Simulation and active control of towed undersea vehicles
Bibliographic record
Abstract
Towed undersea vehicles are used extensively in the field for activities such as fish stock assessment, petroleum exploration, and minehunting. The accurate positioning of the vehicle in time and space is often critical to ensuring useful measurements are obtained from onboard sensors. This paper discusses the development of a simulation facility aimed as a design tool for the development of towed undersea vehicles. The simulation was developed in two parts: the cable dynamics simulation, and the vehicle simulation. The cable is simulated using a lumped mass approach in which the cable is discretized into a number of nodes and the equations of motion are then written for each node. The vehicle simulation is a 2D implementation of a previously-developed 3D untethered vehicle model. Both the cable and vehicle models were validated using data available in the literature. The two models were then coupled to form the complete towed vehicle simulation. The system of differential equations describing the motion of the nodes and vehicle is solved using a fourth order fixed time step Runge Kutta integrator. The use of this simulation is then demonstrated in an evaluation of the depth and pitch control obtained in a bottom-following task.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".