A Multi-Region CFD Model for Aircraft Ground Deicing by Dispersed Liquid Spray
Bibliographic record
Abstract
The aircraft ground deicing (AGD) process is a mandatory step before taking off in a cold climate. The development of CFD (computational fluid dynamics) tools to simulate AGD could help the industry reduce its costs and limit pollution. Previous works have modelled some parts of the AGD process. Building on these previous works, this paper presents a three-dimensional (3D) CFD algorithm to simulate the process in full scale. The algorithm comprises a multi-region model where a Lagrangian method solves the spray particle equations, and an enthalpy–porosity approach with an Eulerian method simulates the ice melting. The multi-region approach is verified in this paper through a spray-tip penetration (STP) test. The STP predicted using the multi-region model had 99% agreement with the STP predicted using a Lagrangian method. Therefore, the multi-region technique correctly modeled the particle momentum between the two regions. This paper also presents a numerical calibration of the permeability coefficient for the extended enthalpy–porosity technique in the context of AGD. The numerical calibration of the permeability coefficient will enable future parametric studies of the AGD process.
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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) | 0.003 | 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".