Anti-icing hot air jet heat transfer augmentation employing inner channels
Bibliographic record
Abstract
Many approaches exist today that employ hot-air from aircraft compressor bleed for anti-icing critical aircraft surfaces. This paper introduces and numerically analyzes the novel application of an inner or etched channel to augment heat transfer from a hot-air jet impinging on a curved surface representing the inner surface of an aircraft wing’s leading edge or slat. The study shows that proper positioning, geometry, and flow characteristics of a channel along the inner surface of the leading edge can significantly enhance heat transfer, boost the anti-icing system performance, and greatly enhance flight safety during critical icing weather conditions. Commercially available CFD software, ANSYS Fluent is used to model and analyze the effect of different geometric and flow parameters typical of those found in small to medium category commercial transport aircraft to help determine the optimum arrangement. These parameters include: (1) jet nozzle height-to-slot diameter ratios from 4 to 8, (2) channel width-to-slot diameter ratios from 0.4 to 1.8, and (3) inner-channel inlet location angles from 10° to 60°. Each configuration resulting from a combination of the above parameters was simulated at Reynolds numbers based on jet-slot diameter of 30,000, 60,000, and 90,000. Empirical relations based on available experimental data are used to validate the results. The main findings of the study reveal that the jet height-to-slot diameter ratio of 6, inner channel height-to-slot diameter ratios of 1.8, and inner-channel inlet angular locations of 10° combination resulted in the highest heat transfer at all Reynolds number as well as higher at increased Reynold numbers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".