Bibliographic record
Abstract
Overview This chapter assesses whether the aircraft being configured, thus far, meets the FAR and customer requirements given in the form of specifications. Coursework follows linearly from the mock market survey (see Chapter 2). Specification requirements addressed in this chapter include aircraft performance to meet the (1) TOFL, (2) LFL, (3) initial rate of climb, (4) maximum speed at initial cruise (especially for civil aircraft design), and (5) payload range. Chapter 16 computes the aircraft DOC, which should follow the aircraft performance estimation. Aircraft performance is a subject that aeronautical schools offer as a separate course. Therefore, to substantiate the FAR and customer requirements, this chapter addresses only what is required – that is, the related governing equations and computational examples associated with the five substantiation parameters listed previously. Substantiation of the payload range requires integrated performances of climb and descent that show fuel consumed, distance covered, and time taken for the flight segments. Integrated climb and descent performances are not specification requirements at this stage; therefore, their detailed computational examples are not provided. Instead, the final results in graphical form carry out the payload-range estimation. It is suggested that readers refer to appropriate textbooks for details on this topic. The turboprop example is not worked out but there is sufficient information to compute it similarly. The remainder of the book after this chapter (except Chapter 16) presents information that aircraft designers should know and apply to their configurations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.039 |
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".