3D fluid simulation of the propagation of a nanosecond discharge in air above a liquid surface: investigation of the pattern formation under various conditions and comparison with experiments
Bibliographic record
Abstract
Abstract Plasma-liquid interaction remains one of the fundamental processes influencing the various applications. Understanding the influence of external parameters on discharge properties, particularly the discharge dynamic at the liquid surface, is therefore essential. In previous studies, we investigated the impact of voltage polarity, gap distance, and liquid dielectric permittivity and electrical conductivity on nanosecond discharges initiated in air at atmospheric pressure in a pin-to-liquid configuration. Herein, we present a 3D fluid model, improved with stochastic photoionization, to simulate the discharge dynamics under the previously mentioned conditions. The model outputs are compared with the discharge dynamics measured experimentally. For instance, filamentation and the homogeneous emission over solution’s surface measured in positive and negative discharges, respectively, are well reproduced by the simulation. Furthermore, the simulation allowed us to report other plasma properties not accessible experimentally such as the spatio-temporal distributions of electric field (E-field) and electron density. Notably, we observe that the E-field at the front of the negative surface ionization wave (SIW) is nearly four times lower than that of the positive SIW, which may explain the absence of filaments for negative discharges. Furthermore, we find that increasing solution conductivity or gap distance reduce the radial propagation velocity of the circular SIW front and stopping its expansion before a destabilization can occur. The simulation allowed investigating the influence of photoionization strength, and we find that increasing the number of ionizing photons leads to suppress the filamentation while keeping the ionization front circular and propagating at high speed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".