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Record W3204383421 · doi:10.4271/01-15-01-0002

Linearization of Aircraft Landing Equations of Motion with Airframe Flexibility Effects

2021· article· en· W3204383421 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSAE International Journal of Aerospace · 2021
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsAirframeFlexibility (engineering)AeronauticsLinearizationAerospace engineeringLanding gearEquations of motionMotion (physics)Computer scienceEngineeringMathematicsPhysicsClassical mechanicsNonlinear systemArtificial intelligence

Abstract

fetched live from OpenAlex

<div>The conventional approach in aircraft landing loads analysis, such as for shock absorber development, is using a nonlinear set of equations and a modal representation of the airframe. For preliminary shock absorber design studies, a linearized set of equations may provide a highly efficient simulation method to limit the parameter space of linear shock absorber models. This article develops a set of linearized equations of motion to simulate the landing touchdown event while capturing airframe flexibility effects using a transfer function. The linearized flexible model demonstrates the ability to generally capture flexibility effects and output responses of interest with a significantly reduced simulation time compared to both fully flexible and nonlinear reduced-order models. The linearization of a Fiala tire model is accomplished by scaling the longitudinal tire stiffness such that the peak tire drag force matches that of the nonlinear model, and the vertical tire stiffness is obtained from a linear regression of a nonlinear vertical force versus deflection curve through an expected range of tire deflection.</div>

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.581
Threshold uncertainty score0.369

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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