Auralization of NASA N+2 Aircraft Concepts from System Noise Predictions

Rizzi, Stephen A.; Burley, Casey L.; Thomas, Russell H.

doi:10.2514/6.2016-2906

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Table 5 shows however that the differences in the sharpness value exceeded for 5% of the time-history are very low, except for FP 3, where a reasonable difference between the sharpness of the measured and synthesized sound can be observed. The sharpness values in Table 5 are consistent with values presented in other references such as [43] by Rizzi et al Looking at the sharpness variation plots for each flight path in Fig. 9, it can be seen that the synthesized and measured sharpness variations largely overlap each other.…”

Section: Sharpness Comparisonsupporting

confidence: 88%

Objective quantification of perceived differences between measured and synthesized aircraft sounds

Sahai

Snellen

Simons

2018

Aerospace Science and Technology

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This paper presents an approach with which perceived audible differences in aircraft sounds can be quantified and presented in an objective manner. The objective quantification of the subjectively heard audible differences is intended to serve two primary goals. It can firstly enable developers of auralization technology to make the auralized sounds more realistic by identifying in which aspects the synthesized sounds differ from their real-life counterparts and to what extent. The quantification can secondly provide an improved and more detailed means of distinguishing between aircraft sounds in general, beyond the conventional metrics of A-weighted Sound Pressure level (dBA) or Effective Perceived Noise Level (EPNL) used currently to assess aircraft noise. In this study sound quality metrics are used to quantify the differences in aircraft sounds. These metrics are widely used in other industries such as the automotive sector. Audio files of a reference aircraft, made over identical flight paths at a noise monitoring station in the vicinity of Schiphol airport, are compared in terms of both conventional and sound quality metrics for four measured and four auralized audio files. It is observed from the comparison that differences that may appear small in the conventional metrics can be significant in terms of the sound quality metrics. Significant differences in measured and synthesized sounds are observed for the aircraft considered in this study with regards to the tonal content and fluctuations in amplitude that occur over time. The conventional metrics are seen to capture the overall loudness aspect of aircraft sounds, but give no clear information regarding which spectral or temporal characteristics cause the sounds to be perceived as audibly different.

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Section: Sharpness Comparisonsupporting

confidence: 88%

Objective quantification of perceived differences between measured and synthesized aircraft sounds

Sahai

Snellen

Simons

2018

Aerospace Science and Technology

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“…Psychoacoustic metrics have been found to outperform conventional noise metrics (e.g. EPNL or SEL) in predicting noise annoyance of fixed-wing aircraft (Rizzi et al, 2016;. Recently, several authors Boucher et al, 2020) (2009).…”

Section: Psychoacoustic Data Analysismentioning

confidence: 99%

Psychoacoustic analysis of contra-rotating propeller noise for unmanned aerial vehicles

Torija

Paruchuri

2021

The Journal of the Acoustical Society of America

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Unmanned aerial vehicle (UAV) technologies are rapidly advancing due to the unlimited number of applications from parcel delivery to people transportation. As the UAV market expands, community noise impact will become a significant problem for public acceptance. Compact drone architectures based on contra-rotating propellers bring significant benefits in terms of aerodynamic performance and redundancy to ensure vehicle control in case of component failure. However, contra-rotating propellers are severely noisy if not designed appropriately. In the framework of a perception-influenced design approach, this paper investigates the optimal rotor spacing distance configuration to minimise noise annoyance. On the basis of a series of psychoacoustic metrics (i.e. loudness, fluctuation strength, roughness, sharpness and tonality) and psychoacoustic annoyance models, the optimal rotor axial separation distance (expressed as a function of propeller blade diameter) is at a range of 0.2 to 0.4. This paper also discusses the performance of currently available psychoacoustic models to predict propeller noise annoyance, and defines further work to develop a psychoacoustic annoyance model optimised for rotating systems.

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“…Sound quality metrics offer a quantitative measure of the qualitative indicators observed in the spectrograms and were calculated from the pseudorecordings. Loudness (N), sharpness (S), roughness, fluctuation strength, and toneto-noise ratio (TNR) metrics were calculated in the manner described in Rizzi et al 41 From the metric time histories, the 5 th percentile, that is, the value of the metric which is exceeded 5% of the measurement time, was calculated. Plotted in Figure 20 and Figure 21 are the 5 th percentiles of loudness (N5) and sharpness (S5) for each of the three auralization methods for each flight operation.…”

Section: Sound Quality Metricsmentioning

confidence: 99%

A Comparison of Aircraft Flyover Auralizations by the Aircraft Noise Simulation Working Group

Rizzi

Legriffon²,

Pieren³

et al. 2020

Aiaa Aviation 2020 Forum

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The Aircraft Noise Simulation Working Group (ANSWr), comprised of NASA, DLR, and ONERA, recently completed an analysis campaign to compare aircraft noise simulation tools, establish guidelines for noise prediction, and launch activities to assess uncertainties associated with the simulation. The campaign included the analyses of two DLR conceptual aircraft, a reference tube-and-wing aircraft, and a low noise aircraft with engines mounted above the fuselage-wing-junction. While the total predicted noise for each of the concepts compared favorably among analyses at the peak level, significant differences were noted at the component level. This paper aims to further that effort by auralizing aircraft flyover noise associated with those predictions. Comparisons are made among the NASA, DLR/Empa, and ONERA generated sounds to determine how differences between the system noise prediction and auralization methods result in changes to the auralized sound.

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Auralization of NASA N+2 Aircraft Concepts from System Noise Predictions

Cited by 6 publications

References 12 publications

Objective quantification of perceived differences between measured and synthesized aircraft sounds

Objective quantification of perceived differences between measured and synthesized aircraft sounds

Psychoacoustic analysis of contra-rotating propeller noise for unmanned aerial vehicles

A Comparison of Aircraft Flyover Auralizations by the Aircraft Noise Simulation Working Group

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