Impact of Wing Box Geometrical Parameters on Stick Model Prediction Accuracy
Why this work is in the frame
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Bibliographic record
Abstract
A stick model is usually used throughout the aircraft development stages for predicting loads and dynamic behavior, while avoiding computational burden associated with a more detailed finite element model. Even if its an inheritance of the aircraft industry history, this simplified model still play an important role. Aircraft behave roughly like a beam therefore, all equations used in the past to predict loads are beam model based. Even though modelisation may seem to be crude and inaccurate, test correlation against such modelisation has demonstrated robustness and accuracy versus low computational cost. However, to the best of the authors’ knowledge, no study providing the fidelity range of such a model has been published yet. In this paper, we propose a first-step approach toward this goal by studying wing stick model accuracy with respect to various geometrical variables. In order to do so, we compared the frequency responses of various stick model configurations with the one obtained from their corresponding global finite element model. Results suggest that the wing aspect ratio has a major influence on the reliability of the stick model. This is followed by the front and rear sweep angles. In contrast, the wing thickness parameter shows no significative effect on the stick model prediction accuracy. Overall, this study highlights some design space regions where the fidelity of the stick model can be questioned, although further investigation is required.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it