Identification of structural surfaces' positions on an F/A-18 using the subspace identification method from flight flutter tests
Bibliographic record
Abstract
In the current paper, a linear state-space mathematical model, identified from flight flutter tests is presented, to simulate the aeroelastic deflections of specific structural parts of the NASA F/A-18 Active Aeroelastic Wing research aircraft. The flight flutter tests were performed in steady-level flight with Schroeder frequency excitation induced on the aircraft ailerons by an on-board excitation system activated by the pilot. The results of the flight flutter tests were used to generate an aeroelastic model in which the deflections of the specific aircraft surfaces are functions of the control inputs combined with the deflections of other aircraft surfaces. The F/A-18 linear model is conceived as nine third-order multiple input-single output (MISO) models. Each model has nine inputs and one output. The nine inputs are the differential ailerons deflection and the deflections of all the other parts of the aircraft. The output of each model is the structural deflection of a given aircraft structure. The model's parameters are estimated with the subspace system identification algorithm, an efficient non-iterative algorithm that computes the system matrices directly from the input and output data. The model's quality is evaluated by calculating the fit and correlation coefficients between the model's outputs and the outputs from flight flutter test data. Although the fit coefficient results are very good - between 89 and 99 per cent - the correlation coefficient method gave the best results (nearly 100 per cent). Finally, resampled inputs were used to validate the F/A-18 model's robustness. The model's aircraft structure was validated for flutter flight tests at different Mach numbers and altitudes. The estimated linear model fits the flight flutter test data very well. The subspace method is therefore very convernvenient for model identification from flight flutter tests.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".