Reconstruction of Large-Scale Coherent Structures in Turbulent Separation Bubbles Using Phase-Consistent DMD
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-4259.vid Experimental measurements of velocity fields using 2D-2C particle image velocimetry are performed on a pressure-induced turbulent separation bubble (TSB) at Re = 5000 based on the momentum thickness. Such flows are largely characterized by two unsteady phenomena occurring within two different frequency regimes. At the higher frequency, a classical vortex shedding develops due to the Kelvin-Helmholtz instability which is associated with the roll-up of the shear layer. At the lower frequency, cycles of contraction and expansion of the entire recirculation region are observed, commonly referred to as “breathing” of the TSB. In view of the size of the TSB, the experimental measurements consist of multiple fields of view (FoVs) to capture the entire recirculation region from incipient detachment to full reattachment of the boundary layer. The data in the different FoVs are therefore not synchronized in time and as of yet only statistical quantities have been reported from this experiment [1, 2]. We explore the use of a novel phase-consistent reduced-order modeling technique recently proposed by Nair et al. [3]. The method is based on dynamic mode decomposition and aligns the phases of the dynamic modes in the different FoVs by taking advantage of spatial overlap in the data. The method permits, for the first time, a phase-consistent modal analysis of the full experimental TSB and therefore the study of time-resolved phenomena upon flow reconstruction. As a result, we illustrate that the low frequency unsteadiness is linked to the passage of large coherent structures throughout the whole TSB.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".