Impact of Wing Box Geometrical Parameters on Stick Model Prediction Accuracy
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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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".