In order to enhance the take-off lift of a butterfly-like flapping wing vehicle (FWV), we implemented an integrated experimental platform and applied a reinforcement learning algorithm. The vehicle, which has a wingspan of 81 cm and is mounted on a stand with a force sensor, is driven by two servos that are powered and controlled wirelessly. To achieve the goal of enhancing take-off lift, we used a model-free, on-policy actor-critic proximal policy optimization algorithm. After 300 learning steps, the average aerodynamic lift force increased significantly from 0.044 N to 0.861 N. This enhanced lift force was sufficient to meet the take-off requirements of the vehicle without the need for any additional aids or airflow. Additionally, we observed a strong lift peak in the upstroke after analyzing the learning results. Further experiments showed that this lift peak is directly related to the elastic release of the wing twist and the opening and closing of the gap between the forewing and hindwing in the early stage of the upstroke. These findings were not easily predicted or discovered using traditional aerodynamic methods. This work provides valuable reinforcement learning experience for the future development of FWVs.

To measure the shape and deformation of an insect wing, a scanning setup adopting laser triangulation and image matching was developed. Only one industry camera with two light sources was employed to scan the transparent insect wings. 3D shape and point to point full field deformation of the wings could be obtained even when the wingspan is less than 3 mm. The venation and corrugation could be significantly identified from the results. The deformation of the wing under pin loading could be seen clearly from the results as well. Calibration shows that the shape and deformation measurement accuracies are no lower than 0.01 mm. Laser triangulation and image matching were combined dexterously to adapt wings’ complex shape, size, and transparency. It is suitable for insect flight research or flapping wing micro-air vehicle development.

The thermo–chemo–mechanical coupling on the thermal shock resistance of 20 vol%-ZrB₂–15 vol%-SiC–graphite composite is investigated with the use of a self-developed material testing system. In each test, a specimen under prescribed constant tensile pre-stress (σ₀ = 0, 10, 20 and 30 MPa) was subjected to 60 cycles of thermal shock. In each cycle, the specimen was heated from room temperature to 2000 °C within 5 s in an air atmosphere or an Ar atmosphere. The residual flexural strength of each specimen was tested, and the fracture morphology was characterized by using scanning electron microscopy (SEM). There were three different regions in the fracture surface of a specimen tested in the air, while no such difference could be observed in the fracture surfaces of the specimens that were tested in Ar. The residual flexural strength of the composite that was tested in Ar generally decreases with the increase of σ₀. However, in the range of 0 ≤ σ₀ ≤ 10 MPa, the residual flexural strength of the composite that was tested in the air ascended with the increase of σ₀ due to the healing effect of oxidation, but it descended thereafter with a further increase of σ₀, as the effect pre-stress that became prominent.

This paper introduces a method for measuring wing motion, deformation, and inertial forces in bio-inspired aircraft research using a camera motion capture system. The method involves placing markers on the wing surface and fitting rigid planes to determine the wing’s spatial axis. This allows for describing the wing’s rigid motion and obtaining deformation characteristics, such as deflection, twist angle, and gap distance of the forewing and hindwing. An image-based method is proposed for determining wing mass distribution, mass blocks, and mass points for inertial force measurement. The study addresses wing motion, deformation, and inertial force measurement in a real butterfly-like flapping wing vehicle and demonstrates the effectiveness of the approach. The results reveal that inertial forces play a negligible role in the generation of lift peaks and contribute minimal lift during the entire flapping cycle. Furthermore, a transitional phase between downstroke and upstroke is found in flexible wing motion, which has high lift production. This measurement approach offers a rapid and effective solution to experimental challenges in bio-inspired aircraft design and optimization.

The paper describes a virtual particle images generation system for 3D-PTV (Particle Tracking Velocimetry) verification. It's used for evaluating the accuracy of PTV algorithms and simulating the real experimental setup. At the first time, mass of virtual particles are generated in the world coordinate system and placed in the virtual flow field. After the camera system conversion from world coordinate to pixel coordinate is applied, the virtual particle images are produced. And the movement of virtual particles can be specified by researchers or given by the flow simulation results in FLUENT. The numbers of cameras and all the parameters of cameras both can be modified in this system. Even more, the illumination model of visual particles, geometry shape variation of visual particles, and defocus blur of visual particles are carefully considered here. Therefore, the virtual particle images generation system can fairly simulate real PTV setup and generate the virtual particle image sequences. It would be helpful to optimize the placement of cameras and the images quality of a real PTV. In addition, a well-known open-source PTV software is used to test the virtual particle images. The result shows that the virtual particles and its tracks can be calculated out as the images from real PTV cameras. As expected, the mismatching of particles in the results can easily be found through the comparison to the specified particles motion.