Model-Scale/Full-Scale Correlation of NRC-OCRE’s Model Resistance, Propulsion and Maneuvering Test Results
Bibliographic record
Abstract
There are a variety of model ices and test techniques adopted by model test facilities. Most often, the clients would ask: “How well can you predict the full scale performance from your model test results?” Model-scale/full-scale correlation becomes an important litmus test to validate a model test technique and its results. This paper summarizes the model-scale/full-scale correlation performed on model test data generated at the National Research Council - Ocean, Coastal, and River Engineering’s (NRC-OCRE) test facility in St. John’s. This correlation includes ship performance predictions, i.e., resistance, propulsion and maneuvering. Selected works from NRC-OCRE on the USCGC icebreaker Healy, the CCGS icebreaker Terry-Fox, the CCGS R-Class icebreakers Pierre Radisson and Sir John Franklin and the CCGS icebreaker Louis S. St. Laurent were reviewed and summarized. The model tests were conducted at NRC-OCRE’s ice tank with the correct density (CD) EGADS model ice. This correlation is based on the concept that a “correlation friction coefficient” (CFC) can be used to predict full-scale ship icebreaking resistance from model test data. The CFCs have been compared for correlation studies using good-quality full-scale information for the five icebreaker models in the NRC-OCRE’s model test database. The review has shown a good agreement between NRCOCRE’s model test predictions and full-scale measurements. The resistance and power correlation were performed for five sets of full-scale data. Although there is substantial uncertainty on ice thickness and ice strength within the full scale data sets that contributes to data scattering, the data suggest a conservative estimate can be obtained to address reasonably this uncertainty by increasing the model prediction by 15% that envelopes most data points. Limited correlation for maneuvering in ice was performed for the USCGC icebreaker Healy. Selected test conditions from the sea trials were duplicated for the maneuvering tests and turning diameters were measured from the arcs of partial circles made in the ice tank. Performance predictions were then compared to the full-scale data previously collected. Despite some discrepancy in ice strength and power level between the model tests and sea trial, the model data agree well with the sea trial data except for three outliers. Otherwise, the maneuvering data show a good correlation between the model test and sea trial results.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".