Reduction of pressure losses and increase of mixing in laminar flows through channels with long-wavelength vibrations
Bibliographic record
Abstract
Pressure losses and mixing in vibrating channels were analysed. The vibrations in the form of long-wavelength travelling waves were considered. Significant reduction of pressure losses can be achieved using sufficiently fast waves propagating downstream, while significant increase of such losses is generated by waves propagating upstream. The mechanisms responsible for pressure losses were identified and discussed. The interaction of the pressure field with the waves can create a force which assists the fluid movement. A similar force can be created by friction, but only under conditions leading to flow separation. An analysis of particle trajectories was carried out to determine the effect of vibrations on mixing. A significant transverse particle movement takes place, including particle trajectories with back loops. The downstream-propagating out-of-the phase waves provide a large reduction of pressure gradient and significant potential for mixing intensification. Analysis of energy requirements demonstrates that it is possible to identify waves which reduce power requirements, i.e. the cost of actuation is smaller than the energy savings associated with the reduction of pressure gradient. The fast forward moving waves provide an opportunity for the development of alternative propulsion methods which can be more efficient than methods based on the pressure difference.
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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 | 0.000 | 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".