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R.C. Van der Grift
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8 records found
1
Aircraft noise has a severe impact on communities around airports. For noise regulation, yearly average noise levels LDEN are modelled for large areas around the airport. To validate and improve the noise model, noise monitoring terminals (NMT) can be used. These NMTs, however, are often placed further away from the airport and in areas with higher background noise levels than ideal measurement conditions. Thresholds placed on the NMTs prevent them from capturing too much background noise but also prohibit them from measuring lower noise levels from aircraft. This research addresses the potential effects of undetected flights on the measured LNight and LDEN . For this, the Doc 29 modelling method is used. The case study is Schiphol Airport, where 41 NMTs are placed at different distances from the runways. Analysis of undetected flights showed that newer aircraft, such as the A320-NEO, were often not measured. Applying weighted least-squares to the available noise level measurements and supporting data gives insights into the possible aircraft-induced noise levels of undetected flights. These insights are used to improve the alignment between measured and modelled LNight and LDEN .
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Aircraft noise has a severe impact on communities around airports. For noise regulation, yearly average noise levels LDEN are modelled for large areas around the airport. To validate and improve the noise model, noise monitoring terminals (NMT) can be used. These NMTs, however, are often placed further away from the airport and in areas with higher background noise levels than ideal measurement conditions. Thresholds placed on the NMTs prevent them from capturing too much background noise but also prohibit them from measuring lower noise levels from aircraft. This research addresses the potential effects of undetected flights on the measured LNight and LDEN . For this, the Doc 29 modelling method is used. The case study is Schiphol Airport, where 41 NMTs are placed at different distances from the runways. Analysis of undetected flights showed that newer aircraft, such as the A320-NEO, were often not measured. Applying weighted least-squares to the available noise level measurements and supporting data gives insights into the possible aircraft-induced noise levels of undetected flights. These insights are used to improve the alignment between measured and modelled LNight and LDEN .
For models that evaluate aircraft noise, thrust is an essential input. From aircraft flight recorder data or measured noise spectra, the engine's rotational speed can be estimated for which a conversion is then needed to obtain the engine's thrust. This research investigates three conversion methods. The first uses the expressions from the ANP database while the second method is based on the fuel flow. The third employs Gas Turbine Simulation Program (GSP) predictions. The thrust estimates are compared to airline performance calculations where significant variations up to 3 dBA in predicted noise were found. Methods one and three were found to be in good agreement with the performance data. An important finding of this paper is that combining methods one and three using least-squares is capable of providing the required conversion expressions, in line with those in the ANP database, but without being limited to a few aircraft types only.
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For models that evaluate aircraft noise, thrust is an essential input. From aircraft flight recorder data or measured noise spectra, the engine's rotational speed can be estimated for which a conversion is then needed to obtain the engine's thrust. This research investigates three conversion methods. The first uses the expressions from the ANP database while the second method is based on the fuel flow. The third employs Gas Turbine Simulation Program (GSP) predictions. The thrust estimates are compared to airline performance calculations where significant variations up to 3 dBA in predicted noise were found. Methods one and three were found to be in good agreement with the performance data. An important finding of this paper is that combining methods one and three using least-squares is capable of providing the required conversion expressions, in line with those in the ANP database, but without being limited to a few aircraft types only.
To regulate aircraft noise impact on communities surrounding airports, best-practice models are used to predict aircraft noise levels. This research evaluates the noise–power–distance (NPD) tables employed in the European Doc 29 noise model using the noise measurements taken around Amsterdam Airport Schiphol. Thrust estimation is based on extracting the blade passing frequency from acoustic measurements and converting it to the engine rotational speed indicator N1%. The N1% estimates are validated with onboard flight data. Even with accurate input parameters (thrust and distance to the observer), discrepancies are observed between modelled and measured noise levels, which can be attributed to the inaccuracies in the NPD tables. To further investigate this, empirical thrust-noise relations are derived from the measurements. These derived relations are found to differ from those in the original NPD tables. When the empirical thrust-noise relations are used, the agreement between the modelled and measured mean noise levels improves. The standard deviation of the differences gets reduced by 25% for departure operations. This finding is subsequently confirmed using independent measurements around Oslo Airport Gardermoen. Beyond improving current best-practice noise modelling, the methodology presented in this research offers insight into the development and validation of NPD tables.
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To regulate aircraft noise impact on communities surrounding airports, best-practice models are used to predict aircraft noise levels. This research evaluates the noise–power–distance (NPD) tables employed in the European Doc 29 noise model using the noise measurements taken around Amsterdam Airport Schiphol. Thrust estimation is based on extracting the blade passing frequency from acoustic measurements and converting it to the engine rotational speed indicator N1%. The N1% estimates are validated with onboard flight data. Even with accurate input parameters (thrust and distance to the observer), discrepancies are observed between modelled and measured noise levels, which can be attributed to the inaccuracies in the NPD tables. To further investigate this, empirical thrust-noise relations are derived from the measurements. These derived relations are found to differ from those in the original NPD tables. When the empirical thrust-noise relations are used, the agreement between the modelled and measured mean noise levels improves. The standard deviation of the differences gets reduced by 25% for departure operations. This finding is subsequently confirmed using independent measurements around Oslo Airport Gardermoen. Beyond improving current best-practice noise modelling, the methodology presented in this research offers insight into the development and validation of NPD tables.
Aircraft noise impacts a growing number of residents around airports. The impact is estimated using noise models such as the European standard Doc 29. These models make use of empirically derived Noise-Power- Distance tables to estimate noise in the areas around the airport. Correction factors are used to account for directionality effects such as engine installation effect and lateral attenuation. Research comparing measured and modelled directionality of the aircraft noise is limited. This research aims to investigate the potential contribution of directionality effects on differences between measured noise levels, obtained around Schiphol Airport using the NOise MOnitoring System (NOMOS), and noise levels predicted by Doc 29. This is done by considering NOMOS measurements at different locations around the airport and use these to retrieve noise levels at the source, i.e., the so-called standardized levels. These noise levels are then used to map the noise levels in lateral and longitudinal directions. By performing the same procedure for modelled sound levels, it is possible to observe differences in directionality patterns. The thus found directivity effects differ significantly from the currently modelled directivity effects. These insights can be used to increase the accuracy of the Doc 29 model without increasing its complexity.
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Aircraft noise impacts a growing number of residents around airports. The impact is estimated using noise models such as the European standard Doc 29. These models make use of empirically derived Noise-Power- Distance tables to estimate noise in the areas around the airport. Correction factors are used to account for directionality effects such as engine installation effect and lateral attenuation. Research comparing measured and modelled directionality of the aircraft noise is limited. This research aims to investigate the potential contribution of directionality effects on differences between measured noise levels, obtained around Schiphol Airport using the NOise MOnitoring System (NOMOS), and noise levels predicted by Doc 29. This is done by considering NOMOS measurements at different locations around the airport and use these to retrieve noise levels at the source, i.e., the so-called standardized levels. These noise levels are then used to map the noise levels in lateral and longitudinal directions. By performing the same procedure for modelled sound levels, it is possible to observe differences in directionality patterns. The thus found directivity effects differ significantly from the currently modelled directivity effects. These insights can be used to increase the accuracy of the Doc 29 model without increasing its complexity.
Conference paper
(2024)
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Anandini Sravya Jayanthi, Rebekka van der Grift, Irene Dedoussi, Mirjam Snellen
To reduce the growing distrust in aircraft noise models felt by communities around the airport, it is imperative to ensure accurate modelling methodologies validated by appropriately measured noise metrics. This is especially crucial in regions farther from the airport where Lden = 45 - 55 dBA because the amount of affected residents in these areas is large. Currently, there is a lack of measured noise levels at such distances and uncertainty about the assumed procedures, such as the aircraft thrust settings. Regarding the latter, before comparing the model and measured noise levels, it’s thus crucial to first create a robust workflow for obtaining accurate input data for the noise predictions. In this contribution, as a first step, audio files from the noise monitoring stations around Rotterdam The Hague airport (RTHA), combined with dedicated array and single microphone measurements, are considered for extracting fan rotational speed, N1. The 64-microphone array and the single microphone system were co-located with one of the monitoring stations at a distance of 1.14 km away from the RTHA runway. The engine settings are retrieved from the intensity-averaged spectrograms obtained from the microphone array. Using the derived thrust settings, the noise levels measured by the monitoring stations are compared with the single-event noise level prediction made by the European Noise model, Doc.29. The aircraft position, i.e., input for the model, is obtained from ADS-B data, which contains the position vector and velocity of the aircraft at 1-second intervals. In the framework of this study, noise predictions for both arrival and take-off procedures for three aircraft types are presented. Finally, this case study aims to investigate the applicability of the data from monitoring stations for the aim of model-data predictions at the mentioned regions.
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To reduce the growing distrust in aircraft noise models felt by communities around the airport, it is imperative to ensure accurate modelling methodologies validated by appropriately measured noise metrics. This is especially crucial in regions farther from the airport where Lden = 45 - 55 dBA because the amount of affected residents in these areas is large. Currently, there is a lack of measured noise levels at such distances and uncertainty about the assumed procedures, such as the aircraft thrust settings. Regarding the latter, before comparing the model and measured noise levels, it’s thus crucial to first create a robust workflow for obtaining accurate input data for the noise predictions. In this contribution, as a first step, audio files from the noise monitoring stations around Rotterdam The Hague airport (RTHA), combined with dedicated array and single microphone measurements, are considered for extracting fan rotational speed, N1. The 64-microphone array and the single microphone system were co-located with one of the monitoring stations at a distance of 1.14 km away from the RTHA runway. The engine settings are retrieved from the intensity-averaged spectrograms obtained from the microphone array. Using the derived thrust settings, the noise levels measured by the monitoring stations are compared with the single-event noise level prediction made by the European Noise model, Doc.29. The aircraft position, i.e., input for the model, is obtained from ADS-B data, which contains the position vector and velocity of the aircraft at 1-second intervals. In the framework of this study, noise predictions for both arrival and take-off procedures for three aircraft types are presented. Finally, this case study aims to investigate the applicability of the data from monitoring stations for the aim of model-data predictions at the mentioned regions.
Aircraft noise is a significant problem for communities surrounding airports. Accurate prediction models are needed to estimate noise levels from aircraft operations. In this research, the accuracy of the sonAIR aircraft noise model in predicting noise levels from departures around Schiphol airport is evaluated by comparison to measurement data from NOMOS and the current best-practice modelling approach Doc29. Results show a significant but consistent underestimation of noise levels by sonAIR, mainly due to a generalisation of emission models. The standard deviation of differences between model results and measurements is lower for sonAIR than for Doc29 by up to 1 dB. Differences between measurement and model results were found in the relation between N1 and noise levels, and for maximum noise levels. The results demonstrate that sonAIR provides more reliable predictions of noise levels on the single flight event level than Doc29. Additionally, this study shows agreement with results from a previous validation study in Zürich, thereby demonstrating the applicability of sonAIR to another airport. This research contributes to better aircraft noise predictions, which will have implications that ultimately lead to a better quality of life for communities affected by aircraft noise.
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Aircraft noise is a significant problem for communities surrounding airports. Accurate prediction models are needed to estimate noise levels from aircraft operations. In this research, the accuracy of the sonAIR aircraft noise model in predicting noise levels from departures around Schiphol airport is evaluated by comparison to measurement data from NOMOS and the current best-practice modelling approach Doc29. Results show a significant but consistent underestimation of noise levels by sonAIR, mainly due to a generalisation of emission models. The standard deviation of differences between model results and measurements is lower for sonAIR than for Doc29 by up to 1 dB. Differences between measurement and model results were found in the relation between N1 and noise levels, and for maximum noise levels. The results demonstrate that sonAIR provides more reliable predictions of noise levels on the single flight event level than Doc29. Additionally, this study shows agreement with results from a previous validation study in Zürich, thereby demonstrating the applicability of sonAIR to another airport. This research contributes to better aircraft noise predictions, which will have implications that ultimately lead to a better quality of life for communities affected by aircraft noise.
To regulate aircraft noise impact on communities surrounding airports, best-practice models are used to predict aircraft noise levels. In this research the Noise-Power-Distance (NPD) tables in the European Doc.29 noise model are evaluated with measurements around Amsterdam Airport Schiphol. Even with accurate input parameters (thrust and distance to the observer), differences in modelled and measured noise levels are found, which are assumed to be due to the errors in the NPD table. To further investigate this, thrust-noise relations are derived from measurements. These relations are found to differ from the original NPD tables. Using the measured thrust-noise relations, the modelled and measured mean noise levels are in agreement and the standard deviation of the differences is reduced by 25% for departure operations. This finding is consequently verified with independent measurements around Oslo Airport Gardermoen. Next to an improvement in best-practice noise modelling, the methods described in this research give insight into the creation and validation of NPD tables.
...
To regulate aircraft noise impact on communities surrounding airports, best-practice models are used to predict aircraft noise levels. In this research the Noise-Power-Distance (NPD) tables in the European Doc.29 noise model are evaluated with measurements around Amsterdam Airport Schiphol. Even with accurate input parameters (thrust and distance to the observer), differences in modelled and measured noise levels are found, which are assumed to be due to the errors in the NPD table. To further investigate this, thrust-noise relations are derived from measurements. These relations are found to differ from the original NPD tables. Using the measured thrust-noise relations, the modelled and measured mean noise levels are in agreement and the standard deviation of the differences is reduced by 25% for departure operations. This finding is consequently verified with independent measurements around Oslo Airport Gardermoen. Next to an improvement in best-practice noise modelling, the methods described in this research give insight into the creation and validation of NPD tables.
Accurate modelling of aircraft noise in different weather conditions is crucial for the reliability of noise predictions and their application worldwide. In best-practice aircraft noise models, such as Doc.29, the change in expected sound level on the ground due to changing atmosphere is modelled with a simplified propagation calculation. The Aircraft Noise and Performance (ANP) database contains several standardised source spectra, known as spectral classes, which are used for these calculations to account for the frequency dependence of the atmospheric effects. The spectral classes consist of Pressure Band Levels (PBL) of 24 1/3rd octave bands. This research focuses on the agreement of these spectral classes with measurements, taken around Amsterdam Schiphol Airport, and quantifies the effect of differences in these spectra for the Doc.29 weather correction. The measurements, taken by an acoustic array close to the runway and by continuous single microphone noise measurement stations (NOMOS) at long range, are propagated to the standard distance of 1000 ft (for which the ANP spectral classes are given) taking into account the geometrical spreading and the actual atmospheric absorption. For the B737-800 and A330, differences in shape are found between the two measured spectra and the spectral class. This is partly due to the low signal-to-noise ratio for the high frequencies in case of large distances between the aircraft and the measurement system. The effect of the application of the measured spectra on the Doc.29 weather correction is found to be smaller than 0.5 dBA for the NOMOS positions, indicating the suitability of the current ANP spectral classes for the weather correction.
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Accurate modelling of aircraft noise in different weather conditions is crucial for the reliability of noise predictions and their application worldwide. In best-practice aircraft noise models, such as Doc.29, the change in expected sound level on the ground due to changing atmosphere is modelled with a simplified propagation calculation. The Aircraft Noise and Performance (ANP) database contains several standardised source spectra, known as spectral classes, which are used for these calculations to account for the frequency dependence of the atmospheric effects. The spectral classes consist of Pressure Band Levels (PBL) of 24 1/3rd octave bands. This research focuses on the agreement of these spectral classes with measurements, taken around Amsterdam Schiphol Airport, and quantifies the effect of differences in these spectra for the Doc.29 weather correction. The measurements, taken by an acoustic array close to the runway and by continuous single microphone noise measurement stations (NOMOS) at long range, are propagated to the standard distance of 1000 ft (for which the ANP spectral classes are given) taking into account the geometrical spreading and the actual atmospheric absorption. For the B737-800 and A330, differences in shape are found between the two measured spectra and the spectral class. This is partly due to the low signal-to-noise ratio for the high frequencies in case of large distances between the aircraft and the measurement system. The effect of the application of the measured spectra on the Doc.29 weather correction is found to be smaller than 0.5 dBA for the NOMOS positions, indicating the suitability of the current ANP spectral classes for the weather correction.