Diesel engine integration into autonomous underwater vehicles
Bibliographic record
Abstract
The critical enabling technologies which have been identified to fully realise the potential of AUVs are: long endurance propulsion/energy systems; geodetic and relative navigation; underwater communications; mission management and control; sensors and signal processing; and vehicle design. However, perhaps the most critical technology for almost every AUV application, and often the operational limiting factor, is the availability of adequate onboard energy/power. Given the specialist nature of the AUV market, research and development into new AUV-specific power systems is inevitably limited by resources. At the present, the relative merits and disadvantages of the competing air-independent power systems (AIPS) are fairly well known. However, the greatest need of advice is with the "total system" and its integration, i.e., how the AIPS is affected by, and affects the overall vehicle design. Hence, with the numerous design considerations of an AUVs, full knowledge and understanding of the total AIPS integration is essential, if a technically and operationally successful vehicle design is to be achieved The aim of this paper is to examine the conceptual design of an AUV with specific emphasis on the integration of an air-independent power system, thereby enabling the initial design of AUVs to be evaluated.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".