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Record W2100368864 · doi:10.1109/oceans.1993.325968

Passive damping to attenuate snap loading on a ROV umbilical cable

2002· article· en· W2100368864 on OpenAlexaff
R. Driscoll, L. Iggins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRemotely operated underwater vehicleBoomStructural engineeringRemotely operated vehicleDamperFinite element methodMarine engineeringEngineeringComputer scienceAerospace engineeringRobot

Abstract

fetched live from OpenAlex

Summary form only given. Snap loading of the umbilical cable connecting a ship to a remotely operated vehicle (ROV) and its cage is a major problem when the ROV system is operating in rough sea conditions. Snap loading is the instantaneous, high magnitude, tensile loading of a cable. This loading arises when the amplitude and frequency of the ship motion is such that it causes parts of the cable to be subjected to compressive forces. Since cables are incapable of resisting such loads, slack can develop in a section of the cable where there is a compressive load. If the rate of retensioning is rapid, the cable will then experience snap loading. This paper describes the design of a passive damping system to attenuate snap loading in the ROV system. The mathematical model consists of the following sub-systems: 1) the boom and ship, 2) the cable, 3) the passive damper, and 4) the cage. The cable is modelled with discrete elements implemented using finite element theory. The total cable length is divided into a large number of elements, and the motion equations are written for each element, and a forcing function is applied to the top element.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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 categoriesInsufficient payload (model declined to judge)
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 score0.890
Threshold uncertainty score0.999

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)0.0020.001

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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2002
Admission routes1
Has abstractyes

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