Bibliographic record
Abstract
Supercavitation is a means of drag reduction in water, wherein a body is enveloped in a gas layer in order to reduce skin friction. Depending on the type of supercavitating vehicle under consideration, the overall drag coefficient can be an order of magnitude less than that of a fully-wetted vehicle. However, even in its simplest conceptualization, supercavitating vehicle dynamics features slope-discontinuous force curves and time-delay effects, control presents special challenges. This paper has presented strategies for the control of the highly-coupled nonlinear system comprising a supercavitating vehicle. A three DOFs hydrodynamical model based on Newton’s Laws was implemented for simulating the behavior of such a system. The linearization of the mathematical model is derived in detail. A Linear Quadratic Regulator (LQR) control design for the vehicle was formulated. Results of dynamical simulation for a specific vehicle were presented both for uncontrolled flight and with LQR-based feedforward-feedback control. The system behavior is dominated by the distinct change in the nature of the forces as the afterbody moves between a planing and non-planing condition, but that under the assumptions made for the three DOFs model the controller can eliminate the most undesirable behavior in either case.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".