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In-Flight Aircraft Trajectory Optimization within Corridors Defined by Ensemble Weather Forecasts

机译:通过集合天气预报定义的走廊内的飞行飞机轨迹优化

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摘要

Today, each flight is filed as a static route not later than one hour before departure. From there on, changes of the lateral route initiated by the pilot are only possible with air traffic control clearance and in the minority. Thus, the initially optimized trajectory of the flight plan is flown, although the optimization may already be based upon outdated weather data at take-off. Global weather data as those modeled by the Global Forecast System do, however, contain hints on forecast uncertainties itself, which is quantified by considering so-called ensemble forecast data. In this study, the variability in these weather parameter uncertainties is analyzed, before the trajectory optimization model TOMATO is applied to single trajectories considering the previously quantified uncertainties. TOMATO generates, based on the set of input data as provided by the ensembles, a 3D corridor encasing all resulting optimized trajectories. Assuming that this corridor is filed in addition to the initial flight plan, the optimum trajectory can be updated even during flight, as soon as updated weather forecasts are available. In return and as a compromise, flights would have to stay within the corridor to provide planning stability for Air Traffic Management compared to full free in-flight optimization. Although the corridor restricts the re-optimized trajectory, fuel savings of up to 1.1%, compared to the initially filed flight, could be shown.
机译:今天,每次航班都在出发前不迟于一小时内提交静态路线。从那里开始,只有通过空中交通管制间隙和少数群体的横向路线的变化仅可能。因此,飞行计划的初始优化的轨迹是飞行的,尽管优化可能已经基于出耗时的过时的天气数据。然而,全球天气数据作为全球预测系统建模的天气数据在预测不确定性本身上包含了提示,这通过考虑所谓的集合预测数据来量化。在这项研究中,分析了这些天气参数不确定因素的可变性,在轨迹优化模型番茄应用于考虑先前量化的不确定性的单个轨迹之前。番茄基于该集合提供的输入数据集生成,该组3D走廊包装所有产生的优化轨迹。假设除了初始飞行计划之外提交了这个走廊,即使在飞行期间也可以更新最佳轨迹,一旦获得更新的天气预报,即使是可用的天气预报也可以更新。作为返回和作为妥协,航班将不得不留在走廊内,以便为空中交通管理提供规划稳定性,而与全匹票的飞行中的空中优化相比。虽然走廊限制了重新优化的轨迹,但与最初提交的飞行相比,省油节省高达1.1%的速度达到1.1%。

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