Numerical Simulation of Cavitating Flow Around a Hydrofoil
Bibliographic record
Abstract
The objective of this paper is to evaluate the applicability of different cavitation models and determine appropriate numerical parameters for cavitating flows around a hydrofoil. The simulations are performed for a NACA 66 foil at 6 degrees angle of attack, Reynolds number of 750 000 and for a cavitation number of 1.49 corresponding to the partial sheet cavitating regime. The incompressible, multiphase Reynolds-averaged Navier-Stokes (RANS) equations are solved by the CFD solver CFX with Kubota and Merkle cavitation models. As part of the work, the Merkle model is implemented into CFX by User Fortran code because this model has shown good cavitation prediction capability according to the literature. The effects of the k-ε and SST turbulence models on the cavitating flow dynamics are compared. Also, an investigation on structured and hybrid meshes with different mesh sizes and concentrations is carried out in order to better understand the mesh influence for this cavitation simulation. The local compressibility effect is considered by correcting the turbulent eddy viscosity inside the mixture vapor/liquid zones. The numerical results are validated by experiments conducted in a cavitation tunnel at the French Naval Academy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".