Numerical Simulation of Cryogenic Fluid Sloshing In Propellant Tank and Influence of Damping with Ring Baffles Under Forced Excitations
Bibliographic record
Abstract
Sloshing phenomena in fluid systems have emerged as a significant challenge with wide-ranging implications across various engineering domains, including aerospace and cryogenic storage.This paper presents a comprehensive study of sloshing effects in cryogenic propellant tanks, focusing on the behaviour of liquid oxygen as the working fluid.Sufficient works are present in literature demonstrating predictability of sloshing dynamics without phase change.Specific parameters governing the evaporation and condensation of cryogenic fluids introduces distinct challenges.In this paper, the numerical behaviour of sloshing is simulated using Ansys Fluent with a Volume of Fluid (VOF) model and the Lee model to handle phase change.Through numerical simulations, the study investigates the effect of baffles with different sizes under various excitation frequencies, to enhance our understanding and mitigation strategies for sloshing-related issues in cryogenic fluid systems.Selection of an appropriate baffles in cryogenic propellant tanks is a concern for safe operation during the entire flight duration in aerospace vehicles.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".