Aeroacoustic optimization of MAV rotors. (January 2022)

• Record Type: Journal Article
• Authors: Li Volsi, Pietro; Gomez-Ariza, David; Gojon, Romain; Jardin, Thierry; Moschetta, Jean-Marc
• Is Part Of: International journal of micro air vehicles. Volume 14(2022)
• Publication Date: 2022-01
• Subjects: Aeroacoustics -- MAV -- multi-objective optimization -- genetic algorithm -- aerodynamic performance -- tonal noise; Aerosonde (Drone aircraft) -- Periodicals; Aerospace engineering -- Periodicals; Aerosonde (Drone aircraft); Aerospace engineering; Periodicals; 623.746905
• DOI: 10.1177/17568293211070827
• Languages: English
• ISSNs: 1756-8293

Abstract

The more restrictive airspace regulations force drone manufacturers to take into account the noise emitted by the drone during the design phase, along with its aerodynamic performance to increase the flight time. A Non-Linear Vortex Lattice Method (NVLM), coupled with the Farassat Formulation-1A of the Ffowcs-Williams and Hawkings acoustic analogy is used to evaluate the aerodynamic and aeroacoustic performance of MAV rotors. Pymoo, a Python-based optimization framework, is employed to modify the geometry, evaluate its performance and extract the set of Pareto optimal solutions. The two objectives are the aerodynamic Figure-of-Merit and the Sound Pressure Level of the Blade Passing Frequency tone for a microphone located at a far-field distance of 1.62 m and 30 ∘ below the rotor plane. The approach proposed in this paper takes into account up to ten different parameters, ranging from the twist and chord distributions, to the rake and skew angles.