Flight alternate optimization scheme in dangerous weather based on multiexpectation
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摘要: 空中多次備降極易導致低油量等不安全事件發生。針對區域內多航班集體備降這一問題，選取其中最復雜情況，即航路或終端區存在雷暴天氣，首先通過搜集和統計氣象數據與歷史航跡，得到雷暴天氣下飛行限制區的劃設標準；然后，將前往備降場方式分為機動飛行與沿航路飛行兩類，分別使用A*與改進灰狼?Dijkstra方法開展改航路徑規劃；先以備降航班飛行總時長最短為單目標，再綜合飛行、管制、機場、航空公司等多方期望構建多目標函數，定義動態決策時間間隔，提出一種基于單目標與多目標的區域內多航班備降動態優化方案；最后，使用“8.12”華北運行數據開展仿真驗證，在單目標與多目標方案中，面向機動飛行A*算法所得結果分別將飛行總時長減少了100 min和62 min，而面向按照航路飛行的改進灰狼?Dijkstra算法所得總時長分別減少73 min和14 min；并且，在多目標方案中，航班恢復飛往原目的地的時間整體提前了63 min，總成本降低了6.29萬元。以上說明，該方案在保證航班備降安全基礎上，可兼顧多方需求，提升經濟與效率。Abstract: Aircraft landing with low fuel is usually caused by diversion decision changes in the air. It could lead to an unsafe event. To solve the problem of collective alternate landings of multiple flights in the area, the most complex situation is selected, that is, when the weather in the route or terminal area is dangerous. However, in this situation, when the pilot announces diversion, many are unacceptable by the airport due to limited aircraft stand. Therefore, this study establishes a decision-making approach to help air traffic controllers and airlines choose suitable alternate airports since accurate fly limit zone conditions can keep the aircraft out of dangerous weather and a short diversion route can avoid the low fuel situation, both of which can increase the safety of the diversion process. The basic conditions of the fly limit zone under dangerous weather are obtained by combining meteorological data and historic tracks. Here, A* and the improved gray wolf Dijkstra algorithm are used to plan the diversion path for two different routes to the alternate aerodrome: maneuvering flight and flying along the route. First, considering the shortest total flight time of alternate flight as the single objective and subsequently integrating the expectations of flight, control, airport, and airline, a multi-objective function is constructed, the dynamic decision-making time interval is defined, and a dynamic optimization scheme of multi-flight alternate in the region based on a single objective and multi-objective is proposed. The single-objective scheme focuses on the safe aspect of diversion, while the multi-objective scheme focuses on preventing airport rejection. The multi-objective scheme can enable the flight to land quickly, ensure safety, and consider the expectations of multiple parties to avoid diverting simultaneously. Using the “8.12” North China large area alternate landing data, the total flight time obtained by selecting the direct flight A* algorithm is reduced by 100 and 62 min, respectively, and the total flight time obtained by the improved gray wolf Dijkstra algorithm based on the route is reduced by 73 and 14 min. Moreover, in the multi-objective scheme, the overall time of flight resumption to the original destination is 63 min earlier, and the total cost is reduced by 62900 yuan. Although multiple diversion still occurs on CA991, the process is within the safety range. Therefore, results indicate that the scheme considers the needs of multiple parties and improves the economy to ensure the safety of flight alternate landing.
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圖 2 MU9882航班航線疊加氣象數據圖. (a) 16:18; (b) 17:18
Figure 2. Superimposed meteorological of MU9882 route: (a) 16:18; (b)17:18
表 1 飛越航班的氣象數據統計
Table 1. Meteorological data statistics of overflights
Reflectivity /dBZ Number of overflights VIL: 0 kg·m?3 VIL: 1–4 kg·m?3 VIL: 5–7 kg·m?3 VIL: >7 kg·m?3 10–15 0 7 0 0 16–20 16 18 0 0 21–25 10 20 0 0 26–30 4 27 0 0 31–35 28 67 0 0 36–40 13 11 4 0 41–45 3 9 1 0 ≥46 0 0 0 1 
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表 2 航班個例分析
Table 2. Meteorological data analyzation cases
Flight Time Reflectivity /dBZ VIL /(kg·m?3) Observatory CZ6408 16:30 41–45 1–4 Tang Gu 16:36 36–40 1–4 G52819 7:30 36–40 5–7 Pu Yang 7:36 26–30 1–4 7:42 26–30 0
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表 3 飛越航班驗證結果
Table 3. Meteorological data validation results
Reflectivity /
dBZNumber of flights VIL: 0 kg·m?3 VIL: 1–4 kg·m?3 VIL: 5–7 kg·m?3 VIL: >7 kg·m?3 21–25 4 6 0 0 26–30 5 10 0 0 31–35 11 51 0 0 36–40 9 8 1 0 41–45 3 4 0 0 ≥46 0 0 0 0
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表 4 備降優化決策結果. (a) 決策過程與備降時間變化; (b) 整體結果
Table 4. Flight alternate decision result: (a) decision process and time change; (b) total result (a)
Times of decision Flight Actual result Single-objective decision scheme Multi-objective decision scheme A* result Time change /
minImproved gray wolf result Time change / min A* result Time change / min Improved gray wolf result Time change / min First
decisionCA1288 ZBTJ ZBTJ 0 ZBTJ 0 ZBTJ 0 ZBTJ 0 Second
decisionCA1290 ZYTX ZYTX 0 ZYTX 0 ZYTX 0 ZYTX 0 CA1238 ZBHH ZBHH 0 ZBHH 0 ZBHH 0 ZBHH 0 CA991 ZYTX ZYTX 0 ZYTX 0 ZBHH — ZBHH — CA8312 ZYTX ZBHH ?33 ZBHH ?27 ZBHH +11 ZBHH ?27 CA4115 ZYTX ZBHH ?30 ZBHH ?23 ZBHH ?30 ZBHH ?23 CA1150 ZBHH ZBHH ?53 ZBHH ?51 ZBHH ?53 ZBHH ?51 Third
decisionCA1290 ZYTX ZYTX 0 ZYTX 0 ZYTX 0 ZYTX 0 CA1238 ZBHH ZBHH 0 ZBHH 0 ZBHH 0 ZBHH 0 CA991 ZYTX ZYTX 0 ZYTX 0 ZYTX +24 ZYTX +42 CA8312 ZYTX ZBHH ZBHH ZYTX ZBHH CA4115 ZYTX ZBHH ZBHH ZBHH ZBHH CA8346 ZBHH ZBHH 0 ZBHH 0 ZBHH 0 ZBHH 0 CA4135 ZBHH ZSJN +16 ZSJN +37 ZBHH ?14 ZYTX +44 HU7794 ZSJN ZSJN 0 ZSJN 0 ZSJN 0 ZSJN 0 Note：Time change in the chart “+” means increase,” ?” means decrease.
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Table . (b)
Scheme of flight alternate decision Total diversion time / min Total diversion cost/ (104 ￥) Total flight recovery time /min Actual result 451 78.29 2644 Single-objective decision scheme A* result 351 66.27 2718 Improved gray wolf result 378 68.91 2661 Multi-objective decision scheme A* result 389 69.40 2642 Improved gray wolf result 437 72.00 2579
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