A Data-Driven Approach for Generating Vortex-Shedding Regime Maps for an Oscillating Cylinder
Bibliographic record
Abstract
This study presents a data-driven approach for generating vortex-shedding maps, which are vital for predicting flow structures in vortex-induced vibration (VIV) wind energy extraction devices, while addressing the computational and complexity limitations of traditional methods. The approach employs unsupervised clustering techniques on subsequences extracted using the matrix profile method from local flow measurements in the wake of an oscillating circular cylinder generated from 2-dimensional computational fluid dynamics simulations of VIV. The proposed clustering methods were validated by reproducing a benchmark map produced at a low Reynolds number (Re = 4000) and then extended to a higher Reynolds number (Re = 10,000) to gain insights into the complex flow regimes. The multi-step clustering methods used density-based and k-Means clustering for the pre-clustering stage and agglomerative clustering using dynamic time warping (DTW) as the similarity measure for final clustering. The clustering methods achieved exceptional performance at high-Reynolds-number flow, with scores in the silhouette index (0.4822 and 0.4694) and Dunn index (0.3156 and 0.2858) demonstrating the accuracy and versatility of the hybrid clustering methods. This data-driven approach enables the generation of more accurate and feasible maps for vortex-shedding applications, which could improve the design and optimization of VIV wind energy harvesting systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".