Flight Trajectory Optimization Through Genetic Algorithms Coupling Vertical and Lateral Profiles

Patrón, Roberto Salvador Félix; Botez, Ruxandra Mihaela

doi:10.1115/imece2014-36510

Cited by 17 publications

(12 citation statements)

References 17 publications

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“…Figure 4 provides a description of the shapes and (normalized) sizes of the contours corresponding to the ellipses described in Table 1. Figure 5 illustrates, for each ellipse configuration presented in Table 1, the relationship between the normalized values of the total distance (P 1 IP 2 ), described by equation (9), and the normalized value of the x coordinate of the point (I) in which the trajectory intersects the ellipse's contour, for a number of 11 normalized values of the half-distance between P 1 and P 2 (d).…”

Section: Ellipse Area Selection On a Plane Surfacementioning

confidence: 99%

“…As part of the general effort, the Laboratory of Research in Active Controls, Avionics, and AeroServoElasticity (LARCASE) assembled a team of researchers that are investigating new algorithms addressing or related to FMS flight trajectory optimization. [9][10][11][12][13][14][15][16][17][18] As mentioned above, one important element that influences the performance of the flight trajectory optimization algorithm is the range of geographical locations explored in the search for an optimal trajectory -i.e. the geographical area considered by the optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Geographical area selection and construction of a corresponding routing grid used for in-flight management system flight trajectory optimization

Dancila

Botez

2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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This paper proposes a new method for selecting an ellipse-shaped geographical area and constructing a routing grid that circumscribes the contour of the designated area. The resulting grid describes the set of points used by the flight trajectory optimization algorithms to determine an aircraft's optimal flight trajectory as a function of given particular atmospheric conditions. This method was developed with the intent of its employment in the context of Flight Management System trajectory optimization algorithms, but can be used in Air Traffic Management environments as well. The routing grid limits the trajectory's maximal total ground distance (between the departure and destination airports), maximizes the geographical area (for a better consideration of the wind conditions) and minimizes the number of grid nodes. The novelty of the proposed method resides in the fact that it allows a distinct and independent parameterization and control of the ellipse's total surface, and the required size of the take-off/landing procedure maneuvering areas at the departure/destination airports. The ellipse contour constructed using this method is, therefore, well adapted to the particular configuration of the trajectory for which the optimization is performed. Each design variables' influence is presented, as well as a set of routing grids generated for trajectories corresponding to different total flight distances, and were further compared with real flight trajectory data retrieved using the website Flight Aware.

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Section: Ellipse Area Selection On a Plane Surfacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geographical area selection and construction of a corresponding routing grid used for in-flight management system flight trajectory optimization

Dancila

Botez

2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…The investigations and the development of flight path optimisation algorithms at the ETS' Research Laboratory in Active Controls, Avionics and Aeroservoelasticity (LARCASE) (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43) provided a good understanding of the trade-offs and limitations imposed on the optimisation algorithms with respect to run times and the size of the set of potential paths, and thus, to the general performance of the optimisation algorithm. This research inspired the quest to find faster, less computing-intensive flight path computation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Vertical flight path segments sets for aircraft flight plan prediction and optimisation

Dancila

Botez

2018

Aeronaut. j.

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The paper presents a method for constructing a set of vertical flight path segments, that would compose an aircraft's vertical flight envelope, by using an aircraft performance model. This method is intended to be used for aircraft flight plan prediction and optimisation algorithms. The goal is to reduce the volume of recurring segment performance computations currently required for flight plan prediction or optimisation. The method presented in this paper applies to a free-flight scenario. The flight-path segments composing the vertical flight envelope belong to one of the unrestricted climb, constant-speed level flight, step-climb and continuous descent segments, performed at the consigned climb, cruise and descent speed schedules and at the consigned air temperature values. The method employs an aircraft model using linear interpolation tables. Nine test scenarios were utilised to assess the performances of the resulting flight envelopes as a function of the number of cruise altitudes and descent flight paths. The set of evaluated performance parameters includes the range of total flight times and still-air flight distances, and the vertical profiles describing the minimum and maximum flight times, and still-air flight distances. The advantages of the proposed method are multiple. First, it eliminates the need for repetitive aircraft performance computations of identical vertical flight plan segments, and provides the means for quick retrieval of the corresponding performance data for use in the construction of a full flight plan. Second, the vertical flight path look-up structure and the vertical flight-path graph describe a set of vertical flight paths that consider an aircraft's and flight plan's configuration parameters and cover its maximum flight envelope. Third, the look-up structure and the graph provide the means for rapid and clear identification of the available options for constructing a flight-plan segment, as well as for detecting the points associated with changes in the flight phases, including climb, cruise, step-climb and descent.

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“…At each cruise waypoint, the aircraft considered a possible deviation in order to profit from tailwinds or to avoid strong headwinds. Genetic algorithms were later used to find the most economical vertical and lateral reference trajectories 50,51 . The artificial bee colony algorithm has been used to optimize the lateral reference trajectory with the novelty of using a dynamic grid 52 .…”

Section: Introductionmentioning

confidence: 99%

New Search Space Reduction Algorithm for Vertical Reference Trajectory Optimization

Murrieta-Mendoza¹,

Gagné²,

Mihaela³

2016

INCAS BULLETIN

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Flight Trajectory Optimization Through Genetic Algorithms Coupling Vertical and Lateral Profiles

Cited by 17 publications

References 17 publications

Geographical area selection and construction of a corresponding routing grid used for in-flight management system flight trajectory optimization

Geographical area selection and construction of a corresponding routing grid used for in-flight management system flight trajectory optimization

Vertical flight path segments sets for aircraft flight plan prediction and optimisation

New Search Space Reduction Algorithm for Vertical Reference Trajectory Optimization

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