Response Surface Models for Analyzing Planing Hull Motions in a Vertical Plane
Bibliographic record
Abstract
A non-linear mathematical model, Planing Hull Motion Program (PHMP) has been developed based on strip theory to predict the heave and pitch motions of planing hull at high speed in head seas. PHMP has been validated against published model test data. For various combinations of design parameters, PHMP can predict the heave and pitch motions and bow and center of gravity accelerations with reasonable accuracy at planing and semi-planing speeds. This paper illustrates an application of modern statistical design of experiment (DOE) methodology to develop simple surrogate models to assess planing hull motions in a vertical plane (surge, heave and pitch) in calm water and in head seas. Responses for running attitude (sinkage and trim) in calm water, and for heave and pitch motions and bow and center of gravity accelerations in head seas were obtained from PHMP based on a multifactor uniform design scheme. Regression surrogate models were developed for both calm water and in head seas for each of the relevant responses. Results showed that the simple one line regression models provided adequate fit to the generated responses and provided valuable insights into the behaviour of planing hull motions in a vertical plane. The simple surrogate models can be a quick and useful tool for the designers during the preliminary design stages.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".