Bibliographic record
Abstract
Abstract Propulsion generated by wall vibrations in the form of traveling waves was investigated. A model problem consisting of two parallel plates free to move with respect to each other was used. Vibration of one of these plates generated movement of the other plate, whose velocity was used to assess the effectiveness of such propulsion. Three types of responses were identified: a “sloshing” response for long waves, a “moving wall” response for short waves, and an “intermediate” response for in-between waves. Long and transitional waves produced propulsion of marginal interest. Short waves produced effective propulsion with the velocity of the plate increasing proportionally to the second power of the wave number and the second power of the amplitude, and approximately proportionally to the wave velocity. The vibrating wall appeared in this limit to the bulk of the fluid as a moving wall. The effectiveness of vibrations significantly increased by tilting waves. The best response for short fast waves was achieved using adjacent discrete elements spaced by about three-fourths of the wavelength. An analysis of waves of arbitrary shapes demonstrated that concentrating the vibration energy in the largest available and dominant wave number (monochromatic waves) resulted in the best system performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".