Vertical Reference Flight Trajectory Optimization with the Particle Swarm Optimisation

Murrieta-Mendoza, Alejandro; Ruiz, H.; Botez, Ruxandra Mihaela

doi:10.2316/p.2017.848-032

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…In the work of Ref. (7), there is no explicit reference to which phases or aircraft models were considered when modelling fuel burn using the geodesic or the optimal trajectory. We assume that authors considered only CCD phases, and regarding the aircraft models, our assumption is based on our research of Flightaware for the most common aircraft models used on the same flights, namely, the Boeing 787-900 for the flights from Montreal to Paris and Toronto to London.…”

Section: Computational Resultsmentioning

confidence: 99%

“…Using this method, we were able to extend the work of Refs. (24), (7) and (8), not only in terms of the number flights evaluated but also for all the flight phases.…”

Section: Discussionmentioning

confidence: 99%

“…The work of Ref. (7) optimises the fuel burn of the vertical profile of a commercial aircraft. The airspace was modelled under the form of a unidirectional graph.…”

Section: Literature Reviewmentioning

confidence: 99%

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Calculating block time and consumed fuel for an aircraft model

Lemos

Woodward

2021

Aeronaut. j.

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In this paper we present a novel approach to calculate Block Time and Fuel (BTF) consumed for an aircraft model during a flight. The BTF model computes the ground distance between the origin and destination airports, derives the flight’s cruise altitude and by integrating two institutional data sets calculates the duration and the fuel consumed for the whole of taxi-out, take-off, climb, cruise, descent, approach, landing and taxi-in phases. We use the French Association for Operational Research and Decision Support (ROADEF) 2009 Challenge flight rotation to sample our model. The statistical analysis of the results consisted of comparing BTF results for the block time and those from the ROADEF Challenge 2009 with the real ones retrieved from Flightaware® for the same origin and destination airports and aircraft model. Statistical results are reported for percentile and root mean square error, and we show that, using simple calculations, the BTF computational results for block time are in a lower percentile and have lower root mean square error than the block times used by the ROADEF 2009 Challenge. To compare the fuel consumed, we used the values for the real flights published in the literature review. We were able to verify a good fit between the BTF results and those values. Since the BTF model computational results are obtained within a few seconds, we also conclude that the BTF model is suited for flight planning and disruption recovery in commercial aviation.

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Section: Computational Resultsmentioning

confidence: 99%

“…Using this method, we were able to extend the work of Refs. (24), (7) and (8), not only in terms of the number flights evaluated but also for all the flight phases.…”

Section: Discussionmentioning

confidence: 99%

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Calculating block time and consumed fuel for an aircraft model

Lemos

Woodward

2021

Aeronaut. j.

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“…The trajectory optimization is different from the path planning problem, which needs to consider the performance limitations of the aircraft and give a reasonable arrival time at each point of the trajectory to ensure that the aircraft can fly according to the arrival time within the flight envelope. The currently used trajectory optimization methods include dynamic planning [7], heuristic methods (genetic algorithm [8], simulated annealing algorithm [9], particle swarm algorithm [10], and differential evolution algorithm [11]), and optimal control methods [12], etc. The optimization objectives consider the flight time and fuel consumption, etc., among which the optimal control algorithm differs from other algorithms in that the motion model is considered, and there may be large errors between the trajectories generated by other algorithms and the real flyable trajectories, the generated trajectory may be infeasible because the aircraft equations of motion are ignored, while the optimal control takes into account the aircraft equations of motion and can generate a very accurate trajectory with guaranteed flyability, which has become the main method used in recent years [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Aircraft Robust Trajectory Optimization Considering Various Predictability Metrics Under Uncertain Wind

Chang,

Hu,

Zhang

2023

IEEE Access

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Trajectory uncertainty presents a major challenge for air traffic management. In order to improving the predictability of flights to facilitate air traffic management while also considering the economic benefits of airlines, in this paper, a trajectory multi-objective optimization method based on robust optimal control with flexible chosen horizontal route is proposed. Both the trajectory predictability and trajectory economic cost are taken as the optimization objectives. The method takes the ensemble weather forecast data as input to consider the impact of wind uncertainty on trajectory operation. To consider the accumulated predictability of arrival time at important sampling points throughout the entire trajectory and the impacts of increased predictability on the passed through sectors with different busyness levels, the concept of robustness coefficient based on sector busyness weighting is proposed and new trajectory predictability objective functions are established. A scenario from Hong Kong to Amsterdam is constructed to verify the effectiveness of the proposed model and algorithm. The results show that the proposed method can improve the predictability of the arrival time at the destination by 67.30% when compared with the initial flight plan, and can further improve the accumulated predictability for the entire trajectory predictability by 21.30% using the entire trajectory predictability metric as the objective. The robustness coefficient's effect on improving the arrival time predictability for busy sectors traversed by the trajectory without bringing unacceptable costs to the trajectory predictability in other sectors is also verified. By the experiments results, we can draw the conclusions that the proposed method can obtain wind optimized trajectory with both economy efficiency and improved predictability.INDEX TERMS Air traffic management, robust optimal control, trajectory optimization, high-altitude wind, ensemble prediction system

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“…In view of such factors, pre-tactical CDO trajectory optimization with various constraints and objectives in singleaircraft or multi-aircraft scenarios attracted great attention [11,12]. In most of the literature, the objectives of trajectory optimization include fuel consumption [13], flight time [14], emission [15] and noise impact [16], etc. A few scholars also assessed trajectories based on optimization in terms of safety, efficiency, airspace capacity, and ecological compatibility [14,17].…”

Section: Introductionmentioning

confidence: 99%

Multi-Attributes Decision-Making for CDO Trajectory Planning in a Novel Terminal Airspace

Yang

Wang

et al. 2021

Sustainability

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Continuous Descent Operations (CDO) has been recognized as an effective way to significantly reduce fuel burn and noise impact. Designing efficient and flexible arrival routes for generating conflict-free and economical trajectories is a cornerstone for fully achieving CDO by high-level automation in high-density traffic scenarios. In this research, inspired by the Point Merge (PM), we design the Inverted Crown-Shaped Arrival Airspace (ICSAA) and its operational procedures to support Omni-directional CDO. In order to generate optimal conflict-free trajectories for upcoming aircraft in an efficient manner, we established a multi-objective trajectory optimization model solved by Non-dominated Sorting Genetic Algorithm with Elitist Strategy (NSGA-II). The Pareto solutions of minimal fuel consumption and trip time were achieved in single aircraft and highly complex multi-aircraft scenarios. Among all the elements of Pareto front, we obtained an unique solution with Entropy-Weights Method and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) to strike a better trade-off among collision probability, fuel consumption, and trip time, which incorporates both air traffic controller’s and pilot’s interests. The effectiveness of CDO performance improvement and computational efficiency in different scenarios were verified. The ICSAA would be a promising structure that promotes the application of automated and flexible CDO.

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Vertical Reference Flight Trajectory Optimization with the Particle Swarm Optimisation

Cited by 4 publications

References 30 publications

Calculating block time and consumed fuel for an aircraft model

Calculating block time and consumed fuel for an aircraft model

Multi-Objective Aircraft Robust Trajectory Optimization Considering Various Predictability Metrics Under Uncertain Wind

Multi-Attributes Decision-Making for CDO Trajectory Planning in a Novel Terminal Airspace

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