Prediction of runway deicer melting capacity using the enthalpymethod
Bibliographic record
Abstract
Runway de-icing plays an important role in ensuring the safety of the aviation industry. Often, chemical deicers are used to lower the water melting point and remove the runway ice. The melting point depression varies based on the concentration of the deicer solution. Road de-icing models exist to predict pavement temperature, covered by snow/ice, during chemical de-icing operation. However, the specificity of airport operations requires a runway de-icing model. The model should simulate the melting front location and predict the melting rate. This article suggests a mathematical model for runway de-icing and validates it against ice-melting test results. The runway model considers the temperature changes with time inside the ice and the deicer mixtures. The model is 1D along the direction normal to the ice surface. The model solves the heat and mass transfer equations between the solid and liquid states with a melting front at the interface, analog to the classical Stefan problem. The enthalpy method solves the Stefan problem. The enthalpy in the solid, liquid, and at the interface predict the temperature with the specific heat. Fick's law models the deicer concentration evolution in time at each location. The melting point temperature is variable due to the dilution of the deicer in the solution. For validation, the deicer agents for the runway are experimentally tested at the Anti-Icing Materials International Laboratory (AMIL) in Chicoutimi. The experimental set-up uses a Petri dish that has an ice sample in it. The experimentalist applies a volume of deicer on the ice sample and monitors the temperature evolution in space (along the surface) and time with a thermal camera. The camera is fixed above the sample in a room at a specified temperature. The final paper compares the model temperatures and melted mass to the experimental results at the Petri dish center. The model calculates the quantity of melted ice using 5g deicer chemical in five minutes. On the one hand, the model calculates the temperature in the normal direction, while, on the other hand, the experiment measures the temperature along the top surface. Consequently, the paper validates the temperature of the deicer solution and the mass of melted ice.
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 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".