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Low-Order Prediction Methodology for Multi-Rotors Tonal Noise

Edoardo Dell'Erba

Low-Order Prediction Methodology for Multi-Rotors Tonal Noise.

Rel. Domenic D'Ambrosio, Renzo Arina, Christophe Schram, Julien Christophe. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Aerospaziale, 2021

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Recent advancements in electric propulsion, autonomous tech solutions, and the need of a faster, safer, and greener way of transports lead to the fast development of Urban Air Mobility. In this background in the past years, electric vertical take-off and landing aircraft (eVTOL) has been largely considered as the solution to the issue of urban traffic congestion. The difficulties associated with those types of vehicles are strictly related to complex aerodynamic caused by the presence of multiple smaller rotors spread across the wings. The potential interactions can significantly affect noise generation. The aim of this thesis is to define a low-cost computational methodology, which is able to approximately predict the tonal noise generated by a specific class of eVTOL: drones. The DJI Phantom 2 is chosen as the subject of the study, considering its wide use in literature. The coupling of unsteady Reynolds-averaged Navier–Stokes(RANS) simulations and an analytical reformulation of the Ffowcs-Williams and Hawkings analogy permit to quantify the tonal noise for different configurations. Following the difficulties related to the development of a simulation with the full drone configuration, the final purpose is reached by simpler step: 1)isolated actuator disk case to study the more appropriate setting in terms of domain characteristics and boundary conditions;2)isolated propeller case to validate the post-processing methodology and the tonal noise computation. In the end, the case with the drone air-frame and four rotating propellers is analyzed. The current results are found to be in good agreement with several observations including the overall thrust generated, the flow structure, and the tonal noise at the low frequencies.

Relators: Domenic D'Ambrosio, Renzo Arina, Christophe Schram, Julien Christophe
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 128
Corso di laurea: Corso di laurea magistrale in Ingegneria Aerospaziale
Classe di laurea: New organization > Master science > LM-20 - AEROSPATIAL AND ASTRONAUTIC ENGINEERING
Aziende collaboratrici: Von Karman Institute for Fluid Dynamics
URI: http://webthesis.biblio.polito.it/id/eprint/18272
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