Systems Design and Performance of Cold Gas Microthruster for Microsatellite Attitude Control
Bibliographic record
Abstract
We discuss our approach to the design and manufacturing of a cold gas micropropulsion system, using micro electromechanical systems (MEMS) devices, for an experimental attitude and orbital control system for the ALMASat microsatellite. ALMASat is a small educational spacecraft entirely designed and assembled in the aerospace laboratories of II School of Engineering of the University of Bologna. Its weight is about 12 kg, carried by a cubical bus (side 30 cm), and it is scheduled for launch from Baikonur (Kazahstan) using the DNEPR Launch Vehicle. The core of the micropropulsion system is a De Laval nozzle which accelerates the fluid, molecular nitrogen, from a plenum, through a 40 micron throat at supersonic velocity. Using the nozzle as a simple cold-gas thruster, good Isp efficiencies are demonstrated, although side-wall boundary layer contamination, no continuity effect and the limitations of MEMS fabrication technologies are clear limiting factors. The micropropulsion system consists of a high pressure tank, a pressure regulator and solenoid valves for the open/close thrusters cycles. The extruded nozzle geometry, made on silicon wafer, has been manufactured in collaboration with the Carlo Gavazzi Space (CGS) and IMM section of the Italian National Research Council (CNR) of Bologna, using a Deep Reactive Ion Etching (DRIE) technique and successive bonding.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".