Turbulent Energy Prediction for an External Flow Around Valeo Cooling Fan by V2-F Modelling and Improved K-? Low Reynolds Model
Bibliographic record
Abstract
The noise field can be defined as the consequence of pressure fluctuations generated by turbulent flows, close to solid walls, which are governed by acoustics conversions and basing on the Lighthill’s theory. This paper is discussing the different results of numerical simulation for an external flow around an asymmetric wing profile (Valeo CD). The Numerical simulation consists of comparing the original Durbin V2-f and the k-? low Reynolds models. Some modifications have been introduced to the k-? model, by replacing the strain rate term and the vorticitiy, in order to improve the turbulent energy prediction of the low Reynolds viscous models. The comparison of the results obtained has been made with full experiments in large wind tunnel at the central school of Lyon, and LES simulation. The V2-f model has shown a good stability and satisfactory turbulent energy prediction near the wall, comparing to the k-? modified model. The improvements were due to the normal velocity fluctuations v², and the anisotropic effects modelled by the elliptic relaxation function close to the solid wall.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".