Use of factorial design in a podded propulsor geometric series
Bibliographic record
Abstract
Factorial design is a method of experimental design that can be used to increase the value of multi-factor experiments. The method estimates the effects of the individual factors tested on the overall result to determine which factors most influence the outcome of the experiment. This allows the experimenter to run an additional test series that studies in detail the primary factors while legitimately treating insignificant factors as negligible. Podded propulsors are a relatively recent addition to propulsion options for the shipping industry and are a popular alternative to traditional propulsors with ship designers. The geometry of the pod that encases the motor and shaft of the podded propulsor has been primarily guided by the size of available motors. As motor design becomes more refined and flexible, the relationship of the various parameters (diameter, length, position of strut) with respect to performance becomes a more important design consideration. There are a number of geometric parameters that can be used to optimize the design of the pod and five were chosen for the test series. The results that are presented in this paper are the first set of results obtained from a new pod test apparatus at Memorial University. Numerical results that validate the experimental values are presented. The preliminary results show that some of the design factors are significant at certain J values.
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 | 0.001 |
| 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".