A Data-Driven Approach to Departure and Arrival Noise Abatement Flight Procedure Development
An aircraft noise modeling framework is presented and used to perform a data-driven exploration of factors correlating with measured aircraft community noise and a model-based validation of the variables found to have the greatest noise impact. Aggregate departure and arrival noise and flight proced...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/145055 |
| _version_ | 1826214690154348544 |
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| author | Mahseredjian, Ara |
| author2 | Hansman, R. John |
| author_facet | Hansman, R. John Mahseredjian, Ara |
| author_sort | Mahseredjian, Ara |
| collection | MIT |
| description | An aircraft noise modeling framework is presented and used to perform a data-driven exploration of factors correlating with measured aircraft community noise and a model-based validation of the variables found to have the greatest noise impact. Aggregate departure and arrival noise and flight procedures were examined so that factors correlating with measured noise could be isolated. Operational flights at Seattle-Tacoma International Airport were examined using a framework that includes ADS-B data, a force balance kinematics model to estimate aircraft performance, and noise monitor recordings. Variation in measured noise within the network was examined as a function of observed data, including aircraft type, aircraft trajectory, airline, wind, temperature, and relative humidity; and inferred variables, including aircraft configuration, weight, and thrust. Airline-specific departure procedures were shown to impact noise measurements. Departure procedures with higher thrust and higher initial climb gradients were observed to have lower measured noise. Arrival procedures that delayed their deceleration were observed to have lower measured noise in some cases. Ambient environmental conditions, including wind, temperature, and relative humidity, were found to impact noise variation. A model-based evaluation of the factors correlating with aircraft noise followed the data-driven exploration. The delayed deceleration approach, a procedure in which aircraft maintain higher speeds, remain cleanly configured, and fly with lower thrust levels for a longer period of time, was identified as having noise reduction potential beyond 8 nm from the airport. Noise from operational Boeing 737, Airbus A320, and Embraer E190 flights at Boston Logan and Seattle Tacoma airports was modeled using the NASA Airplane Noise Prediction Program and was compared with ground noise monitor measurements. When corrected for atmospheric conditions, modeled noise results were consistent with noise monitor readings under reasonable flap deployment assumptions during various early, intermediate, and delayed deceleration approach procedures. Measured noise results indicated that compared to aircraft that decelerated early, aircraft performing delayed deceleration approaches reduced noise by an average of 3-6 dB across different aircraft types. |
| first_indexed | 2024-09-23T16:09:49Z |
| format | Thesis |
| id | mit-1721.1/145055 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:09:49Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1450552022-08-30T03:05:01Z A Data-Driven Approach to Departure and Arrival Noise Abatement Flight Procedure Development Mahseredjian, Ara Hansman, R. John Huynh, Jacqueline L. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics An aircraft noise modeling framework is presented and used to perform a data-driven exploration of factors correlating with measured aircraft community noise and a model-based validation of the variables found to have the greatest noise impact. Aggregate departure and arrival noise and flight procedures were examined so that factors correlating with measured noise could be isolated. Operational flights at Seattle-Tacoma International Airport were examined using a framework that includes ADS-B data, a force balance kinematics model to estimate aircraft performance, and noise monitor recordings. Variation in measured noise within the network was examined as a function of observed data, including aircraft type, aircraft trajectory, airline, wind, temperature, and relative humidity; and inferred variables, including aircraft configuration, weight, and thrust. Airline-specific departure procedures were shown to impact noise measurements. Departure procedures with higher thrust and higher initial climb gradients were observed to have lower measured noise. Arrival procedures that delayed their deceleration were observed to have lower measured noise in some cases. Ambient environmental conditions, including wind, temperature, and relative humidity, were found to impact noise variation. A model-based evaluation of the factors correlating with aircraft noise followed the data-driven exploration. The delayed deceleration approach, a procedure in which aircraft maintain higher speeds, remain cleanly configured, and fly with lower thrust levels for a longer period of time, was identified as having noise reduction potential beyond 8 nm from the airport. Noise from operational Boeing 737, Airbus A320, and Embraer E190 flights at Boston Logan and Seattle Tacoma airports was modeled using the NASA Airplane Noise Prediction Program and was compared with ground noise monitor measurements. When corrected for atmospheric conditions, modeled noise results were consistent with noise monitor readings under reasonable flap deployment assumptions during various early, intermediate, and delayed deceleration approach procedures. Measured noise results indicated that compared to aircraft that decelerated early, aircraft performing delayed deceleration approaches reduced noise by an average of 3-6 dB across different aircraft types. S.M. 2022-08-29T16:29:55Z 2022-08-29T16:29:55Z 2022-05 2022-06-09T16:14:40.513Z Thesis https://hdl.handle.net/1721.1/145055 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mahseredjian, Ara A Data-Driven Approach to Departure and Arrival Noise Abatement Flight Procedure Development |
| title | A Data-Driven Approach to Departure and Arrival Noise Abatement Flight Procedure Development |
| title_full | A Data-Driven Approach to Departure and Arrival Noise Abatement Flight Procedure Development |
| title_fullStr | A Data-Driven Approach to Departure and Arrival Noise Abatement Flight Procedure Development |
| title_full_unstemmed | A Data-Driven Approach to Departure and Arrival Noise Abatement Flight Procedure Development |
| title_short | A Data-Driven Approach to Departure and Arrival Noise Abatement Flight Procedure Development |
| title_sort | data driven approach to departure and arrival noise abatement flight procedure development |
| url | https://hdl.handle.net/1721.1/145055 |
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