A numerical and experimental test-bed for low-speed fans
Bibliographic record
Abstract
An extensive experimental and numerical database provides detailed information on some transition and noise mechanisms encountered in the low-Reynolds-number flows of low-speed axial fans for the first time. Two different similarly instrumented mock-ups built from the same industrial controlled-diffusion airfoil have allowed the first consistent comparison of wall pressure and near-field velocity statistics on the same geometry with and without rotation in perfect similitude of Mach and Reynolds numbers. The experimental and numerical results on the stationary airfoil constitute the largest unique aeroacoustic data set for airfoil trailing-edge noise characterization including installation effects. A similar experimental aeroacoustic database has been built on the rotating controlled-diffusion blade in the Michigan State University-Automotive Fan Research and Development (MSU-AFRD) test facility for different fan configurations, rotational speeds and flow rates. This yields a unique test-bed for fan code validation. The comparisons between the stationary and rotating airfoils suggest that the wall pressure statistics are hardly influenced by rotation in the trailing-edge region, and that the differences in the velocity statistics in the near-wake are a more energetic wake with smaller velocity deficits and diffusion, and a far-less uniform inviscid region in the rotating case.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".