5,121 research outputs found
Effects of speed reduction in climb, cruise and descent phases to generate linear holding at no extra fuel cost
Best paper Award in Trajectory Optimisation Track - ICRAT 2016Speed reduction strategies have proved to be useful
to recover delay if air traffic flow management regulations are
cancelled before initially planned. Considering that for short-
haul flights the climb and descent phases usually account for
a considerable percentage of the total trip distance, this paper
extends previous works on speed reduction in cruise to the whole
flight. A trajectory optimization software is used to compute
the maximum airborne delay (or linear holding) that can be
performed without extra fuel consumption if compared with
the nominal flight. Three cases are studied: speed reduction
only in cruise; speed reduction in the whole flight, but keeping
the nominal cruise altitude; and speed reduction for the whole
flight while also optimizing the cruise altitude to maximize delay.
Three representative flights have been simulated, showing that
the airborne delay increases significantly in the two last cases
with nearly 3-fold time for short-haul flights and 2-fold for mid-
hauls with the first case. Results also show that fuel and time are
traded along different phases of flight in such a way the airborne
delay is maximized while the total fuel burn is kept constant.Peer ReviewedAward-winningPostprint (published version
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Comparison of stochastic methods for control in air traffic management
This paper provides a direct comparison of two stochastic optimisation techniques (Markov Chain Monte Carlo and Sequential Monte Carlo) when applied to the problem of conflict resolution and aircraft trajectory control in air traffic management. The two methods are then also compared to another existing technique of Mixed-Integer Linear Programming
which is also popular in distributed control.Work supported by EPSRC (Engineering and Physical Sciences Research Council - UK) Grant No. EP/G066477/1International Federation of Automatic Control World Congress, 201
Sequential Monte Carlo simulation of collision risk in free flight air traffic
Within HYBRIDGE a novel approach in speeding up Monte Carlo simulation of rare events has been developed. In the current report this method is extended for application to simulating collisions with a stochastic dynamical model of an air traffic operational concept. Subsequently this extended Monte Carlo simulation approach is applied to a simulation model of an advanced free flight operational concept; i.e. one in which aircraft are responsible for self separation with each other. The Monte Carlo simulation results obtained for this advanced concept show that the novel method works well, and that it allows studying rare events that stayed invisible in previous Monte Carlo simulations of advanced air traffic operational concepts
Online Learning for Ground Trajectory Prediction
This paper presents a model based on an hybrid system to numerically simulate
the climbing phase of an aircraft. This model is then used within a trajectory
prediction tool. Finally, the Covariance Matrix Adaptation Evolution Strategy
(CMA-ES) optimization algorithm is used to tune five selected parameters, and
thus improve the accuracy of the model. Incorporated within a trajectory
prediction tool, this model can be used to derive the order of magnitude of the
prediction error over time, and thus the domain of validity of the trajectory
prediction. A first validation experiment of the proposed model is based on the
errors along time for a one-time trajectory prediction at the take off of the
flight with respect to the default values of the theoretical BADA model. This
experiment, assuming complete information, also shows the limit of the model. A
second experiment part presents an on-line trajectory prediction, in which the
prediction is continuously updated based on the current aircraft position. This
approach raises several issues, for which improvements of the basic model are
proposed, and the resulting trajectory prediction tool shows statistically
significantly more accurate results than those of the default model.Comment: SESAR 2nd Innovation Days (2012
Runway exit designs for capacity improvement demonstrations. Phase 2: Computer model development
The development is described of a computer simulation/optimization model to: (1) estimate the optimal locations of existing and proposed runway turnoffs; and (2) estimate the geometric design requirements associated with newly developed high speed turnoffs. The model described, named REDIM 2.0, represents a stand alone application to be used by airport planners, designers, and researchers alike to estimate optimal turnoff locations. The main procedures are described in detail which are implemented in the software package and possible applications are illustrated when using 6 major runway scenarios. The main output of the computer program is the estimation of the weighted average runway occupancy time for a user defined aircraft population. Also, the location and geometric characteristics of each turnoff are provided to the user
An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem
The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficienc
Testing Method for Multi-UAV Conflict Resolution Using Agent-Based Simulation and Multi-Objective Search
A new approach to testing multi-UAV conflict resolution algorithms is presented. The problem is formulated as a multi-objective search problem with two objectives: finding air traffic encounters that 1) are able to reveal faults in conflict resolution algorithms and 2) are likely to happen in the real world. The method uses agent-based simulation and multi-objective search to automatically find encounters satisfying these objectives. It describes pairwise encounters in three-dimensional space using a parameterized geometry representation, which allows encounters involving multiple UAVs to be generated by combining several pairwise encounters. The consequences of the encounters, given the conflict resolution algorithm, are explored using a fast-time agent-based simulator. To find encounters meeting the two objectives, a genetic algorithm approach is used. The method is applied to test ORCA-3D, a widely cited open-source multi-UAV conflict resolution algorithm, and the method’s performance is compared with a plausible random testing approach. The results show that the method can find the required encounters more efficiently than the random search. The identified safety incidents are then the starting points for understanding limitations of the conflict resolution algorithm
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