Effect of vehicle configuration on the performance of a submersible pulsed-jet vehicle at intermediate Reynolds number
Bibliographic record
Abstract
Recent results have demonstrated that pulsed-jet propulsion can achieve propulsive efficiency greater than that for steady jets when short, high frequency pulses are used, and the pulsed-jet advantage increases as Reynolds number decreases into the intermediate range (∼50). An important aspect of propulsive performance, however, is the vehicle configuration. The nozzle configuration influences the jet speed and, in the case of pulsed-jets, the formation of the vortex rings with each jet pulse, which have important effects on thrust. Likewise, the hull configuration influences the vehicle speed through its effect on drag. To investigate these effects, several flow inlet, nozzle, and hull tail configurations were tested on a submersible, self-propelled pulsed-jet vehicle ('Robosquid' for short) for jet pulse length-to-diameter ratios (L/D) in the range 0.5-6 and pulsing duty cycles (St(L)) of 0.2 and 0.5. For the configurations tested, the vehicle Reynolds number (Re(υ)) ranged from 25 to 110. In terms of propulsive efficiency, changing between forward and aft-facing inlets had little effect for the conditions considered, but changing from a smoothly tapered aft hull section to a blunt tail increased propulsive efficiency slightly due to reduced drag for the blunt tail at intermediate Re(υ). Sharp edged orifices also showed increased vehicle velocity and propulsive efficiency in comparison to smooth nozzles, which was associated with stronger vortex rings being produced by the flow contraction through the orifice. Larger diameter orifices showed additional gains in propulsive efficiency over smaller orifices if the rate of mass flow was matched with the smaller diameter cases, but using the same maximum jet velocity with the larger diameter decreased the propulsive efficiency relative to the smaller diameter cases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".