Statistical modeling of aircraft takeoff weight
The Takeoff Weight (TOW) of an aircraft is an important aspect of aircraft performance, and impacts a large number of characteristics, ranging from the trajectory to the fuel burn of the flight. Due to its dependence on factors such as the passenger and cargo load factors as well as operating strate...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
ATM
2018
|
| Online Access: | http://hdl.handle.net/1721.1/115247 https://orcid.org/0000-0001-7664-4230 https://orcid.org/0000-0002-8624-7041 |
| _version_ | 1826214203485061120 |
|---|---|
| author | Chati, Yashovardhan Sushil Balakrishnan, Hamsa |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Chati, Yashovardhan Sushil Balakrishnan, Hamsa |
| author_sort | Chati, Yashovardhan Sushil |
| collection | MIT |
| description | The Takeoff Weight (TOW) of an aircraft is an important aspect of aircraft performance, and impacts a large number of characteristics, ranging from the trajectory to the fuel burn of the flight. Due to its dependence on factors such as the passenger and cargo load factors as well as operating strategies, the TOW of a particular flight is generally not available to entities outside of the operating airline. The above observations motivate the development of accurate TOW estimates that can be used for fuel burn estimation or trajectory prediction. This paper proposes a statistical approach based on Gaussian Process Regression (GPR) to determine both a mean estimate of the TOW and the associated confidence interval, using observed data from the takeoff ground roll. The predictor variables are chosen by considering both their ease of availability and the underlying aircraft dynamics. The model development and validation are conducted using Flight Data Recorder archives, which also provide ground truth data. The proposed models are found to have a mean TOW error of 3%, averaged across eight different aircraft types, resulting in a nearly 50% smaller error than the models in the Aircraft Noise and Performance (ANP) database. In contrast to the ANP database which provides only point estimates of the TOW, the GPR models quantify the uncertainty in the estimates by providing a probability distribution. Finally, the developed models are used to estimate aircraft fuel flow rate during ascent. The TOW estimated by the GPR models is used as an input to the fuel flow rate estimation. The proposed statistical models of the TOW are shown to enable a better quantification of uncertainty in the fuel flow rate as compared to the deterministic ANP models, or to models that do not use the TOW as an explicit input. |
| first_indexed | 2024-09-23T16:01:45Z |
| format | Article |
| id | mit-1721.1/115247 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:01:45Z |
| publishDate | 2018 |
| publisher | ATM |
| record_format | dspace |
| spelling | mit-1721.1/1152472022-10-02T05:47:25Z Statistical modeling of aircraft takeoff weight Chati, Yashovardhan Sushil Balakrishnan, Hamsa Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Chati, Yashovardhan Sushil Balakrishnan, Hamsa The Takeoff Weight (TOW) of an aircraft is an important aspect of aircraft performance, and impacts a large number of characteristics, ranging from the trajectory to the fuel burn of the flight. Due to its dependence on factors such as the passenger and cargo load factors as well as operating strategies, the TOW of a particular flight is generally not available to entities outside of the operating airline. The above observations motivate the development of accurate TOW estimates that can be used for fuel burn estimation or trajectory prediction. This paper proposes a statistical approach based on Gaussian Process Regression (GPR) to determine both a mean estimate of the TOW and the associated confidence interval, using observed data from the takeoff ground roll. The predictor variables are chosen by considering both their ease of availability and the underlying aircraft dynamics. The model development and validation are conducted using Flight Data Recorder archives, which also provide ground truth data. The proposed models are found to have a mean TOW error of 3%, averaged across eight different aircraft types, resulting in a nearly 50% smaller error than the models in the Aircraft Noise and Performance (ANP) database. In contrast to the ANP database which provides only point estimates of the TOW, the GPR models quantify the uncertainty in the estimates by providing a probability distribution. Finally, the developed models are used to estimate aircraft fuel flow rate during ascent. The TOW estimated by the GPR models is used as an input to the fuel flow rate estimation. The proposed statistical models of the TOW are shown to enable a better quantification of uncertainty in the fuel flow rate as compared to the deterministic ANP models, or to models that do not use the TOW as an explicit input. National Science Foundation (U.S.) (Award 0931843) 2018-05-07T17:34:36Z 2018-05-07T17:34:36Z 2017-06 2018-03-14T16:34:33Z Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/115247 Chati, Yashovardhan S. and Hamsa Balakrishnan. "Statistical Modeling of Aircraft Takeoff Weight." Twelfth USA/Europe Air Traffic Management Research and Development Seminar (ATM 2017), 26-30 June, 2017, Seattle, Washington, ATM, 2017. https://orcid.org/0000-0001-7664-4230 https://orcid.org/0000-0002-8624-7041 http://www.atmseminarus.org/12th-seminar/papers/ Twelfth USA/Europe Air Traffic Management Research and Development Seminar (ATM 2017) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf ATM MIT Web Domain |
| spellingShingle | Chati, Yashovardhan Sushil Balakrishnan, Hamsa Statistical modeling of aircraft takeoff weight |
| title | Statistical modeling of aircraft takeoff weight |
| title_full | Statistical modeling of aircraft takeoff weight |
| title_fullStr | Statistical modeling of aircraft takeoff weight |
| title_full_unstemmed | Statistical modeling of aircraft takeoff weight |
| title_short | Statistical modeling of aircraft takeoff weight |
| title_sort | statistical modeling of aircraft takeoff weight |
| url | http://hdl.handle.net/1721.1/115247 https://orcid.org/0000-0001-7664-4230 https://orcid.org/0000-0002-8624-7041 |
| work_keys_str_mv | AT chatiyashovardhansushil statisticalmodelingofaircrafttakeoffweight AT balakrishnanhamsa statisticalmodelingofaircrafttakeoffweight |