RESISTANCE PREDICTION USING ARTIFICIAL NEURAL NETWORKS FOR PRELIMINARY TRI-SWACH DESIGN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it