Predicted performance of a two-phase underwater ramjet with a Laval nozzle
Bibliographic record
Abstract
To predict engine performance and further instruct the integral engine design, a more reasonable and accurate numerical model of the two-phase underwater ramjet was introduced in this article by considering the bubble formation process. Two-fluid model was used to examine the bubbly flow in the nozzle and its mathematical model was solved by a fourth-order Runge–Kutta method. Subsequently, the influences of vessel velocity, gas mass flow rate, navigational depth, and orifice diameter of the bubble injector on the performance of the engine were discussed. Results show that, compared with convergent nozzle, Laval nozzle is proved to improve the thrust of the engine, especially at relatively high velocity and gas mass flow rate. With the other conditions fixed, there is an optimum vessel velocity for the ramjet, in which maximum thrust is generated. And a smaller orifice diameter always promotes the engine performance, while this promotion is negligible when the orifice diameter is smaller than 1 mm. Besides, increasing backpressure will cause serious performance drop, which means that the the two-phase underwater ramjet is only efficient for shallow depths.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".