Computational Modal Analysis of Half Scale Generic Business Jet Substructures
Bibliographic record
Abstract
Abstract The primary objective of this work was to determine the modal parameters of two substructures of a half scale generic business jet model with pre-stressed skin panels. The effect that pre-stiffened skin panels has on the modal parameters of an aircraft fuselage subsection is not well documented in the literature. First, bending pre-stress on stiffened plates was empirically determined to increase stiffness without changing mode shapes. Second, preliminary finite element models of the substructures determined that the effect of skin pre-stress was significant in one of the two substructures. Finally, an updating technique to account for stiffening effects was proposed and validated to be effectively used in the substructure, improving computational results across all metrics. It is recommended that the model updating procedure developed in this work be used to model skin pre-stress for aircraft fuselage substructures. The improved accuracy of the updated computational model should be of significant interest to the aerospace industry. Future work can be performed to further develop the model updating technique introduced in this work to allow for widespread application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".