Framework for a physics-based digital twin of a towed cable-body system
Bibliographic record
Abstract
• A digital twin for estimating the dynamic behaviour of cables is proposed. • Validation of a finite element cable model for oscillatory motions is presented. • Framework to predict cable dynamics from ship motion data is presented. • Ability to estimate kinematics and kinetics of cable in real-time is demonstrated. Marine towed cable-body systems undergo significant tension variations from the wave-induced ship motion, potentially causing damage or unsafe conditions due to the cable becoming slack. Accurate real-time estimation of the cable tension would enable the development of automated methods for preventing slack cable. Instead of measuring the tension using a force sensor, which may be costly and impractical in the marine environment, a Digital Twin is proposed which utilizes readily available sensor data and simulates the cable dynamics in parallel with the physical system, outputting virtual estimates of cable tension and displacement in real-time. This paper details a framework for a Digital Twin, utilizing a finite element model of a marine towed cable-body system. The finite element model is presented including definitions of the forces acting on the cable and towbody and the constraints for enforcing ship motion and cable motions. Using experimental data obtained from the literature, preliminary validation of the model was performed. The operation of the Digital Twin in real-time was demonstrated using synthetic ship motion data for a simple towing scenario, demonstrating the ability of the Digital Twin to estimate the cable tension and profile in real-time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".