Multi-Fidelity Numerical Acoustic Optimization of UAV Propellers
Roberto NAVARRO, Luis Miguel GACÍA-CUEVAS, Jorge GARCÍA-TISCAR, Federico Nahuel RAMÍREZ, Marco LAUDATO, Mihai MIHAESCU
Abstract. In this work, we propose a multi-fidelity approach to optimize the geometry of a small fixed-pitch propeller to reduce its acoustic impact. This way, the advantages of low-fidelity methods (low cost and fast results) can be combined with those of higher-order ones (better resolution and more reliable results) to obtain an optimized solution within a reasonable timeframe. We train a fully connected network surrogate that maps propeller geometry (chord and twist) and thrust to optimal rotating speed, torque, and sound pressure level (SPL) at the blade passing frequency (BPF) at 17 different angles, and benchmark it on three datasets: low-order blade element momentum theory (BEMT), higher-order vortex particle method (VPM), and a balanced Mixed set.
Keywords
Noise, UAS, Drones, AAM, VPM, CFD
Published online 7/20/2026, 5 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Roberto NAVARRO, Luis Miguel GACÍA-CUEVAS, Jorge GARCÍA-TISCAR, Federico Nahuel RAMÍREZ, Marco LAUDATO, Mihai MIHAESCU, Multi-Fidelity Numerical Acoustic Optimization of UAV Propellers, Materials Research Proceedings, Vol. 69, pp 314-318, 2026
DOI: https://doi.org/10.21741/9781644904251-56
The article was published as article 56 of the book CEAS – AIDAA Conference 2025
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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