Construction and dynamic identification of aeroelastic test models for flexible roofs
Bibliographic record
Abstract
Abstract The aeroelastic wind tunnel testing of flexible roofs made of hyperbolic paraboloid cable nets is a challenging task for designers and researchers, with very limited documented experiences in the literature. The reduced-scale model construction and its dynamic identification are the main issues to address when approaching this problem, mainly because of (i) the very small mass of the roof, (ii) the strict aeroelastic criteria to satisfy and (iii) a large number of very closely spaced significant natural frequencies. To suggest an approach to follow to investigate the wind—structure interaction for this structural typology, this paper discusses the aeroelastic scaling, the aeroelastic model construction, the dynamic modal identification and the FEM predictive numerical modelling of hyperbolic paraboloid roofs (HPRs) with square, rectangular and circular plan shapes and two different curvatures. Modal identification is especially challenging due to the presence of several closely spaced modes and it is here tackled by different methods such as Welch’s method, random decrement technique (RDT), Empirical mode decomposition with a time-varying filter (TVF-EMD) and frequency domain decomposition method (FDD). The satisfying accuracy of the aeroelastic scaling has been verified by comparing the wind-induced vertical displacements of the prototypes against those of the experimental models. Furthermore, an extensive qualitative investigation of the natural mode shapes has been carried out revealing that test models reproduce most of the prototype modes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".