CRJ 700 Aerodynamic Coefficients Identification in Dynamic Stall Conditions using Neural Networks
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Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-2577.vid This paper presents a methodology to predict aircraft aerodynamic coefficients in both linear and non-linear stall conditions along the hysteresis curve, using Neural Networks. The variations of the lift and drag aerodynamic coefficients were estimated during an aircraft stall maneuver. A Level-D Bombardier CRJ-700 Virtual Research Simulator (VRESIM), designed and manufactured by CAE Inc. and Bombardier, was used to gather flight test data in both linear and non-linear stall phases. According to the Federal Aviation Administration (FAA), the Level-D is the highest certification level for the flight dynamics model of an aircraft, which means that its flight dynamics data is very close to real aircraft flight dynamics data. These data are then used to create a database of aerodynamics coefficients for the complete flight envelope of the aircraft. Multilayer Perceptron (MLP) and Recurrent Neural Networks (RNN) were trained to learn the aerodynamic coefficients and their correlation with flight parameters. The choice of the neural network hyperparameters is also explained. Finally, the obtained models are validated by comparing the predicted aerodynamic coefficients with their corresponding experimental data from the Level-D Bombardier CRJ 700 flight simulator. The results obtained showed that both MLP and RNN were able to predict the lift and drag aerodynamic coefficients with an average relative error of 2 %.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it