Use Of Surface Vibration For Resistance Reduction In Parallel Movement Of Plates
Bibliographic record
Abstract
The effects of plate vibrations on the external force required to maintain relative movement of parallel plates has been explored using a two-dimensional model problem. Surface roughness increases the overall resistance that this force needs to overcome. Wall vibrations are introduced to investigate if the driving force can be reduced. The main motivation behind this research is the determination of strategies which could be used for lowering of this resistance and, thus, lowering the energy cost associated with transportation. Vibrations in the form of travelling waves induced along the lower plate lead to progressive area of contractions and expansions which propagate in the streamwise direction creating peristaltic pumping effect. The main objective of this work is to quantify potential for reduction of the driving force due to utilization of this effect. The flow problem represents an unsteady moving boundary problem due to the plate movements. Use of the Galilean transformation reduces this problem to a fixed boundary problem with an irregular boundary shape dictated by the wave form, which is solved with spectral accuracy using the Immersed Boundary Conditions method. This is a gridless algorithm which provides means for analysis of a wide range of wave shapes with a significant reduction of manual labor required by typical grid-based algorithms. Its efficient implementation relying on specialized linear solvers provides basis for fast and accurate analysis of multiple geometries using desktop computers and can lead to shape optimization. As energy needs to be expanded for creation of vibrations, concept considered in this work can be viewed as an alternative propulsion method where the classical propulsion is assisted by boundary vibrations.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".