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Record W2044593289 · doi:10.2514/1.17311

Elastodynamic Analysis of Aerial Refueling Hose Using Curved Beam Element

2006· article· en· W2044593289 on OpenAlexaff
Zheng Zhu, S. A. Meguid

Bibliographic record

VenueAIAA Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Control Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBeam (structure)Finite element methodStructural engineeringMechanicsPhysicsEngineering

Abstract

fetched live from OpenAlex

The elastodynamic analysis of an aerial refueling hose by classic cable theory suffers the singularity problem when the hose slackens under dynamic loadings. The difficulty is addressed and overcome by modeling the refueling hose with a new three-noded locking-free curved beam element. The large deformations and rotations of curved beams are formulated in terms of an updated Lagrangian framework with consistently coupled quintic polynomial displacement fields to satisfy the membrane locking-free condition. The stability and accuracy of the new element is validated by experiments involving an instrumented free-swinging steel cable. Good agreement is observed between the experimental results and the predictions of the new element. The numerical capability of modeling a refueling hose and drogue system has been demonstrated by simulating 1) the oscillation of hose due to the disturbance from the tanker and the vortex-induced velocity and 2) a receiver coupling with a hose reel malfunction. The analysis results show clearly the formation and propagation of oscillations along the hose, the consequent whipping near the drogue, and the associated variation of hose tension. The results of new element agree well with field observations and existing analysis results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), 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

Citations54
Published2006
Admission routes1
Has abstractyes

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