Environmental Impact by Air Traffic: Assessing Aircraft Noise nearby HK Airport
Project Description

The environmental noise pollution due to civilian aviation now constitutes the primary obstacle to a sustainable future for air transportation, the continuous growth (+5% every year) of which comes along with increased levels of societal and health concerns worldwide. This not only threatens the development of air travel (expected to double by 2040) but may also prevent the emergence of future airspace solutions (e.g. supersonic transportation). In particular, it has become critical to further mitigate the noise impact due to air traffic around airports of major cities – to begin with Hong Kong, which ranks 6th (resp. 1st) in the world for what concerns the yearly volume of passengers (resp. cargo).

Since decades, air traffic noise mitigation was achieved by constantly reducing the aircraft noise at its source, for instance through the development of quieter engines and/or low-noise aircraft architectures. A more recent and very promising alternative is to mitigate the overall noise impact on the ground, for instance by optimizing the aircraft flight-paths (e.g., the so-called Continuous Descent Approach, which is now adopted by major airports, including Hong Kong International Airport, HKIA). Doing so, however, requires developing accurate prediction means (e.g. aircraft noise prediction software) and optimization tools (e.g. multidisciplinary optimization platform) that may allow modelling the entire set of aircraft operations around airports. These computational means must be validated against actual noise data coming from actual aircraft movements (take-off and climb, approach and landing).

For helping the on-going development of such an optimization platform by HKUST researchers, this UROP project will consist in acquiring and exploiting a representative and reliable set of aircraft noise samples around HKIA. This will be achieved through in-situ audio recordings of aircraft during take-off/landing flight phases, along with an accurate tracking of their characteristics (aircraft and engines type, flight-paths, atmospheric conditions, etc). The audio datasets will then have to be post-processed (e.g. background noise removal) and exploited (e.g. spectral analyses) so as to feed the needs of computational platform validation.

Successfully conducting this research action will imply overcoming various technical challenges. This will require the UROP participant(s) to acquire and master specific knowledge and skills, whether theoretical (noise physics, signal theory) or practical (experimental preparation/acquisition/exploitation). Besides, this research action will have to be conducted in close coordination with the HKUST researchers involved in this underlying effort of air traffic noise mitigation. On another hand, this research action shall provide the UROP participant(s) an exciting opportunity to taste the water of what R&D is all about, by tackling a challenging problem within an actual research framework.

Supervisor
REDONNET Stephane
Co-Supervisor
REDONNET Stephane
HORNER, Andrew Brian
Quota
3
Course type
UROP1000
UROP1100
UROP2100
Applicant's Roles

The UROP participant(s) will
- plan, execute and exploit a full-scale experimental campaign
- perform in-situ audio recordings of aircraft during take-off/landing flight phases, using professional means available at HKUST
- track the aircraft and flight characteristics (aircraft and engines type, flight paths, atmospheric conditions, etc).
- post-process (e.g. background noise removal) and exploit (e.g. spectral analyses) the audio datasets
- support the validation efforts of the computational platform

Applicant's Learning Objectives

1. Learn how to conduct academic - and yet applied - research
2. Learn how to plan, execute and exploit a full-scale experiment
3. Learn how to cope with the unavoidable challenges/issues of conducting experiments out of a preserved/clean laboratory environment
4. Learn about aircraft, air traffic, and airport operations
5. Learn about noise physics and beyond (e.g. signal theory)

Complexity of the project
Challenging