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Using reduced hydrodynamic models to accelerate the predictor–corrector convergence of implicit 6-DOF URANS submarine manoeuvring simulations

2014· article· en· W2035072829 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComputers & Fluids · 2014
Typearticle
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsUniversity of New BrunswickDefence Research and Development Canada
FundersNatural Sciences and Engineering Research Council of CanadaAtlantic Canada Opportunities AgencyCanada Foundation for InnovationDefence Research and Development Canada
KeywordsJacobian matrix and determinantPredictor–corrector methodConvergence (economics)MathematicsApplied mathematicsControl theory (sociology)Mathematical analysisReynolds numberMechanicsPhysicsComputer scienceTurbulence

Abstract

fetched live from OpenAlex

An implicit predictor–corrector method is presented for the simultaneous integration of the six degrees-of-freedom (DOF) equations of motion for a manoeuvring submarine and the unsteady Reynolds-Averaged Navier Stokes (URANS) equations describing the vehicle hydrodynamics. The novel method uses coefficient-based hydrodynamic models for estimating the Jacobian matrix for Newton iteration. The method is applied to emergency rising and horizontal plane zig-zag manoeuvres. It is shown to converge faster at each timestep than under-relaxed fixed-point iteration with an optimum relaxation parameter. A simple model containing only primary linear hydrodynamic coefficients that are relatively easy to estimate or measure was found to be adequate for modelling the Jacobian matrix in these simulations.

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)0.000
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 score0.388
Threshold uncertainty score0.907

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 head0.028
GPT teacher head0.244
Teacher spread0.215 · 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