Numerical modelling and validation of seakeeping performance of a fishing vessel
Bibliographic record
Abstract
Seakeeping performance prediction is a traditional naval architecture capability, but it remains a challenging task due to the complexities of underlying physics. The combination of vessel speeds, hull geometry and various marine environmental conditions contribute to the complexity of seakeeping analysis for Atlantic Canadian fishing vessels. This paper investigates the seakeeping performance of a fishing boat using two different 3D panel codes, ANSYS-AQWA and ShipMo3D. The simulation results from both numerical tools were validated with the physical model test data, specifically at representative speeds and headings. Response amplitude operators (RAO) for heave, roll and pitch were investigated first, and the motion responses under irregular waves were assessed using root mean square (RMS) values. It was confirmed from the comparison study that the simulation results from both numerical tools agreed well with the model test results and proved to be an appropriate tool for an Atlantic fishing boat with a relative short and wide hull shape (i.e., a low length/beam ratio). The validated models were then utilized to predict the seakeeping performance of the fishing vessel at different advancing speeds, wave conditions and headings. Using the results, the overall seakeeping performance of the vessel was evaluated and discussed in detail in the paper. This work confirms that numerical models can be utilized at the early stage of designing a fishing boat for assessing the operational risk under various conditions and different speeds.
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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".