RV
R.C. Van der Grift
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1
Thrust and weight estimation for Doc. 29 noise models
Using ACMS data to more accurately predict noise levels at Amsterdam Airport Schiphol
Master thesis
(2026)
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E.S.D. van Pijlen, M. Snellen, R.C. Van der Grift, J.A. Melkert, A. Amiri Simkooei, J. Sun
This paper investigates whether aircraft noise modeling can be improved by more accurately predicting the aircraft weight and thrust compared to the current methodology ECAC (European Civil Aviation Conference) described in Doc. 29 (Document 29). Using ACMS (Aircraft Condition Monitoring System) data from multiple aircraft types, two new weight estimation methods are proposed for departures: a climb slope and distance based approach, and a specific-energy method. The MAPE (Mean Average Percentage Error) of the current stage length approach is compared to the newly proposed methods. For thrust estimation, departures during the initial take-off roll and climb out are modeled using weight-dependent interpolations of the FPPs (Fixed-Point Profiles). For the other parts of the departure process, median FPPs, for which boundaries are determined by a flight segmentation model, are used. Arrival thrust values are predicted using a random forest regression model trained on flight path angle, calibrated airspeed, and corrected net thrust. This random forest model accurately captures thrust peak magnitudes and locations for most flights. Noise contour plots are generated for an original Doc. 29 model, an ACMS Doc. 29 and a new weight and thrust Doc. 29 model. For the ACMS Doc. 29 model, the ACMS thrust and weight data is directly used as input data for the noise model. The new weight and thrust estimates reveal closer agreement with ACMS Doc. 29 contours than with the original Doc. 29 method. This result indicates the rigidity of the FPPs and outdated ANP (Aircraft Noise Performance) database entries contribute to current modeling inaccuracies. The results demonstrate that the incorporation of performance relationships can significantly improve the theoretical Doc. 29 model.
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This paper investigates whether aircraft noise modeling can be improved by more accurately predicting the aircraft weight and thrust compared to the current methodology ECAC (European Civil Aviation Conference) described in Doc. 29 (Document 29). Using ACMS (Aircraft Condition Monitoring System) data from multiple aircraft types, two new weight estimation methods are proposed for departures: a climb slope and distance based approach, and a specific-energy method. The MAPE (Mean Average Percentage Error) of the current stage length approach is compared to the newly proposed methods. For thrust estimation, departures during the initial take-off roll and climb out are modeled using weight-dependent interpolations of the FPPs (Fixed-Point Profiles). For the other parts of the departure process, median FPPs, for which boundaries are determined by a flight segmentation model, are used. Arrival thrust values are predicted using a random forest regression model trained on flight path angle, calibrated airspeed, and corrected net thrust. This random forest model accurately captures thrust peak magnitudes and locations for most flights. Noise contour plots are generated for an original Doc. 29 model, an ACMS Doc. 29 and a new weight and thrust Doc. 29 model. For the ACMS Doc. 29 model, the ACMS thrust and weight data is directly used as input data for the noise model. The new weight and thrust estimates reveal closer agreement with ACMS Doc. 29 contours than with the original Doc. 29 method. This result indicates the rigidity of the FPPs and outdated ANP (Aircraft Noise Performance) database entries contribute to current modeling inaccuracies. The results demonstrate that the incorporation of performance relationships can significantly improve the theoretical Doc. 29 model.
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 is evaluated in predicting noise levels around Schiphol airport 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, maximum noise levels and frequency spectra. These 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 confirming the applicability of sonAIR to another airport. This research contributes to better aircraft noise predictions, which will have implications ultimately leading 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 is evaluated in predicting noise levels around Schiphol airport 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, maximum noise levels and frequency spectra. These 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 confirming the applicability of sonAIR to another airport. This research contributes to better aircraft noise predictions, which will have implications ultimately leading to a better quality of life for communities affected by aircraft noise.