Visualizing three-dimensional vortex shedding through evolution surface clusters
Bibliographic record
Abstract
Turbulent vortex shedding in the wake of a bluff body often contains cycle-to-cycle variations in the shape, trajectory, and intensity of vortices. Existing flow visualization techniques cannot effectively present these variations and, consequently, their influence on the aerodynamics to the user. This paper explores a new flow visualization approach to represent quasi-periodic vortex shedding over multiple shedding cycles concurrently. This approach uses a reduced-dimension representation of spatiotemporal vortex progression (called evolution surfaces) and ensemble visualization techniques (clustering). The resulting visualization can be used to identify topological changes in the behavior and strengths of coherent structures (i.e., vortices) in unsteady flows. This approach is applied in two case studies of bluff body wakes with Reynolds Numbers Re = 1200 (Hemmati et al. in Progress in Turbulence VII, Bertinoro, Italy, pp 227–232, 2017. https://doi.org/10.1007/978-3-319-57934-4_32 ) and Re = 300 (Morton et al. in J Heat Fluid Flow 72:109–122, 2018. https://doi.org/10.1016/J.IJHEATFLUIDFLOW.2018.05.014 ). In prior work, classification of these wakes’ dynamics was based on energy fluctuation and shedding topology. However, these techniques are not well suited for representing characteristic changes between shedding regimes. In the present work, it has been shown that evolution surface clusters help to identify topological changes characterizing cycle-to-cycle variations in vortex behavior, while reducing visual clutter. The results indicate that evolution surface clusters are a promising visualization tool for comparative analysis of unsteady vortex dynamics in turbulent wakes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".