Inverse determination of aircraft loading using artificial neural network analysis of structural response data with statistical methods
Why this work is in the frame
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Bibliographic record
Abstract
An artificial Neural Network (ANN) system has been developed that can analyse aircraft flight data to provide a reconstruction of the aerodynamic loads experienced by the aircraft during flight, including manoeuvre, buffet and distributed loading. For this research data was taken from the International Follow-On Structural Test Project (IFOSTP) F/A-18 fatigue test conducted by the Royal Australian Air Force and Canadian Forces. This fatigue test involved the simultaneous application of both manouevre and buffet loads using airbag actuators and shakers. The applied loads were representative of the actual loads experienced by an FA/18 during flight tests. Following an evaluation of different ANN types an Ellman network with three linear layers was selected. The Elman back-propagation network was tested with various parameters and structures. The network was trained using the MATLAB 'traingdx' function with is a gradient descent with momentum and adaptive learning rate back-propagation algorithm. The ANN was able to provide a good approximation of the actual manoeuvre or buffet loads at the location where the training loads data were recorded even for input values which differ from the training input values. In further tests the ability to estimate distributed loading at locations not included in the training data was also demonstrated. The ANN was then modified to incorporate various methods for the calculation and prediction of output error and reliability Used in combination and in appropriate circumstances, the addition of these capabilities significantly increase the reliability, accuracy and therefore usefulness of the ANN system's ability to estimate aircraft loading.To demonstrate the ANN system's usefulness as a fatigue monitoring tool it was combined with a formulae for crack growth analysis. Results inficate the ANN system may be a useful fatigue monitoring tool enabling real time monitoring of aircraft critical components using existing strain gauge sensors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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