Influence of wall shape on vortex formation in modulated channel flow
Bibliographic record
Abstract
The flow inside channels with periodic, wavy walls of arbitrary shape is considered numerically. Solutions are obtained using either a perturbation approach, for weak modulation amplitude, or a finite volume technique, for strong amplitude. The flow is examined for sinusoidal, arched and triangular modulation over a wide range of amplitude, wavelength and Reynolds number in the steady laminar regime. For weak wall modulation (ε<0.3, α<2, where ε and α are the dimensionless half-wave height and wavelength, respectively), it is found that the flow behavior along the modulated wall is of the boundary-layer type. As such, the critical Reynolds number, Rec, for separation for each modulation shape can be expressed as an explicit function of ε and α, while the location of separation and pressure distribution along the modulated wall scale with ε, α, and Rec. For strong modulations, the boundary layer model is no longer satisfactory to predict the flow behavior and deviations from the trends found for weaker modulations are observed. It is also shown that the driving force required to sustain a given flow rate increases as ε increases. For all modulation amplitudes, the sinusoidal wave shape is found to require the largest pressure gradient to maintain a given flow rate through the channel and, consequently, yields the highest friction factor. Finally, the existence of a stable recirculating flow regime is discussed in the light of earlier stability analyses.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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 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".