Calculation of the fan rotational speed based on flyover recordings for improving aircraft noise prediction models

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

The enforcement of noise control regulations around airports depends on the estimations of aircraft noise prediction models. Current best–practice noise contour calculation methods assume default engine thrust values depending on the engine type and the altitude of the aircraft. These prediction tools provide a single noise level for a certain aircraft type in a certain flight phase and at a specific distance from the observer. In practice, however, changes in the thrust occur and cause variations in the noise levels of several decibels. In this paper, an approach is presented to estimate the fan rotational speed N1% (and hence the thrust) directly from flyover audio recordings. This method estimates the blade passing frequency (BPF) of the fan by searching its characteristic tonal peak (and its higher harmonics) and accounting for the Doppler effect. This method was applied to more than 400 measurements of Airbus A330–300 aircraft. The results show a significant correlation between the recorded noise levels and the fan rotational speed, explaining up to 43% of the variability in noise levels. Considering the calculated N1% values in the noise prediction models notably increases the agreement of the estimations with the recorded noise levels.
Original languageEnglish
Title of host publication23rd International Congress on Acoustics, 9-13 September 2019. Aachen, Germany
Number of pages8
ISBN (Electronic)978-3-939296-15-7
Publication statusPublished - 2019
EventICA 2019: 23rd International Congress on Acoustics - Aachen, Germany
Duration: 9 Sep 201913 Sep 2019

Conference

ConferenceICA 2019: 23rd International Congress on Acoustics
CountryGermany
CityAachen
Period9/09/1913/09/19

Fingerprint Dive into the research topics of 'Calculation of the fan rotational speed based on flyover recordings for improving aircraft noise prediction models'. Together they form a unique fingerprint.

  • Cite this