Flow and thrust vectoring characteristics of underwater high-speed gas jet
Bibliographic record
Abstract
When utilizing high-speed gas jet for the propulsion of underwater vehicles, complex flow phenomena such as ventilated cavitation, bubble expansion, and contraction are formed, along with corresponding complex thrust characteristics. In this paper, an experimental study was conducted on the thrust and flow field evolution characteristics of vector-deflected high-speed gas jets produced by a Laval nozzle under co-flow conditions. Under the experimental conditions of this study, the venting position of the pulsating foam tail cavity shifts with the increase in the nozzle vector angle θ. The axial component of thrust exhibits a noticeable loss as the vector angle θ increases, and its oscillation is correlated with the pressure pulsation of the tail cavity. The nozzle thrust vector angle operates within an optimal range, with the lateral force peaking at θ = 6°. Beyond this angle, the lateral force diminishes as θ progresses further. The amplitude of the lateral force is related to the vent channel, with unobstructed channels corresponding to the peak values of the lateral force. This paper can provide a reference for the design of vector jet propulsion systems for underwater vehicles. The unique phenomena and patterns of underwater vector jets revealed through experiments lay the foundation and offer insights for more in-depth mechanistic studies.
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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 | 0.000 |
| 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".