New Methodology for Longitudinal Flight Dynamics Modelling of the UAS-S4 Ehecatl towards its Aerodynamics Estimation Modelling
Bibliographic record
Abstract
Unmanned Aerial Vehicle modelling has found diverse utilities in both civil and military applications. In order to develop an accurate model of their flight dynamics, it is important to properly estimate their aerodynamics coefficients. For this purpose, several methods are usually applied. This paper presents a methodology to obtain the flight dynamics of an Unmanned Aerial Vehicle, for which its aerodynamics coefficients were found based on its geometrical properties. This methodology was applied to the UAS-S4, designed and manufactured by Hydra Technologies, using DATCOM and TORNADO codes. The aerodynamic model thus found was compared with another model obtained by use of ANSYS Fluent software. The model was completed with a propulsion system developed by use of Javaprop. Results have shown that the obtained model is capable of estimating with accuracy the aerodynamic behaviour of the UAS-S4.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".