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Record W4395661696 · doi:10.5957/josr.06220019

Determination of Maneuvering Force Coefficients for a Destroyer Model with OpenFOAM

2024· article· en· W4395661696 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Ship Research · 2024
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsDefence Research and Development CanadaMemorial University of Newfoundland
Fundersnot available
KeywordsHullComputational fluid dynamicsTurbulenceComputer scienceMotion (physics)MechanicsSimulationDynamics (music)FidelityPhysicsEngineeringMarine engineeringAcousticsArtificial intelligence

Abstract

fetched live from OpenAlex

_ Ship maneuvering performance can be predicted using various methods, including physical model tests, rapid simulations using coefficient-based forces, and high-fidelity computational fluid dynamics. This article considers the application of computational fluid dynamics for the prediction of hull force coefficients to be used as inputs to rapid maneuvering simulations. The open-source software OpenFOAM was used to simulate forces on the destroyer model DTMB 5415 for steady drift, oscillatory pure sway motion, and oscillatory yaw motion. Recommendations are provided regarding best practices in a number of areas, including domain size, mesh refinement, time step size, and turbulent modeling. Predicted forces for steady drift angles up to 12 degrees are typically within 10%of experimental values. For oscillatory sway and yaw motions, predicted forces are typically within 25%of experimental values. Keywords maneuvering; OpenFOAM; best modeling practices

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.

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)0.002
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 score0.214
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.071
GPT teacher head0.368
Teacher spread0.297 · 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