Electron Collection to Arbitrarily Shaped Space Electrodynamic Tethers: A Kinetic Model
Bibliographic record
Abstract
Tether electron current collection is one of the key mechanisms allowing for power and/or thrust generation applications of space electrodynamic tethers and depends on such parameters as tether geometry and the velocity of plasma flow.This paper presents the initial validation of a novel computational approach, based on kinetic theory, that will allow for the assessment of the efficiency of electron collection to bare tethers of arbitrary cross sections from a flowing space plasma.The drift velocity associated with the translational speed of the tether is incompatible with 1-D, cylindrically symmetric models1i2 and its effects on current collection are not well understood.A kinetic model is developed for the two-dimensional plasma surrounding the tether, which consists in self-consistently solving the Vlasov and Poisson equations through a semi-analytical, seminumerical process.A drifting Maxwellian velocity distribution is assumed for the plasma species (electrons and ions) at the outer boundary of the solution space; no assumption is made regarding the velocity distributions in the computational region surrounding the tether, allowing for fully poly-energetic species.Simulation results are validated with Langmuir cylindrical probe theory for the non-flowing case.For flowing cases, some initial results show a qualitative agreement with results from vacuum chamber experiments g but further work is needed to improve the consistency of the results.Work is underway t o allow the use of the model for arbitrary tether geometries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".