Eulerian–Lagrangian CFD model for prediction of heat transfer between aircraft deicing liquid sprays and a surface
Bibliographic record
Abstract
Purpose The aim of this paper is to present a Eulerian–Lagrangian model of aircraft ground deicing that avoids the scale’s dispersion problem caused by the great distance between the spray nozzle and the surface to be deiced. Verification is done using the case of a hot particle liquid spray impinging on a horizontal flat plate. The impinged particles flow outwards radially from the impingement zone and form a hot film wall. The computed wall heat distribution is verified. In the end, an inclination spray’s angle study is presented. Design/methodology/approach The problem is divided into two regions. First, a 3D region is created for the evolution of the Lagrangian particles spray. A second 2D region is provided for the formation of a liquid film. The two regions exchange mass, momentum and energy through an interface. Heat losses are modelled through particles and liquid-film cooling and evaporation, particles splash and heat transfer to a fixed temperature plate. Findings For a chamber pressure of 1 bar, the predicted spray penetration is within 10 per cent of the experimental results. For this study case, the heat transfer is maximized with an inclination angle of approximately 30° of the spray. Originality/value The model presented makes it possible to simulate the impingement and heat transfer of a large-scale liquid spray with a reasonable computational cost. To the best of the authors’ knowledge, this model is a first attempt of the computational fluid dynamics simulation of ground deicing.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".