基于历史航迹数据的民用航空器极曲线估算方法

王兵; 张博雯; 彭瑛

doi:10.19818/j.cnki.1671-1637.2025.04.023

基于历史航迹数据的民用航空器极曲线估算方法

doi: 10.19818/j.cnki.1671-1637.2025.04.023

王兵^{1, 2, ,},
张博雯^{1, 2},
彭瑛^{1, 3}

1.
南京航空航天大学空中交通管理系统全国重点实验室, 江苏南京 211106
2.
南京航空航天大学通用航空与飞行学院, 江苏溧阳 213300
3.
南京航空航天大学民航学院, 江苏南京 211106

基金项目:

国家重点研发计划 2022YFB2602401

详细信息

作者简介:
王兵(1979-)，男，河南洛阳人，南京航空航天大学讲师，工学博士，从事民用航空器轨迹运行研究

通讯作者:
WANG Bing (1979-), male, lecturer, PhD, evanwb@163.com

中图分类号: U8
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- 文章访问数: 341
- HTML全文浏览量: 59
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2024-07-25
- 录用日期: 2025-03-12
- 修回日期: 2025-01-06
- 刊出日期: 2025-08-28

Estimation method for civil aircraft drag polar based on historical trajectory data

WANG Bing^{1, 2
, ,},
ZHANG Bo-wen^{1, 2},
PENG Ying^{1, 3}

1.
State Key Laboratory of Air Traffic Management System, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China
2.
College of General Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Liyang 213300, Jiangsu, China
3.
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

Funds:

National Key R&D Program of China 2022YFB2602401

Article Text (Baidu Translation)

摘要

摘要: 为有效解决民用航空器极曲线参数不能通过公开渠道获取的问题，提出了一种基于历史航迹数据的航空器极曲线估算方法；根据航空器性能与推力模型构建了极曲线参数优化模型；通过基于NUTS采样器的马尔可夫链蒙特卡洛(MCMC)算法对优化模型求解，得到了航空器的极曲线参数；分别以某A320航空器的3次直飞航班及当前民用航空的12种主流机型(1 564个航班)为例，通过估算极曲线并对航线爬升阶段进行航迹预测，对比预测剖面与快速存取记录器(QAR)数据中的爬升剖面，验证了方法的有效性与普适性。研究结果表明：对于典型样本航班，生成的爬升剖面(爬升至巡航高度34 100英尺，约10 400 m)与QAR数据中的爬升剖面相比，平均爬升率相对误差为1.16%，气压高度最大绝对误差在500英尺(约152 m)以内，与使用传统的航空器性能数据库中参考极曲线所预测得到的爬升剖面相比，预测精度得到了明显提高；对于批量样本航班，其中占比96.48%的航班预测爬升剖面绝对误差在1 000英尺(约300 m)以内，所有航班最大绝对误差的平均值为497.71英尺(约151.7 m)。所建立的航空器极曲线估算方法适用于大批量航班，可为高精度民用航空器轨迹仿真与预测工作提供技术支撑。
- 空中交通 /
- 航迹预测 /
- 极曲线 /
- MCMC算法 /
- 航空器推力模型 /
- 航空器性能模型
Abstract: To effectively solve the problem of the unavailability of civil aircraft's drag polar parameters through public channels, a method for estimating the drag of aircraft polar based on historical flight trajectory data was presented. An optimization model for drag polar parameters was constructed based on aircraft performance and thrust models. The optimization model was solved using the Markov Chain Monte Carlo (MCMC) algorithm based on the NUTS. The aircraft's drag polar parameters were obtained. To verify the method's effectiveness and generalizability, three direct flights of an A320 aircraft and 12 major types of current civilian aircraft (1 564 flights) were taken as examples. Estimation of drag polar and trajectory prediction for the climb phase were conducted. The predicted climb profiles were compared against actual climb profiles in quick access recorder (QAR) data. Analysis results show that for typical sample flight, the generated climb profile (climb to the cruising altitude of 341 00 feet) has a relative error of 1.16% of mean climb rate and an absolute error of pressure altitude within 500 feet compared to the climb profile in QAR data. This prediction accuracy is significantly improved compared to the predicted climb profile using reference drag polar from the traditional base of aircraft data. Among the results of bulk sample flights, 96.48% of the flights' predicted climb profiles have an absolute error within 1 000 feet, with average maximum absolute error of 497.71 feet for all flights. Therefore, the proposed method for estimating aircraft's drag polar is suitable for a large number of flights and can provide technical support for high-precision simulation and prediction of civilian aircraft trajectories.
- air traffic /
- trajectory prediction /
- drag polar /
- MCMC algorithm /
- aircraft thrust model /
- aircraft performance model

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图 1 某航班爬升阶段实际爬升率与修正爬升率对比

Figure 1. Comparison of actual and calibrated climb rates during a flight's climb phase

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图 2 基于NUTS采样的MCMC算法流程

Figure 2. Flow of MCMC algorithm based on NUTS

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图 3 典型爬升剖面

Figure 3. Typical flight climb profile

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图 4 某样本航班高度剖面

Figure 4. Altitude profile of the sample flight

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图 5 样本航班极曲线示例

Figure 5. Example of sample flight drag polar

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图 6 样本航班不同爬升剖面及爬升率对比

Figure 6. Comparison of different climb profiles and climb rate of sample flight

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图 7 各机型预测剖面绝对误差最大值分布

Figure 7. Distribution of maximum absolute error of different flights

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表 1 从QAR数据中所提取的字段信息

Table 1. Data fields extracted from QAR

字段名	全称	描述
TIME	Time stamp	时间戳
PA	Pressure altitude	气压高度
CAS	Calibrated air speed	校正空速
Mach	Mach number	马赫数
GW	Gross weight	航空器总重
TAT	Total air temperature	总温
BANK	Bank	坡度

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表 2 某A320航空器的C_D0与λ计算结果

Table 2. Results of C_D0 and λ of the A320 aircraft

马赫数	C_D0	λ	马赫数	C_D0	λ
0.50	0.006 9	0.037 5	0.65	0.014 2	0.040 2
0.51	0.007 1	0.037 5	0.66	0.014 3	0.040 3
0.52	0.007 4	0.037 6	0.67	0.014 3	0.040 3
0.53	0.007 6	0.037 7	0.68	0.014 4	0.040 3
0.54	0.008 5	0.038 1	0.69	0.014 7	0.040 4
0.55	0.008 6	0.038 1	0.70	0.015 0	0.040 4
0.56	0.009 1	0.038 3	0.71	0.015 5	0.040 6
0.57	0.009 6	0.038 5	0.72	0.016 1	0.040 9
0.58	0.010 6	0.038 8	0.73	0.016 5	0.041 0
0.59	0.010 8	0.038 8	0.74	0.016 8	0.041 1
0.60	0.011 2	0.039 1	0.75	0.017 3	0.041 3
0.61	0.011 6	0.039 3	0.76	0.017 5	0.041 5
0.62	0.013 2	0.039 8	0.77	0.017 8	0.041 6
0.63	0.013 6	0.040 0	0.78	0.018 5	0.042 0
0.64	0.014 2	0.040 2	0.79	0.019 3	0.042 4

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表 3 批量航班绝对误差分布情况

Table 3. Distribution of absolute errors of batch flights

机型	航班数	预测剖面最大绝对误差不大于1 000英尺		参考剖面最大绝对误差不大于1 000英尺		预测剖面最大绝对误差平均值/英尺	参考剖面最大绝对误差平均值/英尺
机型	航班数	航班数	占比/%	航班数	占比/%	预测剖面最大绝对误差平均值/英尺	参考剖面最大绝对误差平均值/英尺
A320ceo	182	176	96.70	37	20.33	434.17	2 241.14
A320neo	299	286	95.65	78	26.09	478.32	1 713.27
A319	36	36	100.00	3	8.33	600.66	2 673.99
A321	124	121	97.58	31	25.00	592.69	1 936.02
A333	74	71	95.95	3	4.05	646.24	3 185.36
A332	34	33	97.06	1	2.94	638.80	4 053.52
B737	67	64	95.52	1	1.49	597.68	4 152.54
B738	672	648	96.43	92	13.69	459.05	2 092.71
B77W	20	19	95.00	0	0.00	638.18	5 418.98
B788	21	21	100.00	0	0.00	557.27	2 414.86
B789	18	17	94.44	0	0.00	554.31	5 224.97
E190	17	17	100.00	0	0.00	515.42	4 496.63
总计	1 564	1 509	96.48	246	15.73	497.71	2 330.00

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