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Record W2088616431 · doi:10.1115/1.1833358

Experimental and Numerical Study of Coupled Dynamic Response of a Mini Tension Leg Platform

2004· article· en· W2088616431 on OpenAlexaff
Anitha Joseph, V. G. Idichandy, S. K. Bhattacharyya

Bibliographic record

VenueJournal of Offshore Mechanics and Arctic Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicWave and Wind Energy Systems
Canadian institutionsCommunity Sector Council Newfoundland and LabradorMemorial University of Newfoundland
Fundersnot available
KeywordsSparHullFlumeWave loadingTension (geology)Coupling (piping)Submarine pipelineStructural engineeringFinite element methodEngineeringMarine engineeringGeologyMechanicsMechanical engineeringGeotechnical engineeringPhysicsFlow (mathematics)

Abstract

fetched live from OpenAlex

Role of mini tension leg platforms (TLP) in oil exploration and production in marginal deepwater fields is becoming increasingly important. Mini TLP combines the simplicity of a spar and favorable response features of a TLP. In this paper, the results of a detailed experimental and numerical investigation of the coupled dynamic behavior of a mini TLP are reported with special attention to hull-tether coupling. The experimental study has been carried out using a scaled model in wave flume with specially designed tethers whose first two “string” natural frequencies are excited by waves, thus achieving strong hull-tether coupling. The numerical study has been carried out using a nonlinear time domain finite element method specifically addressed to compliant offshore platforms using a combination of potential theory based wave loading and Morison-type wave loading. Extensive comparisons between numerical and experimental results have been made both for platform motions and deflected shapes of the tethers and conclusions drawn.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
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 head0.215
Teacher spread0.206 · 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

Citations15
Published2004
Admission routes1
Has abstractyes

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