Research on the simplified vehicle window buffeting noise with a modified LRN CLES model using a transition-code based method
Bibliographic record
Abstract
Purpose This paper aims to propose a precise turbulence model for vehicle aerodynamics, especially for vehicle window buffeting noise. Design/methodology/approach Aiming at the fact that commonly used turbulence models cannot precisely predict laminar-turbulent transition, a transition-code-based improvement is introduced. This improvement includes the introduction of total stress limitation (TSL) and separation-sensitive model. They are integrated into low Reynolds number (LRN) k- ε model to concern transport properties of total stress and precisely capture boundary layer separations. As a result, the ability of LRN k- ε model to predict the transition is improved. Combined with the constructing scheme of constrained large-eddy simulation (CLES) model, a modified LRN CLES model is achieved. Several typical flows and relevant experimental results are introduced to validate this model. Finally, the modified LRN CLES model is used to acquire detailed flow structures and noise signature of a simplified vehicle window. Then, experimental validations are conducted. Findings Current results indicate that the modified LRN CLES model is capable of achieving acceptable accuracy in prediction of various types of transition at various Reynolds numbers. And, the ability of this model to simulate the vehicle window buffeting noise is greater than commonly used models. Originality/value Based on the TSL idea and separation-sensitive model, a modified LRN CLES model concerning the laminar-turbulent transition for the vehicle window buffeting noise is first proposed.
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How this classification was reachedexpand
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 | 0.005 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".