Low Order Model Dynamics of the Forced Cylinder Wake
Bibliographic record
Abstract
The periodically forced cylinder wake exhibits complex but highly symmetrical patterns. In recent work, the authors have exploited symmetry-group equivariant bifurcation theory to derive low order equations describing, approximately, the dominant nonlinear dynamics of wake mode interactions. The models have been shown to qualitatively predict the observed bifurcations suggesting that the Karman wake remains, dynamically, a fairly simple system at least when viewed in 2D. Preliminary experimental data are presented supporting the feasibility of using 2D simulation results for the derivation of the low order model parameters. A POD analysis of the wake PIV velocity field yields flow modes closely similarly to those obtained via 2D CFD computations for Re in the 1000 range. The paper presents new results of simulations for Re = 200. For this low Reynolds number, the forced Karman wake exhibits rich dynamics dominated by quasi-periodicity, mode locking, torus doubling and chaos. The low Re torus breakdown may be explained by the Afraimovich-Shilnikov theorem. Interestingly, in a previous analysis for the higher Re number, Re = 1000, transition to a period-doubled flow state was found to occur via a route akin to the Takens-Bogdanov bifurcation scenario.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".