New Methodologies for Aircraft Stability Derivatives Determination from Its Geometrical Data
Bibliographic record
Abstract
The common practice for aircraft design and certifi cation is usually based on its flight test data. The aircraft stability derivatives are t he main unknowns to be determined from its flight dynamics model. These aircraft stability der ivatives data are the intrinsic parameters used in the aircraft design - and are dependent on its geometry and on its flight conditions. There is the need of determination with the highest precision of aircraft stability derivatives for all regimes in order to determine the best airc raft flight model possible by use of simulation tools rather than expensive flight test data. A way of obtaining the dynamic and static stability derivatives is using a semi-empiri cal method DATCOM presented in USAF Stability and Control DATCOM reference. A new code called FDerivatives was conceived by us where new algorithms and methods were added, with respect to the DATCOM classical FORTRAN code, to improve the stability derivatives calculations for an aircraft in the subsonic regime. This new FDerivatives code was written under MATLAB 7.4.0 (R2007a) version and has a complex structure which contains a graphical interface to facilitate the potential users work. The new code and interface would allow aircraft designers to evaluate aircraft new design concepts, predict its performan ces, and therefore bring the necessary changes to its design. This code would provide impo rtant savings in man-hours and other resources needed for flight tests. Results obtained in terms of stability derivatives values with the new FDerivatives code are here presented a nd validated with the flight test data results for the Hawker 800 XP aircraft by use of it s aircraft geometry knowledge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".