Temperature Variation of Optical Sensors on a wing during wind tunnel tests
Bibliographic record
Abstract
In this paper, wind tunnel measurements are presented for the airflow fluctuation detection using pressure optical sensors. A number of 21 wind tunnel test runs for various Mach numbers, angles of attack and Reynolds numbers were performed in the 6’×9’ wind tunnel at the Institute for Aerospace Research at the National Research Council Canada (IAR/NRC). A rectangular finite aspect ratio half wing, having a NACA 4415 cross-section, was considered with its upper surface instrumented with pressure taps, pressure optical sensors and one Kulite transducer. The Mach number was varied from 0.1 to 0.3 and the angles of attack range was within -3 to 3. Unsteady pressure signals were recorded and a thorough comparison, in terms of unsteady and mean pressure coefficients, was performed between the measurements from the three sets of the pressure transducers. Temperature corrections were considered in the pressure measurements by optical sensors. Comparisons were also performed against theoretical predictions using XFoil CFD code, and mean errors smaller than 10% was noticed between the measured and the predicted data.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".