Time frequency analysis of sound from a maneuvering rotorcraft

Stephenson, James H.; Tinney, Charles E.; Greenwood, Eric; Watts, Michael E.

doi:10.1016/j.jsv.2014.05.018

Cited by 21 publications

(11 citation statements)

References 14 publications

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“…This was shown previously in Ref. 6 during an investigation of multiple flight conditions. Therefore, a filtering method can be developed given the simultaneous occurrence of higher harmonic content that exceeds some amplitude threshold relative to the strength of the main rotor harmonic.…”

Section: Blade-vortex Interaction Extraction Methodssupporting

confidence: 83%

“…The tail rotor noise signal is primarily identified in the midrange frequency, whereas the BVI noise signals are identified by their higher harmonics (Refs. 3,6,23,24).…”

Section: Blade-vortex Interaction Extraction Methodsmentioning

confidence: 99%

“…The main rotor scaling process was performed for relative main rotor energies ranging from 35 dB below the peak BVI signal ( MRE = −35) to 7 dB above. Each extraction technique was applied to every MRE value, and the extracted signals are analyzed using the metric given in Eq, (7). It can be seen, in Fig.…”

Section: Synthetic Signal Wavelet and Fourier Transform Comparisonmentioning

confidence: 99%

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Extracting Blade–Vortex Interactions Using Continuous Wavelet Transforms

Stephenson¹,

Tinney²

2017

j am helicopter soc

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A framework for using continuous wavelet transforms to isolate and extract blade-vortex interaction noise from helicopter acoustic signals is described. The extraction method allows for the investigation of blade-vortex interactions independent of other sound sources. Experimentally acquired acoustic data from full-scale helicopter flyover tests are first transformed into time-frequency space through the wavelet transformation, with blade-vortex interactions identified and filtered by their high-amplitude, high-frequency impulsive content. The filtered wavelet coefficients are then used to create a pressure signal solely related to blade-vortex interactions. Analysis of a synthetic data set is conducted and shows that blade-vortex interactions can be accurately extracted so long as the blade-vortex interaction wavelet energy is comparable to the wavelet energy in the main rotor harmonic.

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Section: Blade-vortex Interaction Extraction Methodssupporting

confidence: 83%

“…The tail rotor noise signal is primarily identified in the midrange frequency, whereas the BVI noise signals are identified by their higher harmonics (Refs. 3,6,23,24).…”

Section: Blade-vortex Interaction Extraction Methodsmentioning

confidence: 99%

Section: Synthetic Signal Wavelet and Fourier Transform Comparisonmentioning

confidence: 99%

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Extracting Blade–Vortex Interactions Using Continuous Wavelet Transforms

Stephenson¹,

Tinney²

2017

j am helicopter soc

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“…with a central frequency of ω ψ 6. The analysis shown here mirrors previous efforts described by Baars and Tinney [16], Stephenson et al [17], and Rojo et al [18] and is performed in the Fourier domain using 81 unique scales distributed logarithmically over the frequency range 100 Hz < f < f s ∕2. Only regions inside the cone of influence are shown and are constructed by overlapping signal partitions comprising N 2 17 samples.…”

Section: Time-frequency Analysismentioning

confidence: 75%

Multirotor Drone Noise at Static Thrust

2018

Self Cite

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A first principles understanding of the sound field produced by multirotor drones in hover is presented. Propeller diameters ranging from 8 to 12 in. are examined and with configurations comprising an isolated rotor, quadcopter, and hexacopter configuration. The drone pitch, defined as the ratio of drone diameter to rotor diameter, is the same for all multirotor configurations and is valued at 2.25. A six-degree-of-freedom load cell is used to assess the aerodynamic performance of each configuration, whereas an azimuthal array of 1∕2 in. microphones, placed between two and three hub-center diameters from the drone center, is used to assess the acoustic near field. The analysis is performed using standard statistical metrics such as sound pressure level and overall sound pressure level and is presented to demonstrate the relationship between the number of rotors, the drone rotor size, and its aerodynamic performance (thrust) relative to the near-field acoustics.

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“…In recent years, identification of time-varying structures based on the measured dynamic responses has been researched thoroughly, and latest application in gas pipelines and a maneuvering rotorcraft could be found in references [1,2]. The simplest way to address the first issue is to subjectively choose a segment that appears stationary [3] or utilize modal parameters from several selected segments of measured signals; however, the selected segments must represent a stationary process.…”

Section: Introductionmentioning

confidence: 99%