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Record W2054562291 · doi:10.1115/1.1287167

A Comparison of Ship-Mounted and Cage-Mounted Passive Heave Compensation Systems

2000· article· en· W2054562291 on OpenAlexaff
Frederick Driscoll, Meyer Nahon, Rolf G. Lueck

Bibliographic record

VenueJournal of Offshore Mechanics and Arctic Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsUniversity of Victoria
FundersOffice of Naval Research
KeywordsCompensation (psychology)Marine engineeringRemotely operated underwater vehicleControl theory (sociology)Tension (geology)PulleyCageSea stateWork (physics)EngineeringStructural engineeringGeologyComputer scienceUltimate tensile strengthMechanical engineeringMaterials science

Abstract

fetched live from OpenAlex

Tethered marine systems experience large tensile loads in their tether when operating in rough seas. Heave compensation systems can be used to reduce these loads and increase the safe operating sea states. In this work, a discrete representation of a passive heave compensator is developed and added to a finite-element model of a deep-sea ROV system to investigate the performance of ship-mounted and cage-mounted compensation systems. Numerical simulations are performed for operating depths ranging from 3280–16,400 ft (1000–5000 m) and a range of compensator stiffnesses. Both ship and cage-mounted systems reduced the natural frequencies, rms cage motion and rms tension, and extended the operating sea state of the ROV. During extreme seas, the cage-mounted compensator effectively eliminated all snap loads. However, the compensator’s characteristics must be carefully chosen because a poorly designed compensator can exacerbate operational problems. [S0892-7219(00)00903-1]

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.239
Teacher spread0.230 · 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 designBench or experimental
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

Citations38
Published2000
Admission routes1
Has abstractyes

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