Diminishing Liquid Propellant Sloshing in Rocket Engines
Bibliographic record
Abstract
Sloshing of liquid propellants is a serious issue when it comes to space flight and rocket propulsion. Liquid propellant sloshing in rocket engines can induce bank angle changes which have been proven to lead to catastrophic failures.There are three times during flight where sloshing is a major concern. The first of which is takeoff, max Q, main engine cut off or second engine cut off. When a spacecraft experiences acceleration, the propellant settles to the bottom of the tank and has a rough and nearly flat free surface perpendicular to the thrust vector of the engine. For the purpose of thrust vector control during a main engine burn, several gimbal actuators may be used to articulate the engine thrust vector and aim the thrust vector through the spacecraft’s center of mass. This introduces lateral acceleration disturbances and the propellant responds by forming standing waves on the free surfaces, which is called sloshing. The main goal of this research is to design a structure to generate sloshing within a tank and collect data with sensors and high-speed cameras. This data would then be used to develop a method to then diminish the sloshing and implemented into future research and designs. We are constructed a sloshing mechanism unit to generate sloshing of liquid propellant within the tank. We will collect data with sensors and high speed cameras. We are awaiting final parts to come in to collect data.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".