Trajectory computation Infrastructure based on BADA Aircraft Performance Model

Gallo, Eduardo; Lopez-Leones, Javier; Vilaplana, Miguel; Navarro, Francisco; Nuić, Angela

doi:10.1109/dasc.2007.4391830

Cited by 26 publications

(16 citation statements)

References 3 publications

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“…7 The aircraft energy can be considered as the sum of its kinetic energy (speed) and its potential energy (altitude). 8 The main challenge of an aircraft during descent is to diminish its energy. This requires adding as little thrust as possible, which supplies energy, while progressively employing less efficient aerodynamic configurations (including different angles of attack, spoilers, flaps, and landing gear) to remove energy.…”

Section: Benchmark Descentmentioning

confidence: 99%

Prediction of descent trajectories based on Aircraft Intent

Gallo

2010

29th Digital Avionics Systems Conference

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A Trajectory Computation Infrastructure (TCI) contains three main modules: the Aircraft Performance Model (APM) that provides the aircraft performances (thrust, drag, and fuel consumption, among others), the Weather Model (WM) that provides the wind and atmospheric properties, and the Trajectory Engine (TE) that integrates the equations of motion to obtain the predicted trajectory.As part of its Advanced Trajectory Technologies (ATT) activities, Boeing Research & Technology Europe (BR&TE) has developed a TCI that employs the Aircraft Intent Description Language (AIDL) as the main input to the TCI. AIDL is a univocal, rigorous, and standardized method to express aircraft intent (AI), unambiguously determining the desired trajectory. AIDL is based on the simultaneous use of various instructions, each of them closing a single degree of freedom of the aircraft motion.This paper describes the elements involved in the trajectory computation process. It then explains the different ways of modeling a descent trajectory by means of AI. Its main objective is to obtain guidelines on the consequences that differences between the actual and expected inputs to the trajectory computation process have on the resulting predicted trajectories, and how these can vary depending on the choices taken to model the AI.The different AIs result in the same trajectory when combined with the expected weather (wind and atmospheric temperature) and initial conditions (aircraft mass), but the results diverge when confronted with actual weather and initial mass that differ from the expected ones. This paper describes the reasons for this divergence, analyzes the differences in geometry and speed among the resulting trajectories, and explains why some AI options may be more robust than others when modeling descents, and the risks the modeler incurs when employing each of them. Finally, the AIs employed by the Flight Management System (FMS) vertical navigation (VNAV) modes are described.

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Section: Benchmark Descentmentioning

confidence: 99%

Prediction of descent trajectories based on Aircraft Intent

Gallo

2010

29th Digital Avionics Systems Conference

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“…Thus a flare-out of constant normal acceleration a n has to be flown with h according to (5) and has to be started at 0 h Δ according to (7).…”

Section: With (4) (5) Andmentioning

confidence: 99%

“…For the second test run the simulated radar noise has been set very highstandard deviation for the range error of 0.06 -0. 5 Figure 8 shows the Phoenix linkage processing architecture. The flight plan server (d-fps) receives ADEXP flight plans from different interface agents and unlinked tracks from the tracker.…”

Section: B Application For Tracker Evaluationmentioning

confidence: 99%

Trajectory computation for tracker evaluation and linkage processing

Natchev

Heidger

2008

2008 Tyrrhenian International Workshop on Digital Communications - Enhanced Surveillance of Aircraft and Vehicles

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“…That must be accomplished taking into account the models used, aircraft intents, speeds and other flight parameters used in each flight sub-phase. 3,4 • To establish air-ground protocols to negotiate also considered to negotiate free of conflict user preferred 4D trajectories 5 .The main argument into the negotiation process is the 4D trajectory, so it is necessary including a 4D trajectory instance properly formalized.…”

Section: Introductionmentioning

confidence: 99%

A 4D trajectory negotiation protocol for Arrival and Approach sequencing

Rodriguez

Gómez²,

Herrero

et al. 2008

2008 Integrated Communications, Navigation and Surveillance Conference

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Future 4D TBO will require effective airground data link communication and negotiation protocols. This issue is especially critical in Arrival and Approach flight phase due to the variability of conditions into a short space-time environment where multiple aircraft simultaneously converge. Besides, several subtasks are closely related with effective air-ground negotiation protocols for 4D TBO in Terminal Areas: predicting accurate arrivals 4D trajectories, performing well established 4D trajectory formats for an effective interoperability between airborne and ground systems, designing efficient real-time aircraft arrival sequencer and scheduler algorithms, etc.In this paper we propose a 4D Trajectory AirGround Negotiation Protocol for Arrival and Approach Sequencing. The Negotiation Protocol has been implemented in an ad hoc multi-agent platform. Based on this proposal we summarize other relevant information that should be incorporated into the 4D trajectory information.

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Trajectory computation Infrastructure based on BADA Aircraft Performance Model

Cited by 26 publications

References 3 publications

Prediction of descent trajectories based on Aircraft Intent

Prediction of descent trajectories based on Aircraft Intent

Trajectory computation for tracker evaluation and linkage processing

A 4D trajectory negotiation protocol for Arrival and Approach sequencing

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