Identification and Validation of the Cessna Citation X Longitudinal Aerodynamic Coefficients in Stall Conditions using Multi-Layer Perceptrons and Recurrent Neural Networks
Bibliographic record
Abstract
The increased number of accidents in general aviation due to loss of aircraft control has necessitated the development of accurate aerodynamic airplane models. These models should indicate the linear variations of aerodynamic coefficients in steady flight and the highly nonlinear variations of the aerodynamic coefficients due to stall and post-stall conditions. This paper presents a detailed methodology to model the lift, drag, and pitching moment aerodynamic coefficients in the stall regime, using Neural Networks (NN). A system identification technique was used to develop aerodynamic coefficients models from flight data. These data were gathered from a level-D Research Aircraft Flight Simulator (RAFS) that was used to execute the stall maneuvers. Multilayer Perceptrons and Recurrent Neural Networks were used to learn from flight data and find correlations between aerodynamic coefficients and flight parameters. This methodology is employed in here to optimize neural network structures and find ideal hyperparameters: training algorithms and activation functions used to learn the data. The developed stall aerodynamic models were successfully validated by comparing the lift, drag, and pitching moment aerodynamic coefficients predicted for given pilot inputs with experimental data obtained from the Cessna Citation X RAFS for the same pilot inputs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".