CRJ 700 Aerodynamic Coefficients Identification in Dynamic Stall Conditions using Neural Networks
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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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".