Experimental Study of Snow Precipitation Over a Generic Deicing Fluid without Fluid Flow
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Deicing and anti-icing fluids are used to remove and prevent ice formation on aircraft before takeoff. Holdover times (HOT) published by the FAA are used by pilots as guidelines indicating the amount of effective time of a fluid under certain freezing precipitation types. However, the times on these tables are based on endurance time tests involving a visual estimate of failure on a flat plate [<span class="xref">1</span>]: when 30% of the fluid is covered with white snow under snow precipitation, although the times have been correlated to aircraft wing tests [<span class="xref">2</span>] they do not address the mechanism of fluid failure. To measure and understand the fluid mechanisms conducting to failure, the Anti-icing Materials International Laboratory (AMIL) developed a simplified test with a generic deicing propylene glycol-based fluid. The test consisted of pouring 400 mL of the generic deicing fluid on a 5 dm by 3 dm level flat plate where the plate edges were rimmed with insolated walls to make a waterproof open box. The flat plate covered with deicing fluid was submitted to snow precipitation in the form of regular snow and simulated snow pellets. The snow precipitation intensities and temperatures tested were based on ARP5485. The standard test method for testing snow indoors includes using a heating pad to compensate for the lack of thermal equilibration which occurs in outside with wind and the larger air mass. It was the energy solicited by the fluid melting the snow from the heat pad that was used to develop a model for fluid failure. Three precipitation intensities and eight temperatures with the generic deicing fluid and commercial Type I and Type IV fluids were studied. At fluid failure, snow mass, and energy provided to melt the snow were independent of snow intensity and type of snow, however endurance time and supplied power were dependent on snow intensity but independent of snow type. Visual observations showed that only a fraction of the falling snow in contact with the fluid covering the aluminium plate melts, the unmelted snowflakes descend by gravity into the fluid and accumulate on the aluminium plate surface. The energy to melt the snow was provided by a heating system. The proposed model assumes that enough energy is available to melt all the snow that a deicing fluid is able to absorb under water form and that the water can diffuse rapidly into the deicing fluid due to multiple diffusion sites resulting in an uniform propylene glycol concentration and predicted well the endurance time and the snow mass at fluid failure for a generic deicing and Type I fluid with an error of 7%. A semi-empirical relation was used in the model to evaluate the melting fraction; this relation, based on a phase diagram, can be used to estimate the propylene glycol concentration, the power and the energy as a function of time at the fluid failure. However, the model could not predict the endurance time of Type IV anti-icing fluids.</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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".