Aircraft vertical profile prediction for continuous climb based on thrust intention
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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%。
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.
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Key words:
- air traffic management /
- trajectory prediction /
- continuous climb /
- performance model /
- thrust intention
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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 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 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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