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Path Steering Error & Turn Analysis of Multiple Aircraft in the Current ECAC Fleet

2020· article· en· W3104448829 on OpenAlex

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aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
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Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsAutopilotAerospace engineeringAeronauticsTrajectoryRange (aeronautics)Computer scienceSimulationEngineeringAutomotive engineering

Abstract

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EUROCONTROL, the Technical University of Berlin and Lufthansa Aviation Training conducted a series of flight tests in the frame of Horizon 2020 SESAR PJ.14-1.1 “CNS Environment Evolution” using CS-FSTD Level D certified Full Motion Flight Simulators to analyse the Path Steering Error (PSE) and budget allocation for this error in the computation of the navigation Total System Error. Additionally, the performance and behavior of aircraft while executing turns in the trajectory was analysed. To make the analysis as broad as possible with an optimal coverage of investigated navigation and flight guidance-systems used in ECAC, the tests were performed in a range of different aircraft types including Airbus A319, Airbus A340-300, Airbus A220-300, Boeing 737-300, Boeing 777-200, Boeing 747-400, Embraer E190, De Havilland Canada DHC-8-Q400, Embraer E145 and Bombardier CRJ-200. In each aircraft, two different trajectories were flown under known operating conditions using both the autopilot and manual flight using Flight Director. Recorded data was used to deduct the resulting Path Steering Error and turn performance indicators (bank angle and turn radius). The generated data provides valuable information about the actual navigation performance of modern aircraft, in contrast to the assumed PSE values and turn performance requirements in the current standards (MOPS DO-283B & MASPS DO-236C). A lower assumed PSE could allow using less accurate navigation sensors for certain PBN applications while maintaining the same overall TSE. For example, if the adjusted PSE is low enough, ground based DME-DME sensor combination could be used to serve Performance Based Navigation (PBN) operations with a Required Navigation Performance of 0.3NM. The demonstrated PSE was in the order of magnitude of 0.1NM for both the trajectories flown using autopilot and manual flight with Flight Director, which is a reduction compared to the values from DO-283B. A wide spread of tracks was observed in the turns, which were all executed as “fly-by” turns. Depending on the track change and aircraft type, applied bank angles ranged from 5 to 30 degrees, with resulting turn radii ranging from 38 to 1 NM. All the tracks were within the fly-by transition area defined in DO-236C. The huge spread of tracks in the turns makes revision of the conservative definitions of the fly-by transition area challenging but not impossible.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.237
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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