MPI Parallelization of Numerical Simulations for Pulsed Vacuum Arc Plasma Plumes Based on a Hybrid DSMC/PIC Algorithm
Bibliographic record
Abstract
The transport characteristics of the unsteady flow field in rarefied plasma plumes is crucial for a pulsed vacuum arc in which the particle distribution varies from 1016 to 1022 m−3. The direct simulation Monte Carlo (DSMC) method and particle-in-cell (PIC) method are generally combined to study this kind of flow field. The DSMC method simulates the motion of neutral particles, while the PIC method simulates the motion of charged ions. A hybrid DSMC/PIC algorithm is investigated here to determine the unsteady axisymmetric flow characteristics of vacuum arc plasma plume expansion. Numerical simulations are found to be consistent with the experiments performed in the plasma mass and energy analyzer (EQP). The electric field is solved by Poisson’s equation, which is usually computationally expensive. The compressed sparse row (CSR) format is used to store the huge diluted matrix and PETSc library to solve Poisson’s equation through parallel calculations. Double weight factors and two timesteps under two grid sets are investigated using the hybrid DSMC/PIC algorithm. The fine PIC grid is nested in the coarse DSMC grid. Therefore, METIS is used to divide the much smaller coarse DSMC grid when dynamic load imbalances arise. Two parameters are employed to evaluate and distribute the computational load of each process. Due to the self-adaption of the dynamic-load-balancing parameters, millions of grids and more than 150 million particles are employed to predict the transport characteristics of the rarefied plasma plume. Atomic Ti and Ti2+ are injected into the small cylinders. The comparative analysis shows that the diffusion rate of Ti2+ is faster than that of atomic Ti under the electric field, especially in the z-direction. The fully diffuse reflection wall model is adopted, showing that neutral particles accumulate on the wall, while charged ions do not—due to their self-consistent electric field. The maximum acceleration ratio is about 17.94.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".