Accurate marine propellers flow field CFD through anisotropic mesh optimization
Bibliographic record
Abstract
Purpose Computational fluid dynamics (CFD) simulation of the flow field around marine propellers is challenging because of geometric complexity and rotational effects. To capture the flow structure, grid quality and distribution around the blades is primordial. This paper aims to demonstrate that solution-based automatic mesh optimization is the most logical and practical way to achieve optimal CFD solutions. Design/methodology/approach In the current paper, open water propeller performance coefficients such as thrust and torque coefficients are numerically investigated. An anisotropic mesh adaptation technique is applied, believed for the first time, to marine propellers and to two computational domains. Findings The current study’s performance coefficients are compared with other previously published CFD results and improvements in terms of accuracy and computational cost are vividly demonstrated for different advance coefficients, as well as a much sharper capture of the complex flow features. Originality/value It will be clearly demonstrated that these two improvements can be achieved, surprisingly, at a much lower meshing and computational cost.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".