RESISTANCE PREDICTION USING ARTIFICIAL NEURAL NETWORKS FOR PRELIMINARY TRI-SWACH DESIGN
Bibliographic record
Abstract
Due to the novel hull form design, at present no standard series or full-scale data is publicly available to predict Tri- SWACH resistance during the preliminary ship design process. This work investigates the viability of using an Artificial Neural Network (ANN) to quickly predict total resistance for preliminary Tri-SWACH design. An ANN was trained using total resistance experimental data obtained from model tests, which varied side hull arrangements. The results highlight strong correlation for model resistance prediction. A Tri-SWACH case study was then developed which had side hull geometric properties different to any previously used to train the ANN. The results, validated against CFD predictions, mimicked the resistance pattern generated by other model experimental data, providing confidence in the ANN’s ability to function as a resistance prediction tool. This work demonstrates the viability of ANN to assess Tri-SWACH resistance as part of a preliminary design process. These results suggest that ANNs can be effective tools for assessing performance given relevant training data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".