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Record W2526043226 · doi:10.5957/jsr.2010.54.1.53

A Force Estimation Method for Viscous Separated Flow Over Slender Axisymmetric Bodies With Tapered Tails

2010· article· en· W2526043226 on OpenAlexaff
Tiger Jeans, A. Gordon L. Holloway, George D. Watt, Andrew G. Gerber

Bibliographic record

VenueJournal of Ship Research · 2010
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsUniversity of New BrunswickDefence Research and Development Canada
Fundersnot available
KeywordsRotational symmetryMechanicsInviscid flowReynolds numberImpulse (physics)Computational fluid dynamicsMissilePhysicsAdded massMoment (physics)Classical mechanicsEngineeringAerospace engineeringTurbulenceAcoustics

Abstract

fetched live from OpenAlex

A new impulse method for modeling the normal force distributions along slender axisymmetric bodies with tapered tails is presented. This method is based on a decomposition of the hydrodynamic impulse along the body into an inviscid, profile-dependent component and a viscous, profile-independent component. It is applicable to separated flows up to 30 deg of incidence. Model results for the normal force distributions, total normal force, and total moment on axisymmetric streamlined bodies with tapered tails are compared with computational fluid dynamics (CFD) simulations and experimental data up to a Reynolds number of 23 × 106 with good agreement. The model was also tested on a missile-shaped body and was found to be in excellent agreement with experimental results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.368
Teacher spread0.338 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2010
Admission routes1
Has abstractyes

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