Bibliographic record
Abstract
Modern aeroengines utilized effusion cooling technology to further protect the components from degrading at the operating temperatures. Most studies did not address the influence of the manufacturing process used to form the cooling holes on the flow physics where percussion laser drilling was a common technique that produced irregularly shaped holes with roughened surfaces. The investigated as-drilled hole surface was statistically homogeneous, non-isotropic, and generally composed of gradually transitioning plateaus that had imperfections with an average height of 0.32 hole diameters. A conjugate heat transfer CFD study was completed on cylindrical, conical nozzle, and as-drilled holes, all yielding the same hole mass flow rates, with the realizable k-ε turbulence model at representative engine conditions. The cylindrical hole had higher film cooling effectiveness due to lower effluent velocity, and better in-hole heat transfer performance due to higher on-average in-hole flow velocities. The as-drilled hole had nominally better film cooling than the conical nozzle hole due to the higher in-hole turbulence production caused by the roughened surface texture. Ultimately, the hole area profile more significantly influenced the averaged metal temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| 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".