Identifying Opportunities for Reducing Nacelle Drag
Bibliographic record
Abstract
The accurate prediction of drag caused by bluff bodies present in aerospace applications, particularly at high angles of attack, was a challenge. An experimental and numerical investigation of a nacelle intended for fuselage-mounted aircraft engines was completed at several angles of attack between 0 deg and 45 deg with a Reynolds number of 6 × 105. Steady-flow simulations were conducted on hybrid grids using ANSYS fluent 15.0 with preference given to the realizable k–ε turbulence model. Both total drag and the pressure-to-viscous drag ratio increased with angle of attack as a consequence of greater flow separation on the suction surface. Near-field and far-field drag predictions had grid uncertainties below 2.5% and were within 10% of experiment, which were less than the uncertainties of the respective force balance and outlet traverse data at all angles of attack. Regions were defined on suction-side x-pressure force plots using the validated computational fluid dynamics (CFD) data-set that showed where and how much drag could be reduced. At 20 deg angle of attack, there was a potential to reduce up to 20% drag contained within the separated flow region.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".