Feasibility of Developing a Refrigerant-Based Propulsion System for Small Spacecraft
Bibliographic record
Abstract
This paper documents the feasibility of developing a low pressure, low-budget, two-phase refrigerant propulsion system for small spacecraft. The spacecraft design teams at the University of Missouri-Rolla, University of Texas at Austin, and Washington University in St Louis have collaboratively researched and assessed the feasibility of using a refrigerant propellant to provide a safe and practical type of propulsion system for the small spacecraft community. As an alternative to a typical inert cold-gas system, the teams investigated two-phase refrigerant-based systems motivated by the excellent propellant storage advantages and the ease of use and inherent safety. A primary benefit is its ability to be stored as a saturated liquid with inherently lower pressures as the constant volume system maintains self-equilibrium at saturation pressure. The associated laboratory safety of using a refrigerant propellant and ease of constructing cold-gas hardware make the propulsion system an ideal choice for low-budget satellite developers. The safety and performance analysis conducted on a general system indicates that with appropriate precautions and conservative design, test and analysis a refrigerant-based propulsion system can be safely implemented on small spacecraft and is a viable propulsion option. This feasibility study has been used as a guide to design and develop propulsion systems for each of the universities.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".