Extended spectral proper orthogonal decomposition for analysis of correlated surrounding flow structures and wind load components of a building
Bibliographic record
Abstract
Proper orthogonal decomposition (POD) has been used in numerous studies in wind engineering to extract key features of a building's surrounding flow field and surface pressure, the connections between which, however, remain difficult to quantify. This study combined the extended POD with spectral POD (SPOD) method into a new method called extended SPOD (ESPOD) to correlate flow structures with surface pressure. SPOD wind force spectra were defined to quantify how much each pair of velocity and pressure modes contribute to the wind force on a building. The method was validated by a case study on a typical isolated high-rise building, in which periodic coherent structures were extracted to reveal the main mechanisms of the wind forces, including the influences from approaching turbulence, wake vortices, and conical vortices. Phase synchronization , which is utilized in ESPOD, is an effective criterion for distinguishing the multiple physical mechanisms at the same frequency. Additional information provided by the correlated velocity mode helps interpret the physical meanings of the relatively less informative pressure modes. Finally, compared to velocity-based approaches, the pressure-based approach can capture the wind force fluctuations more completely, and the velocity modes are not distorted too much by the non-optimal decomposition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".