In this study, acoustic measurements of a hover condition are taken on isolated rotor–airframe configurations representative of small-scale, rotary-wing unmanned aircraft systems (UAS). Each rotor–airframe configuration consists of two fixed-pitch blades powered by a brushless motor, with a simplified airframe geometry intended to represent a generic multicopter arm. In addition to acoustic measurements, computational fluid dynamics–based aeroacoustic predictions are implemented on a subset of the experimentally tested rotor–airframe configurations in an effort to better understand the noise content of the rotor–airframe systems. Favorable agreements are obtained between acoustic measurements and predictions, based on both time- and frequency-domain postprocessing techniques. Results indicate that close proximity of airframe surfaces results in the generation of considerable tonal acoustic content in the form of harmonics of the rotor blade passage frequency (BPF). Analysis of the acoustic prediction data shows that the presence of the airframe surfaces can generate noise levels either comparable to or greater than the rotor blade surfaces under certain rotor tip clearance conditions. Analysis of the on-surface Ffowcs Williams and Hawkings source terms provides insight as to the predicted physical noise-generating mechanisms on the rotor and airframe surfaces.

Microphone array processing algorithms often assume straight-line source-to-observer wave propagation. However, when the microphone array is placed outside an open-jet test section, the presence of the shear layer refracts the acoustic waves and causes the wave propagation times to vary from a free-space model. With a known source location in space, the propagation time delay can be determined using Amiet's theoretical method. In this study, the effects of shear layer refraction are examined using a pulsed laser system to generate a plasma point source in space and time for several different test section flow speeds and configurations. An array of microphones is used to measure the pulse signal, allowing for the use of qualitative beamforming and quantitative timing analysis. Results indicate that Amiet's method properly accounts for planar shear layer refraction time delays within experimental uncertainty. This is true both when the source is in the inviscid core of the open-jet test section, as well as when the source is located in different model wakes of varying complexity. However, the method breaks down where the thin layer assumption fails, such as in the region where the tunnel test section's open jet interacts with the facility jet collector.

Microphone arrays are commonly used for noise source localization and power estimation in aeroacoustic measurements. The delay-and-sum (DAS) beamformer, which is the most widely used beamforming algorithm in practice, suffers from low resolution and high sidelobe level problems. Therefore, deconvolution approaches, such as the deconvolution approach for the mapping of acoustic sources (DAMAS), are often used for extracting the actual source powers from the contaminated DAS results. However, most deconvolution approaches assume that the sources are uncorrelated. Although deconvolution algorithms that can deal with correlated sources, such as DAMAS for correlated sources, do exist, these algorithms are computationally impractical even for small scanning grid sizes. This paper presents a covariance fitting approach for the mapping of acoustic correlated sources (MACS), which can work with uncorrelated, partially correlated or even coherent sources with a reasonably low computational complexity. MACS minimizes a quadratic cost function in a cyclic manner by making use of convex optimization and sparsity, and is guaranteed to converge at least locally. Simulations and experimental data acquired at the University of Florida Aeroacoustic Flow Facility with a 63-element logarithmic spiral microphone array in the absence of flow are used to demonstrate the performance of MACS.

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