Comparison of numerical and experimental results over a naca0025 airfoil undergoing separation
Bibliographic record
Abstract
This paper examines unsteady numerical simulation of a three-dimensional flow over two different symmetric NACA airfoils.The airfoils are at various angles of attack and various low Reynolds numbers ( ) 4 6 5 10 ~ 5 10 × × .The Spalart-Allmaras, and k ε models, as well as LES and E-LES approaches are used and compared to experimental results.The capability of each turbulent model to capture characteristics at low Reynolds number is discussed.The main objective in this study is to capture the behavior of boundary-layer separation with respect to experiments at critical Reynolds numbers and then to establish a useful solution methodology describing transition in the boundary layer of airfoils accurately.Fully attached flow, boundary-layer separation, and boundary-layer with reattachment conditions are studied.Although LES and E-LES provide the highest computation cost, results show the accuracy of these methods at low Reynolds numbers (<150 x10 3 ).For higher Reynolds number, turbulence models provide a fair agreement with experiments; while the processing cost of LES method is very high.Finally, the impact of airfoil thickness, Reynolds number, and angle of attack on the boundary-layer separation and consequently airfoil performance are studied in detail.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".