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Efficient prediction of propeller noise in non-axial uniform inflow conditions

Furkat Yunus, Damiano Casalino, Gianluca Romani, Mirjam Snellen

2024Aerospace Science and Technology11 citationsDOIOpen Access PDF

Abstract

This paper investigates the prediction accuracy and time efficiency of two distinct low-fidelity methods for predicting the tonal and broadband noise of a drone rotor in axial and non-axial inflow conditions. These are both derived from an aerodynamic rotor model based on the blade element momentum theory, respectively coupled with a time- and a frequency-domain solution of the Ffowcs Williams-Hawkings integral equation applied to a radial distribution of acoustically compact and non-compact sources. Experimental data and scale-resolving lattice-Boltzmann/very-large eddy simulation results for a two-bladed small unmanned aerial system in transitional boundary layer conditions are used to validate the low-fidelity approaches. Comparison between low-fidelity, high-fidelity and experimental results reveal that the underlying sound generation mechanisms are accurately modeled by the low-fidelity methods, which therefore constitute a valid tool for the preliminary design of quiet drone rotors and for the estimation of the community noise impact of drone operations. • Efficient, accurate methods are needed to predict propeller noise in non-axial inflow, common in takeoff and landing flights. • Efficient tools are vital for evaluating low-noise flight procedures and optimizing trajectories. • Blade-element-momentum theory with 2D unsteady models, like Sears' function, reliably predicts loads in non-axial inflows. • Low-order models predict propeller noise in non-axial inflow, matching high-fidelity simulations and measurements.

Topics & Concepts

InflowPropellerNoise (video)Marine engineeringAcousticsPhysicsEnvironmental scienceEngineeringMechanicsComputer scienceArtificial intelligenceImage (mathematics)Aerodynamics and Acoustics in Jet FlowsAcoustic Wave Phenomena ResearchCavitation Phenomena in Pumps
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