Identification of the Bombardier CRJ-700 Stall Dynamics Model Using Neural Networks
Bibliographic record
Abstract
This paper aims to present a new methodology to model the aerodynamic coefficients and predict the aircraft dynamics under stall conditions, including the hysteresis cycle, using neural networks. The aerodynamic coefficients variations required for the identification process were estimated from flight data collected during different stall maneuvers. Then, a level-D-qualified Bombardier CRJ-700 virtual research equipment simulator (VRESIM) developed by CAE, Inc. and Bombardier was used to gather flight data in both linear and nonlinear stall phases. According to the Federal Aviation Administration (FAA), level D is the highest qualification level for flight dynamics and propulsion models. Multilayer perceptron (MLP) and recurrent neural networks were trained for the aerodynamic coefficients learning and their correlation with flight parameters. A new methodology for tuning the neural network parameters, such as the optimal number of layers and neurons, was developed. The resulting models were validated by comparing predicted flight data with experimental data obtained from the level D Bombardier CRJ-700 VRESIM by considering the same pilot inputs. The models developed using the proposed methodology were able to predict the CRJ-700 flight dynamics in both static and dynamic stall conditions, with very good precision, within the tolerances of the FAA.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".