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Record W4391329976 · doi:10.2514/6.2024-1735

Modeling of the Longitudinal Ground Dynamics of the Cessna Citation X

2024· article· en· W4391329976 on OpenAlexaff
Elias E. Zohreh Nejad, Han Wang, Georges Ghazi, Ruxandra Mihaela Botez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsDynamics (music)Aerospace engineeringComputer sciencePhysicsEngineeringAcoustics

Abstract

fetched live from OpenAlex

This study aims to present a new method developed at the Laboratory of Applied Research in Active Control, Avionics, and AeroServoElasticity (LARCASE) for modeling the longitudinal ground dynamics of the Cessna Citation X (CCX) business jet aircraft. Based on a Matlab/Simulink platform, the model proposed is able to reproduce the aircraft behavior for different operating conditions on the ground. In this study, a nonlinear model for the landing gears was developed to simulate the vertical reaction forces of its three components. A detailed study was carried out to estimate the runway friction coefficient according to the runway condition: dry, and wet with two different water depths (5 mm and 12 mm), aircraft weight, center of gravity, velocity, and acceleration. A comprehensive database was created for the identification process from a Research Aircraft Flight Simulator (RAFS) for the CCX, designed and manufactured by CAE Inc. with a Level D (the highest level of certification). Finally, the ground model was validated using additional tests from the RAFS and the Criteria Manual for the Qualification of Flight Simulation Training Devices. According to the results, the proposed model provided excellent ground dynamics predictions, with a maximum relative error of 3%.

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.

How this classification was reachedexpand

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: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.119

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.197
Teacher spread0.187 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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