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Record W2088308240 · doi:10.1115/1.4027718

Nonlinear Hydrostatic Restoring of Floating Platforms

2014· article· en· W2088308240 on OpenAlexaff
Mohammed Khair Al‐Solihat, Meyer Nahon

Bibliographic record

VenueJournal of Computational and Nonlinear Dynamics · 2014
Typearticle
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsMcGill University
Fundersnot available
KeywordsBuoyancyHydrostatic equilibriumRestoring forceBARGECylinderFictitious forceStructural engineeringOffshore wind powerEngineeringStiffnessDisplacement (psychology)Nonlinear systemTension (geology)MechanicsTurbineMarine engineeringPhysicsMechanical engineeringClassical mechanicsMoment (physics)

Abstract

fetched live from OpenAlex

This paper provides a comprehensive theoretical analysis to determine the nonlinear hydrostatic restoring loads and stiffnesses of a floating offshore platform. A new approach is developed to calculate the buoyancy forces and the corresponding moments for general 3D displacements of offshore platforms that utilize cylindrical floaters, such as barge (rectangular cylinder), spar, tension leg platform (TLP), and semisubmersible (circular cylinders) offshore platforms. The exact buoyancy force magnitude and point of action (center of buoyancy) and hydrostatic restoring moments for general fully coupled 3D displacements are derived. Exact expressions for the coupled water plane area restoring moments in pitch, roll, and yaw are derived in the body and inertial frames. The analysis is subsequently used to evaluate the hydrostatic loads and stiffness of floating cylinders that undergo large displacement, such as floating wind turbine (FWT) platforms. Moreover, it can be used to determine the equilibrium positions and orientation of free floating cylindrical bodies.

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)0.001
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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