Wind Tunnel Testing with a Vertical Stabilizer Common Research Model in Ground Icing Conditions
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">As part of a larger research program on behalf of Transport Canada and the Federal Aviation Administration, APS Aviation Inc. conducted a series of representative scaled tests in the National Research Council Canada 3 m × 6 m Icing Wind Tunnel evaluating contaminated fluid flow-off from a common research model vertical stabilizer. The goal of this work is to help understand the impact of de/anti-icing fluids with and without precipitation on the performance of vertical surfaces, using existing allowance times that were developed for horizontal surfaces to guide the test exposure times. The data include a qualitative analysis of the appearance of the surfaces and a quantitative evaluation using aerodynamic data from an external balance and manual measurements of the fluid thicknesses on the model surface. The model was evaluated in a clean and dry configuration to establish the baseline aerodynamic performance, with sandpaper roughness testing used to as a substitute for fluids in order to investigate model performance in a static manner. Tests with de/anti- fluids and precipitation, including snow, ice pellets, and freezing rain, were conducted in both static and dynamic takeoff simulations to determine the impact of the fluids and precipitation on the model performance. In general, fluid, fluid and contamination, and roughness testing all had comparable side force losses; however, the worst-case conditions may not have been explored yet as testing was generally limited to warmer temperatures above −10°C. As a proof-of-concept, laser scanning of the model with ice contamination was successfully completed for both pre- and post-simulated takeoff conditions.</div></div>
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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".