Analysis of the integrated pattern of hoverable flapping wing micro-air vehicle

Abstract

Flying creatures, such as insects and hummingbirds, display superior flight capabilities that have inspired the development of Flapping-Wing Micro Air Vehicles (FWMAVs). Although these vehicles have achieved certain milestones, integrating subsystems under stringent size, weight, and power (SWaP) constraints poses significant energy and weight management challenges. These vehicles' common patterns and critical differences still need to be clearly understood, indicating that their integrated patterns still need to be defined.

To determine the integrated pattern of these aircraft, this study began with analyzing existing data on hover-capable bionic aircrafts. It then formulated a set of indices through the permutation and combination of subsystem parameters. Key indices were identified by examining the universality and specificity of system indices through case studies of aircraft, which helped establish the integrated pattern of FWMAVs.

The study revealed certain commonalities in these aircraft; for example, the actuation system, comprising the drive, transmission, and manipulation systems, accounts for 51.72 % of the take-off weight. Combining the transmission system and structure, the airframe system represents 27.62 % of the take-off weight. The battery system accounts for about 20.18 %, while the electronic system usually constitutes 12.78 % of the aircraft's take-off weight. Additionally, significant variability was observed among aircrafts in parameters such as $C_{\mathrm{D\_}5}$ and $C_{\mathrm{D\_}7}$, which represent the distribution of control functions, and $C_{\mathrm{S\_}4}$, which tests the integration of the structure with the transmission system. This paper constructs an integrated pattern for such aircraft based on these findings.

The integrated pattern derived from this work provides a practical range of parameter values for subsystems during the initial design phase of these aircraft. This is crucial for enhancing the iterative convergence speed in engineering, facilitating the parallel development and design of subsystems, and validating the practicality of numerical optimization results from optimization algorithms. Ultimately, this contributes to advancing future designs and developments of hover-capable bio-inspired flying aircraft.