Micro-machined Surface Channels Applied to Engine Intake Flow and Heat Transfer
Bibliographic record
Abstract
This article examines the effects of surface microchannels, when altering the boundary layer flow along a helicopter engine bay cooling inlet. The purpose of the study is to investigate the potential benefits of such micro-channels for shedding off runback water from the surface. The local fluid motion and convective heat transfer are considered to be affected by the profiles of the surface micro-channels. Converging, diverging and parallel micro-channels are investigated. It is observed that the converging micro-channels affect the vorticity distribution on the downstream side of the intake surface. Results are presented for experiments involving PIV (Particle Image Velocimetry) in a water tunnel. Aircraft icing is largely affected by convective heat transfer and the runback flow of unfrozen water along the ice surface (Messinger [1]). During glaze ice, the energy imparted by impinging droplets and phase change is sufficient to sustain a flowing supercooled surface film (Naterer et al. [2, 3]). The modes of heat exchange include convection, conduction through the ice and surface film, phase change and droplet kinetic energy imparted on impact (Myers, Hammond [4]). Unlike glaze ice, the formation of rime ice arises when impinging droplets are solidified immediately upon impact on the ice surface (Naterer [5]).
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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) | 0.000 | 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".