Comparison of computational algorithms for simulating an electrospray plume with a n-body approach
Bibliographic record
Abstract
In order to better evaluate the trade-offs between different simulation options for an electrospray thruster plume, we have developed a multi-scale n-body code to compute the evolution of a single emitter electrospray plume in the pure ionic regime. The electrostatic force computations in the simulation are captured through the use of three different computational algorithms with various degrees of approximation. The results of the simulations for a simple test case are compared in terms of computational speed and accuracy. The test case utilizes a single operating point (323nA) for a stable meniscus solution for the ionic liquid EMI-BF4 firing in the positive pure ion mode. Complex species and probabilistic fragmentation processes are neglected. An overview is provided of the trade-off between accuracy and computational speed for the three algorithms in the context of simulating the electrostatic interactions between particles. For a large number of particles, the faster algorithms show a significant reduction in computational time while maintaining a high level of accuracy with a proper choice of tuning parameters.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".