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Record W2006255641 · doi:10.1109/oceans.2014.7003269

Mooring and hydrostatic restoring of offshore floating wind turbine platforms

2014· article· en· W2006255641 on OpenAlexaff
Mohammed Khair Al‐Solihat, Meyer Nahon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsStiffnessBARGEMooringSparMarine engineeringHydrostatic equilibriumOffshore wind powerTurbineEngineeringSurgeStructural engineeringStiffness matrixMechanical engineeringPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper investigates the restoring stiffness of the main platform concepts proposed for offshore floating wind turbine (FWT) systems; namely, barge, spar, tension leg platform (TLP). The overall system stiffness is partly due to the hydrostatics, and partly due to mooring. The hydrostatic stiffness matrix is formulated using the linear hydrostatic approach that assumes small platform rotation. A new analytical form of the mooring stiffness matrix for a taut-leg platform is presented and subsequently used to formulate the TLP mooring stiffness. While a numerical approach, is used for the other two platform types. The hydrostatic and mooring stiffness coefficients for the surge, sway, heave, roll, pitch and yaw degrees of the freedom (DOF) are computed for the different types of platforms. For each DOF, the magnitude of stiffness from both hydrostatics and moorings are compared.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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