容量受限下城市对航班四维航迹优化

doi:10.7527/S1000-6893.2021.25581

电子电气工程与控制

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容量受限下城市对航班四维航迹优化

谢华¹, 黎子弘¹, 杨磊¹, 朱永文², 刘芳子^1,3

1. 南京航空航天大学民航学院, 南京 211106;
2. 国家空域技术重点实验室, 北京 100085;
3. 中国民用航空局空中交通管理局战略发展部, 北京 100020

收稿日期:2021-03-29 修回日期:2021-06-21 出版日期:2022-08-15 发布日期:2021-06-18
通讯作者: 黎子弘,E-mail:zhli15@nuaa.edu.cn E-mail:zhli15@nuaa.edu.cn
基金资助:
国家自然科学基金(61903187);江苏省自然科学基金(BK20190414)

Optimization of four-dimensional trajectory of city pair with limited capacity

XIE Hua¹, LI Zihong¹, YANG Lei¹, ZHU Yongwen², LIU Fangzi^1,3

1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Key Laboratory of National Airspace Technology, Beijing 100085, China;
3. Strategic Development Department, Air Traffic Management Bureau, Civil Aviation Administration of China, Beijing 100020, China

Received:2021-03-29 Revised:2021-06-21 Online:2022-08-15 Published:2021-06-18
Supported by:
National Natural Science Foundation of China (61903187);Natural Science Foundation of Jiangsu Province (BK20190414)

摘要/Abstract

摘要： 航班燃油效率的提高是全球航空运输系统转型发展的关键目标和重要挑战之一。基于航迹运行(TBO)的实施将对于增强空中交通可预测性和提高飞行效率具有重大作用,也为航班节能减排提供了新手段。综合考虑航空器动力学性能限制、可用航路限制、扇区容量约束、空中交通管制对于航班运行高度和速度的限制等,提出了容量受限下城市对"跑道-跑道"四维航迹多目标规划方法,适应性改进带精英策略的非支配排序遗传算法并进行求解。以上海虹桥-北京首都城市对航线为例,结果表明,在容量受限下,通过协同规划航班飞行路径、垂直剖面和飞行速度最多可以减少油耗8.79%,同时探讨了空域拥堵的时空特征对于航空器燃油效率的影响,阐述了优化结果随着拥堵发生的空间位置和严重程度的变化规律。为容量受限下科学配置城市对间空域资源提供了方法框架,将有利于促进TBO在我国的推广应用。

关键词: 空中交通管理, 基于航迹运行, 航迹规划, 燃油效率, 容流平衡

Abstract: Improvement of aircraft fuel efficiency is one of the key goals and important challenges for the transformation and development of the global air transportation system. Implementation of Trajectory-Based Operation (TBO) will play a major role in enhancing the predictability of air traffic and flight efficiency, and will also provide new means for the aircraft to save energy and reduce emission. Based on a comprehensive consideration of the influence of aircraft dynamics, available routes, sector capacity, and air traffic control on the aircraft’s operating altitude and speed, this paper proposes a multi-objective planning method for the "runway-runway" four-dimensional trajectory of the city pair with limited capacity, and adaptively improves the non-dominated sorting genetic algorithm with the elite strategy and solves the algorithm. The Shanghai Hongqiao-Beijing Capital city pair route is used as an example. The results show that with limited capacity, cooperative planning of flight paths, vertical profile and flight speed can reduce fuel consumption by up to 8.79%. The influence of the temporal and spatial characteristics of airspace congestion on the fuel efficiency of aircraft is discussed, and the pattern that how optimal solutions evolve with different locations and severity of congestion is also analyzed. This paper provides a framework for scientific allocation of city pair airspace resources with limited capacity, which will facilitate popularization and application of TBO in China.

Key words: air traffic management, trajectory-based operation, trajectory planning, fuel efficiency, demand and capacity balancing

中图分类号:

V355

谢华, 黎子弘, 杨磊, 朱永文, 刘芳子. 容量受限下城市对航班四维航迹优化[J]. 航空学报, 2022, 43(8): 325581-325581.

XIE Hua, LI Zihong, YANG Lei, ZHU Yongwen, LIU Fangzi. Optimization of four-dimensional trajectory of city pair with limited capacity[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(8): 325581-325581.

参考文献

[1] BAI B H, LI X D, CHEN H X. Aerodynamic and aeroacoustics optimization design of multi-element airfoil by a genetic algorithm[C]//25th AIAA/CEAS Aeroacoustics Conference. Reston: AIAA, 2019: 2762.
[2] AIGNER B, STUMPF E, HINZ A, et al. An integrated design framework for aircraft with hybrid electric propulsion[C]//AIAA Scitech 2020 Forum. Reston: AIAA, 2020: 1501.
[3] QIU R, XU J P, XIE H P, et al. Carbon tax incentive policy towards air passenger transport carbon emissions reduction[J]. Transportation Research Part D: Transport and Environment, 2020, 85: 102441.
[4] SOBIERALSKI J B, HUBBARD S M. The effect of jet fuel tax changes on air transport, employment, and the environment in the US[J]. Sustainability, 2020, 12(8): 3352.
[5] MURRIETA-MENDOZA A, BOTEZ R M. Commercial aircraft trajectory optimization to reduce flight costs and pollution: Metaheuristic algorithms[M]. Berlin:Springer, 2020: 33-62.
[6] LIM Y, GARDI A, SABATINI R, et al. Optimal energy-based 4D guidance and control for terminal descent operations[J]. Aerospace Science and Technology, 2019, 95: 105436.
[7] 唐建军, 郭卫东, 徐东光, 等. 飞机电动滑行系统驱动特性及节能减排性能分析[J]. 北京航空航天大学学报, 2020, 46(8): 1545-1554. TANG J J, GUO W D, XU D G, et al. Driving characteristics and energy saving and emission reduction performance of aircraft electric taxiing system[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(8): 1545-1554 (in Chinese).
[8] JANIC' M. An assessment of the potential of alternative fuels for "greening" commercial air transportation[J]. Journal of Air Transport Management, 2018, 69: 235-247.
[9] ICAO. 2019 environmental report: Aviation and environment:19/09/11-01[R]. Montréal: International Civil Aviation Organization, 2019.
[10] SOLER M, OLIVARES A, STAFFETTI E, et al. Framework for aircraft trajectory planning toward an efficient air traffic management[J]. Journal of Aircraft, 2012, 49(1): 341-348.
[11] CLARKE J P B, HO N T, REN L, et al. Continuous descent approach: Design and flight test for Louisville International Airport[J]. Journal of Aircraft, 2004, 41(5): 1054-1066.
[12] VILLEGAS DÍAZ M, GóMEZ COMENDADOR V F, GARCÍA-HERAS CARRETERO J, et al. Environmental benefits in terms of fuel efficiency and noise when introducing continuous climb operations as part of terminal airspace operation[J]. International Journal of Sustainable Transportation, 2020, 14(12): 903-913.
[13] SÁEZ R, DALMAU R, PRATS X. Optimal assignment of 4D close-loop instructions to enable CDOs in dense TMAs[C]//2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC). Piscataway: IEEE Press, 2018: 1-10.
[14] 李东亚, 胡荣, 张军峰, 等. 航空器持续下降进近技术的发展现状与展望[J]. 航空计算技术, 2016, 46(5): 131-134. LI D Y, HU R, ZHANG J F, et al. Review and prospects on continuous descent approach technology of aircraft[J]. Aeronautical Computing Technique, 2016, 46(5): 131-134 (in Chinese).
[15] 王建忠, 王超, 张宝成. 基于点融合进近的航空器进场4D航迹规划[J]. 科学技术与工程, 2017, 17(14): 333-337. WANG J Z, WANG C, ZHANG B C. 4D trajectory planning method for arrivals based on merging point approach[J]. Science Technology and Engineering, 2017, 17(14): 333-337 (in Chinese).
[16] ERRICO A, VITO V D. Aircraft operating technique for efficient sequencing arrival enabling environmental benefits through CDO in TMA[C]//AIAA Scitech 2019 Forum. Reston: AIAA, 2019.
[17] 杨磊, 李文博, 刘芳子, 等. 柔性空域结构下连续下降航迹多目标优化[J]. 航空学报, 2021, 42(2):324157.YANG L, LI W B, LIU F Z, et al. Multi-objective optimization of continuous descending trajectories in flexible airspace[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(2): 324157 (in Chinese).
[18] LOVEGREN J A A. Estimation of potential aircraft fuel burn reduction in cruise via speed and altitude optimization strategies:ICAT-2011-03[R]. Cambridge: MIT International Center for Air Transportation (ICAT), 2015.
[19] WILLIAMS V, NOLAND R B, TOUMI R. Air transport cruise altitude restrictions to minimize contrail formation[J]. Climate Policy, 2003, 3(3): 207-219.
[20] 王宇, 杨振博, 余雄庆, 等. 考虑风场、雷雨区的下一代客机轨迹多目标优化[J]. 航空计算技术, 2019, 49(2): 19-23. WANG Y, YANG Z B, YU X Q, et al. Multi-objective optimization of next Gen transport trajectory considering wind field and thunderstorm area[J]. Aeronautical Computing Technique, 2019, 49(2): 19-23 (in Chinese).
[21] HARTJES S, VISSER H G, HUBAR M E G. Trajectory optimization of extended formation flights for commercial aviation[J]. Aerospace, 2019, 6(9): 100.
[22] TIAN Y, HE X Q, XU Y, et al. 4D trajectory optimization of commercial flight for green civil aviation[J]. IEEE Access, 2020, 8: 62815-62829.
[23] HARADA A, EZAKI T, WAKAYAMA T, et al. Air traffic efficiency analysis of airliner scheduled flights using collaborative actions for renovation of air traffic systems open data[J]. Journal of Advanced Transportation, 2018, 2018: 2734763.
[24] WICKRAMASINGHE N K, BROWN M, HIRABAYASHI H, et al. Feasibility study on constrained optimal trajectory application in the Japanese airspace[C]//AIAA Modeling and Simulation Technologies Conference. Reston: AIAA, 2017.
[25] GARCÍA-HERAS J, SOLER M, SÁEZ F J. Collocation methods to minimum-fuel trajectory problems with required time of arrival in ATM[J]. Journal of Aerospace Information Systems, 2016, 13(7): 243-265.
[26] DALMAU R, PRATS X. Controlled time of arrival windows for already initiated energy-neutral continuous descent operations[J]. Transportation Research Part C: Emerging Technologies, 2017, 85: 334-347.
[27] GARCÍA-HERAS J, SOLER M, SÁEZ F J. A comparison of optimal control methods for minimum fuel cruise at constant altitude and course with fixed arrival time[J]. Procedia Engineering, 2014, 80: 231-244.
[28] MENDOZA A M, BUNEL A, BOTEZ R M. Aircraft vertical reference trajectory optimization with a RTA constraint using the ABC algorithm[C]//16th AIAA Aviation Technology, Integration, and Operations Conference. Reston: AIAA, 2016: 4208.
[29] GU R P, YUAN J, HAN X L, et al. Flight performance optimization considering environmental impact under multi-RTA constraints[J]. International Journal of Aeronautical and Space Sciences, 2019, 20(4): 964-977.
[30] DALMAU R, ALENKA J, PRATS X. Combining the assignment of pre-defined routes and RTAs to sequence and merge arrival traffic[C]//17th AIAA Aviation Technology, Integration, and Operations Conference. Reston: AIAA, 2017.
[31] EUROCONTROL. Experimental Centre. User Manual for the base of aircraft data (BADA), Revision 3.11: 13/04/16-01[R]. Brussels: EEC, 2013.
[32] GLOVER W, LYGEROS J. Simplified multi-aircraft models for conflict detection and resolution algorithms[J]. Computer, 2004(1): 1-28.
[33] STENGEL R F. Flight Dynamics[M]. Princeton: Princeton University Press, 2015.
[34] AYO B S. An improved genetic algorithm for flight path re-routes with reduced passenger impact[J]. Journal of Computer and Communications, 2017, 5(7): 65-75.
[35] MIRJALILI S. Genetic algorithm[M].Evolutionary Algorithms and Neural Networks. Berlin: Springer, 2019: 43-55.
[36] LI F F, LI Z Z. Research on rapid planning of intelligent aircraft trajectory under multiple constraints[J]. Journal of Physics: Conference Series, 2020, 1592(1): 012021.
[37] JONES D F, MIRRAZAVI S K, TAMIZ M. Multi-objective meta-heuristics: an overview of the current state-of-the-art[J]. European Journal of Operational Research, 2002, 137(1): 1-9.
[38] ZITZLER E, DEB K, THIELE L. Comparison of multi-objective evolutionary algorithms: empirical results[J]. Evolutionary Computation, 2000, 8(2): 173-195.
[39] PSIAKI M L. Backward-smoothing extended Kalman filter[J]. Journal of Guidance, Control, and Dynamics, 2005, 28(5): 885-894.
[40] RUIZ S, GUICHARD L, PILON N, et al. A new air traffic flow management user-driven prioritisation process for low volume operator in constraint: simulations and results[J]. Journal of Advanced Transportation, 2019, 2019: 1208279.
[41] LIU F Z. Estimating aircraft fuel consumption using radar tracks data[J]. International Journal of Performability Engineering, 2018: 14(10): 2249-2260.
[42] YU X B, LU Y Q, YU X R. Evaluating multiobjective evolutionary algorithms using MCDM methods[J]. Mathematical Problems in Engineering, 2018, 2018: 9751783.

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

容量受限下城市对航班四维航迹优化

Optimization of four-dimensional trajectory of city pair with limited capacity

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