Bibliographic record
Abstract
Overview An important task in aircraft design is to make the best possible estimation of all the different types of drag associated with aircraft aerodynamics. Commercial aircraft design is sensitive to the DOC, which is aircraft-drag–dependent. Just one count of drag (i.e., C D = 0.0001) could account for several million U.S. dollars in operating cost over the lifespan of a small fleet of midsized aircraft. This will become increasingly important with the increasing trend in fuel costs. Accurate estimation of the different types of drag remains a central theme. (Equally important are other ways to reduce DOC as described in Section 2.1; these are discussed in Chapter 17.) For a century, a massive effort has been made to understand and estimate drag, and the work is still continuing. Possibly some of the best work on aircraft drag in English is compiled by NACA/NASA, RAE, AGARD, ESDU, DATCOM, Royal Aeronautical Society (RAeS), AIAA, and others. These publications indicate that the drag phenomena are still not fully understood and that the way to estimate aircraft drag is by using semi-empirical relations. CFD (see Chapter 14) is gaining ground but it is still some way from supplanting the proven semi-empirical relations. In the case of work on excrescence drag, efforts are lagging. The 2D-surface skin friction drag, elliptically loaded induced drag, and wave drag can be accurately estimated – together, they comprise most of the total aircraft drag.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 0.028 |
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".