Impact of particle number and cell size on energy-conserving PIC applied to RF-driven bounded plasmas
Bibliographic record
Abstract
Recent studies have shown that variations in particle-per-cell count and cell size can significantly affect the accuracy of 1D implicit energy- and charge-conserving electrostatic particle-in-cell simulations of capacitively coupled radio frequency discharges, even when the sheath is resolved and the quasi-neutral region is relaxed. This challenges the expected advantages of implicit schemes and may stem from the complexity of active stochastic sheath heating. To test whether passive sheaths can mitigate this issue, we study a similar 1D discharge with perpendicular electron heating, which produces passive sheath dynamics. We find that the same resolution sensitivity persists: coarser spatial resolution requires more particles per cell and still yields reduced accuracy compared to well resolved solutions. Furthermore, we observe that in our implementations, it is difficult to identify simulation parameters (time step, cell size, and particle-per-cell count) where implicit schemes are both more accurate and faster than momentum-conserving explicit codes. On non-uniform grids, we show that energy-conserving explicit and implicit methods yield similar results when the time step is small. Finally, we explore how trends in the resultant discharge characteristics vary with reduced resolution in energy-conserving simulations beyond what is feasible with momentum-conserving explicit schemes and find that while overall trends are generally preserved, the specific values and the magnitude of their changes can differ significantly.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".