Shuxia Jiang

This study investigates the unsteady aerodynamic mechanisms underlying the efficient flight of birds and proposes a biomimetic flapping-wing aircraft design utilizing a double-crank double-rocker mechanism. Building upon a detailed analysis of avian flight dynamics, a two-stage foldable flapping mechanism was developed, integrating an optimized double-crank double-rocker structure with a secondary linkage system. This design enables synchronized wing flapping and spanwise folding, significantly enhancing aerodynamic efficiency and dynamic performance. The system’s planar symmetric layout and high-ratio reduction gear configuration ensure movement synchronicity and stability while reducing mechanical wear and energy consumption. Through precise modeling, the motion trajectories of the inner and outer wing segments were derived, providing a robust mathematical foundation for motion control and optimization. Computational simulations based on trajectory equations successfully demonstrated the characteristic figure-eight wingtip motion. Using 3D simulations and CFD analysis, key parameters—including initial angle of attack, aspect ratio, flapping frequency, and flapping speed—were optimized. The results indicate that optimal aerodynamic performance is achieved at an initial angle of attack of 9°, an aspect ratio of 5.1, and a flapping frequency and speed of 4–5 Hz and 4–5 m/s, respectively. These findings underscore the potential of biomimetic flapping-wing aircraft in applications such as UAVs and military technology, providing a solid theoretical foundation for future advancements in this field.

In order to solve the problems that the prediction accuracy of the traditional centrifugal fan is low and the cost is high, a noise prediction model for centrifugal fan based on improved particle swarm optimization (IPSO) optimized BP neural network was presented. The initial weights and thresholds of BP neural network were optimized by using IPSO. The 17 parameters were collected by the liancheng company and be used to establish the regression equation to obtain the standard regression coefficient. The importance of the fan parameters was ranked and four key characteristic parameters were determined as input values by the optimization algorithm to build the IPSO-BP centrifugal fan noise prediction model. After comparative study, IPSO-BP model has better prediction effect than PSO-BP model and BP model, and the prediction error is only 0. 97%. The research shows that IPSO-BP model can effectively shorten the fan design period and save the design cost.