A Computational Study to Predict the Seakeeping Performance of a Surfaced Submarine in Irregular Waves
Bibliographic record
Abstract
Abstract In general, submarines are designed to optimize operation below the water surface because they spend most of their time underwater. On the other hand, the performance in the free surface condition is also important because submarines face a variety of scenarios to complete operational missions, and the free surface condition is unavoidable for port departure and arrival. In the case of a submarine, the numerical accuracy of the potential theory for seakeeping analysis is excellent in submerged conditions, but it is poor in free surface conditions because of nonlinear effects near the free surface area. In this study, star-ccm+ was used as a Reynolds-averaged Navier–Stokes (RANS) solver to estimate the seakeeping performance of a Canadian Victoria Class submarine in irregular waves. The results were compared with those of model tests from a published paper. In addition, the potential theory code was also used to assess the seakeeping performance and compare it with computational fluid dynamics (CFD) results. From the calculation results, the motion responses in irregular waves using CFD showed similar trends to the experimental results. In contrast, motion responses from potential code showed significantly larger values than the experimental results. In conclusion, CFD simulations with irregular waves can be a good solution to predict the seakeeping performance of submarines in free surface conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".