Evolution Surfaces for Spatiotemporal Visualization of Vortex Features
Bibliographic record
Abstract
Turbulent fluid flow data are often 4-D, spatially and temporally complex, and require specific techniques for visualization. Common visualization techniques neglect the temporal aspect of this data, limiting the ability to convey feature motion, or offering the user a complicated visualization. To remedy this, we present an approach-evolution surfaces-focused on the spatiotemporal rendering of user-selected flow features (i.e., vortices). By abstracting the spatial representation of these features, the approach renders their spatiotemporal behavior with reduced visual complexity. The behavior of vortex features is presented as surfaces, with textures indicating properties of motion and evolution events (e.g., bifurcation and amalgamation) represented by the surface topology. We evaluated the approach on two data sets generated from empirical measurement and computational simulation (Re = 28 000 and Re = 1200, respectively). Our approach's focus on handling evolution events makes it capable of visualizing higher Reynolds number (Re) flows than other surface-based techniques. This approach has been assessed by fluid dynamicists to assert the validity for flow analysis. Evolution surfaces offer a compact visualization of spatiotemporal vortex behaviors, opening potential avenues for exploration and analysis of fluid flows.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".