Simulation of Freezing and Frozen Precipitation for the Evaluation of De/anti-icing Systems
Bibliographic record
Abstract
Simulating freezing and frozen precipitation in an indoor laboratory setting can permit year round evaluation of de/anti-icing systems and fluids. At AMIL, freezing rain, freezing drizzle, icing fog and in-cloud icing as well as frost, snow, ice pellets and icing clouds can be simulated in a variety of cold chambers of different heights and with different wind conditions using specialized spraying systems and temperature set-ups. Freezing rain is simulated using a 9 m high vertical chamber capable of supercooling water droplets from 100 to 1000 μm, so they freeze not long after impact. The freezing drizzle is simulated in a 4 m high chamber where supercooled droplets from 50 to 250 μm freeze on impact. Icing fog and in-cloud icing are simulated with the help of a pneumatic spray nozzle system which allows for a finer water spray, in the 20 μm diameter range. The frost is simulated by saturating a cold room with humidity generated from a heated, temperature controlled water bath. Snow is simulated with a fine pneumatic water spray which forms a soft rime simulating the effect of natural snow. Ice pellets are made in the 9 m high vertical cold chamber using strong turbulent wind that increases the air residency and convective air cooling of water droplets so they freeze before impact. Finally, icing wind tunnels allow for the simulation of an object iced through a passing supercooled cloud. For all simulations, humidity can be controlled by using a heated water bath to increase it, or by using an independent refrigeration unit or molecular sieves to decrease it. This paper reviews the various simulated precipitation obtained at AMIL.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".