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Record W4413391763 · doi:10.1115/omae2025-157416

An Experimental Analysis of Second-Order Roll Motions of New Generation FPSOs

2025· article· en· W4413391763 on OpenAlexaff
Victor Cappelaro, Pedro Cardozo de Mello, Asdrubal N. Queiroz Filho, Jordi Mas-Soler, Alexandre N. Simos, Allan C. de Oliveira, Marcos D. Ferreira

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsComputer scienceOrder (exchange)

Abstract

fetched live from OpenAlex

Abstract Floating Production, Storage and Offloading (FPSO) units represent the main type of platform adopted nowadays for oil & gas production offshore Brazil. Since the first versions in the early 1990s, the FPSOs designed to operate in deep water fields underwent several modifications, and, more recently, those of the new generations are characterized by a significant increase of hull size and topside weight. One of the consequences of this increase in the FPSOs size are the higher values of their natural periods of roll motions, making them more susceptible to (low-frequency) resonant motions induced by waves. The present work brings more details on a set of experimental results designed to evaluate the difference-frequency roll motions of a small-scale model with geometry and mass distribution within the range representative of the new FPSO units. The main objective of the tests was to provide the basis for the verification of the predictions obtained by numerical models that are often used for the units’ seakeeping analysis. Besides identifying the resonant roll motions induced by second-order wave effects in the experimental records, some preliminary comparisons are presented with computational predictions. Results show that a reasonable level of adherence may be obtained, notwithstanding the well-known limitations of the numerical procedure.

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 categoriesInsufficient payload (model declined to judge)
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 score0.609
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.258
Teacher spread0.248 · 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.

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
Published2025
Admission routes1
Has abstractyes

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