Computational Fluid Dynamics Investigation of Transient Effects of Aircraft Ground Deicing Jets
Bibliographic record
Abstract
The present Paper investigates the heat transfer and momentum created by a hot turbulent propylene glycol jet impinging on a horizontal plate at below the freezing-point temperature. A model for the simulation of the initial formation of liquid film at the beginning of an aircraft ground deicing process is proposed. The volume of fluid model coupled with the film formulation is employed using computational fluid dynamics to capture the interface in multiphase flow (liquid propylene glycol/liquid water/air). The three-dimensional Reynolds averaged Navier–Stokes equations are numerically solved using a finite volume discretization under unsteady conditions. The ground deicing case consists of a box and a convergent divergent nozzle installed inside the box. Approximately 512,000 rectangular cells with 0.006 m of thickness are used to refine the mesh of the liquid film on the wall. Flow conditions set are 60°C for the propylene glycol inlet temperature and 0°C for the static ambient temperature. The computed impinged surface skin Nusselt number gives the most noticeable discrepancies at the stagnation point, where the analysis results in a lower skin Nusselt number compared to the experimental results. The magnitudes of heat transfer for the ground deicing case study are found to be in good agreement with experimental hot jet data obtained from the literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".