| Citation: | DU Z M，ZHANG J F，MIAO H L，et al. Aircraft vertical profile prediction for continuous climb based on thrust intention[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（4）：1347-1353 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0446
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Accurate continuous climb profile prediction can improve conflict detection reliability and departure scheduling precision. A method of modeling aircraft thrust intention based on the thrust setting information is proposed. A vertical profile prediction method for the continuous ascent is suggested, taking into account temperature data, wind vector, thrust purpose, and the total energy model. The case studies and comparative analysis are based on quick access recorder (QAR) data. The analysis is focused on the error between the predicted and actual values of true airspeed, altitude, and fuel flow at each sampling data from QAR. In addition, the evaluation is done on the mean absolute error of the duration and distance to the top of climb (TOC) between the actual and anticipated values. The results indicate that the TOC arrival time mean absolute error could be controlled to within 1 minute by the proposed prediction method. The arrival time mean absolute error at TOC can be reduced by approximately 52% compared to prediction methods without considering the thrust intent.
| [1] |
曾雯, 胡荣, 宋文, 等. 中国民用航空器CO2减排潜力的区域划分[J]. 北京航空航天大学学报, 2023, 49(9): 2455-2462.
ZENG W, HU R, SONG W, et al. Regional classification of CO2 emission reduction potential of China’s civil aircraft[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(9): 2455-2462 (in Chinese).
|
| [2] |
ZENG W L, CHU X, XU Z F, et al. Aircraft 4D trajectory prediction in civil aviation: A review[J]. Aerospace, 2022, 9(2): 91. doi: 10.3390/aerospace9020091
|
| [3] |
ZHANG J F, LIU J, HU R, et al. Online four dimensional trajectory prediction method based on aircraft intent updating[J]. Aerospace Science and Technology, 2018, 77: 774-787. doi: 10.1016/j.ast.2018.03.037
|
| [4] |
ZHANG J F, PENG Z H, YANG C W, et al. Data-driven flight time prediction for arrival aircraft within the terminal area[J]. IET Intelligent Transport Systems, 2022, 16(2): 263-275. doi: 10.1049/itr2.12142
|
| [5] |
WANG Z Y, LIANG M, DELAHAYE D. A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area[J]. Transportation Research Part C:Emerging Technologies, 2018, 95: 280-294. doi: 10.1016/j.trc.2018.07.019
|
| [6] |
LIANG M, DELAHAYE D, MARECHAL P. Conflict-free arrival and departure trajectory planning for parallel runway with advanced point-merge system[J]. Transportation Research Part C:Emerging Technologies, 2018, 95: 207-227. doi: 10.1016/j.trc.2018.07.006
|
| [7] |
FEUSER FERNANDES H, MÜLLER C. Optimization of the waiting time and makespan in aircraft departures: A real time non-iterative sequencing model[J]. Journal of Air Transport Management, 2019, 79: 101686. doi: 10.1016/j.jairtraman.2019.101686
|
| [8] |
HO-HUU V, HARTJES S, VISSER H G, et al. Integrated design and allocation of optimal aircraft departure routes[J]. Transportation Research Part D:Transport and Environment, 2018, 63: 689-705. doi: 10.1016/j.trd.2018.07.006
|
| [9] |
GUO Y, HU M H, ZOU B, et al. Air traffic flow management integrating separation management and ground holding: An efficiency-equity Bi-objective perspective[J]. Transportation Research Part B:Methodological, 2022, 155: 394-423. doi: 10.1016/j.trb.2021.12.004
|
| [10] |
张军峰, 葛腾腾, 陈强, 等. 离场航空器四维航迹预测及不确定性分析[J]. 西南交通大学学报, 2016, 51(4): 800-806. doi: 10.3969/j.issn.0258-2724.2016.04.027
ZHANG J F, GE T T, CHEN Q, et al. 4D trajectory prediction and uncertainty analysis for departure aircraft[J]. Journal of Southwest Jiaotong University, 2016, 51(4): 800-806 (in Chinese). doi: 10.3969/j.issn.0258-2724.2016.04.027
|
| [11] |
SUN J Z, ELLERBROEK J, HOEKSTRA J M. WRAP: An open-source kinematic aircraft performance model[J]. Transportation Research Part C:Emerging Technologies, 2019, 98: 118-138. doi: 10.1016/j.trc.2018.11.009
|
| [12] |
SUN J Z, ELLERBROEK J, HOEKSTRA J M. Aircraft initial mass estimation using Bayesian inference method[J]. Transportation Research Part C:Emerging Technologies, 2018, 90: 59-73. doi: 10.1016/j.trc.2018.02.022
|
| [13] |
ALLIGIER R, GIANAZZA D, DURAND N. Learning the aircraft mass and thrust to improve the ground-based trajectory prediction of climbing flights[J]. Transportation Research Part C:Emerging Technologies, 2013, 36: 45-60. doi: 10.1016/j.trc.2013.08.006
|
| [14] |
ALLIGIER R, GIANAZZA D, DURAND N. Machine learning and mass estimation methods for ground-based aircraft climb prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6): 3138-3149. doi: 10.1109/TITS.2015.2437452
|
| [15] |
ALLIGIER R, GIANAZZA D. Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study[J]. Transportation Research Part C:Emerging Technologies, 2018, 96: 72-95. doi: 10.1016/j.trc.2018.08.012
|
| [16] |
Eurocontrol Experimental Center. User manual for the base of aircraft data (BADA) [Z]. Paris: Eurocontrol Experimental Center, 2014: 22.
|
| [17] |
European Civil Aviation Conference. Report on standard method of computing noise contours around civil airports – Volume 2: Technical guide [S]. Paris: European Civil Aviation Conference, 2016: B5-B6.
|
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