Oscillating Airfoil Icing Tests in the NASA Glenn Research Center Icing Research Tunnel
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">A team from the USA rotorcraft industry, NASA, and academia was established to create a validated high-fidelity computational fluid dynamics (CFD) icing tool for rotorcraft. Previous work showed that an oscillating blade with a periodic variation in angle of attack causes changes in the accreted ice shape and this makes a significant change in the airfoil drag. Although there is extensive data for ice accumulation on a stationary airfoil section, high-quality icing-tunnel data on an oscillating airfoil is scarce for validating the rotorcraft icing problem. In response to this need, a two-dimensional (2D) oscillating airfoil icing test was recently performed in the Icing Research Tunnel at the NASA Glenn Research Center. Three leading-edge specimens for an existing 15-inch chord test apparatus were designed and instrumented to provide the necessary data for the CFD code validation. The test apparatus was run in steady and oscillatory operation mode to collect data for variations in liquid water content, tunnel temperature, and airspeed. Data obtained from the test include ice tracings, photographs, surface pressure measurements, wake traverse data, and force balance data. This paper presents an overview of the test including model details, test instrumentation, sample results, and a summary of key findings.</div></div>
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.007 |
| 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".