Effect of Variable Melting Temperature on the Enthalpy Model for Runway Deicing
Bibliographic record
Abstract
One of the most significant challenges for the aviation industry in the winter is the deicing operations on runways. As a result, deicer chemicals can pollute the environment if used in a large amount. A mathematical model could help optimize the use of deicer chemicals. Road deicing models exist to predict pavement temperature covered by snow/ice during deicing operations. However, the specificity of airport operations requires a model for the runway deicing to simulate the mass of ice melted with usage of deicing agents. Here we propose a model for runway deicing and validate it against experimental results. Our model considers temperature, diffusive flux, and time changes in a normal direction. It also calculates the mass and heat transfer in three regions (liquid, mushy, and solid). We used the enthalpy method to determine the temperature and the interface location at each time step. In the liquid and solid, the deicer concentration is obtained by Fick’s law and updated at each time step and location. The melting point temperature is variable due to the dilution of the deicer in the solution. Therefore, melting points are updated depending on the concentration at each location and time. The model uses the phases diagram for water and deicer agent mixture, considering eutectic point, for melting point calculation. The mesh dependency of the model is first investigated. To verify the model sensitivity, the paper proposes parametric studies for the heat transfer coefficient and the diffusion coefficients. Then, to validate the model, the Anti-Icing Materials International Laboratory (AMIL) in Chicoutimi conducted experimental tests on deicer agents for runways. Validation of the model is achieved for potassium acetate and potassium formate, two types of deicers.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".