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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".