This study presents a stereophotogrammetry approach to achieve full-field displacement measurements of helicopter rotor blades. The method is demonstrated in the wind tunnel test of a 2 m diameter rotor, conducted at the Aeroacoustic Wind Tunnel of China Aerodynamics Research and Development Center (CARDC). By arranging the retroreflective targets on the special hat installed directly above the rotor hub, the dynamic motion of the rotor shaft was tracked accurately, and a unified coordinate system was established on the rotor. Therefore, three-dimensional coordinates of instantaneously measured targets attached on the blade could be transformed to the unified rotor coordinate system, thereby providing a basis for consistently calculating the blade displacements at different test conditions. Moreover, location deviations of the blade caused by the vibration of the measuring system or the rotor due to freestream and rotor rotation were also effectively corrected through coordinate transformation. Comparisons of experimental and simulation results for a range of hover and forward flight conditions show good magnitude and trend agreements.

Small object detection presents various challenges across different domains, with UAV aerial image detection being particularly significant and complex. The detection accuracy is primarily influenced by the high density of small objects, substantial object scale variations and background complexity. Nevertheless, existing object detection algorithms exhibit deficiencies in feature retention and multi-scale feature fusion, thereby limiting detection performance in intricate scenes. To address these challenges, this paper proposes an innovative multi-dimensional feature enhancement and multi-scale feature adaptive aggregation and diffusion small object detection network (MFEAD-SODNet) for UAV aerial images. First, a backbone network integrating edge and spatial feature enhancement is developed to enhance feature representation from multiple perspectives, which improves small object recognition accuracy and detection performance. Second, the multi-scale feature adaptive aggregation and diffusion feature pyramid network (MFAD-FPN) is innovatively introduced. This network effectively preserves multi-scale information through adaptive feature fusion driven by channel selection. Additionally, it employs a cross-layer feature aggregation and adjacent layer feature diffusion mechanism to shorten feature transfer paths and minimize information propagation loss. Finally, a Lightweight shared detail-enhanced detection head is proposed to balance computational complexity while enhancing detailed feature representation. To evaluate the effectiveness of the proposed algorithm, experiments were conducted using VisDrone2019 as the baseline dataset. Results indicate that, compared to the baseline model, MFEAD-SODNet improves Mean Average Precision (mAP)@0.5 and mAP@0.5:0.95 by 7.6% and 5.1 %, respectively, while reducing the number of parameters by 23.3 %. Furthermore, the effectiveness and generalization of the MFEAD-SODNet model for small object detection were further validated using additional public and self-built datasets.

The landing gear is widely concerned as the main noise source of airframe noise. The flow characteristics and aerodynamic noise characteristics of the landing gear were numerically simulated based on Large Eddy Simulation and Linearized Euler Equation, and the feasibility of the simulation model was verified by experiments. Then the wind speed effect on the flow and acoustic characteristics of the minor cavity structures in a two-wheel landing gear were analyzed. The results show that the interaction of vortices increases with the increase of velocity at the brake disc, resulting in a slight increase in the amplitude of pressure fluctuation at 55 m·s⁻¹~75 m·s⁻¹. With the increase of speed, the obstruction at the lower hole of torque link decreases, and many vortical structures flow out of the lower hole and are dissipated, so that the pressure fluctuation amplitude of 75 m·s⁻¹ almost does not increase relative to 55 m·s⁻¹. The contribution of each part in the landing gear to the overall noise is as follows: shock strut > tire > torque link > brake disc. At the speed of 34 m·s⁻¹~55 m·s⁻¹, the contribution of each component to the total noise increases with the increase of speed, and the small components such as torque link and brake disc contribute more to the total noise. At the speed of 55 m·s⁻¹~75 m·s⁻¹, the increase of overall noise mainly comes from the main components such as shock strut and tire, and the brake disc and torque link contribute very little to the overall noise. It provides a reference for the further noise reduction optimization design of the landing gear.

This study experimentally investigated the unsteady pressure fluctuations caused by high-speed dual-jet impingement and the resulting far-field acoustic pressure, focusing on the correlation patterns between near-field and far-field pressure fluctuations and the mechanisms behind the formation of impingement tones on the obstacle. Fast-responding pressure-sensitive paint (fast PSP) measurements were performed on dual-impinging jets with varying impinging distances (h/D = 2–10, where D is the nozzle exit diameter), Mach numbers (M = 0.8–1.2), and a fixed nozzle separation distance (S/D = 3.5). Far-field acoustic signals were synchronously measured with the PSP using an array of eleven acoustic sensors. Three distinct impingement types were identified: stable impingement pattern (which can be further divided into single-frequency tone-dominated and multi-frequency tone-dominated), underdeveloped impingement pattern, and unstable impingement pattern. It was revealed that the primary distinction between these impingement patterns lies in the relative strength of pressure fluctuations in the jet core region and the dual-jet interference region. Coherent output power analysis was employed to link the near-field impinging pressure fluctuations to the far-field acoustic pressure fluctuations and to identify the sources of impingement tones on the obstacle. The direct impact of the jet core on the obstacle, the interaction between the wall-jet and the obstacle, and the interference between wall jets were found to be the three main mechanisms responsible for the formation of impingement tone sources.