Large aperture microphone array measurement at operating airport for sound source model creation of J-FRAIN

In 2022, authors conducted microphone array measurements at an operating airport to create sound source models for commercial aircraft based on the noise prediction framework J-FRAIN. A similar measurement was also performed by our colleagues in 2019, but it was a trial to establish the concept of the framework and was done using a microphone array in which the number of microphones was reduced to about half. In order to improve the accuracy of the sound source models, the microphone array with a 30-meter diameter consisting of 195 microphones mounted on the ground was used in 2022. Recording-based simultaneous noise measurement using sound level meters was also performed around the airport to validate prediction results in terms of time histories of 1/3 octave band sound pressure levels. This paper describes an overview of the microphone array measurements conducted in 2022 and a case study of noise prediction around the airport using sound source models created from the data of microphone array measurements conducted in 2022. Predicted results are compared with 1/3 octave band data measured using sound level meters mounted at a height of 4 m above the ground at sites of noise monitoring stations around the airport.

Authors

Institutions

Publication Details

Journal
NOISE-CON proceedings
Published
2024-10-04
DOI
https://doi.org/10.3397/in_2024_3995
Primary Topic
Aerodynamics and Acoustics in Jet Flows
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Large aperture microphone array measurement at operating airport for sound source model creation of J-FRAIN

Taro Imamura, Yasuaki Kawase, Toshiyasu Nakazawa, Naoaki Shinohara et al.
NOISE-CON proceedings
Aerodynamics and Acoustics in Jet Flows
article

Large aperture microphone array measurement at operating airport for sound source model creation of J-FRAIN

Taro Imamura, Yasuaki Kawase, Toshiyasu Nakazawa, Naoaki Shinohara, Takehisa TAKAISHI, Tomohiro Kobayashi
article en

Abstract

In 2022, authors conducted microphone array measurements at an operating airport to create sound source models for commercial aircraft based on the noise prediction framework J-FRAIN. A similar measurement was also performed by our colleagues in 2019, but it was a trial to establish the concept of the framework and was done using a microphone array in which the number of microphones was reduced to about half. In order to improve the accuracy of the sound source models, the microphone array with a 30-meter diameter consisting of 195 microphones mounted on the ground was used in 2022. Recording-based simultaneous noise measurement using sound level meters was also performed around the airport to validate prediction results in terms of time histories of 1/3 octave band sound pressure levels. This paper describes an overview of the microphone array measurements conducted in 2022 and a case study of noise prediction around the airport using sound source models created from the data of microphone array measurements conducted in 2022. Predicted results are compared with 1/3 octave band data measured using sound level meters mounted at a height of 4 m above the ground at sites of noise monitoring stations around the airport.

NOISE-CON proceedingsVol. 270(4)
Japan Aerospace Exploration Agency (JP), Kobayasi Institute of Physical Research (JP), Port and Airport Research Institute (JP), The University of Tokyo (JP)
Affordable and clean energy
Openalex Percentile: Top 73%
Aerodynamics and Acoustics in Jet Flows
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.