Analysis of Variance to Determine the Effect of Hull Form Parameters on Resistance and Seakeeping Performance for PSV Hulls
Bibliographic record
Abstract
Throughout the world many Platform Supply Vessel designs have been proposed as the optimal form for their given operating environment, but evaluating these claims has been difficult due to a poor understanding of the relationships between hull form shapes and performance for these vessels. This paper presents the results of analysis aimed at determining these relationships. Results of CFD calculations to determine the Effective Horsepower/tonne for a series of PSV designs were presented in the paper A step towards an optimum PSV Hull form. This paper presents results for 16 separate hull forms, which were designed as each possible combination of four two-level hull form parameters. The hull form features considered were bow shape (vertical stem or bulbous), flat of bottom (flat or deadrise), length of parallel mid body (short or long), and stern shape (convention or integrated); resistance was calculated at two typical operating speeds (10 and 14 knots). This set of results was favourable for analysis using the statistical design of experiments technique: analysis of variance, which was used to determine the relationship between the hull and resistance performance. The same hull form series was used to study the effects of the hull form parameters on motions in head waves. A 2 level factorial experiment was designed based on the hull parameters with the heave and pitch response calculated using the potential flow ship motion prediction code Shipmo3D, for each of two representative wave conditions (summer light seas and winter heavy seas) at the zero speed and 10 knot operating speed. Analysis of variance was used to analyze the heave and pitch responses measured, and was used to determine the relationship between each hull parameter and each response. In both cases a 5% F-test was used to determine the significance of each parameter studied, and the significant effects were analyzed to determine their contributions to the overall model of the data. The results have found the relationships between the hull design parameters and the Effective Horespower/tonne, heave, and pitch response of the vessel, indicating which factors provide the largest contribution to minimizing each response. The interaction effects between factors were also examined to allow for a generalized understanding of the resulting effect of selecting one hull parameter over another. A numerical model combining all significant factors was fitted to the data, allowing for multiple objective optimization to determine which hull forms provide the most desirable performance for each response.
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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".