Control, Simulation, and Testbed Development for Improving Maritime Launch and Recovery Operations
Bibliographic record
Abstract
Improvements to launch and recovery operations at sea are driven by the desire to increase safety and operational availability.This thesis presents various tools to improve motion compensation strategies in maritime launch and recovery: a 3D computer simulator to examine wave synchronization, a signal prediction algorithm for Go-NoGo states and a hardware set-up to simulate ship and wave motion.The 3D simulator of towed body dynamics was advanced to model the wave interactions with the cable and towed body as the body exits the water.Small scale simulations were run to investigate the inclusion of wave synchronization in established active heave compensation strategies where the hypothesis that wave synchronization would reduce variations in cable tension was not supported; the simulations demonstrated that wave synchronization increased variations in cable tension compared to simulations not using motion compensation.The use of a signal prediction method that forecasts a periodic signal based only on historic data of the signal was explored.The method is a means to predict safe breach events where the prediction algorithm was advanced and tuned to determine Go-NoGo states.A Go scenario identified by the mean and one standard deviation of the predicted signal was found to produce a Go-NoGo signal that agreed most with the desired Go-NoGo signal for forecasts up to 10 s.For the development of laboratory equipment for the Carleton University flume tank, a ship motion simulator was designed and built to emulate 5 degrees-of-freedom of ship motion and a kinematic analysis was performed to characterize the system workspace.For producing waves in the flume tank, a design methodology was developed for the design of a plunger-type wavemaker.A numerical model for determining the wave amplitude to actuator stroke length ratio was advanced to include the effects of a flow current.The design methodology enables the designer to select an appropriate actuator and plunger shape based on an operating point that incorporates multiple design variables.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".