Dynamic modeling of cable system using a new nodal position finite element method
Bibliographic record
Abstract
Abstract The handling of cable systems onboard surface ships and submarines presents a significant technical challenge to design engineers in defense and ocean industries. The current approaches rely heavily on empirical methods and time‐consuming/expensive prototype testing. Computer simulation provides a cost effective way to reduce the high risks associated with the design of the cable‐towed system. This paper presents a new nodal position finite element method (FEM) to effectively simulate the dynamics of cable system experiencing large rigid body rotation coupled with small elastic deformation. The existing FEM deals with the large rigid body rotation by linearized incremental approximation that is prone to numerical inaccuracies resulting from large 3D rotations. By solving for the position directly instead of indirectly via the displacement, the new FEM does not need to decouple the elastic deformation from the rigid body rotation and thus eliminates the error source arising from the linearized incremental approximation in existing FEM. Analysis results demonstrate that the new FEM algorithm is simple and robust by comparing with numerical benchmark test and experiments including sea trial data. Copyright © 2008 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".