Fluid-structure interaction of flexible collectors affects particle capture efficiency at ecologically relevant collector Reynolds numbers
Bibliographic record
Abstract
Introduction: The capture of suspended particles, which is an important process in many aquatic and terrestrial ecosystems, has been modeled using stationary rigid collectors and, more recently those that move in response to flow-induced vibrations. These models do not, however, account for collector flexibility, despite the fact that many biological collectors, especially aquatic collectors, exhibit flexibility. Methods: This study examined the effect of collector flexibility (indicated by the Young’s Modulus, E; range = 10–3–102 GPa) on particle capture efficiency (η; flux of captured particles: flux of particles) at different collector Reynolds numbers (Rec where the collector diameter [dc] is the length scale; range = 30–508) in a recirculating flow chamber. Results: Patterns in η were generally similar for flexible and rigid collectors until moderate Rec (∼374) when higher η were observed on the most flexible collectors. This threshold corresponded to periods of vortex induced motions in which the oscillation frequency of the collector transverse to the flow direction was >4 Hz and the maximum amplitude of the oscillation relative to dc was >60% in the transverse and >100% in the longitudinal direction. Discussion: Given the range in E examined in this study, it is likely that particle capture on flexible natural collectors has been underestimated using the standard model of a rigid stationary or oscillating collector. The role of collector flexibility should be considered in models and studies of particle capture in natural systems.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".