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Record W2048660505 · doi:10.1115/fedsm2005-77152

A Critical Review of Classical Force Estimation Methods for Streamlined Underwater Vehicles Using Experimental and CFD Data

2005· review· en· W2048660505 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

Venuenot available
Typereview
Languageen
FieldEngineering
TopicShip Hydrodynamics and Maneuverability
Canadian institutionsDefence Research and Development CanadaUniversity of New Brunswick
Fundersnot available
KeywordsComputational fluid dynamicsHullMarine engineeringSubmarineReynolds numberMoment (physics)UnderwaterVortexMechanicsAerospace engineeringComputer scienceSimulationEngineeringGeologyPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

Classical hydrodynamic force estimation methods are widely used by industrial designers of underwater vehicles for whom captive model experiments and CFD based simulations are uneconomical. They are also used in the preliminary design of submarines and when real time submarine simulations are required. These methods poorly estimate the contribution of the hull to the forces, especially at moderate to high incidence angles. This paper critically reviews the classical hull force estimation methods developed by Munk, Allen, Perkins and Jorgensen, and Sarpkaya. It compares the methods with experimentally validated CFD predictions of a streamlined body at incidence angles up to 30 degrees and for Reynolds numbers from 2.3 to 230 million. The comparison shows that inadequately modeled flow separation and leeside body vortices explain the poor force and moment predictions. This is partly due, at least, to the lack of a streamlined tail on the truncated missile shapes for which the estimation methods were developed.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.881
Threshold uncertainty score0.921

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.184
GPT teacher head0.485
Teacher spread0.301 · 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

Quick stats

Citations7
Published2005
Admission routes1
Has abstractyes

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