Bibliographic record
Abstract
Overview This chapter is concerned with the aerodynamic information required at the conceptual design stage of a new aircraft design project. It provides details that influence shaping and other design considerations and defines the various parameters integral to configuring aircraft mould lines. Any object moving through air interacts with the medium at each point of the wetted (i.e., exposed) surface, creating a pressure field around the aircraft body. An important part of aircraft design is to exploit this pressure field by shaping its geometry to arrive at the desired performance of the vehicle, including shaping to generate lifting surfaces, to accommodate payload, to house a suitable engine in the nacelle, and to tailor control surfaces. Making an aircraft streamlined also makes it looks elegant. Aeronautical engineering schools offer a series of aerodynamic courses, starting with the fundamentals and progressing toward the cutting edge. It is assumed that readers of this book have been exposed to aerodynamic fundamentals; if so, then readers may browse through this chapter for review and then move on to the next chapter. Presented herein is a brief compilation of applied aerodynamics without detailed theory beyond what is necessary. Many excellent textbooks are available in the public domain for reference. Because the subject is so mature, some nearly half century-old introductory aerodynamics books still serve the purpose of this course; however, more recent books relate better to current examples.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.034 |
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".