Deep learning-based reduced-order modeling of unsteady wake dynamics of unequal-height tandem finite wall-mounted circular cylinders
Bibliographic record
Abstract
This study presents proper-orthogonal decomposition (POD)-based deep learning-reduced-order model (DL-ROM) predictions of the unsteady wake dynamics of a single finite wall-mounted circular cylinder (FWMC) and unequal-height tandem FWMCs. The DL-ROMs, based on long short-term memory (LSTM) and bidirectional LSTM (BLSTM) neural networks, were trained using time-resolved particle image velocimetry (TR-PIV) measurements from the symmetry plane and three horizontal planes along the span of the FWMCs. The FWMCs were fully submerged in a turbulent boundary layer with a Reynolds number, based on the cylinder diameter and freestream velocity, of Re = 5540. The single cylinder (SC) had an aspect ratio of 7.0, while the tandem cylinders (TC) had a spacing ratio of 4.0 and a height ratio of 0.75, with the downstream cylinder (DC) matching the height of the SC. The SC exhibited a quadruple wake structure with enhanced vortex shedding at mid-height, while the TC, due to partial sheltering, featured downwash impingement on the frontal surface of the DC and an induced upwash in the wake of the DC, which led to an enhanced recirculation bubble and vortex shedding near the free end of the DC. The POD results showed that these salient wake dynamics were effectively captured by the first 200 modes and were therefore used for training the models. Transfer learning was applied to the horizontal planes, reducing computational cost and improving accuracy. Both LSTM- and BLSTM-based models exhibited commendable predictions of the POD coefficients and the reconstructed instantaneous and time-averaged flow fields for each test case; however, errors were relatively larger in the complex regions around the DC due to partial sheltering effects.
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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 | 0.001 | 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".