J.S. Pockelé
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5 records found
1
The influence of ambient noise in the perception of wind turbine noise is evaluated in this exploratory study. For this purpose, experimental field measurements of an NTK wind turbine at different wind speeds and background noise levels are considered. Synthetic wind turbine noise auralizations are then computed to replicate the weather and operational conditions during the experiments. Different background noise recordings were then synthetically added to the simulated auralizations to investigate the effect in sound quality metrics, such as loudness, roughness, or the psychoacoustic annoyance model by Zwicker. A least-squares analysis was applied to the resulting sound signals. It was found that adding background noise to the auralisations notably reduced the differences in metrics between simulations and experiments. However, the behaviour with respect to the A-weighted signal-to-noise ratio then becomes background-noise dependent and, hence, more challenging to predict. Therefore, for perceptual studies, it is recommended to use experimental recordings with low background noise as a ground truth.
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The influence of ambient noise in the perception of wind turbine noise is evaluated in this exploratory study. For this purpose, experimental field measurements of an NTK wind turbine at different wind speeds and background noise levels are considered. Synthetic wind turbine noise auralizations are then computed to replicate the weather and operational conditions during the experiments. Different background noise recordings were then synthetically added to the simulated auralizations to investigate the effect in sound quality metrics, such as loudness, roughness, or the psychoacoustic annoyance model by Zwicker. A least-squares analysis was applied to the resulting sound signals. It was found that adding background noise to the auralisations notably reduced the differences in metrics between simulations and experiments. However, the behaviour with respect to the A-weighted signal-to-noise ratio then becomes background-noise dependent and, hence, more challenging to predict. Therefore, for perceptual studies, it is recommended to use experimental recordings with low background noise as a ground truth.
The human perception of two wind turbines of different sizes, a small NTK turbine and a larger NREL model, was evaluated through their synthetically auralised sound. A wide range of wind speed conditions and observer locations was considered. The simulated sounds were analyzed using equivalent sound pressure levels and psychoacoustic sound quality metrics. Moreover, listening experiments were conducted to evaluate the human response to the same sounds. The least-squares models fitted to the results provided scaling laws for the different sound metrics as a function of wind speed (divided into low- and high-speed regimes) and distance to the observer. At lower wind speeds, the NREL turbine’s noise and annoyance levels increase faster with increasing wind speed than the NTK turbine. The results of the NREL turbine at high wind speeds seem to indicate that turbulent boundary layer trailing-edge noise contributes more to annoyance than leading edge turbulent inflow noise. In the listening experiments, the larger wind turbine was perceived roughly 30% more annoying than the smaller one for the same conditions. The equivalent A-weighted sound pressure level and the psychoacoustic annoyance model by Zwicker were reported to closely represent the annoyance ratings reported in the listening experiment.
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The human perception of two wind turbines of different sizes, a small NTK turbine and a larger NREL model, was evaluated through their synthetically auralised sound. A wide range of wind speed conditions and observer locations was considered. The simulated sounds were analyzed using equivalent sound pressure levels and psychoacoustic sound quality metrics. Moreover, listening experiments were conducted to evaluate the human response to the same sounds. The least-squares models fitted to the results provided scaling laws for the different sound metrics as a function of wind speed (divided into low- and high-speed regimes) and distance to the observer. At lower wind speeds, the NREL turbine’s noise and annoyance levels increase faster with increasing wind speed than the NTK turbine. The results of the NREL turbine at high wind speeds seem to indicate that turbulent boundary layer trailing-edge noise contributes more to annoyance than leading edge turbulent inflow noise. In the listening experiments, the larger wind turbine was perceived roughly 30% more annoying than the smaller one for the same conditions. The equivalent A-weighted sound pressure level and the psychoacoustic annoyance model by Zwicker were reported to closely represent the annoyance ratings reported in the listening experiment.
This study explores the potential of simulation methodologies in the early stages of the acoustic design of advanced air mobility cabins. With perceptual assessment as a priority, the approach includes conducting listening experiments based on the auralization of simulation results. For this, a cabin was simulated under the stochastic load of a turbulent boundary layer and auralized as representative cabin noise. The listening experiments investigated the impact of cabin parameter variations—specifically Young's modulus, skin thickness, and fluid bulk modulus—on the participants' perception and preferences. The findings show that utilizing the presented methodology within an early design scope produced audible differences for these parameter variations. With significant changes to the signals' preference probabilities, the proposed method is able to provide a better understanding and statistical depth to the cabin acoustic design process. Loudness and A-weighted sound pressure levels reliably predicted preferences, whereas other psychoacoustic metrics were of little significance, mainly due to the stochastic, stationary, and low-frequency characteristics of the noise samples. Furthermore, the position of the passenger within the cabin model significantly affected the preferences. Adding authentic cabin sounds to the auralizations did not significantly alter the parameter variations' preference distributions.
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This study explores the potential of simulation methodologies in the early stages of the acoustic design of advanced air mobility cabins. With perceptual assessment as a priority, the approach includes conducting listening experiments based on the auralization of simulation results. For this, a cabin was simulated under the stochastic load of a turbulent boundary layer and auralized as representative cabin noise. The listening experiments investigated the impact of cabin parameter variations—specifically Young's modulus, skin thickness, and fluid bulk modulus—on the participants' perception and preferences. The findings show that utilizing the presented methodology within an early design scope produced audible differences for these parameter variations. With significant changes to the signals' preference probabilities, the proposed method is able to provide a better understanding and statistical depth to the cabin acoustic design process. Loudness and A-weighted sound pressure levels reliably predicted preferences, whereas other psychoacoustic metrics were of little significance, mainly due to the stochastic, stationary, and low-frequency characteristics of the noise samples. Furthermore, the position of the passenger within the cabin model significantly affected the preferences. Adding authentic cabin sounds to the auralizations did not significantly alter the parameter variations' preference distributions.
The current study reports the results of a psychoacoustic listening experiment investigating the human response to the noise emissions from various types of drone flyovers, recorded during acoustic field experiments. The investigation covers six quadcopters with single propellers, a quadcopter with counterrotating propellers, and two types of hybrid electric vertical take-off and landing (eVTOL) drones. These recorded audio samples were employed in a dedicated listening experiment conducted at the Psychoacoustic Listening Laboratory (PALILA) in the faculty of Aerospace Engineering of Delft University of Technology, involving 57 participants. The two eVTOL drones were perceived as considerably less annoying than their quadcopter counterparts, whereas the coaxial-propeller quadcopter was found to be the most annoying drone. Strong correlations were found between the mass and volume of the quadcopter drones and the annoyance ratings from the listening experiments. Psychoacoustic annoyance metrics from different models proved to predict the perceived noise annoyance more accurately than conventional sound metrics typically employed in noise assessment.
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The current study reports the results of a psychoacoustic listening experiment investigating the human response to the noise emissions from various types of drone flyovers, recorded during acoustic field experiments. The investigation covers six quadcopters with single propellers, a quadcopter with counterrotating propellers, and two types of hybrid electric vertical take-off and landing (eVTOL) drones. These recorded audio samples were employed in a dedicated listening experiment conducted at the Psychoacoustic Listening Laboratory (PALILA) in the faculty of Aerospace Engineering of Delft University of Technology, involving 57 participants. The two eVTOL drones were perceived as considerably less annoying than their quadcopter counterparts, whereas the coaxial-propeller quadcopter was found to be the most annoying drone. Strong correlations were found between the mass and volume of the quadcopter drones and the annoyance ratings from the listening experiments. Psychoacoustic annoyance metrics from different models proved to predict the perceived noise annoyance more accurately than conventional sound metrics typically employed in noise assessment.
Current validation of wind turbine noisemodels primarily focuses on sound levels averaged over time, typicallyexpressed in metrics such as the equivalent A-weighted sound pressure level(LA,eq). Whereas valid for regulatory purposes, these methods are notsufficient for psychoacoustic research, as time-averaged levels alone do notfully explain the measured noise annoyance. Therefore, this research aims toestablish whether psychoacoustic sound quality metrics (SQMs) provideadditional value when analysing wind turbine noise models. This work employsthe Horizontal Axis Wind turbine simulation Code 2 (HAWC2) to generate noisesource spectrograms, which are propagated through the atmosphere with aGaussian beam-tracing approach. The final sound signals are retrieved throughan inverse Short-Time Fourier Transform (iSTFT). This methodology is applied toa case study featuring a stall-controlled, horizontal-axis, Nordtank NTK 500/41wind turbine. The results are evaluated against measurements by consideringLA,eq, SQMs, and a comparative listening experiment. The LA,eq metric shows aconsistent underprediction of the simulations with respect to measurements,which is partly explained by a ground reflection modelling error. In thehigh-frequency range, stall noise is known to be significantly underpredictedby the aero-acoustic simulation model. This usually translates in increasingdiscrepancies between measurements and models as the wind speed increases. Thecomparative listening experiment confirms that participants experience thesimulations and the measurements as significantly different. The differenceratings show a good agreement with the differences in the psychoacousticannoyance and loudness metrics. It is more difficult to relate the results fromthe listening experiment to LA,eq. These findings confirm that an evaluationwith psychoacoustic metrics next to conventional methods provides additionalvalue in validating wind turbine noise models for human perception research.
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Current validation of wind turbine noisemodels primarily focuses on sound levels averaged over time, typicallyexpressed in metrics such as the equivalent A-weighted sound pressure level(LA,eq). Whereas valid for regulatory purposes, these methods are notsufficient for psychoacoustic research, as time-averaged levels alone do notfully explain the measured noise annoyance. Therefore, this research aims toestablish whether psychoacoustic sound quality metrics (SQMs) provideadditional value when analysing wind turbine noise models. This work employsthe Horizontal Axis Wind turbine simulation Code 2 (HAWC2) to generate noisesource spectrograms, which are propagated through the atmosphere with aGaussian beam-tracing approach. The final sound signals are retrieved throughan inverse Short-Time Fourier Transform (iSTFT). This methodology is applied toa case study featuring a stall-controlled, horizontal-axis, Nordtank NTK 500/41wind turbine. The results are evaluated against measurements by consideringLA,eq, SQMs, and a comparative listening experiment. The LA,eq metric shows aconsistent underprediction of the simulations with respect to measurements,which is partly explained by a ground reflection modelling error. In thehigh-frequency range, stall noise is known to be significantly underpredictedby the aero-acoustic simulation model. This usually translates in increasingdiscrepancies between measurements and models as the wind speed increases. Thecomparative listening experiment confirms that participants experience thesimulations and the measurements as significantly different. The differenceratings show a good agreement with the differences in the psychoacousticannoyance and loudness metrics. It is more difficult to relate the results fromthe listening experiment to LA,eq. These findings confirm that an evaluationwith psychoacoustic metrics next to conventional methods provides additionalvalue in validating wind turbine noise models for human perception research.