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Record W2136407266 · doi:10.1115/omae2012-83104

Prediction of Stabilizing Moments and Effects of U-Tube Anti-Roll Tank Geometry Using CFD

2012· article· en· W2136407266 on OpenAlexfundno aff
Worakanok Thanyamanta, David Molyneux

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsnot available
FundersAtlantic Canada Opportunities Agency
KeywordsComputational fluid dynamicsMoment (physics)Tube (container)Marine engineeringEngineeringSimulationMechanicsComputer scienceMechanical engineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

U-tube type stabilizers have been used in ships and offshore vessels to reduce roll motion. The motion of the fluid in the tank creates moments that counteracts the roll moment of the ship. With space and weight constraints, a well designed U-tube tank is crucial for providing effective stabilization. Geometric parameters play important roles in defining the stabilizing performance of such tanks. The effects of these parameters are sometimes interrelated. Computational Fluid Dynamics (CFD) is a useful tool that can be used to predict stabilizing moments and phase lags of U-tube tanks in a short period of time. In order to use the CFD code reliably, validation is required. In this paper, a CFD code’s ability to predict stabilizing moments created by fluid in various U-tube tank geometries was validated. The effects of tank parameters on the stabilizing moments were investigated and compared with published experiment data. The results were found to be in good agreement with the model test data. The code can be used to give a preliminary evaluation of tank performance for design purposes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.256

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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