Suppressing tip vortex cavitation through passive deformation of a hydrofoil. II. Twisting
Bibliographic record
Abstract
In this paper, we investigate geometric twisting as a passive control strategy to mitigate tip vortex cavitation (TVC) in marine propellers and hydrofoils. Large eddy simulation of tip vortex flow past an elliptical NACA (National Advisory Committee for Aeronautics) 66(2)-415 hydrofoil in various twisting configurations is conducted in non-cavitating and cavitating conditions. Twisting is parameterized by the twisting span and tip-twisting angle, and a variety of twisting configurations are investigated. The configurations include quarter-, half-, and full-span twisting with tip-twisting angles ranging from −9° to 9°. An analysis of tip vortex core pressure in non-cavitating conditions and tip vortex cavity in cavitating conditions reveals the potential of geometric twisting by positive tip-twisting angles in alleviating TVC. To ensure practicality, the effect of twisting on hydrodynamic performance is evaluated. Partial-span twisting with positive tip-twisting angles leads to no adverse impact, or even a slight improvement, in hydrodynamic performance. Examining the integrated performance index indicates improved performance in twisted cases with positive tip-twisting angles, considering both hydrodynamic performance and TVC intensity simultaneously. A detailed investigation of the flow field reveals that the variations in the spanwise loading distribution, tip vortex–wake interactions, and boundary layer behavior are the major mechanisms involved in TVC mitigation achieved by twisting. The findings demonstrate geometric twisting as an effective and practical TVC mitigation strategy for hydrofoils and marine propellers, which offers suppression of cavitation without compromising and potentially improving hydrodynamic performance.
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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 | 0.000 |
| 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) | 0.001 | 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".