Numerical study on effects of leading-edge manufacturing defects on cavitation performance of a full-scale propeller. II. Simulation for the full-scale propeller with defects
Bibliographic record
Abstract
Paper II of this two-part paper investigated the effects of leading-edge (LE) manufacturing defects on the open-water cavitation performance of a full-scale propeller based on the geometry of David Taylor Model Basin propeller by using the three-dimensional (3D) steady Reynolds-Averaged Navier–Stokes solver. Various simulation parameters, including domain size, grid size, stretch ratio, first-grid spacing, y+, and turbulence model, were carefully examined for their effects on the solutions, leading to the development of the best modeling practices for the full-scale propeller with LE defects. Employing these recommended best-practice settings, simulations were conducted on the full-scale propellers with 0.10, 0.25, and 0.50 mm LE defects. Compared to the predictions from Paper I [Jin et al., “Numerical study on effects of leading-edge manufacturing defects on cavitation performance of a full-scale propeller—Paper I: Simulation for the model- and full-scale propellers without defect,” Phys. Fluids 36, 105179 (2024).], which did not account for LE defects, the results showed that the LE defects within International Standards Organization (ISO) 484 Class S tolerances narrow the cavitation buckets. As a consequence, such LE defects can result in more than 40% reduction in cavitation inception speed, which is similar to the conclusions drawn from earlier two-dimensional (2D) studies [Jin et al., “2D CFD studies on effects of leading-edge propeller manufacturing defects on cavitation performance,” in SNAME Maritime Convention (The Society of Naval Architects and Marine Engineers, 2020).]. Note that Paper I [Jin et al., “Numerical study on effects of leading-edge manufacturing defects on cavitation performance of a full-scale propeller—Paper I: Simulation for the model- and full-scale propellers without defect,” Phys. Fluids 36, 105179 (2024).] presents the simulations for the model- and full-scale propellers without LE defects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".