Numerical and Experimental Investigation of Flow Confinement Effects on UAV Rotor Noise

Nardari, Clement; Casalino, Damiano; Polidoro, Francesco; Coralic, Vedran; Lew, Phoi-Tack; Brodie, J. Douglas

doi:10.2514/6.2019-2497

Cited by 33 publications

(13 citation statements)

References 14 publications

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“…This noise source requires the flow incident on the propeller disc to be turbulent. Turbulent inflow noise can be predicted using a computational method such as that described in [21] or analytical methods such as that described by [22]. Note that tests conducted in anechoic chambers or confined spaces will produce a recirculating flow, which can produce high levels of turbulent inflow noise [23,24]; •…”

mentioning

confidence: 99%

Quantification of the Psychoacoustic Effect of Noise from Small Unmanned Aerial Vehicles

Hui

Kingan

Hioka

et al. 2021

IJERPH

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This paper presents the results of a study evaluating the human perception of the noise produced by four different small quadcopter unmanned aerial vehicles (UAVs). This study utilised measurements and recordings of the noise produced by the quadcopter UAVs in hover and in constant-speed flight at a fixed altitude. Measurements made using a ½″ microphone were used to calculate a range of different noise metrics for each noise event. Noise recordings were also made using a spherical microphone array (an Eigenmike system). The recordings were reproduced using a 3D sound reproduction system installed in a large anechoic chamber located at The University of Auckland. Thirty-seven participants were subjected to the recordings and asked to rate their levels of annoyance in response to the noise, and asked to perform a simple cognitive task in order to assess the level of distraction caused by the noise. This study discusses the noise levels measured during the test and how the various noise metrics relate to the annoyance ratings. It was found that annoyance strongly correlates with the sound pressure level and loudness metrics, and that there is a very strong correlation between the annoyance caused by a UAV in hover and in flyby at the same height. While some significant differences between the distraction caused by the UAV noise for different cases were observed in the cognitive distraction test, the results were inconclusive. This was likely due to a ceiling effect observed in the participants’ test scores.

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mentioning

confidence: 99%

Quantification of the Psychoacoustic Effect of Noise from Small Unmanned Aerial Vehicles

Hui

Kingan

Hioka

et al. 2021

IJERPH

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“…The LB method is chosen because it has been validated for aerodynamics and aeroacoustics studies for similar phenomena [11,[13][14][15][16]. Its implementation in the commercial software 3DS-Simulia PowerFLOW 5.4b is used.…”

Section: B Methodologymentioning

confidence: 99%

Aerodynamic and Aeroacoustic Effects of Swirl Recovery Vanes Length

Avallone

Ende

et al. 2019

Journal of Aircraft

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A numerical investigation of a propeller with swirl recovery vanes (SRVs), for which experimental data exist, is performed. A second SRV geometry, with shorter vanes to avoid the impingement of the propeller tip vortices, is also investigated. For the baseline SRVs, the efficiency of the propulsive system increases of 2.4% with respect to the isolated propeller. This is obtained by converting angular momentum in axial momentum. A reduction of the swirl angle in the near wake by 48% is found. Most of the thrust is generated at the root of the vanes. Leading-edge impingement noise is the dominant source. The vanes cause noise increase by 20 dB with respect to the isolated propeller in the axial direction, where noise from the propeller vanishes. In the axial direction, sound pressure level spectra show tonal peaks at harmonics of the second blade passing frequency (BPF) while, in the other directions, peaks are present at harmonics of the first BPF. However, the overall isolated propeller noise is 23 dB higher than the noise generated by the SRVs. Shortening the vane length causes 13% reduction of the thrust generated by the vanes with respect the baseline case, but no variation of the far-field noise. Nomenclature c = chord [m] C T = thrust coefficient [-] D = propeller diameter [m] J = advance ratio [-] n = rotational velocity [rev/s] P = power [W] r = local blade radius [m]

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“…A significant increase in noise levels before the first BPF is observed in the permeable surface (wake envelope) predictions relative to the predictions using blade surface for both coarse and fine mesh cases. Blade-wake interactions and leading edge interactions are known to impact at the lower frequencies [36], and therefore, it can be inferred that the leading-edge interactions are better resolved by using wake envelope. It is also interesting to observe that the decay of the spectra at higher frequencies is better captured by the predictions from propeller surface as compared to the wake envelope.…”

Section: Far-field Characteristicsmentioning

confidence: 99%

Numerical characterisation of noise generated by a distributed-propulsion propeller

Sharma

Ambrose

Jefferson-Loveday

2022

28th AIAA/CEAS Aeroacoustics 2022 Conference

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The shift towards sustainable and eco-friendly future of aviation is further catalysed by the aggressive targets set by administrative and industrial stakeholders. It has emerged as one of the solutions with the potential to increase overall efficiency and improve noise emissions. This paper characterises the flow and acoustic field associated with a DEP propeller. A hybrid methodology using scale-resolving turbulence formulation and an acoustic analogy is used to predict aerodynamic noise generation and acoustic wave propagation to the far-field for an isolated DEP propeller. The numerical predictions are observed to be in reasonable agreement with the measured values for both aerodynamic and aero-acoustic attributes. The sensitivity of far-field acoustic spectra to the integration surface is also presented. The results are further analysed to identify dominant noise sources in the system including propeller and wake flows. I. Nomenclature= density RANS = Reynolds-averaged Navier-Stokes LES = Large eddy simulation WALE = Wall-adopted local eddy-viscosity DEP = Distributed electric propulsion

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Numerical and Experimental Investigation of Flow Confinement Effects on UAV Rotor Noise

Cited by 33 publications

References 14 publications

Quantification of the Psychoacoustic Effect of Noise from Small Unmanned Aerial Vehicles

Quantification of the Psychoacoustic Effect of Noise from Small Unmanned Aerial Vehicles

Aerodynamic and Aeroacoustic Effects of Swirl Recovery Vanes Length

Numerical characterisation of noise generated by a distributed-propulsion propeller

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