds number could be predicted through the VLM based on the ideal fluid assumption. From the RANS calculations, it was possible to present the difference in the aerodynamic performance when propellers of the same geometry with different ratios were operated using different Reynolds numbers. It was confirmed that each numerical method matched well with the wind tunnel test results in the range of the advance ratio that produced the maximum efficiency, and from the results, it was possible to observe the change in aerodynamic performance that differed according to the scale change.

Currently, the detection of targets using drone-mounted imaging equipment is a very useful technique and is being utilized in many areas. In this study, we focus on acoustic signal detection with a drone detecting targets where sounds occur, unlike image-based detection. We implement a system in which a drone detects acoustic sources above the ground by applying a phase difference microphone array technique. Localization methods of acoustic sources are based on beamforming methods. The background and self-induced noise that is generated when a drone flies reduces the signal-to-noise ratio for detecting acoustic signals of interest, making it difficult to analyze signal characteristics. Furthermore, the strongly correlated noise, generated when a propeller rotates, acts as a factor that degrades the noise source direction of arrival estimation performance of the beamforming method. Spectral reduction methods have been effective in reducing noise by adjusting to specific frequencies in acoustically very harsh situations where drones are always exposed to their own noise. Since the direction of arrival of acoustic sources estimated from the beamforming method is based on the drone’s body frame coordinate system, we implement a method to estimate acoustic sources above the ground by fusing flight information output from the drone’s flight navigation system. The proposed method for estimating acoustic sources above the ground is experimentally validated by a drone equipped with a 32-channel time-synchronized MEMS microphone array. Additionally, the verification of the sound source location detection method was limited to the explosion sound generated from the fireworks. We confirm that the acoustic source location can be detected with an error performance of approximately 10 degrees of azimuth and elevation at the ground distance of about 150 m between the drone and the explosion location.

Position estimation of sound source using ray tracing method is introduced based on wind field measurement. TDOA (time difference of arrival) estimate error occurs during outdoor sound measurement due to the atmospheric condition such as wind, temperature, humidity and so on. Such condition can possibly change the trajectories of rays. It produces erroneous propagation time information accordingly. In this study, simplex method is used to estimate the source position using ray tracing. The basic concept is to find the estimation point which generates smallest mean square error of propagation distances or times from the candidate points. At first the estimation area is set around the first estimate which is calculated from the TDOA estimation. The simplex method is then applied to each point in the estimation area. Nelder-Mead simplex method is used to find the best position that produces the minimum error in travelling distances. In order to apply the ray tracing, the wind distribution is interpolated based on the wind measurements around the boundary. Simulation and experiments are conducted to verify the performance of the algorithm.

Localization of Underwater Noise Sources Using TDOA (Time Difference of Arrival) Method Marine propeller;Cavitation;Singing;Underwater noise;Time Difference Of Arrival(TDOA);Localization; As considerable interests in noise emission from a ship have been increased, the need for localization of noise sources of the marine propeller generating cavitation and singing noise is looming large. In many practical cases, cavitation and singing noise occur on a particular position of the certain blade of the propeller. It is so important to know the position of noise source correctly in order to eliminate or suppress unwanted noise. In this study, we develop