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首页> 外文期刊>International Journal of Innovative Research in Science, Engineering and Technology >Algorithms for Automatic Elaboration of Optimized Trajectories for Continuous Descent Operations
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Algorithms for Automatic Elaboration of Optimized Trajectories for Continuous Descent Operations

机译:自动拟定连续下降操作的最佳轨迹的算法

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摘要

To reduce the effects of aircraft noise, fuel use and related pollutant atmospheric emissions, due to conventional step-down procedures for approach and landing, designed curved and continuous descent approach trajectories can be adopted in order to provide suitably optimized vertical profiles by combining flexible lateral paths and continuous descent operations such that level flight segments can be completely eliminated. This paper describes a methodology for the automatic generation of efficient Curved and Continuous Descent Approach (CCDA) trajectories to reduce the aircraft fuel consumption during the approach phase. Furthermore, the proposed method takes into account the airport and external environmental constraints by providing the obstacles mapping from aeronautical charts georeferencing processes and suitable DSMs and DTMs. This allows to automatically generate a modified set of trajectories that optimizes the track distance within a tolerable range. The Italian Aerospace Research Center (CIRA) has developed this study within the framework of the Efficient Air Transport System (EATS) project. The identification of all influencing factors in TMA has been here provided for a specified airport and related Aerodrome Traffic Zone and Control Zones in collaboration with the University of the Study of Naples “Parthenope”. Applications and simulations results of the proposed methodology have been reported and discussed.
机译:为了降低飞机噪声,燃料使用和相关污染物大气排放的影响,由于进近和着陆的常规降压程序,可以采用设计的弯曲和连续下降进场轨迹,以便通过组合灵活的横向装置来提供适当优化的垂直剖面路径和连续下降操作,从而可以完全消除水平飞行段。本文介绍了一种自动生成有效的弯曲和连续下降进近(CCDA)轨迹以减少进近阶段飞机油耗的方法。此外,通过从航空图地理参考过程以及合适的DSM和DTM提供障碍物映射,所提出的方法考虑了机场和外部环境的限制。这允许自动生成一组修改的轨迹,该轨迹集可在允许范围内优化轨道距离。意大利航空研究中心(CIRA)在高效航空运输系统(EATS)项目的框架内开展了这项研究。与那不勒斯研究大学“ Parthenope”合作,为特定的机场以及相关的机场交通区和管制区提供了TMA中所有影响因素的识别。已经报告和讨论了所提出方法的应用和仿真结果。

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