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摘要:
航空运输是中国综合交通领域的重要支柱，其绿色运行的研究对于可持续发展的意义重大。以绿色优化为核心，以减少碳排放为目标，面向终端区对航空器的轨迹进行优化研究，有效减少了碳排放，并得出有效结论。探索终端区飞行对于环境的影响，建立发动机排放模型；基于现有的广播式自动相关监视（ADS-B） 数据和航空器A321机型巡航性能，对数据进行分析归纳；建立划分航段的整数规划模型，研究飞机在航段内的具体情况，结合飞机改变高度、平飞2种状态对航段区间水平距离长短进行分类讨论，得到航迹排放模型，依据模型假设和航行规则建立约束，利用粒子群优化(PSO) 算法进行求解，模型增加了高度层改变约束，优化飞行状态，减轻管制负荷，使优化后航迹的碳排放较优化前降低了8.45%。通过对比优化前后航迹发现，在减少污染物排放的目标下，连续下降的进近策略比梯度下降策略更加高效。
Abstract:Air transport is an important pillar in the field of comprehensive transportation in China, and the research on its green operation is of great significance to sustainable development. To achieve green optimization and reduce carbon emissions, the trajectory of the aircraft in the terminal area was optimized, which effectively reduced carbon emissions and drew effective conclusions. The influence of the flight in the terminal area on the environment was explored, and the engine emission model was established. Based on the existing automatic dependent surveillance-broadcast（ADS-B） data and the cruise performance of aircraft A321, the data were analyzed and summarized. An integer programming model was established to divide flight segments and study the specific circumstances of the aircraft in the flight segment. The trajectory emission model was obtained by considering the two states of changing aircraft altitude and keeping horizontal flight and discussing the length of horizontal distance between flight segments. Then, the constraints were established according to the model assumptions and navigation rules, and the particle swarm optimization (PSO) algorithm was used to solve the problem. The model increased the altitude layer change constraint, optimized the flight state, reduced the control load, and lowered the carbon emission by 8.45% after trajectory optimization. By comparing the trajectory before and after optimization, it was found that under the goal of reducing emissions, the approach strategy of continuous descent was more efficient than the strategy of gradient descent.
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Key words:
- air transport /
- terminal area /
- green operation /
- carbon emission /
- trajectory optimization
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图 2 垂直剖面上划分航段后的空域模型
Figure 2. Airspace model after dividing flight segments on vertical section
图 4 区间长度足够时，以爬升为例的航段内变高度层飞行模型
Figure 4. Flight model with varying altitude in flight segment, taking climbing as an example, when interval length is enough
图 5 区间长度不够时，以爬升为例的航段内变高度层飞行模型
Figure 5. Flight model with varying altitude in flight segment, taking climbing as an example, when interval length is not enough
图 6 模型的进近航迹（以爬升为例）
Figure 6. Approach trajectory of model,taking climbing as an example
表 1 相对高度层编号对应的飞行高度层
Table 1. Flight altitude layers corresponding to relative altitude layer numbers
相对高度层编号 高度/m 1 0～900 2 900～ 1500 3 1500 ～2100 4 2100 ～2700 5 2700 ～3300 6 3300 ～3900 7 3900 ～4500 8 4500 ～5100 9 5100 ～5700 10 5700 ～6300 11 6300 ～6900 12 6900 ～7500 
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表 2 航班进近过程的ADS-B数据
Table 2. ADS-B data of flight approach process
时刻 航向/(°) 速度/(km·h−1) 高度/m 下降率/(m·s−1) 12:47:42 25 708 7468 −292 12:49:46 25 689 6828 −465 12:50:56 25 666 6149 −595 12:51:29 25 653 5814 −588 12:52:46 25 634 5075 −554 12:54:07 25 581 4511 −315 12:56:41 25 562 3932 −174 13:00:47 327 523 3330 −143 13:03:23 327 505 2629 −443 13:07:00 329 478 2247 −410 13:09:00 332 394 1849 −389 13:11:00 333 372 1363 −362 13:13:00 336 288 10 −312
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表 3 A321各高度层发动机参数与飞行性能数据
Table 3. A321 engine parameters and flight performance data at different altitudes
高度/m 相对高度层编号 爬升功率/(kg·s−1) 飞行速度/(m·s−1) 平飞功率/(kg·s−1) 爬升率/(m·s−1) 下降功率/(kg·s−1) 下降率/(m·s−1) 900 1 1.56 83.33 4.60 9.86 0.38 11.45 1500 2 1.77 105.56 4.48 9.04 0.37 11.50 2100 3 1.91 133.60 4.36 4.84 0.37 12.48 2700 4 2.00 140.28 4.23 4.24 0.36 12.10 3300 5 2.08 145.28 4.11 6.96 0.35 12.55 3900 6 2.14 156.11 3.99 5.38 0.35 10.16 4500 7 2.18 161.39 3.86 3.21 0.34 9.34 5100 8 2.20 176.11 3.74 5.88 0.33 11.10 5700 9 2.18 181.39 3.61 4.25 0.32 10.78 6300 10 2.14 185.00 3.48 7.87 0.32 13.48 6900 11 2.06 191.39 3.35 5.61 0.31 13.87 7500 12 1.94 196.67 3.23 3.50 0.30 11.33
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[1] 樊守彬. 美国机场大气污染物控制途径及效果[J]. 环境科学与管理, 2011, 36(3): 40-43.FAN S B. Control measures and efficiency of air pollutant from aircraft in USA[J]. Environmental Science and Management, 2011, 36(3): 40-43(in Chinese). [2] 殷润泽. 基于环境影响的终端区进离场航线优化研究[D]. 南京: 南京航空航天大学, 2018: 1-38.YIN R Z. Research on optimization of arrival and departure routes for environmental impact in TMA[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2018: 1-38(in Chinese). [3] LÜHRS B, NIKLASS M, FROEMMING C, et al. Cost-benefit assessment of 2D and 3D climate and weather optimized trajectories[C]//Proceedings of the 16th AIAA Aviation Technology, Integration, and Operations Conference. Reston: AIAA, 2016. [4] SIDIROPOULOS S, MAJUMDAR A, HAN K, et al. A framework for the classification and prioritization of arrival and departure routes in multi-airport systems terminal manoeuvring areas[C]//Proceedings of the 15th AIAA Aviation Technology, Integration, and Operations Conference. Reston: AIAA, 2015. [5] ZHOU J, CAFIERI S, DELAHAYE D, et al. Optimal design of SIDs/STARs in TMA using simulated annealing[C]//Proceedings of the IEEE/AIAA 35th Digital Avionics Systems Conference. Piscataway: IEEE Press, 2016: 1-8. [6] ZHOU J, CAFIERI S, DELAHAYE D, et al. Optimization-based design of departure and arrival routes in terminal maneuvering area[J]. Journal of Guidance, Control, and Dynamics, 2017, 40(11): 2889-2904. doi: 10.2514/1.G002728 [7] 孙见忠, 左洪福, 刘鹏鹏, 等. 航空发动机污染物排放量估算方法[J]. 交通运输工程学报, 2012, 12(2): 53-61.SUN J Z, ZUO H F, LIU P P, et al. Estimation method of aeroengine pollution emissions[J]. Journal of Traffic and Transportation Engineering, 2012, 12(2): 53-61(in Chinese). [8] 陈林. 我国航空运输LTO阶段和巡航阶段排放量测算与预测[J]. 北京交通大学学报(社会科学版), 2013, 12(4): 27-33.CHEN L. Measurement and forecast of the emissions from Chinese air transportation LTO and cruise phases[J]. Journal of Beijing Jiaotong University (Social Sciences Edition), 2013, 12(4): 27-33(in Chinese). [9] 闫国华, 吴鹏. 飞机完整航线二氧化碳排放量估算[J]. 装备制造技术, 2013(8): 29-31.YAN G H, WU P. The aircraft the complete routes CO2 emissions estimate[J]. Equipment Manufacturing Technology, 2013(8): 29-31(in Chinese). [10] 田勇, 万莉莉, 叶博嘉, 等. 基于降低温室效应的飞行高度层分配优化[J]. 西南交通大学学报, 2018, 53(2): 400-405.TIAN Y, WAN L L, YE B J, et al. Flight level allocation optimization to reduce greenhouse effect[J]. Journal of Southwest Jiaotong University, 2018, 53(2): 400-405(in Chinese). [11] TIAN Y, WAN L L, HAN K, et al. Optimization of terminal airspace operation with environmental considerations[J]. Transportation Research Part D: Transport and Environment, 2018, 63: 872-889. doi: 10.1016/j.trd.2018.06.018 [12] LIU F Z, HU M H, LV W Y, et al. Research on optimization of aircraft climb trajectory considering environmental impact[J]. Journal of Advanced Transportation, 2021, 2021(S1): 6677329. [13] 李杰, 杨好天, 王兵, 等. 民航飞机起飞推力对油耗和排放的影响分析[J]. 交通运输系统工程与信息, 2021, 21(6): 283-288.LI J, YANG H T, WANG B, et al. Influence of takeoff thrust on fuel consumption and emissions of civil aircraft[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(6): 283-288(in Chinese). [14] 胡荣, 王德芸, 冯慧琳, 等. 碳达峰视角下的机场航空器碳排放预测[J]. 交通运输系统工程与信息, 2021, 21(6): 257-263.HU R, WANG D Y, FENG H L, et al. Prediction of aircraft CO2 emission from perspective of CO2 emission peak[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(6): 257-263(in Chinese). [15] 郑丽君, 胡荣, 张军峰, 等. 高峰时段下离港航空器绿色滑行策略设计与评价[J]. 北京航空航天大学学报, 2019, 45(11): 2320-2326.ZHENG L J, HU R, ZHANG J F, et al. Design and evaluation of green taxiing strategy for departure aircraft during peak hours[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2320-2326(in Chinese). [16] 王中凤燕. 航空器绿色轨迹优化研究[D]. 南京: 南京航空航天大学, 2017: 32-56.WANG Z F Y. Aircraft trajectory optimization based on environmental impact[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017: 32-56(in Chinese). -
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