Shared-Energy Prediction Model for Ship-Ice Interactions
Bibliographic record
Abstract
Low- and non-ice-class ship-ice interactions are modelled with a shared-energy approach, which typically models the internal mechanics with nonlinear finite element methods. For applications like the preliminary design phase and quick operational assessments of the ship’s structural capabilities, a finite element shared-energy approach can be time consuming and information intensive, therefore, an analytical share-energy algorithm is proposed. The proposed algorithm applies the upper bound energy methodology by equating the external collision energy, determined with the Popov collision model (Popov, et al., 1967), to the sum of the internal ice and structural response energies. The distribution of the internal energy, between the ice and the structure, is determined by iterating through possible shared contact forces until the sum of the internal response energies equals the external energy introduced into the system. The ice-crushing energy is modelled with Daley’s (1999) energy based ice collision force models, and the internal structural strain energy is modelled through a combination of classical beam theory and design of experiments methodology. The proposed model is benchmarked against a finite element ice wedge-ship grillage structure interaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".