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Record W2076859792 · doi:10.1115/omae2004-51311

Simulation of Submerged Slack Tethers and Their Interaction With the Environment

2004· article· en· W2076859792 on OpenAlexaff
Juan A. Carretero, Bradley J. Buckham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsUniversity of VictoriaUniversity of New Brunswick
Fundersnot available
KeywordsRemotely operated underwater vehicleUnderwaterContact dynamicsComputer scienceMarine engineeringWork (physics)Scope (computer science)Contact forceSimulationSystem dynamicsVehicle dynamicsAerospace engineeringControl engineeringEngineeringMechanical engineeringRobotMobile robotGeologyPhysicsArtificial intelligenceMechanics

Abstract

fetched live from OpenAlex

Tethered systems, underwater or otherwise, are nowadays used for very diverse tasks. Due to the complexity of such systems, it is necessary to simulate them for design, operation and training purposes. This paper deals with an approach to simulation of tethered systems, in particular underwater remotely operated vehicles (ROVs), by incorporating contact forces acting between the tether and the environment into the dynamic model of the tether. This will ensure model fidelity when the tethered system is operated in a dense environment. In this paper, methods used to compute contact forces are described. In the calculation of contact dynamics, the distance between the tethered system and the environment is of utmost interest. Algorithms to determine the separation distance between the tether and the environment are discussed in the scope of this work. These algorithms are then incorporated into an existing dynamics model of the ROV tether. Finally, this paper concludes with a simple numerical example where a tether is moved in a concave environment. The distance between the tether and the environment is computed as the tether’s location and three-dimensional profile change with time.

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 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: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.084

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 head0.013
GPT teacher head0.194
Teacher spread0.181 · 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.

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

Citations0
Published2004
Admission routes1
Has abstractyes

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