考虑推力意图的航空器连续爬升垂直剖面预测

杜卓铭; 张军峰; 苗洪连; 王斌

doi:10.13700/j.bh.1001-5965.2022.0446

考虑推力意图的航空器连续爬升垂直剖面预测

doi: 10.13700/j.bh.1001-5965.2022.0446

1.
南京航空航天大学民航学院，南京 210016
2.
中国民用航空中南地区空中交通管理局，广州 510403

基金项目: 国家自然科学基金(U1933117)

详细信息

通讯作者:
E-mail：zhangjunfeng@nuaa.edu.cn

中图分类号: V352
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- 被引次数: 0
出版历程
- 收稿日期: 2022-05-31
- 录用日期: 2022-07-29
- 网络出版日期: 2022-09-14
- 整期出版日期: 2024-04-29

Aircraft vertical profile prediction for continuous climb based on thrust intention

1.
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2.
Central and Southern Regional Air Traffic Management Bureau of the Civil Aviation Administration of China, Guangzhou 510403, China

Funds: National Natural Science Foundation of China (U1933117)

More Information

Corresponding author: E-mail：zhangjunfeng@nuaa.edu.cn

摘要

摘要:
航空器连续爬升剖面的准确预测可以提高冲突探测的可靠性和离场排序的精确性。基于推力设置信息，提出航空器推力意图的建模方法。利用航空器的全能量方程，考虑风速向量与温度信息，提出考虑推力意图的航空器连续爬升垂直剖面的预测方法。利用快速存取记录器(QAR)数据，进行多案例的对比分析，以QAR每一数据帧为采样点，重点对预测剖面的真空速、高度和燃油流量等进行误差分析。考察不同预测方法，对比到达爬升顶点(TOC)的时间和距离的误差。结果表明：采用所提预测方法可以将到达TOC时间平均绝对误差控制在1 min内；与不考虑推力意图的预测方法相比，可以降低到达TOC时间平均绝对误差约52%。
- 空中交通管理 /
- 航迹预测 /
- 连续爬升 /
- 性能模型 /
- 推力意图
Abstract:
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.
- air traffic management /
- trajectory prediction /
- continuous climb /
- performance model /
- thrust intention

HTML全文

图 1 航空器连续爬升垂直剖面的基础意图示意图

Figure 1. Aircraft basic intent for vertical profiles under continuous climb operation

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图 2 不同推力意图对垂直剖面影响

Figure 2. Impact on vertical profiles of different thrust intentions

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图 3 风分量与标准温差

Figure 3. Wind component and standard temperature deviation

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图 4 高度和速度剖面预测

Figure 4. Prediction of altitude and speed profile

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图 5 推力预测

Figure 5. Prediction of thrust

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图 6 燃油流量预测

Figure 6. Prediction of fuel flow

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图 7 航空器状态变量误差箱线图

Figure 7. Absolute error box diagram of aircraft state variebles

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表 1 推力意图与推力对应关系

Table 1. Correspondence of thrust intention and thrust

推力意图	描述	对应推力
MAN TOGA	最大起飞推力	ANP 最大起飞推力
THR CLB	爬升推力	BADA 最大爬升推力
SPEED	加速	保持原阶段推力
MAN FLX	灵活推力	110% BADA爬升推力
THR DCLB1	一挡减推力	ANP一挡减推力
THR DCLB2	二挡减推力	85% BADA推力
THR DCLB3	三挡减推力	ANP二挡减推力

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表 2 到达TOC时间位置误差

Table 2. Time and position error of arrival in TOC

组号	$ {t_{{\text{TOC}}}} $/s	${s_{{\text{TOC}}}}$/km	$ \varepsilon _{{\text{AE}}}^t $/s			$ \varepsilon _{{\text{AE}}}^s $/km
组号	$ {t_{{\text{TOC}}}} $/s	${s_{{\text{TOC}}}}$/km	传统方法	考虑气象方法	本文预测方法	传统方法	考虑气象方法	本文预测方法
1	416	52	17	13	46	6	1	11
2	557	84	149	138	74	25	26	16
3	399	49	1	8	51	8	0	11
4	512	69	137	130	16	15	18	7
5	519	68	135	131	14	13	17	5
6	478	55	88	83	66	1	7	17
7	512	69	137	130	16	15	18	7

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参考文献(17)

[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.