Pattern interaction : can we generate propulsion?
Bibliographic record
Abstract
The propulsive effect generated by heated grooves has been studied using parallel horizontal plates with the upper plate free to move, while the lower plate is equipped with grooves and subjected to periodic heating. Two effects were found to contribute to the propulsion. The first effect relies on nonlinear thermal streaming, which occurs due to sinusoidal heating in a smooth channel. It is observed that this effect exists for all heating wavenumbers when sufficient heating intensity is applied. This effect is represented by pitchfork bifurcation and can cause the flow to move in either the positive or negative x-direction. The second effect relies on the thermal drift effect originating from the combination of heating and groove patterns. It is known that this effect can be modulated by changing the relative positions of the heating and groove patterns. We have only considered cases where the heating and grooves are represented by the same wavenumber. The thermal drift is maximized when the groove and heating peaks are a quarter of the wavelength away from each other and minimized when the peaks are at the same location or half a wavelength from each other. The direction of the flow can also be controlled by placing the heating wave to the right or left of the grooves. Both propulsion methods have the potential to drive flow with relatively minimal flow losses. The strength of propulsion can also be increased by increasing the heating amplitude or adding uniform heating.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".