Modelling and Simulation of Marine Cables with Dynamic Winch and Sheave Contact
Bibliographic record
Abstract
Cable-sheave systems are commonly used on marine vessels for lifting and towing applications.As a result of the motion of the vessel, the cable can detach from the surface of the sheave.This thesis presents a simulation of a towed cable system which includes the interaction of the cable with the sheave surface in order to examine variations in the contact between the cable and the sheave.A three-dimensional description of the sheave geometry is implemented in order to accurately model the contact forces as the vessel undergoes six degree-of-freedom motion.To assess the performance of the model, the simulated cable behavior is compared to small scale experimental measurements.Experiments were carried out with a pulley supporting a cable and a swinging load.Good agreement with the measured cable tension and wrap angle of the cable around the pulley was shown.Using existing experimental data, the motion of a small towbody in a flume tank was compared with the simulated motion.The simulation demonstrated good agreement with the experimental towbody motion, predicting the volume of the enclosing ellipsoid to within 27%.Finally, a case study was performed to demonstrate the usage of the simulation to examine variations in cable tension and contact forces for a full scale system.The method demonstrated can be used in future studies to examine cable detachment behavior.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".