Lightweight Material Selection for Heavy Payload Multi-Rotor Unmanned Aerial Vehicles Using Alloys, Composites, and Hybrid Composites: An Investigation into Different Loading Conditions Based on Maneuvering

The primary aim of the work is to identify a suitable hybrid composite material for heavy payload multi-rotor unmanned aerial vehicles (UAV) by conducting fluid-structure-interaction (FSI) experiments on the airframe. The UAV's airframe has an estimated payload lifting capacity of 25 kg. Therefore, it is necessary to determine the structural integrity of the strength parameters for tensile and compressive loads under various operating situations. Instead of conventional computational models, this work imposed a unique computational composite molding prepared through ANSYS ACP tool. Additionally, the various composite computational models have been specifically developed in the ANSYS ACP tool to evaluate the airframe's capacity to endure different composite materials, alloys, and hybrid materials. This study thoroughly investigates the computational analysis of more than 20 materials, encompassing both individual materials and hybrid systems. This study primarily examines hybrid structures, which, due to their reduced weight, have a significant impact on the aviation industry. Within the framework of FSI, the airframe is subjected to an external load in the form of aerodynamic pressure exerted by ANSYS Fluent. The computational fluid dynamics investigations are computed on the UAV’s airframe under the targeted mission profile’s speed and so the aerodynamic pressures are noted. Through advanced system coupling approach, the relevant aerodynamic loads are transferred into structural body of the airframe in an efficient manner. To confirm the precision of the computational advancement, specific analytical methods are applied, and the computational results are authenticated. In conclusion, the GFRP-FR-4 woven-based composite and its associated hybrid composites are the most durable choice for the airframes of heavy-payload drones.