Best modeling practice for self-propulsion simulation of ship model in calm water
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the development of best Reynolds-Averaged Navier–Stokes modeling practices for simulations of propeller–hull interaction in calm water using Star-CCM+. Extensive convergence studies were carried out to examine effects of various propeller modeling methods and parameters, such as non-dimensional wall distance, grid resolution/distribution, and turbulence model. Bare-hull resistance and propeller open-water performance were first examined. For propeller–hull interaction, a simplified body-force method and a detailed propeller modeling method were applied to predict the wake fraction and propeller performance behind the hull. The difference in accuracy of the two methods was quantified, and the best modeling practices were recommended based on the convergence studies. Validation studies were carried out for the Korea Research Institute of Ships and Ocean Engineering Container Ship model. The pseudo-effective wake fraction determined from computational fluid dynamics simulations was introduced and compared with the experimental effective wake fraction.
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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 it