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Record W4403639449 · doi:10.1063/5.0218500

Numerical study on effects of leading-edge manufacturing defects on cavitation performance of a full-scale propeller. I. Simulation for the model- and full-scale propellers without defect

2024· article· en· W4403639449 on OpenAlexaff
Shanqin Jin, Heather Peng, Wei Qiu

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPropellerPhysicsCavitationFull scaleScale (ratio)MechanicsComputer simulationScale effectsLeading edgeEnhanced Data Rates for GSM EvolutionMarine engineeringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Paper I of this two-part paper focuses on predicting the open-water cavitation performance of the model- and full-scale propellers based on the geometry of David Taylor model basin 5168 propeller without leading-edge (LE) defect. Simulations were conducted using the three-dimensional (3D) steady Reynolds-Averaged Navier–Stokes solver in the commercial software package Star-CCM+. Effects of simulation parameters, including domain size, grid size, stretch ratio, first-grid spacing, y+, and turbulence model on the solutions were carefully examined and the best-practice settings for the model propeller and the full-scale propeller with no LE defect were developed. Validation studies were carried out for the propeller model against the experimental data. Results for the full-scale propeller were compared with those of the model-scale propeller. The differences in model and full-scale predictions are generally insignificant. To further confirm this finding, the scale effect was investigated with four additional scale ratios, λ = 2, 3, 4, and 5. The results showed that the scale effect is in general minimal and, therefore, the developed best-practice settings can be applied to the open-water simulation of the full-scale propeller. Paper II presents the simulation for the full-scale propellers with LE defects.

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 score0.029
Threshold uncertainty score0.655

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 head0.016
GPT teacher head0.260
Teacher spread0.243 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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