Effects of Curvature on Spacecraft Propellant Management Surface Tension Screen Capillary Capability
Bibliographic record
Abstract
In the development and operations of spacecraft, the application of a propellant management device, or PMD, is one solution to mitigate propellant slosh and deliver gas-free propellant to the system’s engines. PMDs range in complexity and are unique to every propulsion system, but a large subset of PMDs use surface tensions screens to directly control the location of gas, and indirectly the location of the liquid, in the tank. The limiting capability of any surface tension screen is defined by its “bubble point,” or the differential pressure at which the surface tension of the liquid propellant on the screen breaks and gas bubbles through. The propellant management community has identified that curving a surface tension screen alters its bubble point, but the effect of curvature isn’t easily quantified and the problem is typically overcome by overdesigning the system to ensure its proper function. This overdesign can cost the system in terms of weight, money, performance, and general uncertainty. The purpose of this investigation was to quantify the effects of curvature on the bubble point of several typical types of surface tension screen. Through NASA’s SEED program, the bubble point was tested in a microgravity environment for three screen types at four different curvatures. The microgravity environment enabled uniform pressure across the screens, so that the bubble point was tested across the entire screen’s surface. Even though the data is noisy, the results show a trend in sensitivity of the bubble point, with a notable degradation in performance as the screen curvature increases. Method, testing procedure, and lessons learned are discussed. The resulting curves from the data of this project can potentially help in PMD design efforts and give insight into this important on-orbit effect.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".