A nonlinear model of AC-field-induced parametric waves on a water surface
Bibliographic record
Abstract
So-called parametric excitations occur as a result of a time-dependent change in a parameter (e.g., rigidity, gravitational acceleration, etc.) of a system. Such a wave can be formed on the surface of a conductive liquid by applying an AC electric field perpendicularly to the surface. It has previously been shown that, using a linearized analysis, this electrohydrodynamic phenomenon can be described mathematically by the Mathieu equation. This linearized analysis is successful at predicting the wavelength and frequency of the parametric wave, but it predicts unlimited growth and therefore cannot determine the resulting amplitude or phase. This paper presents a nonlinear extension of the linearized model, resulting in a nonlinear form of the Mathieu equation. The nonlinear model, which accounts for the change in the force due to surface tension as the wave amplitude grows, results in a prediction of the finite wave amplitude, and gives a prediction of the phase of the finite wave with respect to the exciting AC field. Viscous damping of the liquid is considered. A spatially sinusoidal wave shape is assumed, but the model can accommodate different curvatures of the upward and downward phases of the wave. The model explains why electric discharging above a water surface often occurs after the applied electric field has started to decline from its peak. Experimental measurements (obtained using a channel of water) of wave amplitude and phase are compared with predictions from the model.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".