Experimental validation of vibration similitude laws for stiffened panels excited by a turbulent boundary layer
Bibliographic record
Abstract
Stiffened structures excited by a turbulent boundary layer (TBL) occur in many engineering applications, particularly in the aerospace and naval sectors. The structures encountered in these applications are generally large and complex, and carrying out experiments in order to characterize their vibration response can be costly, time-consuming and difficult. To overcome these constraints, the similitude theory can be used to estimate the response of a full scale structure from the response of a reduced scale structure. A simplifying step to determine these similitude laws for stiffened panels consists in modeling the panel as an equivalent orthotropic panel. Then, using the governing equation of the orthotropic panel and considering the TBL excitation, the vibration similitude laws are derived implying similitude conditions to be respected on geometrical and material properties of the structure as well as flow velocity. In this paper, we present the developments of these similitude laws and an experimental validation for panels in air. The similitude conditions are discussed to adequately choose the properties of scaled stiffened panels. Experimental results are presented to show that the structural response of the reference panel can be recovered from the response of the reduced scale panel using the related scaling laws.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".