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Record W2003807967 · doi:10.1115/omae2013-10066

Prediction of Sloshing Effects Using a Coupled Non-Linear Seakeeping and CFD Code

2013· article· en· W2003807967 on OpenAlexfundno aff
Worakanok Thanyamanta, Don Bass, David Molyneux

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics Simulations and Interactions
Canadian institutionsnot available
FundersAtlantic Canada Opportunities Agency
KeywordsSeakeepingComputational fluid dynamicsSlosh dynamicsMarine engineeringShip motionsFlow (mathematics)Free surfaceEngineeringCode (set theory)Storage tankSimulationComputer scienceMechanicsStructural engineeringHullMechanical engineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, a numerical approach for predicting sloshing or roll-stabilization effects is proposed. A 3D non-linear time domain seakeeping code, MOTSIM, was coupled with a commercial CFD code (Flow-3D) and used to predict roll stabilizing performance of an unconventional U-tube tank installed in an oceanographic vessel. The codes were fully coupled and thus provided coupled effects of the external flow field and the motion of the fluid with a free surface inside the anti-roll tank on the ship motion in six degrees of freedom. MOTSIM is a well validated code that has been proven to provide accurate motion prediction for various vessels. The CFD code allows for modeling of complex tank geometry as well as detailed investigation of locations in the tank where severe loads might be experienced. Comparisons of the simulation results with experimental data showed good agreement and significant effects of the anti-roll tank on decreasing the ship’s roll motion. This study also demonstrated the coupled code’s potential use for any type of sloshing problems including the design of roll-stabilization tanks and LNG carriers.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.281

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 head0.229
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

Citations0
Published2013
Admission routes1
Has abstractyes

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