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Record W4386215794 · doi:10.32920/24043446.v1

Cable Tension Test

2023· preprint· en· W4386215794 on OpenAlexaff
Rutuja Rajendra Anandgaonkar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTension (geology)PulleyStructural engineeringDrop (telecommunication)Drop testTensile testingMaterials scienceForensic engineeringEngineeringComposite materialMechanical engineeringUltimate tensile strength

Abstract

fetched live from OpenAlex

<p>The report focuses on operations of a cable pulley system within the nose landing gear of a Bombardier Global Express. The Manual release system is observed to lose pre-tension in the nose landing gear cable loop. Series of tests were performed to single out two possibilities that could be responsible for the tension loss. The two main causes were identified as structural deformation within the system and cable creep over time. With these tests it was confirmed that there is loss in tension when pulley cluster assembly was subjected to fixed load for longer duration of time, whereas, when the pulley cluster assembly was tested under similar loading for 5 days, negligible loss in tension was observed. The test was designed to last for 53 days but due to the outbreak of a pandemic, the entire test took 105 days to be completed. Additional investigation to eliminate stress relaxation within the cable within the structure, as a possible cause for the tension loss, will have to be carried after this test. Unwinding of the cable in the pulley cluster assembly was determined to be the primary cause of tension loss. The reason for the loss in tension could be a combination of both the above-mentioned causes. However, the test protocols performed here reveal the tension drop but more tests are required to conclusively determine the reason for tension drop. </p>

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 categoriesInsufficient payload (model declined to judge)
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 score0.959
Threshold uncertainty score1.000

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)0.0010.001

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 head0.033
GPT teacher head0.241
Teacher spread0.208 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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